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126 results on '"Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe"'

1. A polygenic risk score for multiple myeloma risk prediction

Author: Angelica Macauda, Matteo Pelosini, Krzysztof Jamroziak, Mario Petrini, Annette Juul Vangsted, Chiara Piredda, Stephane Minvielle, Marek Dudziński, Hervé Avet-Loiseau, Aleksandra Butrym, Ulla Vogel, Niels Abildgaard, Fabienne Lesueur, Waldemar Tomczak, Vibeke Andersen, Herlander Marques, Daniele Campa, Judit Várkonyi, Rui Manuel Reis, Katalin Kadar, Gabriele Buda, Małgorzata Raźny, Charles Dumontet, Juan Sainz, Anna Suska, Enrico Orciuolo, Agnieszka Druzd-Sitek, Marcin Kruszewski, Daria Zawirska, Niels Frost Andersen, Artur Jurczyszyn, Grzegorz Mazur, Marcin Rymko, Federica Gemignani, Edyta Subocz, Federico Canzian, Katia Beider, Jan Maciej Zaucha, Marzena Wątek, Arnon Nagler, Genomic Epidemiology Group [Heidelberg, Germany] (GEP / DKFZ), German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), University of Pisa - Università di Pisa, University Hospital of Cracow/Szpital Uniwersytecki w Krakowie [Poland] (SUK), Aarhus University Hospital, Chaim Sheba Medical Center, Sea Hospital [Gdynia, Poland] (SH), Wroclaw Medical University [Wrocław, Pologne] (WMU), Hospices Civils de Lyon (HCL), Holycross Cancer Center of Kelce, Hematology Clinic, Kielce, Institute of Hematology and Transfusion Medicine [Warsaw, Poland] (IHTM), Centre for Genomics and Oncological Research Pfizer [Granada, Spain] (GENYO), University of Granada [Granada]-Andalusian Regional Government [Granada, Spain], Hospital Universitario Virgen de las Nieves [Granada, Spain] (HUVN), Semmelweis University [Budapest], Odense University Hospital (OUH), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Teaching Hospital No 1 [Rzeszów, Poland] (TH1), University of Copenhagen = Københavns Universitet (KU), Military Institute of Medicine [Warsaw, Poland] (MIM), Rydygier Specialistic Hospital [Cracow, Poland] (RSH), University of Minho [Braga], Jagiellonian University Medical College [Cracow, Poland] (JUMC), Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology (MCMCC), The National Research Center for Work Environment [Copenhagen, Denmark] (NRCWE), University of Southern Denmark (SDU), ICVS/3B's - PT Government Associate Laboratory [Braga/Guimarães, Portugal] (AL), Barretos Cancer Hospital [São Paulo, Brazil] (BCH), Institut Universitaire du Cancer de Toulouse - Oncopole (IUCT Oncopole - UMR 1037), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-CHU Toulouse [Toulouse]-Institut National de la Santé et de la Recherche Médicale (INSERM), University Hospital Bydgoszcz [Bydgoszcz, Poland] (UHB), Medical University of Lublin, N. Copernicus Town Hospital [Torun, Poland] (NCTH), Integrative Oncogenomics of Multiple Myeloma Pathogenesis and Progression (CRCINA-ÉQUIPE 11), Centre de Recherche en Cancérologie et Immunologie Nantes-Angers (CRCINA), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Nantes - UFR de Médecine et des Techniques Médicales (UFR MEDECINE), Université de Nantes (UN)-Université de Nantes (UN)-Centre hospitalier universitaire de Nantes (CHU Nantes)-Centre National de la Recherche Scientifique (CNRS)-Université d'Angers (UA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Nantes - UFR de Médecine et des Techniques Médicales (UFR MEDECINE), Université de Nantes (UN)-Université de Nantes (UN)-Centre hospitalier universitaire de Nantes (CHU Nantes)-Centre National de la Recherche Scientifique (CNRS)-Université d'Angers (UA), Bernardo, Elizabeth, Wrocław Medical University, Centre de Recherche en Cancérologie de Lyon (UNICANCER/CRCL), Centre Léon Bérard [Lyon]-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Universidad de Granada = University of Granada (UGR)-Andalusian Regional Government [Granada, Spain], Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Copenhagen = Københavns Universitet (UCPH), Uniwersytet Jagielloński w Krakowie = Jagiellonian University (UJ), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Université d'Angers (UA)-Université de Nantes (UN)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre hospitalier universitaire de Nantes (CHU Nantes)-Université d'Angers (UA)-Université de Nantes (UN)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre hospitalier universitaire de Nantes (CHU Nantes)
Subjects: Oncology, medicine.medical_specialty, Population, [SDV.CAN]Life Sciences [q-bio]/Cancer, Single-nucleotide polymorphism, Genome-wide association study, Polymorphism, Single Nucleotide, 03 medical and health sciences, 0302 clinical medicine, [SDV.CAN] Life Sciences [q-bio]/Cancer, Risk Factors, Internal medicine, Genetics, medicine, Genetic predisposition, Humans, Genetic Predisposition to Disease, education, Genetics (clinical), Multiple myeloma, 030304 developmental biology, Genetic association, 0303 health sciences, education.field_of_study, business.industry, medicine.disease, Penetrance, 3. Good health, 030220 oncology & carcinogenesis, Polygenic risk score, Multiple Myeloma, business, Genome-Wide Association Study
Abstract: This work was partially supported by intramural funds of the University of Pisa, DKFZ, and University Hospital of Southern Jutland, Denmark, and by a grant of the French National Cancer Institute (INCA). The authors wish to thank Dr. Dominic Edelmann (Division of Biostatistics, DKFZ) for helpful advice about data analysis., There is overwhelming epidemiologic evidence that the risk of multiple myeloma (MM) has a solid genetic background. Genome-wide association studies (GWAS) have identified 23 risk loci that contribute to the genetic susceptibility of MM, but have low individual penetrance. Combining the SNPs in a polygenic risk score (PRS) is a possible approach to improve their usefulness. Using 2361 MM cases and 1415 controls from the International Multiple Myeloma rESEarch (IMMEnSE) consortium, we computed a weighted and an unweighted PRS. We observed associations with MM risk with OR = 3.44, 95% CI 2.53-4.69, p = 3.55 x 10(-15) for the highest vs. lowest quintile of the weighted score, and OR = 3.18, 95% CI 2.1 = 34-4.33, p = 1.62 x 10(-13) for the highest vs. lowest quintile of the unweighted score. We found a convincing association of a PRS generated with 23 SNPs and risk of MM. Our work provides additional validation of previously discovered MM risk variants and of their combination into a PRS, which is a first step towards the use of genetics for risk stratification in the general population., University of Pisa, DKFZ, University Hospital of Southern Jutland, Denmark, Institut National du Cancer (INCA) France
Published: 2021

2. Parental-to-embryo switch of chromosome organization in early embryogenesis

Author: Wing Leung, Samuel Collombet, Maud Borensztein, Tarak Shisode, Katia Ancelin, Csilla Várnai, Tristan Piolot, Nicolas Servant, Edith Heard, Rafael Galupa, Takashi Nagano, Peter Fraser, Noémie Ranisavljevic, European Molecular Biology Laboratory [Heidelberg] (EMBL), Génétique et Biologie du Développement, Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université Paris sciences et lettres (PSL), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Curie [Paris], and Centre National de la Recherche Scientifique (CNRS)
Subjects: Male, Parents, Embryonic Development, Polycomb-Group Proteins, Biology, Methylation, Histones, Chromosome conformation capture, Genomic Imprinting, Mice, 03 medical and health sciences, Histone H3, 0302 clinical medicine, X Chromosome Inactivation, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Animals, Medicine, Chromosome Positioning, [SDV.BDD]Life Sciences [q-bio]/Development Biology, Gene, Alleles, ComputingMilieux_MISCELLANEOUS, Genomic organization, 030304 developmental biology, Epigenomics, Genetics, Regulation of gene expression, 0303 health sciences, Genome, Multidisciplinary, business.industry, Gene Expression Regulation, Developmental, Obstetrics and Gynecology, General Medicine, Chromosomes, Mammalian, Chromatin, Blastocyst, Germ Cells, [SDV.BDD.EO]Life Sciences [q-bio]/Development Biology/Embryology and Organogenesis, Histone, Fertilization, biology.protein, Female, Single-Cell Analysis, business, Genomic imprinting, 030217 neurology & neurosurgery
Abstract: Paternal and maternal epigenomes undergo marked changes after fertilization1. Recent epigenomic studies have revealed the unusual chromatin landscapes that are present in oocytes, sperm and early preimplantation embryos, including atypical patterns of histone modifications2–4 and differences in chromosome organization and accessibility, both in gametes5–8 and after fertilization5,8–10. However, these studies have led to very different conclusions: the global absence of local topological-associated domains (TADs) in gametes and their appearance in the embryo8,9 versus the pre-existence of TADs and loops in the zygote5,11. The questions of whether parental structures can be inherited in the newly formed embryo and how these structures might relate to allele-specific gene regulation remain open. Here we map genomic interactions for each parental genome (including the X chromosome), using an optimized single-cell high-throughput chromosome conformation capture (HiC) protocol12,13, during preimplantation in the mouse. We integrate chromosome organization with allelic expression states and chromatin marks, and reveal that higher-order chromatin structure after fertilization coincides with an allele-specific enrichment of methylation of histone H3 at lysine 27. These early parental-specific domains correlate with gene repression and participate in parentally biased gene expression—including in recently described, transiently imprinted loci14. We also find TADs that arise in a non-parental-specific manner during a second wave of genome assembly. These de novo domains are associated with active chromatin. Finally, we obtain insights into the relationship between TADs and gene expression by investigating structural changes to the paternal X chromosome before and during X chromosome inactivation in preimplantation female embryos15. We find that TADs are lost as genes become silenced on the paternal X chromosome but linger in regions that escape X chromosome inactivation. These findings demonstrate the complex dynamics of three-dimensional genome organization and gene expression during early development. Single-cell allelic HiC analysis, combined with allelic gene expression and chromatin states, reveals parent-of-origin-specific dynamics of chromosome organization and gene expression during mouse preimplantation development.
Published: 2020

3. Inference of genome 3D architecture by modeling overdispersion of Hi-C data

Author: Nelle Varoquaux, William Stafford Noble, Jean-Philippe Vert, Univ. Grenoble Alpes, CNRS, CHU Grenoble Alpes, Grenoble INP, TIMC-IMAG, 38000 Grenoble, France, Department of Genome Sciences [Seattle] (GS), University of Washington [Seattle], Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, Google Brain, Paris, Centre de Bioinformatique (CBIO), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Translational microbial Evolution and Engineering (TIMC-TrEE), Translational Innovation in Medicine and Complexity / Recherche Translationnelle et Innovation en Médecine et Complexité - UMR 5525 (TIMC ), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Google Research [Paris], Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), and Varoquaux, Nelle
Subjects: 0303 health sciences, Computer science, [SDV]Life Sciences [q-bio], Negative binomial distribution, Inference, Function (mathematics), Poisson distribution, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [SDV] Life Sciences [q-bio], 03 medical and health sciences, symbols.namesake, 0302 clinical medicine, Overdispersion, symbols, Poisson regression, Multidimensional scaling, Algorithm, 030217 neurology & neurosurgery, 030304 developmental biology, Count data
Abstract: We address the challenge of inferring a consensus 3D model of genome architecture from Hi-C data. Existing approaches most often rely on a two step algorithm: first convert the contact counts into distances, then optimize an objective function akin to multidimensional scaling (MDS) to infer a 3D model. Other approaches use a maximum likelihood approach, modeling the contact counts between two loci as a Poisson random variable whose intensity is a decreasing function of the distance between them. However, a Poisson model of contact counts implies that the variance of the data is equal to the mean, a relationship that is often too restrictive to properly model count data.We first confirm the presence of overdispersion in several real Hi-C data sets, and we show that the overdispersion arises even in simulated data sets. We then propose a new model, called Pastis-NB, where we replace the Poisson model of contact counts by a negative binomial one, which is parametrized by a mean and a separate dispersion parameter. The dispersion parameter allows the variance to be adjusted independently from the mean, thus better modeling overdispersed data. We compare the results of Pastis-NB to those of several previously published algorithms: three MDS-based methods (ShRec3D, ChromSDE, and Pastis-MDS) and a statistical methods based on a Poisson model of the data (Pastis-PM). We show that the negative binomial inference yields more accurate structures on simulated data, and more robust structures than other models across real Hi-C replicates and across different resolutions.A Python implementation of Pastis-NB is available at https://github.com/hiclib/pastis under the BSD licenseSupplementary information is available at https://nellev.github.io/pastisnb/
Published: 2022

4. The kinesin KIF1C transports APC-dependent mRNAs to cell protrusions

Author: Emeline Coleno, Marion Peter, Marie-Cécile Robert, Tianhong Wang, Xavier Pichon, Kazem Zibara, Arthur Imbert, Edouard Bertrand, Florian Mueller, Thomas Walter, Stavroula Mili, Racha Chouaib, Konstadinos Moissoglu, Institut de Génétique Moléculaire de Montpellier (IGMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174) (FEMTO-ST), Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'économie et de sociologie du travail (LEST), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Hospices Civils de Lyon (HCL), Imagerie et Modélisation - Imaging and Modeling, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Equipe labellisée Ligue contre le Cancer, National Cancer Institute [Bethesda] (NCI-NIH), National Institutes of Health [Bethesda] (NIH), Institut de génétique humaine (IGH), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Lebanese University [Beirut] (LU), Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), This project was supported by France BioImaging (ANR-10-INBS-04), the Agence Nationale de la Recherche (ANR-11-BSV8-018-02, ANR-14-CE10-0018-01 and ANR-19-CE12-0007-03), and the Ligue Nationale Contre le Cancer and the Fondation pour la Recherche Médicale. This work was supported by the Labex EpiGenMed, from the framework 'Investissements d’avenir'. This work was supported in part by the Intramural Research Program of the National Cancer Institute, NIH., ANR-10-INBS-0004,France-BioImaging,Développment d'une infrastructure française distribuée coordonnée(2010), ANR-11-BSV8-0018,EJCbirth,Impacts de la transcription par l'ARN polymérase II sur l'assemblage de l'EJC(2011), ANR-14-CE10-0018,HI-FISH,Etude systématique de l'expression des gènes à l'échelle des molécules uniques d'ARN(2014), ANR-19-CE12-0007,TRANSFACT,Caracterisation des foyers de traduction: de nouvelles structures qui organisement finement le cytoplasme(2019), ANR-10-LABX-0012,EpiGenMed,From Genome and Epigenome to Molecular Medicine: turning new paradigms in biology into the therapeutic strategies of tomorrow(2010), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), and Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Cytoplasm, RNA localization, [SDV]Life Sciences [q-bio], Adenomatous Polyposis Coli Protein, RNA transport, Kinesins, Biology, Article, RNA Transport, 03 medical and health sciences, local translation, 0302 clinical medicine, Live cell imaging, MRNA transport, Animals, Humans, RNA, Messenger, Molecular Biology, cytoplasmic protrusions, 030304 developmental biology, 0303 health sciences, Messenger RNA, RNA, Cell biology, Kinesin, Cell Surface Extensions, 030217 neurology & neurosurgery, HeLa Cells
Abstract: International audience; RNA localization and local translation are important for numerous cellular functions. In mammals, a class of mRNAs localize to cytoplasmic protrusions in an APC-dependent manner, with roles during cell migration. Here, we investigated this localization mechanism. We found that the KIF1C motor interacts with APC-dependent mRNAs and is required for their localization. Live cell imaging revealed rapid, active transport of single mRNAs over long distances that requires both microtubules and KIF1C. Two color imaging directly revealed single mRNAs transported by single KIF1C motors, with the 3’UTR being sufficient to trigger KIF1C-dependent RNA transport and localization. Moreover, KIF1C remained associated with peripheral, multimeric RNA clusters and was required for their formation. These results reveal a widespread RNA transport pathway in mammalian cells, in which the KIF1C motor has a dual role in transporting RNAs and clustering them within cytoplasmic protrusions. Interestingly, KIF1C also transports its own mRNA suggesting a possible feedback loop acting at the level of mRNA transport.
Published: 2021

5. Neural network fast‐classifies biological images through features selecting to power automated microscopy

Author: Maël Balluet, Florian Sizaire, Baptiste Giroux, Jérémy Pont, Youssef El Habouz, Otmane Bouchareb, Marc Tramier, Jacques Pecreaux, Thomas Walter, Institut de Génétique et Développement de Rennes (IGDR), Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Université de Rennes (UR)-Centre National de la Recherche Scientifique (CNRS)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Centre de Bioinformatique (CBIO), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Institut Curie [Paris], Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Inscoper, ANR‐10‐INBS‐04 (France BioImaging), Agence Nationale de la Recherche, PhD fellowship Maël Balluet (2017‐1589), Association Nationale de la Recherche et de la Technologie, ATIP starting grant Pecreaux, Centre National de la Recherche Scientifique, ANR-10-INBS-0004,France-BioImaging,Développment d'une infrastructure française distribuée coordonnée(2010), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-19-CE45-0011,SAMic,Microscopie intelligente autonome(2019), Pécréaux, Jacques, Développment d'une infrastructure française distribuée coordonnée - - France-BioImaging2010 - ANR-10-INBS-0004 - INBS - VALID, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, and Microscopie intelligente autonome - - SAMic2019 - ANR-19-CE45-0011 - AAPG2019 - VALID
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Histology, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [PHYS.PHYS.PHYS-BIO-PH]Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph], Image processing, 02 engineering and technology, [SDV.BC.BC]Life Sciences [q-bio]/Cellular Biology/Subcellular Processes [q-bio.SC], Pathology and Forensic Medicine, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, embedded system, cell biology, 0202 electrical engineering, electronic engineering, information engineering, Redundancy (engineering), [SDV.BC.BC] Life Sciences [q-bio]/Cellular Biology/Subcellular Processes [q-bio.SC], ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, 0303 health sciences, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Artificial neural network, machine vision and scene understanding, [PHYS.PHYS.PHYS-BIO-PH] Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph], business.industry, Pattern recognition, Linear discriminant analysis, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Random forest, image processing, Data set, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Discriminant, Feature (computer vision), microscopy, 020201 artificial intelligence & image processing, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract: International audience; Artificial intelligence is nowadays used for cell detection and classification in optical microscopy during post-acquisition analysis. The microscopes are now fully automated and next expected to be smart by making acquisition decisions based on the images. It calls for analysing them on the fly. Biology further imposes training on a reduced data set due to cost and time to prepare the samples and have the data sets annotated by experts. We propose a real-time image processing compliant with these specifications by balancing accurate detection and execution performance. We characterised the images using a generic, high-dimensional feature extractor. We then classified the images using machine learning to understand the contribution of each feature in decision and execution time. We found that the non-linear-classifier random forests outperformed Fisher's linear discriminant. More importantly, the most discriminant and time-consuming features could be excluded without significant accuracy loss, offering a substantial gain in execution time. It suggests a feature-group redundancy likely related to the biology of the observed cells. We offer a method to select fast and discriminant features. In our assay, a 79.6 ± 2.4% accurate classification of a cell took 68.7 ± 3.5 ms (mean ± SD, 5-fold cross-validation nested in 10 bootstrap repeats), corresponding to 14 cells per second, dispatched into eight phases of the cell cycle, using 12 feature groups and operating a consumer market ARM-based embedded system. A simple neural network offered similar performances paving the way to faster training and classification, using parallel execution on a general-purpose graphic processing unit. Finally, this strategy is also usable for deep neural networks paving the way to optimizing these algorithms for smart microscopy.
Published: 2021

6. Autoimmunity affecting the biliary tract fuels the immunosurveillance of cholangiocarcinoma

Author: Franck Letourneur, Jérémy Augustin, Mark J. Kurth, Julie Le Naour, Sylvie Lachkar, Pamela Caudana, Eliane Piaggio, Youra Kim, Sarah Levesque, Jie S. Zhu, Guido Kroemer, Jonathan Pol, Bouchra Lekbaby, Andrea Checcoli, Patrick S.C. Leung, Allan Sauvat, Shashi Gujar, Agathe Delaune, Chantal Housset, Pierre de la Grange, Paule Opolon, Laura Fouassier, Noémie Robil, Laurence Zitvogel, Patrick Soussan, Juliette Paillet, Céleste Plantureux, Jimena Tosello Boari, M. Eric Gershwin, Isabelle Martins, Maria Chiara Maiuri, Cédric Coulouarn, Gautier Stoll, Norma Bloy, Gwennhael Autret, Juliette Hamroune, Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Institut Gustave Roussy (IGR), Institut Curie [Paris], Immunité et cancer (U932), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Curie [Paris], Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), GenoSplice [Paris], Institut Cochin (IC UM3 (UMR 8104 / U1016)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Paris-Centre de Recherche Cardiovasculaire (PARCC (UMR_S 970/ U970)), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), University of California [Los Angeles] (UCLA), University of California, Centre de Recherche Saint-Antoine (CR Saint-Antoine), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Saint-Antoine [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Dalhousie University [Halifax], Chemistry, Oncogenesis, Stress and Signaling (COSS), Institut National de la Santé et de la Recherche Médicale (INSERM)-CRLCC Eugène Marquis (CRLCC)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), CRLCC Eugène Marquis (CRLCC), Université Paris-Saclay, Immunologie des tumeurs et immunothérapie (UMR 1015), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Gustave Roussy (IGR)-Université Paris-Sud - Paris 11 (UP11), Centre d'Investigation Clinique en Biotherapie des cancers (CIC 1428 , CBT 507 ), Institut Gustave Roussy (IGR)-Institut National de la Santé et de la Recherche Médicale (INSERM), Karolinska University Hospital [Stockholm], Association pour la lutte contre les maladies inflammatoires du foie et des voies biliaires, Dalhousie Medical Research Foundation, Agence National de la Recherche, ERA-Net for Research on Rare Diseases, Association pour la recherche sur le cancer, Cancéropôle Ile-de-France, Chancellerie des universités de Paris, Fondation pour la Recherche Médicale, Elior, European Research Area Network on Cardiovascular Diseases, Gustave Roussy Odyssea, European Union Horizon 2020, Canadian Cancer Society, Project Oncobiome, Fondation Carrefour, GDW20171100085, High-end Foreign Expert Program in China, Institut National du Cancer, Inserm, Institut Universitaire de France, LeDucq Foundation, LabEx, Recherche Hospitalo-Universitaire Torino Lumière, Seerave Foundation, Canadian Institutes of Health Research, Site de Recherche intégrée sur le Cancer Stratified Oncology Cell DNA Repair and Tumor Immune Elimination, Cancer Research and Personalized Medicine, Multi-Organism Institute (ITMO) Aviesan Cancer, Association Française d'Hépatologie, Site de Recherche intégrée sur le Cancer Cancer Research and Personalized Medicine, Ligue contre le Cancer, École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), University of California (UC), Centre de Recherche Saint-Antoine (CRSA), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Université de Rennes (UR)-CRLCC Eugène Marquis (CRLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Paris-Sud - Paris 11 (UP11)-Institut Gustave Roussy (IGR)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Institut Gustave Roussy (IGR)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)
Subjects: CD4-Positive T-Lymphocytes, Cholangitis, Autoimmunity, CD8-Positive T-Lymphocytes, Inbred C57BL, medicine.disease_cause, Medical and Health Sciences, Malignant transformation, Cholangiocarcinoma, Mice, 0302 clinical medicine, Immunologic, Neoplasms, 2.1 Biological and endogenous factors, Immunology and Allergy, Medicine, Aetiology, Cancer, 0303 health sciences, Tumor, Liver Disease, Forkhead Transcription Factors, 3. Good health, Immunosurveillance, medicine.anatomical_structure, Liver, Biliary tract, 030220 oncology & carcinogenesis, Hepatocellular carcinoma, cardiovascular system, Cytokines, Female, Biotechnology, Liver Cancer, Monitoring, T cell, Chronic Liver Disease and Cirrhosis, Immunology, Context (language use), [SDV.CAN]Life Sciences [q-bio]/Cancer, Autoimmune Disease, digestive system, Cell Line, Primary sclerosing cholangitis, Experimental, 03 medical and health sciences, Rare Diseases, Monitoring, Immunologic, Cell Line, Tumor, parasitic diseases, Animals, cardiovascular diseases, Digestive Diseases - (Gallbladder), 030304 developmental biology, business.industry, Neoplasms, Experimental, medicine.disease, digestive system diseases, Mice, Inbred C57BL, Bile Duct Neoplasms, Cancer research, Digestive Diseases, business
Abstract: International audience; Cholangiocarcinoma (CCA) results from the malignant transformation of cholangiocytes. Primary sclerosing cholangitis (PSC) and primary biliary cholangitis (PBC) are chronic diseases in which cholangiocytes are primarily damaged. Although PSC is an inflammatory condition predisposing to CCA, CCA is almost never found in the autoimmune context of PBC. Here, we hypothesized that PBC might favor CCA immunosurveillance. In preclinical murine models of cholangitis challenged with syngeneic CCA, PBC (but not PSC) reduced the frequency of CCA development and delayed tumor growth kinetics. This PBC-related effect appeared specific to CCA as it was not observed against other cancers, including hepatocellular carcinoma. The protective effect of PBC was relying on type 1 and type 2 T cell responses and, to a lesser extent, on B cells. Single-cell TCR/RNA sequencing revealed the existence of TCR clonotypes shared between the liver and CCA tumor of a PBC host. Altogether, these results evidence a mechanistic overlapping between autoimmunity and cancer immunosurveillance in the biliary tract.
Published: 2021

7. A high-risk retinoblastoma subtype with stemness features, dedifferentiated cone states and neuronal/ganglion cell gene expression

Author: André Nicolas, Céline Desbrousses, Mariona Suñol, Sylvain Baulande, Fabiana Lubieniecki, Sandrine Arrufat, David Gentien, Nanor Sirab, Eric Letouzé, Clément Hua, Simon Saule, Elodie Chapeaublanc, Paul Fréneaux, Fariba Nemati, Laurie Tonon, Claude Houdayer, Odette Mariani, Alain Viari, Magalie Larcher, Xavier Sastre-Garau, Sacha Reichman, Daniela Ottaviani, Nathalie Cassoux, Marion Gauthier-Villars, Isabelle Aerts, Céline Vallot, Guillermo Chantada, Aurélien de Reyniès, Genoveva Correa Llano, Liliana Mirabal-Ortega, Livia Lumbroso-Le Rouic, Angel M. Carcaboso, Jérôme Couturier, Dominique Stoppa-Lyonnet, Hervé Brisse, Jaume Català-Mora, Florent Dufour, Isabelle Bernard-Pierrot, François Doz, Meriem Sefta, Paula Schaiquevich, Emmanuel Barillot, Laurence Desjardins, Rosario Aschero, Nabila Elarouci, Celio Pouponnot, Tatiana Popova, F. Radvanyi, Catherine Dehainault, Jing Liu, Gabriela Lamas, Narjesse Karboul, Didier Decaudin, Alexandre Matet, Lisa Golmard, Céline Brulard, Sylvain Guibert, Guillem Pascual-Pasto, Olivier Goureau, Anne Biton, Institut Curie, CNRS, UMR144, Equipe Labellisée Ligue contre le Cancer, PSL Research University, 75005, Paris, France, Sorbonne Université (SU), Programme Cartes d'Identité des Tumeurs, Ligue Nationale Contre le Cancer, 75013, Paris, France, Equipe de recherche européenne en algorithmique et biologie formelle et expérimentale (ERABLE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Synergie Lyon Cancer-Platform of Bioinformatics-Gilles Thomas, Centre de Recherche en Cancérologie de Lyon (UNICANCER/CRCL), Centre Léon Bérard [Lyon]-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre Léon Bérard [Lyon]-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Biologie Cellulaire et Cancer, Institut Curie [Paris]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), (le programme) Cartes d'identité des tumeurs (CIT), Ligue Nationales Contre le Cancer (LNCC), Hospital Nacional de Pediatría J.P. Garrahan, Centre Léon Bérard [Lyon], Département de Biologie des Tumeurs, Institut Curie [Paris], Institut de la Vision, Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Institut Pasteur [Paris] (IP)-Université Paris Cité (UPCité), Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Signalisation, radiobiologie et cancer, Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Imagerie et cerveau (iBrain - Inserm U1253 - UNIV Tours ), Université de Tours (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM), Hôpital Bretonneau, Centre Hospitalier Régional Universitaire de Tours (CHRU Tours), Unité de génétique et biologie des cancers (U830), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Département de Recherche Translationnelle, Centre de recherche de l'Institut Curie [Paris], Genomics Consulting (GeCo), Institut de Recerca Pediàtrica Hospital Sant Joan de Déu [Barcelona, Spain], Hospital Sant Joan de Déu [Barcelona], Consejo Nacional de Investigaciones Científicas y Técnicas [Buenos Aires] (CONICET), CHU Rouen, Normandie Université (NU), Département d'Imagerie Médicale [Institut Curie], Génomique fonctionnelle des tumeurs solides = Functional Genomics of Solid Tumors [CRC] (FunGeST), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), CHI Créteil, École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École pratique des hautes études (EPHE), and Pouponnot, Celio
Subjects: Male, Retinal Ganglion Cells, genetic structures, Cell, Gene Expression, General Physics and Astronomy, Stem cell marker, Metastasis, Transcriptome, 0302 clinical medicine, Neoplasm Metastasis, Cancer genetics, N-Myc Proto-Oncogene Protein, 0303 health sciences, Multidisciplinary, Retinoblastoma, 3. Good health, medicine.anatomical_structure, Child, Preschool, 030220 oncology & carcinogenesis, Retinal Cone Photoreceptor Cells, Female, Science, Retinal Neoplasms, [SDV.CAN]Life Sciences [q-bio]/Cancer, Biology, Malignancy, Article, General Biochemistry, Genetics and Molecular Biology, Eye cancer, Paediatric cancer, Genetic Heterogeneity, 03 medical and health sciences, [SDV.CAN] Life Sciences [q-bio]/Cancer, Biomarkers, Tumor, medicine, Humans, Transcriptomics, Data mining, 030304 developmental biology, Retina, Infant, Cancer, General Chemistry, Cell Dedifferentiation, DNA Methylation, medicine.disease, eye diseases, Mutation, Cancer research, sense organs
Abstract: Retinoblastoma is the most frequent intraocular malignancy in children, originating from a maturing cone precursor in the developing retina. Little is known on the molecular basis underlying the biological and clinical behavior of this cancer. Here, using multi-omics data, we demonstrate the existence of two retinoblastoma subtypes. Subtype 1, of earlier onset, includes most of the heritable forms. It harbors few genetic alterations other than the initiating RB1 inactivation and corresponds to differentiated tumors expressing mature cone markers. By contrast, subtype 2 tumors harbor frequent recurrent genetic alterations including MYCN-amplification. They express markers of less differentiated cone together with neuronal/ganglion cell markers with marked inter- and intra-tumor heterogeneity. The cone dedifferentiation in subtype 2 is associated with stemness features including low immune and interferon response, E2F and MYC/MYCN activation and a higher propensity for metastasis. The recognition of these two subtypes, one maintaining a cone-differentiated state, and the other, more aggressive, associated with cone dedifferentiation and expression of neuronal markers, opens up important biological and clinical perspectives for retinoblastomas., Retinoblastoma is the most frequent intraocular paediatric malignancy whose molecular basis remains poorly understood. Here, the authors perform multi-omic analysis and identify two subtypes; one in a cone differentiated state and one more aggressive showing cone dedifferentiation and expressing neuronal markers.
Published: 2021

8. The Phosphatase PRL-3 Is Involved in Key Steps of Cancer Metastasis

Author: Laura Duciel, S Saule, Maria Kondratova, Luis Cristobal Monraz Gomez, Inna Kuperstein, Signalisation, radiobiologie et cancer, Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Bioinformatique (CBIO), MINES ParisTech - École nationale supérieure des mines de Paris, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)
Subjects: endocrine system, Carcinogenesis, Angiogenesis, [SDV]Life Sciences [q-bio], Systems biology, Phosphatase, Apoptosis, Computational biology, Biology, medicine.disease_cause, Metastasis, 03 medical and health sciences, 0302 clinical medicine, Cell Movement, Structural Biology, Cell Line, Tumor, Cell Adhesion, medicine, Humans, Neoplasm Metastasis, Molecular Biology, Cytoskeleton, 030304 developmental biology, 0303 health sciences, Systems Biology, Cell Cycle, Cancer, Cell cycle, medicine.disease, Neoplasm Proteins, 3. Good health, Disease Progression, Protein Tyrosine Phosphatases, Signal transduction, hormones, hormone substitutes, and hormone antagonists, 030217 neurology & neurosurgery, Signal Transduction
Abstract: PRL-3 belongs to the PRL phosphatase family. Its physiological role remains unclear, but many studies have identified that PRL-3 is a marker of cancer progression and shown it to be associated with metastasis. Evidence implicating PRL-3 in various elements of the metastatic process, such as the cell cycle, survival, angiogenesis, adhesion, cytoskeleton remodeling, EMT, motility and invasion, has been reported. Furthermore, several molecules acting as direct or indirect substrates have been identified. However, this information was obtained in many different studies, and it remains difficult to see the larger picture. We therefore systematically collected the published information together and used it to develop a comprehensive signaling network map. By analyzing this network map, we were able to retrieve the signaling pathways via which PRL-3 governs the key steps of the metastatic process in cancer. In this review, we summarize current knowledge of the role of PRL-3 in cancer and the molecular mechanisms involved. We also provide the web-based open-source PRL-3 signaling network map, for use in further studies.
Published: 2019

9. Gene network and biological pathways associated with susceptibility to differentiated thyroid carcinoma

Author: Claire Mulot, Frédérique Rachédi, Pascal Guénel, Om Kulkarni, Claire Schvartz, Anne Boland-Auge, Evgenia Ostroumova, Ausrele Kesminiene, Anne-Valérie Guizard, Julie Guibon, Christine Lonjou, Juan J Lence-Anta, Rosa Ortiz, Vincent Souchard, Thérèse Truong, Robert Olaso, Carole Rubino, Constance Xhaard, Delphine Bacq-Daian, Florent de Vathaire, Fabienne Lesueur, Jean-François Deleuze, Pierre-Emmanuel Sugier, Marie-Christine Boutron-Ruault, Pierre Laurent-Puig, Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Gustave Roussy (IGR), Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Centre National de Recherche en Génomique Humaine (CNRGH), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Défaillance Cardiovasculaire Aiguë et Chronique (DCAC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Cardiovascular and Renal Clinical Trialists [Vandoeuvre-les-Nancy] (INI-CRCT), Institut Lorrain du Coeur et des Vaisseaux Louis Mathieu [Nancy], French-Clinical Research Infrastructure Network - F-CRIN [Paris] (Cardiovascular & Renal Clinical Trialists - CRCT ), Centre d'investigation clinique plurithématique Pierre Drouin [Nancy] (CIC-P), Centre d'investigation clinique [Nancy] (CIC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Registre Général des Tumeurs du Calvados, CHU Caen, Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN)-Centre Régional de Lutte contre le Cancer François Baclesse [Caen] (UNICANCER/CRLC), Normandie Université (NU)-UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-UNICANCER, Unité de recherche interdisciplinaire pour la prévention et le traitement des cancers (ANTICIPE), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-CHU Caen, Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN)-Tumorothèque de Caen Basse-Normandie (TCBN)-Centre Régional de Lutte contre le Cancer François Baclesse [Caen] (UNICANCER/CRLC), Normandie Université (NU)-UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-UNICANCER-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Jean Godinot [Reims], UNICANCER, Centre International de Recherche contre le Cancer - International Agency for Research on Cancer (CIRC - IARC), Organisation Mondiale de la Santé / World Health Organization Office (OMS / WHO), Gestionnaire, Hal Sorbonne Université, École pratique des hautes études (EPHE), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU)-UNICANCER, Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN)-Université de Caen Normandie (UNICAEN), and Normandie Université (NU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Régional de Lutte contre le Cancer François Baclesse [Caen] (UNICANCER/CRLC)
Subjects: Adult, Male, Adolescent, Genotype, [SDV]Life Sciences [q-bio], Science, ved/biology.organism_classification_rank.species, Gene regulatory network, Genome-wide association study, Computational biology, Steroid biosynthesis, Biology, Polymorphism, Single Nucleotide, Article, Thyroid cancer, Biological pathway, 03 medical and health sciences, Cancer epidemiology, 0302 clinical medicine, Cancer genomics, Humans, Gene Regulatory Networks, Genetic Predisposition to Disease, Thyroid Neoplasms, KEGG, Child, Model organism, Gene, 030304 developmental biology, 0303 health sciences, Multidisciplinary, ved/biology, Infant, Newborn, Infant, Middle Aged, Computational biology and bioinformatics, Neoplasm Proteins, 3. Good health, [SDV] Life Sciences [q-bio], Risk factors, Child, Preschool, 030220 oncology & carcinogenesis, Medicine, Female, Genome-Wide Association Study, FOXE1
Abstract: Variants identified in earlier genome-wide association studies (GWAS) on differentiated thyroid carcinoma (DTC) explain about 10% of the overall estimated genetic contribution and could not provide complete insights into biological mechanisms involved in DTC susceptibility. Integrating systems biology information from model organisms, genome-wide expression data from tumor and matched normal tissue and GWAS data could help identifying DTC-associated genes, and pathways or functional networks in which they are involved. We performed data mining of GWAS data of the EPITHYR consortium (1551 cases and 1957 controls) using various pathways and protein–protein interaction (PPI) annotation databases and gene expression data from The Cancer Genome Atlas. We identified eight DTC-associated genes at known loci 2q35 (DIRC3), 8p12 (NRG1), 9q22 (FOXE1, TRMO, HEMGN, ANP32B, NANS) and 14q13 (MBIP). Using the EW_dmGWAS approach we found that gene networks related to glycogenolysis, glycogen metabolism, insulin metabolism and signal transduction pathways associated with muscle contraction were overrepresented with association signals (false discovery rate adjusted p-value
Published: 2021

10. Prognostic value of intratumoral Fusobacterium nucleatum and association with immune-related gene expression in oral squamous cell carcinoma patients

Author: Caroline Hoffmann, Marc Hilmi, Maud Kamal, Charlotte Lecerf, Odette Mariani, Eliane Piaggio, Cindy Neuzillet, Maria Lesnik, Ivan Bièche, Renaud Leclere, Sophie Vacher, Coraline Dubot, Edith Borcoman, Didier Meseure, Emmanuelle Jeannot, Valentin Calugaru, Manon Marchais, Nathalie Badois, Jerzy Klijanienko, Anne Schnitzler, Christophe Le Tourneau, Institut Curie [Paris], Immunité et cancer (U932), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Curie [Paris], Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Cochin (IC UM3 (UMR 8104 / U1016)), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)
Subjects: Male, Cancer microenvironment, Oncology, medicine.medical_specialty, Multivariate analysis, Science, [SDV]Life Sciences [q-bio], Article, Tumour biomarkers, 03 medical and health sciences, 0302 clinical medicine, stomatognathic system, Internal medicine, Tumor Microenvironment, medicine, Humans, Head and neck cancer, Lymph node, Retrospective Studies, 030304 developmental biology, 0303 health sciences, Multidisciplinary, Fusobacterium nucleatum, biology, Squamous Cell Carcinoma of Head and Neck, business.industry, Oral cancer, Middle Aged, biology.organism_classification, medicine.disease, Head and neck squamous-cell carcinoma, Gastrointestinal Microbiome, 3. Good health, Gene Expression Regulation, Neoplastic, stomatognathic diseases, Real-time polymerase chain reaction, medicine.anatomical_structure, Head and Neck Neoplasms, 030220 oncology & carcinogenesis, Cohort, Medicine, Tumour immunology, Immunohistochemistry, Female, business, CD163
Abstract: Changes in the oral microbiome, particularly Fusobacterium nucleatum, are associated with oral squamous cell carcinoma (OSCC). F. nucleatum has been reported to modulate local immunity in cancers. We aimed to assess the association between intratumoral F. nucleatum and clinico-pathological features, relapse, and overall survival (OS) in two independent cohorts of patients with OSCC, and to explore the interplay with immune-related genes. We retrospectively analyzed tissue samples from a first cohort of 122 patients with head and neck squamous cell carcinoma, including 61 OSCC (cohort #1), and a second cohort of 90 additional OSCC (cohort #2). We then performed a sensitivity analysis on the merged cohort of OSCC patients (N = 151). F. nucleatum 16S rRNA gene sequences were quantified using real-time quantitative PCR. The presence of gram-negative bacteria and macrophages was confirmed by LPS and CD163 immunostainings, respectively. F. nucleatum positivity was associated with older age, less alcohol and combined alcohol plus tobacco consumption, and less frequent lymph node invasion. There was a trend for a lower recurrence rate in F. nucleatum-positive cases, with less metastatic relapses compared to F. nucleatum-negative tumors, and significantly longer OS, relapse-free and metastasis-free survival. F. nucleatum status was independently associated with OS in multivariate analysis. Immune-related gene and immunohistochemistry analyses showed that gram-negative bacteria load inversely correlated with M2 macrophages. F. nucleatum-associated OSCC has a specific immune microenvironment, is more frequent in older, non-drinking patients, and associated with a favorable prognosis.
Published: 2021

11. Human papilloma virus (HPV) integration signature in Cervical Cancer: identification of MACROD2 gene as HPV hot spot integration site

Author: Kamal, Maud, Lameiras, Sonia, Deloger, Marc, Morel, Adeline, Vacher, Sophie, Lecerf, Charlotte, Dupain, C. lia, Jeannot, Emmanuelle, Girard, Elodie, Baulande, Sylvain, Dubot, Coraline, Kenter, Gemma, Jordanova, Ekaterina S., Berns, Els M. J. J., Bataillon, Guillaume, Popovic, Marina, Rouzier, Roman, Cacheux, Wulfran, le Tourneau, Christophe, Nicolas, Alain, Servant, Nicolas, Scholl, Suzy M., Bièche, Ivan, de la Rochefordiere, Anne, Fumoleau, Pierre, Mandic, Aljosa, Samet, Nina, Kamoun, Choumouss, Rondoff, Windy, Armanet, Sebastien, Rohel, Alexandra, Neffati, Souhir, Legrier, Marie-Emmanuelle, Mabiala, Sinette Ngoumou, Dureau, Sylvain, Errera, Coralie, Craina, Marius, Margan, Madalin, Samuels, Sanne, Zijlmans, Henry, Hillemanns, Peter, Dema, Sorin, Dema, Alis, Malenkovic, Goran, Djuran, Branislav, Floquet, Anne, Guyon, Frédéric, Colombo, Pierre Emmanuel, Fabbro, Michel, Kerr, Christine, Medical Oncology, Université Grenoble Alpes (UGA), Amsterdam Reproduction & Development (AR&D), CCA - Cancer biology and immunology, Obstetrics and gynaecology, AII - Infectious diseases, Institut Curie [Paris], Université Paris sciences et lettres (PSL), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Netherlands Cancer Institute (NKI), Antoni van Leeuwenhoek Hospital, Leiden University Medical Center (LUMC), Erasmus University Medical Center [Rotterdam] (Erasmus MC), Université Paris-Saclay, Hôpital privé Pays de Savoie, Dynamique de l'information génétique : bases fondamentales et cancer (DIG CANCER), Centre National de la Recherche Scientifique (CNRS)-Institut Curie [Paris]-Sorbonne Université (SU), Institut Cochin (IC UM3 (UMR 8104 / U1016)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Institute for Advanced Biosciences / Institut pour l'Avancée des Biosciences (Grenoble) (IAB), Centre Hospitalier Universitaire [Grenoble] (CHU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Etablissement français du sang - Auvergne-Rhône-Alpes (EFS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), ARD - Amsterdam Reproduction and Development, and Obstetrics and Gynaecology
Subjects: Oncology, Cancer Research, medicine.medical_specialty, Class I Phosphatidylinositol 3-Kinases, Hydrolases, Molecular biology, Anal Carcinoma, Virus Integration, [SDV]Life Sciences [q-bio], Uterine Cervical Neoplasms, [SDV.CAN]Life Sciences [q-bio]/Cancer, Article, 03 medical and health sciences, 0302 clinical medicine, SDG 3 - Good Health and Well-being, Hpv integration, Internal medicine, Chromosome instability, TP63, medicine, Humans, Papillomaviridae, 030304 developmental biology, Cervical cancer, Human papilloma virus, 0303 health sciences, Molecular medicine, business.industry, Papillomavirus Infections, virus diseases, Middle Aged, Prostate-Specific Antigen, medicine.disease, Progression-Free Survival, female genital diseases and pregnancy complications, 3. Good health, DNA Repair Enzymes, 030220 oncology & carcinogenesis, Cohort, Carcinoma, Squamous Cell, Female, Kallikreins, MACROD2 gene, business, Biomarkers
Abstract: Background Cervical cancer (CC) remains a leading cause of gynaecological cancer-related mortality with infection by human papilloma virus (HPV) being the most important risk factor. We analysed the association between different viral integration signatures, clinical parameters and outcome in pre-treated CCs. Methods Different integration signatures were identified using HPV double capture followed by next-generation sequencing (NGS) in 272 CC patients from the BioRAIDs study [NCT02428842]. Correlations between HPV integration signatures and clinical, biological and molecular features were assessed. Results Episomal HPV was much less frequent in CC as compared to anal carcinoma (p 300 different HPV-chromosomal junctions (inter- or intra-genic). The most frequent integration site in CC was in MACROD2 gene followed by MIPOL1/TTC6 and TP63. HPV integration signatures were not associated with histological subtype, FIGO staging, treatment or PFS. HPVs were more frequently episomal in PIK3CA mutated tumours (p = 0.023). Viral integration type was dependent on HPV genotype (p p = 0.011). Conclusions This is to our knowledge the first study assessing the prognostic value of HPV integration in a prospectively annotated CC cohort, which detects a hotspot of HPV integration at MACROD2; involved in impaired PARP1 activity and chromosome instability.
Published: 2021

12. The kinesin KIF1C transports APC-dependent mRNAs to cell protrusions

Author: Thomas Walter, Xavier Pichon, Edouard Bertrand, Racha Chouaib, Florian Mueller, Emeline Coleno, Tianhong Wang, Marion Peter, Kazem Zibara, Arthur Imbert, Stavroula Mili, Konstadinos Moissoglu, Centre de Bioinformatique (CBIO), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Université Paris sciences et lettres (PSL)
Subjects: 0303 health sciences, Messenger RNA, RNA localization, Chemistry, RNA, RNA transport, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Cell biology, 03 medical and health sciences, 0302 clinical medicine, Cytoplasm, Live cell imaging, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Kinesin, MRNA transport, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], 030217 neurology & neurosurgery, 030304 developmental biology
Abstract: RNA localization and local translation are important for numerous cellular functions. In mammals, a class of mRNAs localize to cytoplasmic protrusions in an APC-dependent manner, with roles during cell migration. Here, we investigated this localization mechanism. We found that the KIF1C motor interacts with APC-dependent mRNAs and is required for their localization. Live cell imaging revealed rapid, active transport of single mRNAs over long distances that requires both microtubules and KIF1C. Two color imaging directly showed single mRNAs transported by single KIF1C motors, with the 3’UTR being sufficient to trigger KIF1C-dependent RNA transport and localization. Moreover, KIF1C remained associated with peripheral, multimeric RNA clusters and was required for their formation. These results reveal an RNA transport pathway in mammalian cells, in which the KIF1C motor has a dual role both in transporting RNAs and in promoting their clustering within cytoplasmic protrusions. Interestingly, KIF1C also transports its own mRNA suggesting a possible feedback loop acting at the level of mRNA transport.
Published: 2021

13. Multiethnic genome-wide association study of differentiated thyroid cancer in the EPITHYR consortium

Author: Pierre Laurent-Puig, Yan Ren, Frédérique Rachédi, Pascal Guénel, Sandya Liyanarachchi, Asta Försti, Albert de la Chapelle, Om Kulkarni, Claire Mulot, Huiling He, Federica Gemignani, Julie Guibon, Anne-Valérie Guizard, Fabienne Lesueur, Rossella Elisei, Ausrele Kesminiene, Anthony F. Herzig, Mojgan Karimi, Pierre-Emmanuel Sugier, Thérèse Truong, Delphine Bacq-Daian, Anne-Louise Leutenegger, Constance Xhaard, Daniel F. Comiskey, Celia M Pereda, Evgenia Ostroumova, Florent de Vathaire, Elisabeth Adjadj, Françoise Borson-Chazot, Jean-François Deleuze, Carole Rubino, Anne Boland-Auge, Hauke Thomsen, Elise A. Lucotte, Marie-Christine Boutron-Ruault, Juan J Lence-Anta, Rosa Ortiz, Claire Schvartz, Institut Gustave Roussy (IGR), Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Université Paris-Saclay, Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Défaillance Cardiovasculaire Aiguë et Chronique (DCAC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Épidémiologie des radiations, épidémiologie clinique des cancers et survie (U1018 (Équipe 3) ), Institut Gustave Roussy (IGR)-Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Centre d'investigation clinique plurithématique Pierre Drouin [Nancy] (CIC-P), Centre d'investigation clinique [Nancy] (CIC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Cardiovascular and Renal Clinical Trialists [Vandoeuvre-les-Nancy] (INI-CRCT), Institut Lorrain du Coeur et des Vaisseaux Louis Mathieu [Nancy], French-Clinical Research Infrastructure Network - F-CRIN [Paris] (Cardiovascular & Renal Clinical Trialists - CRCT ), Service de biostatistique et d'épidémiologie (SBE), Direction de la recherche clinique [Gustave Roussy], Institut Gustave Roussy (IGR)-Institut Gustave Roussy (IGR), Centre National de Recherche en Génomique Humaine (CNRGH), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Université Paris Diderot, Sorbonne Paris Cité, Paris, France, Université Paris Diderot - Paris 7 (UPD7), Institut Jean Godinot [Reims], UNICANCER, Registre Général des Tumeurs du Calvados, CHU Caen, Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN)-Centre Régional de Lutte contre le Cancer François Baclesse [Caen] (UNICANCER/CRLC), Normandie Université (NU)-UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-UNICANCER, Unité de recherche interdisciplinaire pour la prévention et le traitement des cancers (ANTICIPE), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-CHU Caen, Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN)-Tumorothèque de Caen Basse-Normandie (TCBN)-Centre Régional de Lutte contre le Cancer François Baclesse [Caen] (UNICANCER/CRLC), Normandie Université (NU)-UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-UNICANCER-Institut National de la Santé et de la Recherche Médicale (INSERM), Hôpital de Taaone, Hôpital Louis Pradel [CHU - HCL], Hospices Civils de Lyon (HCL), Health Service and Performance Research (HESPER), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Institute of Oncology and Radiobiology, Ohio State University [Columbus] (OSU), University of Pisa - Università di Pisa, Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), Hopp Children's Cancer Center Heidelberg [Heidelber, Germany] (KITZ), German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ)-Heidelberg University Hospital [Heidelberg], German Cancer Consortium [Heidelberg] (DKTK), Génétique, génomique fonctionnelle et biotechnologies (UMR 1078) (GGB), Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO)-Université de Brest (UBO)-EFS-Institut National de la Santé et de la Recherche Médicale (INSERM), Maladies neurodéveloppementales et neurovasculaires (NeuroDiderot (UMR_S_1141 / U1141)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), Centre International de Recherche contre le Cancer - International Agency for Research on Cancer (CIRC - IARC), Organisation Mondiale de la Santé / World Health Organization Office (OMS / WHO), Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation, MESRI: 2102 918823 National Institute of Ecology, NIE Agence Nationale de la Recherche, ANR: ANR‐10‐COHO‐0006 Fondation ARC pour la Recherche sur le Cancer, ARC: PGA120150202302 Ligue Contre le Cancer Fondation de France Institut National Du Cancer, INCa: 9533 Agence Nationale de Sécurité Sanitaire de l’Alimentation, de l’Environnement et du Travail, ANSES Mutuelle Générale de l'Education Nationale, MGEN Electricité de France, EDF: EP 2019‐01, We thank Dr Yannick Rougier and Dr Dominique Baron-Dubourdieu for providing pathological reports for the NC study, as well as Dr Sylvie Laumond, Dr Jean-Paul Grangeon (Direction des affaires sanitaires et sociales de Nouvelle-Cal?donie) and the country's provincial health authorities (DPASS Sud, DPASS Nord, DPASS Iles Loyaut?) for support during data collection in the NC study. We thank Milagros Velasco, Mae Chappe and Idalmis Infante (Institute of Oncology and Radiobiology, La Havana, Cuba) and Silvia Turcios (National Institute of Endocrinology, La Havana, Cuba) for helping in the collection of the data in the Cuban Study. We thank John Paoaafaite and Joseph Teuri who contacted and interviewed cases and controls for the study. Finally, we also thank P. Morales, J. Iltis, P. Giraud, P. Didiergeorge, M. Brisard, G. Soubiran, B. Caillou, P. Dupire, J. Ienfa, G. de Clermont, N. Cerf, B. Oddo, M. Bambridge, C. Baron, A. Mouchard-Rachet, O. Simonet, D. Lamarque, J. Vabret, J. Delacre, M.P. Darquier and J. Leninger, for their help in the collection of the cases or in the organization of study in French Polynesia. We would like to thank the Association Centre de Regroupement Informatique et Statistique en Anatomie Pathologique en Provence-Alpes-C?te d'Azur (CRISAP PACA), as well as Dr Arlette Danzon, Dr Genevi?ve Sasolas, Dr Marc Christophe Sattonnet, Dr Marc Colonna, Dr Brigitte Lacour, Dr Michel Velten, Dr Enora Clero, Dr St?phane Maillard, Dr Laurent Bailly, Dr Eug?nia Marin? Barjoan, Dr Jean-Luc Lassalle, Dr Z Hafdi-Nejjari, Dr P Delafosse, Dr Elisabeth Adjadj, Kami-Marie Moreau, Cyrielle Orenes, Laurianne Sarrazin, St?phanie Bonnay, Fr?d?rique Chatelain, Maryse Barouh, Evelyne Rapp, Julie Festra?ts, Julie Valbousquet, Yusuf Atilgan, Jean Chappellet, Lallia Bedhouche, Florent Dayet and Ziyan Fami, for their help in the collection of cases, the organization and the management of the Young-Thyr study. We acknowledge Stefano Landi for the Italian GWAS data and Subhayan Chattopadhyay and Yasmeen Niazi (Division of Molecular Genetic Epidemiology, German Cancer Research Center?DKFZ) for technical assistance in these data analysis. We are grateful to Dr Herv? Perdry for his help in using the GASTON package. The EPITHYR GWAS was supported by Institut National du Cancer (grant number 9533) and Fondation ARC (grant number PGA120150202302). The E3N cohort received support from the MGEN, Gustave Roussy and Ligue contre le cancer for its setup and maintenance. The E3N cohort was also supported by a state grant from the Agence Nationale pour la Recherche (ANR) (grant number ANR-10-COHO-0006) within the Investissement d'Avenir program and from the French Ministry of Higher Education, Research and Innovation (MESRI, grant number 2102 918823). The other participating studies were funded by Ligue Nationale Contre le Cancer, ANR, the Direction G?n?rale de la Sante, the Agence Fran?aise de S?curit? Sanitaire de l'alimentation, de l'environnement et du travail (ANSES), CHILDTHYR EEC program, and the Fondation de France. JG was the recipient of a PhD fellowship from R?gion Ile-de-France, part of OK was the recipient of a post-doc fellowship from Electricit? de France (conseil scientifique de Radioprotection d'EDF, grant EP 2019-01)., The EPITHYR GWAS was supported by Institut National du Cancer (grant number 9533) and Fondation ARC (grant number PGA120150202302). The E3N cohort received support from the MGEN, Gustave Roussy and Ligue contre le cancer for its setup and maintenance. The E3N cohort was also supported by a state grant from the Agence Nationale pour la Recherche (ANR) (grant number ANR‐10‐COHO‐0006) within the Investissement d'Avenir program and from the French Ministry of Higher Education, Research and Innovation (MESRI, grant number 2102 918823). The other participating studies were funded by Ligue Nationale Contre le Cancer, the Direction Générale de la Sante, the Agence Française de Sécurité Sanitaire de l'alimentation, and the Fondation de France. JG was the recipient of a PhD fellowship from Région Ile‐de‐France, part of OK was the recipient of a post‐doc fellowship from Electricité de France (conseil scientifique de Radioprotection d'EDF, grant EP 2019‐01)., Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), EFS-Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)
Subjects: Male, Cancer Research, Native Hawaiian or Other Pacific Islander, [SDV]Life Sciences [q-bio], Ethnic group, Genome-wide association study, 0302 clinical medicine, Gene Frequency, Informed consent, Epidemiology, thyroid cancer, Chromosomes, Human, Medicine, Prospective cohort study, Thyroid cancer, Genetics, 0303 health sciences, education.field_of_study, Incidence (epidemiology), Middle Aged, Checklist, 3. Good health, Oncology, 030220 oncology & carcinogenesis, Pacific islanders, Female, Adult, medicine.medical_specialty, case-control study, education, Population, Biology, Pacific Islands, Polymorphism, Single Nucleotide, White People, 03 medical and health sciences, Humans, Genetic Predisposition to Disease, Thyroid Neoplasms, Aged, 030304 developmental biology, genome-wide association study, business.industry, Case-control study, medicine.disease, Clinical trial, Case-Control Studies, Family medicine, business
Abstract: Incidence of differentiated thyroid carcinoma (DTC) varies considerably between ethnic groups, with particularly high incidence rates in Pacific Islanders. Here, we conducted a genome-wide association study (GWAS) involving 1,554 cases/1,973 controls of European ancestry and 301 cases/348 controls of Oceanian ancestry from the EPITHYR consortium. Our results confirmed the association with the known DTC susceptibility loci at 2q35, 8p12, 9q22.33 and 14q13.3 in the European ancestry population and suggested two novel signals at 1p31.3 (rs334729) and 16q23.2 (rs16950982), which were associated with TSH levels in previous GWAS. We additionally replicated an association with 5p15.33 reported previously in Chinese and European populations. Except at 1p31.3, all associations were in the same direction in the population of Oceanian ancestry. The frequency of risk alleles at 2q35, 5p15.33 and 16q23.2 were significantly higher in Oceanians than in Europeans and may explain part of the highest DTC incidence observed in Oceanians.Competing Interest StatementThe authors have declared no competing interest.Funding StatementINCA (grant number 9533) and ARC (grant number PGA120150202302), Ligue Nationale Contre le Cancer, Agence Nationale pour la Recherche (ANR), the Direction Generale de la Sante, the Agence Francaise de Securite Sanitaire de l alimentation, de lenvironnement et du travail (ANSES), CHILDTHYR EEC program, and the Fondation de France. JG and CX were the recipient of a PhD fellowship from Region Ile-de-France.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:All participants provided informed consent and each study was approved by their governing ethics committee.All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesThe data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. (Less)
Published: 2021

14. Setting the basis of best practices and standards for curation and annotation of logical models in biology—highlights of the [BC]2 2019 CoLoMoTo/SysMod Workshop

Author: Denis Thieffry, Ioannis Xenarios, Sylvain Soliman, Claudine Chaouiya, Laurence Calzone, Aurélien Naldi, Julio Saez-Rodriguez, Cristina Casals-Casas, Vasundra Touré, Tomáš Helikar, Rahuman S. Malik Sheriff, Gautier Stoll, Martin Kuiper, Vincent Noël, Tom C. Freeman, Anna Niarakis, Marek Ostaszewski, Eugenia Oshurko, Paul Thomas, Laboratoire de recherche européen pour la polyarthrite rhumatoïde (GenHotel), Université d'Évry-Val-d'Essonne (UEVE)-Université Paris-Saclay, Norwegian University of Science and Technology [Trondheim] (NTNU), Norwegian University of Science and Technology (NTNU), University of Luxembourg [Luxembourg], European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, Swiss-Prot Group, Swiss Institute of Bioinformatics [Genève] (SIB), Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), University of Edinburgh, University of Southern California (USC), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Heidelberg University Hospital [Heidelberg], Laboratoire de l'Informatique du Parallélisme (LIP), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Ludwig Institute for Cancer Research, Computational systems biology and optimization (Lifeware), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut de Mathématiques de Marseille (I2M), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), University of Nebraska [Lincoln], University of Nebraska System, Institut de biologie de l'ENS Paris (IBENS), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École normale supérieure - Lyon (ENS Lyon), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Soliman, Sylvain, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), University of Nebraska–Lincoln, École Pratique des Hautes Études (EPHE), and École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL)
Subjects: Best practice, 0206 medical engineering, 02 engineering and technology, Plan (drawing), Logical modelling, Models, Biological, 03 medical and health sciences, Annotation, Model reusability, Annotation standards, Molecular Biology, Method Review, 030304 developmental biology, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], 0303 health sciences, Biological data, Computational model, Computational Biology, Reproducibility of Results, Data science, Reproducibility, Variety (cybernetics), Biocuration, Practice Guidelines as Topic, Key (cryptography), [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], 020602 bioinformatics, Information Systems
Abstract: The fast accumulation of biological data calls for their integration, analysis and exploitation through more systematic approaches. The generation of novel, relevant hypotheses from this enormous quantity of data remains challenging. Logical models have long been used to answer a variety of questions regarding the dynamical behaviours of regulatory networks. As the number of published logical models increases, there is a pressing need for systematic model annotation, referencing and curation in community-supported and standardised formats. This article summarises the key topics and future directions of a meeting entitled ‘Annotation and curation of computational models in biology’, organised as part of the 2019 [BC]2 conference. The purpose of the meeting was to develop and drive forward a plan towards the standardised annotation of logical models, review and connect various ongoing projects of experts from different communities involved in the modelling and annotation of molecular biological entities, interactions, pathways and models. This article defines a roadmap towards the annotation and curation of logical models, including milestones for best practices and minimum standard requirements.
Published: 2021

15. Identification of Tissue of Origin and Guided Therapeutic Applications in Cancers of Unknown Primary Using Deep Learning and RNA Sequencing (TransCUPtomics)

Author: Joshua J. Waterfall, Anne Vincent-Salomon, François-Clément Bidard, Alain Livartowski, Delphine Guillemot, Odette Mariani, Camille Benoist, Sylvain Baulande, Gaëlle Pierron, Christophe Le Tourneau, Sarah Watson, Julien Vibert, Olivier Delattre, Nadège Gruel, Unité de génétique et biologie des cancers (U830), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Curie [Paris], Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre d'Investigation Clinique en Biotherapie des cancers (CIC 1428 , CBT 507 ), Institut Gustave Roussy (IGR)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Versailles Saint-Quentin-en-Yvelines - UFR Sciences de la santé Simone Veil (UVSQ Santé), and Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)
Subjects: Adult, Male, Oncology, medicine.medical_specialty, [SDV]Life Sciences [q-bio], Pathology and Forensic Medicine, 03 medical and health sciences, Deep Learning, 0302 clinical medicine, Internal medicine, Biomarkers, Tumor, medicine, Humans, Prospective Studies, RNA-Seq, Aged, Retrospective Studies, 030304 developmental biology, Interpretability, Aged, 80 and over, 0303 health sciences, business.industry, Deep learning, Cancer, Retrospective cohort study, Middle Aged, medicine.disease, Primary tumor, Autoencoder, Data Accuracy, 3. Good health, Identification (information), 030220 oncology & carcinogenesis, Unknown primary, Neoplasms, Unknown Primary, Molecular Medicine, Female, Artificial intelligence, Gene Fusion, Transcriptome, business
Abstract: International audience; Cancers of unknown primary (CUP) are metastatic cancers for which the primary tumor is not found despite thorough diagnostic investigations. Multiple molecular assays have been proposed to identify the tissue of origin (TOO) and inform clinical care; however, none has been able to combine accuracy, interpretability, and easy access for routine use. We developed a classifier tool based on the training of a variational autoencoder to predict tissue of origin based on RNA-sequencing data. We used as training data 20,918 samples corresponding to 94 different categories, including 39 cancer types and 55 normal tissues. The TransCUPtomics classifier was applied to a retrospective cohort of 37 CUP patients and 11 prospective patients. TransCUPtomics exhibited an overall accuracy of 96% on reference data for TOO prediction. The TOO could be identified in 38 (79%) of 48 CUP patients. Eight of 11 prospective CUP patients (73%) could receive first-line therapy guided by TransCUPtomics prediction, with responses observed in most patients. The variational autoencoder added further utility by enabling prediction interpretability, and diagnostic predictions could be matched to detection of gene fusions and expressed variants. TransCUPtomics confidently predicted TOO for CUP and enabled tailored treatments leading to significant clinical responses. The interpretability of our approach is a powerful addition to improve the management of CUP patients. Copyright
Published: 2021

16. Multimodal liquid biopsy for early monitoring and outcome prediction of chemotherapy in metastatic breast cancer

Author: Charlotte Proudhon, Jérôme Lemonnier, Anthony Gonçalves, Frédérique Berger, Shufang Renault, Jean-Yves Pierga, François-Clément Bidard, Marie-Laure Tanguy, Christelle Levy, Marc Debled, Isabelle Desmoulins, Elodie Girard, Sylvain Baulande, Coraline Dubot, Christelle Jouannaud, Amanda Bortolini Silveira, Caroline Hego, Benoit Albaud, William Jacot, Maria Rios, Jean-Marc Ferrero, Florence Dalenc, Olivier Tredan, Marie-Ange Mouret-Reynier, Aurore Rampanou, CIC1428 IGR-CURIE, Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Versailles Saint-Quentin-en-Yvelines - UFR Sciences de la santé Simone Veil (UVSQ Santé), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Université Paris-Saclay, Institut Curie [Paris], Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Léon Bérard [Lyon], Departement d'Oncologie médicale [Paris], Institut du Cancer de Montpellier (ICM), Institut de Recherche en Cancérologie de Montpellier (IRCM - U1194 Inserm - UM), CRLCC Val d'Aurelle - Paul Lamarque-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université (AMU)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Département d'oncologie médicale, Institut Bergonié [Bordeaux], UNICANCER-UNICANCER, Centre Régional de Lutte contre le Cancer François Baclesse [Caen] (UNICANCER/CRLC), Normandie Université (NU)-UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN), Centre de Lutte contre le Cancer Antoine Lacassagne [Nice] (UNICANCER/CAL), UNICANCER-Université Côte d'Azur (UCA), Institut Jean Godinot [Reims], UNICANCER, Institut de Cancérologie de Lorraine - Alexis Vautrin [Nancy] (UNICANCER/ICL), Centre Jean Perrin [Clermont-Ferrand] (UNICANCER/CJP), Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse - Oncopole (IUCT Oncopole - UMR 1037), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Institut National de la Santé et de la Recherche Médicale (INSERM), Département d'oncologie médicale [Centre Georges-François Leclerc], Centre Régional de Lutte contre le cancer Georges-François Leclerc [Dijon] (UNICANCER/CRLCC-CGFL), Génétique et Biologie du Développement, Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université Paris Cité (UPCité), ANR-10-EQPX-0031,FIT,Internet du Futur (des Objets)(2010), ANR-10-INBS-0009,France-Génomique,Organisation et montée en puissance d'une Infrastructure Nationale de Génomique(2010), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Institut National de la Santé et de la Recherche Médicale (INSERM), COLO, Mouniati, Internet du Futur (des Objets) - - FIT2010 - ANR-10-EQPX-0031 - EQPX - VALID, Organisation et montée en puissance d'une Infrastructure Nationale de Génomique - - France-Génomique2010 - ANR-10-INBS-0009 - INBS - VALID, Institut Curie [Saint-Cloud], Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL), Institut de recherche en cancérologie de Montpellier (IRCM - U896 Inserm - UM1), Université Montpellier 1 (UM1)-CRLCC Val d'Aurelle - Paul Lamarque-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Aix Marseille Université (AMU), UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-CHU Toulouse [Toulouse]-Institut National de la Santé et de la Recherche Médicale (INSERM), and Université de Paris (UP)
Subjects: Oncology, medicine.medical_specialty, Bevacizumab, medicine.medical_treatment, [SDV.CAN]Life Sciences [q-bio]/Cancer, Predictive markers, 03 medical and health sciences, 0302 clinical medicine, Breast cancer, Circulating tumor cell, [SDV.CAN] Life Sciences [q-bio]/Cancer, Internal medicine, medicine, Pharmacology (medical), Radiology, Nuclear Medicine and imaging, Liquid biopsy, RC254-282, 030304 developmental biology, 0303 health sciences, Chemotherapy, business.industry, GATA3, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, medicine.disease, Metastatic breast cancer, 3. Good health, 030220 oncology & carcinogenesis, [SDV.SP.PHARMA] Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, [SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, Outcome prediction, business, medicine.drug
Abstract: Circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) are two cancer-derived blood biomarkers that inform on patient prognosis and treatment efficacy in breast cancer. We prospectively evaluated the clinical validity of quantifying both CTCs (CellSearch) and ctDNA (targeted next-generation sequencing). Their combined value as prognostic and early monitoring markers was assessed in 198 HER2-negative metastatic breast cancer patients. All patients were included in the prospective multicenter UCBG study COMET (NCT01745757) and treated by first-line chemotherapy with weekly paclitaxel and bevacizumab. Blood samples were obtained at baseline and before the second cycle of chemotherapy. At baseline, CTCs and ctDNA were respectively detected in 72 and 74% of patients and were moderately correlated (Kendall’s τ = 0.3). Only 26 (13%) patients had neither detectable ctDNA nor CTCs. Variants were most frequently observed in TP53 and PIK3CA genes. KMT2C/MLL3 variants detected in ctDNA were significantly associated with a lower CTC count, while the opposite trend was seen with GATA3 alterations. Both CTC and ctDNA levels at baseline and after four weeks of treatment were correlated with survival. For progression-free and overall survival, the best multivariate prognostic model included tumor subtype (triple negative vs other), grade (grade 3 vs other), ctDNA variant allele frequency (VAF) at baseline (per 10% increase), and CTC count at four weeks (≥5CTC/7.5 mL). Overall, this study demonstrates that CTCs and ctDNA have nonoverlapping detection profiles and complementary prognostic values in metastatic breast cancer patients. A comprehensive liquid-biopsy approach may involve simultaneous detection of ctDNA and CTCs.
Published: 2021

17. Review of clinical applications of radiation-enhancing nanoparticles

Author: E. Chajon, Valentin Calugaru, N. Scher, C. Le Tourneau, Sylvie Bonvalot, Juliette Thariat, C. Verry, Institut Curie [Paris], Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Curie - Saint Cloud (ICSC), Centre Eugène Marquis (CRLCC), CHU Grenoble, Synchrotron Radiation for Biomedicine = Rayonnement SynchroTROn pour la Recherche BiomédicalE (STROBE), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Radiothérapie [Centre François Baclesse], Centre Régional de Lutte contre le Cancer François Baclesse [Caen] (UNICANCER/CRLC), Normandie Université (NU)-UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU)-UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN), CCSD, Accord Elsevier, and MINES ParisTech - École nationale supérieure des mines de Paris
Subjects: 0106 biological sciences, Oncology, medicine.medical_specialty, Gadolinium, medicine.medical_treatment, [SDV]Life Sciences [q-bio], lcsh:Biotechnology, chemistry.chemical_element, Review Article, 01 natural sciences, Applied Microbiology and Biotechnology, 03 medical and health sciences, 010608 biotechnology, Internal medicine, lcsh:TP248.13-248.65, medicine, 030304 developmental biology, Radiothérapie, Cancer, Cervical cancer, 0303 health sciences, Radiotherapy, business.industry, Nanoparticules, medicine.disease, Head and neck squamous-cell carcinoma, 3. Good health, Clinical trial, Radiation therapy, [SDV] Life Sciences [q-bio], chemistry, Nanoparticles, Sarcoma, Liver cancer, business, Biotechnology
Abstract: Highlights • Inorganic nanoparticles activated by radiotherapy (RT) increase dose deposition within cancer cells compared to RT alone. • Recently, clinical evidence of the radiation-enhancing effects of NP has emerged. • Two radio-enhancement NP are currently under investigation in clinical trials: hafnium oxide NP and gadolinium-based NP. • So far, 229 patients have been treated with NP and RT for soft tissue sarcoma, head and neck cancers or liver cancer. • Intratumoral hafnium oxide nanoparticles were safe and improved efficacy in locally advanced sarcoma., Purpose Clinical evidence of the radiation-enhancing effects of nanoparticles has emerged. Materials and methods We searched the literature in English and French on PubMed up to October 2019. The search term was “nanoparticle” AND “radiotherapy”, yielding 1270 results. Results The two main NP used in clinical trials were hafnium oxide and gadolinium involving a total of 229 patients. Hafnium oxide NP were used in three phase 1/2 trials on sarcoma, head and neck squamous cell carcinoma or liver cancer and one phase 2/3 trial. There are six ongoing phase 1/2 clinical trials to evaluate the combination of gadolinium-based NP and RT for the treatment of brain metastases and cervical cancer. Conclusion So far, intratumoral hafnium oxide nanoparticles were safe and improved efficacy in locally advanced sarcoma.
Published: 2020

18. A Dual Protein-mRNA Localization Screen Reveals Compartmentalized Translation and Widespread Co-translational RNA Targeting

Author: Thomas Walter, Emeline Coleno, Florian Mueller, Adham Safieddine, Xavier Pichon, Nikolay Tsanov, Arthur Imbert, Marie-Cécile Robert, Aubin Samacoits, Edouard Bertrand, Racha Chouaib, Anthony A. Hyman, Kazem Zibara, Christophe Zimmer, Matthias Peter, Ina Poser, Oh Sung Kwon, Hervé Le Hir, Abdel-Meneem Traboulsi, Institut de Génétique Moléculaire de Montpellier (IGMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Laboratory of Stem Cells [Lebanese, Beirut] (ER045-PRASE), Lebanese University [Beirut] (LU), Centre de Bioinformatique (CBIO), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de biologie de l'ENS Paris (IBENS), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Imagerie et Modélisation - Imaging and Modeling, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG), Max-Planck-Gesellschaft, Equipe labellisée Ligue contre le Cancer, This project was supported by France BioImaging (ANR-10-INBS-04), the Agence Nationale de la Recherche (ANR-11-BSV8-018-02 and ANR-14-CE10-0018-01), the Institut Pasteur, the Ligue Nationale Contre le Cancer, and the Fondation pour la Recherche Médicale (FRM grant to E.B.). Furthermore, this work was supported by the French government under management of Agence Nationale de la Recherche as part of the 'Investissements d'avenir' program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute) and by the Labex EpiGenMed from the framework 'Investissements d’avenir.', ANR-10-INBS-0004,France-BioImaging,Développment d'une infrastructure française distribuée coordonnée(2010), ANR-11-BSV8-0018,EJCbirth,Impacts de la transcription par l'ARN polymérase II sur l'assemblage de l'EJC(2011), ANR-14-CE10-0018,HI-FISH,Etude systématique de l'expression des gènes à l'échelle des molécules uniques d'ARN(2014), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-10-LABX-0012,EpiGenMed,From Genome and Epigenome to Molecular Medicine: turning new paradigms in biology into the therapeutic strategies of tomorrow(2010), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris], Zimmer, Christophe, Développment d'une infrastructure française distribuée coordonnée - - France-BioImaging2010 - ANR-10-INBS-0004 - INBS - VALID, BLANC - Impacts de la transcription par l'ARN polymérase II sur l'assemblage de l'EJC - - EJCbirth2011 - ANR-11-BSV8-0018 - BLANC - VALID, Appel à projets générique - Etude systématique de l'expression des gènes à l'échelle des molécules uniques d'ARN - - HI-FISH2014 - ANR-14-CE10-0018 - Appel à projets générique - VALID, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, and Laboratoires d'excellence - From Genome and Epigenome to Molecular Medicine: turning new paradigms in biology into the therapeutic strategies of tomorrow - - EpiGenMed2010 - ANR-10-LABX-0012 - LABX - VALID
Subjects: RNA localization, Endosome, ASPM, translation factories, [SDV.BC.BC]Life Sciences [q-bio]/Cellular Biology/Subcellular Processes [q-bio.SC], Biology, General Biochemistry, Genetics and Molecular Biology, smFISH, Cell Line, 03 medical and health sciences, 0302 clinical medicine, local translation, Polysome, Gene expression, [SDV.BC.BC] Life Sciences [q-bio]/Cellular Biology/Subcellular Processes [q-bio.SC], Humans, RNA, Messenger, Molecular Biology, Gene, 030304 developmental biology, Centrosome, 0303 health sciences, Messenger RNA, co-translational targeting Manuscript, Translation (biology), Cell Biology, Translocon, Cell biology, Protein Transport, Gene Expression Regulation, Polyribosomes, Protein Biosynthesis, Beta-catenin, RNA, RNA transport, co-translational targeting, 030217 neurology & neurosurgery, Developmental Biology
Abstract: International audience; Local translation allows spatial control of gene expression. Here, we performed a dual protein-mRNA localization screen, using smFISH on 523 human cell lines expressing GFP-tagged genes. 32 mRNAs displayed specific cytoplasmic localizations with local translation at unexpected locations, including cytoplasmic protrusions, cell edges, endosomes, Golgi, the nuclear envelope, and centrosomes, the latter being cell-cycle-dependent. Automated classification of mRNA localization patterns revealed a high degree of intercellular heterogeneity. Surprisingly, mRNA localization frequently required ongoing translation, indicating widespread co-translational RNA targeting. Interestingly, while P-body accumulation was frequent (15 mRNAs), four mRNAs accumulated in foci that were distinct structures. These foci lacked the mature protein, but nascent polypeptide imaging showed that they were specialized translation factories. For β-catenin, foci formation was regulated by Wnt, relied on APC-dependent polysome aggregation, and led to nascent protein degradation. Thus, translation factories uniquely regulate nascent protein metabolism and create a fine granular compartmentalization of translation.
Published: 2020

19. A cis-acting mechanism mediates transcriptional memory at Polycomb target genes in mammals

Author: Dina Zielinski, Cecilia Lövkvist, Damarys Loew, Setareh Aflaki, Michel Wassef, Raphaël Margueron, Bérangère Lombard, Daniel Holoch, Tiphaine Hery, Martin Howard, Institut Curie [Paris], Génétique et Biologie du Développement, Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), John Innes Centre [Norwich], Biotech Research and Innovation Centre (BRIC), University of Copenhagen = Københavns Universitet (KU), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Laboratoire de Spectrométrie de Masse Protéomique, Gestionnaire, Hal Sorbonne Université, Biotechnology and Biological Sciences Research Council (BBSRC), University of Copenhagen = Københavns Universitet (UCPH), Mines Paris - PSL (École nationale supérieure des mines de Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)
Subjects: Male, Transcriptional Activation, Cellular differentiation, Polycomb-Group Proteins, Context (language use), Biology, Epigenesis, Genetic, 03 medical and health sciences, Mice, 0302 clinical medicine, Transcription (biology), [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Gene expression, Genetics, Gene silencing, Animals, Humans, Epigenetics, 030304 developmental biology, Mammals, 0303 health sciences, Polycomb Repressive Complex 2, Chromatin, Cell biology, Histone Code, Histone, biology.protein, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Female, 030217 neurology & neurosurgery
Abstract: Epigenetic inheritance of gene expression states enables a single genome to maintain distinct cellular identities. How histone modifications contribute to this process remains unclear. Using global chromatin perturbations and local, time-controlled modulation of transcription, we establish the existence of epigenetic memory of transcriptional activation for genes that can be silenced by the Polycomb group. This property emerges during cell differentiation and allows genes to be stably switched after a transient transcriptional stimulus. This transcriptional memory state at Polycomb targets operates in cis; however, rather than relying solely on read-and-write propagation of histone modifications, the memory is also linked to the strength of activating inputs opposing Polycomb proteins, and therefore varies with the cellular context. Our data and computational simulations suggest a model whereby transcriptional memory arises from double-negative feedback between Polycomb-mediated silencing and active transcription. Transcriptional memory at Polycomb targets thus depends not only on histone modifications but also on the gene-regulatory network and underlying identity of a cell. Polycomb proteins can regulate epigenetic transcriptional memory during cell differentiation. This memory operates in cis and is linked to the strength of activating inputs.
Published: 2020

20. A localization screen reveals translation factories and widespread co-translational RNA targeting

Author: Ina Poser, Xavier Pichon, Edouard Bertrand, Nikolay Tsanov, Christophe Zimmer, Emeline Coleno, Hervé Le Hir, Oh Sung Kwon, Adham Safieddine, Thomas Walter, Marie-Cécile Robert, Marion Peter, Abdel-Meneem Traboulsi, Arthur Imbert, Kazem Zibara, Florian Mueller, Aubin Samacoits, Racha Chouaib, Anthony A. Hyman, Institut de Génétique Moléculaire de Montpellier (IGMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Equipe labelisée Ligue Contre le Cancer, Laboratory of Stem Cells [Lebanese, Beirut] (ER045-PRASE), Lebanese University [Beirut] (LU), Faculty of Sciences [Lebanese University], Equipe labellisée Ligue contre le Cancer, Centre de Bioinformatique (CBIO), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Imagerie et Modélisation - Imaging and Modeling, Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris], Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG), Max-Planck-Gesellschaft, Université Paris sciences et lettres (PSL), This project was supported by France BioImaging (ANR-10-INBS-04), the Agence Nationale de la Recherche (ANR-11-BSV8-018-02 and ANR-14-CE10-0018-01), the Institut Pasteur, the Ligue Nationale Contre le Cancer and the Fondation pour la Recherche Médicale (FRM grant to EB). This work was supported by the Labex EpiGenMed, from the framework 'Investissements d’avenir'., ANR-10-INBS-0004,France-BioImaging,Développment d'une infrastructure française distribuée coordonnée(2010), ANR-14-CE10-0018,HI-FISH,Etude systématique de l'expression des gènes à l'échelle des molécules uniques d'ARN(2014), Lemesle, Marie, Développment d'une infrastructure française distribuée coordonnée - - France-BioImaging2010 - ANR-10-INBS-0004 - INBS - VALID, Appel à projets générique - Etude systématique de l'expression des gènes à l'échelle des molécules uniques d'ARN - - HI-FISH2014 - ANR-14-CE10-0018 - Appel à projets générique - VALID, Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Faculty of Sciences [Lebanese University] | Faculté des Sciences [Université Libanaise], Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de biologie de l'ENS Paris (IBENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0303 health sciences, Messenger RNA, RNA localization, Translation (biology), translation factories, Biology, Translocon, smFISH, Cell biology, 03 medical and health sciences, 0302 clinical medicine, local translation, Cytoplasm, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Polysome, co-translational targeting +Equal contributions, Gene expression, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, RNA transport, Gene, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract: Publié sur BioRxiv le 21 mai 2020 : https://www.biorxiv.org/content/10.1101/2020.05.20.106989v1; Local translation allows a spatial control of gene expression. Here, we performed a dual protein/mRNA localization screen, using smFISH on 523 human cell lines expressing GFP-tagged genes. A total of 32 mRNAs displayed specific cytoplasmic localizations, and we observed local translation at unexpected locations, including cytoplasmic protrusions, cell edges, endosomes, Golgi, the nuclear envelope and centrosomes, the latter being cell cycle dependent. Quantitation of mRNA distribution and automatic pattern classification revealed a high degree of localization heterogeneity between cells. Surprisingly, mRNA localization frequently required ongoing translation, indicating widespread co-translational RNA targeting. Interestingly, while P-body accumulation was frequent (15 mRNAs), four mRNAs accumulated in foci that were distinct structures. These foci lacked the mature protein, but nascent polypeptide imaging showed that they were specialized translation factories. For β-catenin, foci formation was regulated by Wnt, relied on APC-dependent polysome aggregation, and led to nascent protein degradation. Thus, translation factories uniquely regulate nascent protein metabolism and create a fine granular compartmentalization of translation.
Published: 2020

21. Biological networks and GWAS: comparing and combining network methods to understand the genetics of familial breast cancer susceptibility in the GENESIS study

Author: Héctor Climente-González, Chloé-Agathe Azencott, Nadine Andrieu, Christine Lonjou, Fabienne Lesueur, Dominique Stoppa-Lyonnet, Centre de Bioinformatique (CBIO), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Service de génétique, Institut Curie Paris, ANR-18-CE45-0021,SCAPHE,Méthodes pour la découverte de combinaisons de SNPs associées avec un phénotype à partir de données génome entier(2018), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), European Project: 666003,H2020,H2020-MSCA-COFUND-2014,IC-3i-PhD(2016), Azencott, Chloé-Agathe, APPEL À PROJETS GÉNÉRIQUE 2018 - Méthodes pour la découverte de combinaisons de SNPs associées avec un phénotype à partir de données génome entier - - SCAPHE2018 - ANR-18-CE45-0021 - AAPG2018 - VALID, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Institut Curie 3-i PhD Program - IC-3i-PhD - - H20202016-09-01 - 2021-09-01 - 666003 - VALID, and MINES ParisTech - École nationale supérieure des mines de Paris
Subjects: Genetics, 0303 health sciences, Cancer, Context (language use), Genome-wide association study, Disease, Biology, medicine.disease, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], 3. Good health, 03 medical and health sciences, 0302 clinical medicine, Breast cancer, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], 030220 oncology & carcinogenesis, medicine, Familial breast cancer, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Gene, Biological network, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], 030304 developmental biology
Abstract: Network approaches to disease use biological networks, which model functional relationships between the molecules in a cell, to generate hypotheses about the genetics of complex diseases. Several among them jointly consider gene scores, representing the association between each gene and the disease, and the biological context of each gene, modeled by a network. Here, we study six such network methods using gene scores from GENESIS, a genome-wide association study (GWAS) on French women with non-BRCA familial breast cancer. We provide a critical comparison of these six methods, discussing the impact of their mathematical formulation and parameters. Using a biological network yields more compelling results than standard GWAS analyses. Indeed, we find significant overlaps between our solutions and the genes identified in the largest GWAS on breast cancer susceptibility. We further propose to combine these solutions into a consensus network, which brings further insights. The consensus network contains COPS5, a gene related to multiple hallmarks of cancer, and 14 of its neighbors. The main drawback of network methods is that they are not robust to small perturbations in their inputs. Therefore, we propose a stable consensus solution, formed by the most consistently selected genes in multiple subsamples of the data. In GENESIS, it is composed of 68 genes, enriched in known breast cancer susceptibility genes (BLM, CASP8, CASP10, DNAJC1, FGFR2, MRPS30, and SLC4A7, P-value = 3 × 10 4) and occupying more central positions in the network than most genes. The network is organized around CUL3, which is involved in the regulation of several genes linked to cancer progression. In conclusion, we showed how network methods help overcome the lack of statistical power of GWAS and improve their interpretation. Project-agnostic implementations of all methods are available at https://github.com/hclimente/gwas-tools.Author summaryGenome-wide association studies (GWAS) scan thousands of genomes to identify variants associated with a complex trait. Over the last 15 years, GWAS have advanced our understanding of the genetics of complex diseases, and in particular of hereditary cancers. However, they have led to an apparent paradox: the more we perform such studies, the more it seems that the entire genome is involved in every disease. The omnigenic model offers an appealing explanation: only a limited number of core genes are directly involved in the disease, but gene functions are deeply interrelated, and so many other genes can alter the function of the core genes. These interrelations are often modeled as networks, and multiple algorithms have been proposed to use these networks to identify the subset of core genes involved in a specific trait. This study applies and compares six such network methods on GENESIS, a GWAS dataset for familial breast cancer in the French population. Combining these approaches allows us to identify potentially novel breast cancer susceptibility genes and provides a mechanistic explanation for their role in the development of the disease. We provide ready-to-use implementations of all the examined methods.
Published: 2020

22. EXPERIMENTALLY-GENERATED GROUND TRUTH FOR DETECTING CELL TYPES IN AN IMAGE-BASED IMMUNOTHERAPY SCREEN

Author: Franck Perez, Zelia Gouveia, Thomas Walter, Joseph Boyd, Centre de Bioinformatique (CBIO), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Paris sciences et lettres (PSL), and ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019)
Subjects: Microscope, High Content Screening, Computer science, Phase contrast microscopy, Computer Vision, Image processing, Signal, law.invention, 03 medical and health sciences, 0302 clinical medicine, law, Fluorescence microscope, 030212 general & internal medicine, 030304 developmental biology, 0303 health sciences, Ground truth, business.industry, deep learning, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, object detection, Fluorescence, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Chimeric antigen receptor, Object detection, phase contrast microscopy, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business
Abstract: International audience; Chimeric antigen receptor is an immunotherapy whereby T lympho-cytes are engineered to selectively attack cancer cells. Image-based screens of CART cells, combining phase contrast and fluorescence microscopy, suffer from the gradual quenching of the fluorescent signal, making the reliable monitoring of cell populations across time-lapse imagery difficult. We propose to leverage the available fluorescent markers as an experimentally-generated ground truth, without recourse to manual annotation. With some simple image processing, we are able to segment and assign cell type classes automatically. This ground truth is sufficient to train a neural object detection system from the phase contrast signal alone, potentially eliminating the need for the cumbersome fluorescent markers. This approach will underpin the development of cheap and robust microscope-based protocols to quantify CART activity against tumor cells in vitro.
Published: 2020

23. Robust and Scalable Learning of Complex Intrinsic Dataset Geometry via ElPiGraph

Author: Evgeny M. Mirkes, Louis Faure, Jonathan Bac, Alexis Martin, Luca Albergante, Huidong Chen, Luca Pinello, Andrei Zinovyev, Emmanuel Barillot, Alexander N. Gorban, Institut Curie [Paris], Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Bioinformatique (CBIO), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Sensyne Health [Oxford], University of Leicester, Lobachevsky State University [Nizhni Novgorod], Centre de Recherches Interdisciplinaires [Paris] (CRI), Harvard Medical School [Boston] (HMS), Broad Institute of MIT and Harvard (BROAD INSTITUTE), Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], ECE Paris – Ecole d'ingénieurs, Medical University of Vienna (MUW), INCa PLBIO program, CALYS, INCA_11692ITMO Cancer SysBio program (MOSAIC)Grant number 2018-182734 from the Chan Zuckerberg Initiative DAF, ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), European Project: 826121,iPaediatricCurie, Mines Paris - PSL (École nationale supérieure des mines de Paris), Zinovyev, Andrei, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, European Union’s Horizon 2020 program (grant No. 826121, iPC project) - iPaediatricCurie - 826121 - INCOMING, and Medizinische Universität Wien = Medical University of Vienna
Subjects: Theoretical computer science, Point cloud, General Physics and Astronomy, lcsh:Astrophysics, topological grammars, Article, principal graphs, 03 medical and health sciences, 0302 clinical medicine, Software, data approximation, lcsh:QB460-466, principal trees, lcsh:Science, Geometry and topology, 030304 developmental biology, Complex data type, 0303 health sciences, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], business.industry, software, Topological graph, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], lcsh:QC1-999, Scalability, Unsupervised learning, Topological graph theory, lcsh:Q, business, lcsh:Physics, 030217 neurology & neurosurgery
Abstract: Multidimensional datapoint clouds representing large datasets are frequentlycharacterized by non‐trivial low‐dimensional geometry and topology which can be recovered byunsupervised machine learning approaches, in particular, by principal graphs. Principal graphsapproximate the multivariate data by a graph injected into the data space with some constraintsimposed on the node mapping. Here we present ElPiGraph, a scalable and robust method forconstructing principal graphs. ElPiGraph exploits and further develops the concept of elasticenergy, the topological graph grammar approach, and a gradient descent‐like optimization of thegraph topology. The method is able to withstand high levels of noise and is capable ofapproximating data point clouds via principal graph ensembles. This strategy can be used toestimate the statistical significance of complex data features and to summarize them into a singleconsensus principal graph. ElPiGraph deals efficiently with large datasets in various fields such asbiology, where it can be used for example with single‐cell transcriptomic or epigenomic datasets toinfer gene expression dynamics and recover differentiation landscapes.
Published: 2020

24. Collective intelligence defines biological functions in Wikipedia as communities in the hidden protein connection network

Author: Urszula Czerwinska, Emmanuel Barillot, Andrei Zinovyev, Laura Cantini, Dima L. Shepelyansky, Klaus M. Frahm, Institut Curie [Paris], Dynamique des interactions membranaires normales et pathologiques (DIMNP), Université Montpellier 1 (UM1)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Information et Chaos Quantiques (LPT), Laboratoire de Physique Théorique (LPT), Institut de Recherche sur les Systèmes Atomiques et Moléculaires Complexes (IRSAMC), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche sur les Systèmes Atomiques et Moléculaires Complexes (IRSAMC), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), MASTODONS-2016/2017 CNRS project APLIGOOGLE, ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-11-IDEX-0002,UNITI,Université Fédérale de Toulouse(2011), Shepelyansky, Dima, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Initiative d'excellence - Université Fédérale de Toulouse - - UNITI2011 - ANR-11-IDEX-0002 - IDEX - VALID, Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université Montpellier 1 (UM1), Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), and MINES ParisTech - École nationale supérieure des mines de Paris
Subjects: Proteomics, [SDV.BIO]Life Sciences [q-bio]/Biotechnology, Protein Extraction, Computer science, Social Sciences, Biochemistry, 0302 clinical medicine, Sociology, Databases, Genetic, Protein Interaction Mapping, Macromolecular Structure Analysis, Cluster Analysis, Biology (General), Biological Phenomena, Extraction Techniques, 0303 health sciences, Google matrix, Markov chains, Applied Mathematics, Simulation and Modeling, [PHYS.PHYS.PHYS-SOC-PH]Physics [physics]/Physics [physics]/Physics and Society [physics.soc-ph], Collective intelligence, Online Encyclopedias, Graph, 3. Good health, Nucleic acids, Physical Sciences, Encyclopedia, Graph (abstract data type), Protein Interaction Networks, Algorithms, Network Analysis, Research Article, Wikipedia networks, Computer and Information Sciences, Protein Structure, QH301-705.5, DNA repair, Research and Analysis Methods, World Wide Web, 03 medical and health sciences, Web page, Global network, Genetics, Mass Media, Protein Interactions, Molecular Biology, Probability, 030304 developmental biology, Internet, protein networks, Computational Biology, Proteins, Biology and Life Sciences, DNA, Gene Wiki, Communications, [SDV.BIO] Life Sciences [q-bio]/Biotechnology, Search Engine, [PHYS.PHYS.PHYS-SOC-PH] Physics [physics]/Physics [physics]/Physics and Society [physics.soc-ph], Encyclopedias, Protein Structure Networks, Mathematics, 030217 neurology & neurosurgery
Abstract: English Wikipedia, containing more than five millions articles, has approximately eleven thousands web pages devoted to proteins or genes most of which were generated by the Gene Wiki project. These pages contain information about interactions between proteins and their functional relationships. At the same time, they are interconnected with other Wikipedia pages describing biological functions, diseases, drugs and other topics curated by independent, not coordinated collective efforts. Therefore, Wikipedia contains a directed network of protein functional relations or physical interactions embedded into the global network of the encyclopedia terms, which defines hidden (indirect) functional proximity between proteins. We applied the recently developed reduced Google Matrix (REGOMAX) algorithm in order to extract the network of hidden functional connections between proteins in Wikipedia. In this network we discovered tight communities which reflect areas of interest in molecular biology or medicine and can be considered as definitions of biological functions shaped by collective intelligence. Moreover, by comparing two snapshots of Wikipedia graph (from years 2013 and 2017), we studied the evolution of the network of direct and hidden protein connections. We concluded that the hidden connections are more dynamic compared to the direct ones and that the size of the hidden interaction communities grows with time. We recapitulate the results of Wikipedia protein community analysis and annotation in the form of an interactive online map, which can serve as a portal to the Gene Wiki project., Author summary The long-standing effort for annotating protein functions from published experimental evidences is still far from being completed, partly due to a limited number of biocurators involved in it. Wikipedia was thought to be a suitable platform for the protein function curation crowdsourcing through exploiting the wisdom of the crowd principle. Starting from 2008, English Wikipedia was automatically populated with thousands of protein pages and links between them (Gene Wiki project), which created a useful and rapidly evolving knowledge resource. However, it remains unclear what is the benefit of hyperlinking protein pages with the whole Wikipedia knowledge corpus. We applied the recently introduced network analysis method, called reduced Google Matrix (REGOMAX), in order to study the structure of direct and indirect (hidden) links between protein pages through the rest of the global Wikipedia network. As expected, the network of direct links had node degree distribution approximately following the power law. In contrast, the network of hidden links was characterized by larger than expected tight communities of proteins related to their known functions, such as involvement in immune system. The “friendship network” of these protein groups can be used for automated annotations of their functions from non-protein Wikipedia pages. We estimated the size of the expert Wikipedia contributor community, specifically working on protein and associated pages, to be nearly 1000 wikipedians with primarily biomedical background. We conclude that the structure of global Wikipedia network can improve the annotation of protein functions by amplifying the wisdom of the crowd effect.
Published: 2020

25. BRCAness, SLFN11, and RB1 loss predict response to topoisomerase I inhibitors in triple-negative breast cancers

Author: Petra ter Brugge, Laetitia Fuhrmann, Fabien Reyal, Rania El-Botty, Thibaut Larcher, Laura Sourd, Sophie Château-Joubert, Agnès Noël, Jean-Luc Servely, Léa Huguet, Ivan Bièche, Pierre Foidart, Jos Jonkers, Philippe La Rosa, Pierre Painsec, Sophie Leboucher, Ludivine Morisset, Fariba Nemati, Georges Lucotte, Elodie Montaudon, Didier Decaudin, Christopher R. Mueller, Florence Coussy, Ahmed Dahmani, Marc-Henri Stern, Marie-France Poupon, Nor Eddine Sounni, Sophie Vacher, Adrien Briaux, Yves Pommier, Anne Vincent Salomon, Elisabetta Marangoni, Cécile Reyes, Tatiana Popova, Translational Research Department,Medical Oncology Department, Genetics Department, Institut Curie, Translational Research Department, BioPôle Alfort, École nationale vétérinaire d'Alfort (ENVA), PSL Research University, UMR3306, Département Physiologie Animale et Systèmes d'Elevage (PHASE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Physiopathologie Animale et bioThérapie du muscle et du système nerveux (PAnTher), Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Genetics Department, University of Santiago de Compostela, Université Paris sciences et lettres (PSL), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), U830, Institut National de la Santé et de la Recherche Médicale (INSERM), Laboratory of Tumor and Developmental Biology, Groupe Interdisciplinaire de Génoprotéomique Appliqué-Cancer (GIGA-Cancer), Université de Liège, Translational Research Department, Medical Oncology Department, Department of Pathology, University of Veterinary and Animal Sciences, Lahore, Surgery Department, CRLCC Paul Strauss, U932, Immunity and Cancer, Queen's cancer reserach institute, Queen's University, Division of Molecular Pathology and Cancer Genomics Centre Netherlands, Netherlands Cancer Institute, Developmental Therapeutics Branch and Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, ProdInra, Archive Ouverte, École nationale vétérinaire - Alfort (ENVA), École nationale vétérinaire, agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Universidade de Santiago de Compostela [Spain] (USC ), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Biologie du développement et reproduction (BDR), École nationale vétérinaire d'Alfort (ENVA)-Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS), Développement et Pathologie du Tissu Musculaire (DPTM), Ecole Nationale Vétérinaire de Nantes-Institut National de la Recherche Agronomique (INRA), PSL Research University, U900, and Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Nantes
Subjects: Anthracycline, DNA repair, Ubiquitin-Protein Ligases, [SDV]Life Sciences [q-bio], Triple Negative Breast Neoplasms, Topoisomerase-I Inhibitor, Irinotecan, Article, 03 medical and health sciences, 0302 clinical medicine, medicine, Humans, CHEK1, skin and connective tissue diseases, 030304 developmental biology, 0303 health sciences, biology, Retinoblastoma, business.industry, Topoisomerase, Nuclear Proteins, General Medicine, medicine.disease, eye diseases, 3. Good health, [SDV] Life Sciences [q-bio], Retinoblastoma Binding Proteins, 030220 oncology & carcinogenesis, Cancer research, biology.protein, Topoisomerase I Inhibitors, business, Schlafen family member 11, medicine.drug
Abstract: Topoisomerase I (TOP1) inhibitors trap TOP1 cleavage complexes resulting in DNA double-strand breaks (DSBs) during replication, which are repaired by homologous recombination (HR). Triple-negative breast cancer (TNBC) could be eligible for TOP1 inhibitors given the considerable proportion of tumors with a defect in HR-mediated repair (BRCAness). The TOP1 inhibitor irinotecan was tested in 40 patient-derived xenografts (PDXs) of TNBC. BRCAness was determined with a single-nucleotide polymorphism (SNP) assay, and expression of Schlafen family member 11 (SLFN11) and retinoblastoma transcriptional corepressor 1 (RB1) was evaluated by real-time polymerase chain reaction (RT-PCR) and immunohistochemistry analyses. In addition, the combination of irinotecan and the ataxia telangiectasia and Rad3-related protein (ATR) inhibitor VE-822 was tested in SLFN11-negative PDXs, and two clinical non-camptothecin TOP1 inhibitors (LMP400 and LMP776) were tested. Thirty-eight percent of the TNBC models responded to irinotecan. BRCAness combined with high SLFN11 expression and RB1 loss identified highly sensitive tumors, consistent with the notion that deficiencies in cell cycle checkpoints and DNA repair result in high sensitivity to TOP1 inhibitors. Treatment by the ATR inhibitor VE-822 increased sensitivity to irinotecan in SLFN11-negative PDXs and abolished irinotecan-induced phosphorylation of checkpoint kinase 1 (CHK1). LMP400 (indotecan) and LMP776 (indimitecan) showed high antitumor activity in BRCA1-mutated or BRCAness-positive PDXs. Last, low SLFN11 expression was associated with poor survival in 250 patients with TNBC treated with anthracycline-based chemotherapy. In conclusion, a substantial proportion of TNBC respond to irinotecan. BRCAness, high SLFN11 expression, and RB1 loss are highly predictive of response to irinotecan and the clinical indenoisoquinoline TOP1 inhibitors.
Published: 2020

26. Risk-reducing salpingo-oophorectomy, natural menopause, and breast cancer risk: an international prospective cohort of BRCA1 and BRCA2 mutation carriers

Author: Mavaddat, N., Antoniou, A.C., Mooij, T.M., Hooning, M.J., Heemskerk-Gerritsen, B.A., Nogues, C., Laborde, L., Breysse, E., Stoppa-Lyonnet, D., Gauthier-Villars, M., Buecher, B., Caron, O., Fourme-Mouret, E., Fricker, J.P., Lasset, C., Bonadona, V., Berthet, P., Faivre, L., Luporsi, E., Mari, V., Gladieff, L., Gesta, P., Sobol, H., Eisinger, F., Longy, M., Dugast, C., Colas, C., Coupier, I., Pujol, P., Corsini, C., Lortholary, A., Vennin, P., Adenis, C., Nguyen, T.D., Delnatte, C., Tinat, J., Tennevet, I., Limacher, J.M., Maugard, C., Bignon, Y.J., Demange, L., Penet, C., Dreyfus, H., Cohen-Haguenauer, O., Venat-Bouvet, L., Leroux, D., Zattara-Cannoni, H., Fert-Ferrer, S., Bera, O., Ellis, S., Barrowdale, D., Frost, D., Evans, D.G., Izatt, L., Adlard, J., Eeles, R., Brewer, C., Tischkowitz, M., Henderson, A., Cook, J., Eccles, D., Hogervorst, F.B.L., Collee, J.M., Asperen, C.J. van, Mensenkamp, A.R., Ausems, M.G.E.M., Meijers-Heijboer, H.E.J., Engelen, K. van, Blok, M.J., Oosterwijk, J.C., Verloop, J., Broek, E. van den, Mourits, M.J.E., Koppert, L.B., Hopper, J.L., John, E.M., Chung, W.K., Andrulis, I.L., Daly, M.B., Buys, S.S., Benitez, J., Caldes, T., Jakubowska, A., Simard, J., Singer, C.F., Tan, Y., Olah, E., Navratilova, M., Foretova, L., Gerdes, A.M., Roos-Blom, M.J., Leeuwen, F.E. van, Arver, B., Olsson, H., Schmutzler, R.K., Engel, C., Kast, K., Phillips, K.A., Terry, M.B., Milne, R.L., Goldgar, D.E., Rookus, M.A., Andrieu, N., Easton, D.F., GENEPSO, EMBRACE, HEBON, kConFab Investigators, IBCCS, kConFab, BCFR, University of Cambridge [UK] (CAM), Netherlands Cancer Institute (NKI), Antoni van Leeuwenhoek Hospital, Erasmus MC Cancer Institute, Rotterdam, Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC), Institut Curie [Paris], Institut Gustave Roussy (IGR), Département de médecine oncologique [Gustave Roussy], Centre Hospitalier Georges Renon [Niort] (CH Georges Renon Niort), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Centre Catherine-de-Sienne [Nantes] (CCS), Manchester University NHS Foundation Trust (MFT), Guy's & St Thomas' NHS Foundation Trust, Chapel Allerton Hospital, University of Leeds, Royal Marsden NHS Foundation Trust, Royal Devon & Exeter Hospital, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Sheffield Children's NHS Foundation Trust, Wessex clinical genetics service, Vrije Universiteit Amsterdam [Amsterdam] (VU), University Medical Center Groningen [Groningen] (UMCG), Department of Medical Genetics, University Medical Center [Utrecht], Centre for MEGA Epidemiology, The University of Melbourne, Victoria, Australia, Department of Epidemiology, Cancer Prevention Institute of California, Columbia University [New York], Departments of Molecular Genetics and Laboratory Medicine and Pathobiology, University of Toronto-Cancer Care Ontario, Division of Population Science, Fox Chase Cancer Center, Department of Internal Medicine, Huntsman Cancer Institute, Departemento Genetica Humana, Centro Nacional Investigaciones Oncologicas, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos [Madrid, Spain] (IdISSC), Department of Genetics and Pathology, International Hereditary Cancer Centre-Pomeranian Medical University [Szczecin] (PUM), Cancer Genomics Laboratory, Centre Hospitalier Universitaire de Québec, Division of Special Gynecology, Medizinische Universität Wien = Medical University of Vienna-Department of OB/GYN, Medizinische Universität Wien = Medical University of Vienna, National Institute of Oncology, Masaryk Memorial Cancer Institute (MMCI), Masaryk University [Brno] (MUNI), Department of Clinical Genetics [Copenhagen], Rigshospitalet [Copenhagen], Copenhagen University Hospital-Copenhagen University Hospital, The Netherlands Cancer Institute [Amsterdam, The Netherlands], Radiumhemmet, Karolinska University Hospital [Stockholm], Department of Oncology, Lund University Hospital, Division of Molecular Gyneco-Oncology, Department of Gynaecology and Obstetrics, Clinical Center Un, Institute for Medical Informatics, Statistics and Epidemiology [Leipzig] (IMISE), Universität Leipzig [Leipzig], Department of Gynaecology and Obstetrics, University Hospital Carl Gustav Carus, Dept of Haematology and Medical Oncology, Peter MacCallum Cancer Center, Department of Epidemiology [Columbia University], Columbia University [New York]-Columbia Mailman School of Public Health, Cancer Epidemiology Centre, Cancer Council Victoria, International Agency for Cancer Research (IACR), Netherlands Cancer Institute, Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Paris sciences et lettres (PSL), Cancer Research U.K. Genetic Epidemiology Unit, Strangeways Research Laboratory, Andrieu, Nadine, Human genetics, Epidemiology and Data Science, Mavaddat, Nasim [0000-0003-0307-055X], Eeles, Ros [0000-0002-3698-6241], Engel, Christoph [0000-0002-7247-282X], Apollo - University of Cambridge Repository, Pomeranian Medical University-International Hereditary Cancer Centre, Masaryk Memorial Cancer Institute (RECAMO), Columbia Mailman School of Public Health-Columbia University [New York], Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Antoniou, Antonis [0000-0001-9223-3116], Tischkowitz, Marc [0000-0002-7880-0628], Easton, Douglas [0000-0003-2444-3247], Medical Oncology, Surgery, Cancer Research UK (Reino Unido), NIH - National Cancer Institute (NCI) (Estados Unidos), United States Department of Health and Human Services, Ministerio de Economía y Competitividad (España), Unión Europea. Comisión Europea. European Research Council (ERC), Norwegian EEA Financial Mechanism, Hungarian Scientific Research Fund (Hungría), Ministry of Education, Youth and Sports (República Checa), MH CZ -DRO (MMCI), National Health and Medical Research Council (Australia), Australian National Breast Cancer Foundation, Cancer Australia, ERA-NET TRANSAN JTC 2012 Cancer, Transcan grant, Biotechnology and Biological Sciences Research Council (Reino Unido), Pink Ribbons Project, Netherlands Organisation of Scientific Research, Dutch Cancer Society (Holanda), Instituto de Salud Carlos III, Deutsche Krebshilfe, National Institute for Health Research (Reino Unido), Unión Europea. Comisión Europea. 7 Programa Marco, Ministry of Economic Development, Innovation and Export Trade-grant, Canadian Institutes of Health Research, Cancer Research UK, NIH National Cancer Institute (NCI), United States Department of Health & Human Services National Institutes of Health (NIH) - USA, Ministerio de Economia y Competividad (MINECO), European Research Council (ERC), Hungarian Research Grants, MEYS -NPS I, National Health and Medical Research Council of Australia, BBMRI grant, Pink Ribbon grants, Netherlands Organisation of Scientific Research grant, KWF Kankerbestrijding, Instituto de Salud Carlos III - ISCIII, National Institute for Health Research (NIHR), European Union Seventh Framework Program (2007-2013), Canadian Institutes of Health Research (CIHR), Damage and Repair in Cancer Development and Cancer Treatment (DARE), Targeted Gynaecologic Oncology (TARGON), United States of Department of Health & Human Services, European Research Council, APH - Quality of Care, APH - Methodology, Graduate School, ARD - Amsterdam Reproduction and Development, RS: GROW - R4 - Reproductive and Perinatal Medicine, and MUMC+: DA KG Lab Centraal Lab (9)
Subjects: endocrine system diseases, SURGERY, [SDV]Life Sciences [q-bio], [SDV.GEN] Life Sciences [q-bio]/Genetics, Cohort Studies, 0302 clinical medicine, BRCA2 Mutation, Breast cancer, Surgical oncology, Prospective Studies, Prospective cohort study, skin and connective tissue diseases, Salpingo-oophorectomy/methods, 0303 health sciences, Natural menopause, Women's cancers Radboud Institute for Molecular Life Sciences [Radboudumc 17], Obstetrics, BRCA1 Protein, Breast Neoplasms/epidemiology, Incidence (epidemiology), Incidence, WOMEN, ASSOCIATION, Middle Aged, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, OVARIAN, BRCA2 Protein/genetics, 3. Good health, Menopause, Risk-reducing salpingo-oophorectomy, [SDV] Life Sciences [q-bio], 030220 oncology & carcinogenesis, SURVIVAL, Female, Research Article, Cohort study, Adult, medicine.medical_specialty, Salpingo-oophorectomy, Breast Neoplasms, [SDV.CAN]Life Sciences [q-bio]/Cancer, [SDV.GEN.GH] Life Sciences [q-bio]/Genetics/Human genetics, lcsh:RC254-282, 03 medical and health sciences, breast cancer, SDG 3 - Good Health and Well-being, [SDV.CAN] Life Sciences [q-bio]/Cancer, medicine, Humans, METAANALYSIS, 030304 developmental biology, BRCA2 Protein, [SDV.GEN]Life Sciences [q-bio]/Genetics, business.industry, CONSORTIUM, risk-reducing salpingo-oophorectomy, International Agencies, medicine.disease, BRCA1, BRCA2, KCONFAB, REDUCTION, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, LINK, Mutation, BRCA1 Protein/genetics, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business, Risk Reduction Behavior
Abstract: The effect of risk-reducing salpingo-oophorectomy (RRSO) on breast cancer risk for BRCA1 and BRCA2 mutation carriers is uncertain. Retrospective analyses have suggested a protective effect but may be substantially biased. Prospective studies have had limited power, particularly for BRCA2 mutation carriers. Further, previous studies have not considered the effect of RRSO in the context of natural menopause. A multi-centre prospective cohort of 2272 BRCA1 and 1605 BRCA2 mutation carriers was followed for a mean of 5.4 and 4.9 years, respectively; 426 women developed incident breast cancer. RRSO was modelled as a time-dependent covariate in Cox regression, and its effect assessed in premenopausal and postmenopausal women. There was no association between RRSO and breast cancer for BRCA1 (HR = 1.23; 95% CI 0.94-1.61) or BRCA2 (HR = 0.88; 95% CI 0.62-1.24) mutation carriers. For BRCA2 mutation carriers, HRs were 0.68 (95% CI 0.40-1.15) and 1.07 (95% CI 0.69-1.64) for RRSO carried out before or after age 45 years, respectively. The HR for BRCA2 mutation carriers decreased with increasing time since RRSO (HR = 0.51; 95% CI 0.26-0.99 for 5 years or longer after RRSO). Estimates for premenopausal women were similar. We found no evidence that RRSO reduces breast cancer risk for BRCA1 mutation carriers. A potentially beneficial effect for BRCA2 mutation carriers was observed, particularly after 5 years following RRSO. These results may inform counselling and management of carriers with respect to RRSO. The BCFR was supported by grant UM1 CA164920 from the National Cancer Institute. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centres in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organisations imply endorsement by the US Government or the BCFR. CNIO was partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) SAF2014-57680-R and the Spanish Research Network on Rare Diseases (CIBERER). CNIO was also partially supported by FISPI16/00440. INHERIT was supported by the Canadian Institutes of Health Research for the 'CIHR Team in Familial Risks of Breast Cancer' program-grant #CRN-87521-and the Ministry of Economic Development, Innovation and Export Trade-grant #PSR-SIIRI-701. The PERSPECTIVE project was supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (GPH-129344), the Ministere de l'Economie, de la Science et de l' Innovation du Quebec through Genome Quebec, and The Quebec Breast Cancer Foundation. Jacques Simard is a Chairholder of the Canada Research Chair in Oncogenetics. EMBRACE is supported by the Cancer Research UK grants C1287/A23382 and C1287/A16563. D. Gareth Evans is supported by an NIHR grant to the Biomedical Research Centre, Manchester (IS-BRC-1215-20007). The investigators at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust are supported by an NIHR grant to the Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. Ros Eeles and Elizabeth Bancroft are supported by the Cancer Research UK grant C5047/A8385. Ros Eeles is also supported by NIHR support to the Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. Antonis C. Antoniou is funded by Cancer Research UK grants C12292/A20861 and C12292/A11174. MT is funded by the European Union Seventh Framework Program (2007-2013)/European Research Council (310018). The German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC) is supported by the German Cancer Aid (grant no 110837, Rita K. Schmutzler). The national French cohort, GENEPSO, had been supported by a grant from the Fondation de France and the Ligue Nationale Contre le Cancer and is being supported by a grant from INCa as part of the European program ERA-NET on Translational Cancer Research (TRANSCAN-JTC2012, no. 2014-008). HCSC was supported by CIBERONC 161200301 from ISCIII (Spain), partially supported by European Regional Development FEDER funds. The HEBON study is supported by the Dutch Cancer Society grants NKI1998-1854, NKI2004-3088, NKI2007-3756, the Netherlands Organisation of Scientific Research grant NWO 91109024, the Pink Ribbon grants 110005 and 2014-187.WO76, the BBMRI grant NWO 184.021.007/CP46, and the Transcan grant JTC 2012 Cancer 12-054. The IHCC was supported by Grant PBZ_ KBN_122/P05/2004 and ERA-NET TRANSAN JTC 2012 Cancer 12-054 (ERA-NET-TRANSCAN/07/2014). This work was supported by grants to kConFab and the kConFab Follow-Up Study from Cancer Australia (809195); the Australian National Breast Cancer Foundation (IF 17); the National Health and Medical Research Council (454508, 288704, 145684); the National Institute of Health U.S.A. (1RO1CA159868); the Queensland Cancer Fund; the Cancer Councils of New South Wales, Victoria, Tasmania and South Australia; and the Cancer Foundation of Western Australia. KAP is an Australian National Breast Cancer Foundation fellow. MODSQUAD-Czech Republic, Brno, was supported by MH CZ -DRO (MMCI, 00209805) and by MEYS -NPS I -LO1413 to LF and MN. The Hungarian Breast and Ovarian Cancer Study was supported by Hungarian Research Grants KTIA-OTKA CK-80745, NKFI OTKA K-112228, and the Norwegian EEA Financial Mechanism HU0115/NA/2008-3/OP-9. Lund-BRCA collaborators are supported by the Swedish Cancer Society, Lund Hospital Funds, and European Research Council Advanced Grant ERC-2011-294576. Stockholm-BRCA collaborators are supported by the Swedish Cancer Society. The funders had no role in the design of the study; the collection, analysis, or interpretation of the data; the writing of the manuscript; or the decision to submit the manuscript for publication. Sí
Published: 2020

27. Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data

Author: Yuliya V. Orlova, Emmanuel Barillot, Alexander N. Gorban, Evgeny M. Mirkes, Jonathan Bac, S. E. Golovenkin, A. Chervov, Andrei Zinovyev, Krasnoyarsk State Medical University (KrasSMU), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Leicester, Lobachevsky State University [Nizhni Novgorod], ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), European Project: 826121,iPaediatricCurie, Zinovyev, Andrei, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, European Union’s Horizon 2020 program (grant No. 826121, iPC project) - iPaediatricCurie - 826121 - INCOMING, and MINES ParisTech - École nationale supérieure des mines de Paris
Subjects: FOS: Computer and information sciences, Computer Science - Machine Learning, J.2, J.3, Clinical Trajectory, Computer science, Pseudotime, I.2.6, computer.software_genre, Machine Learning (cs.LG), 0302 clinical medicine, [SHS.STAT] Humanities and Social Sciences/Methods and statistics, Cluster Analysis, 0303 health sciences, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [SHS.STAT]Humanities and Social Sciences/Methods and statistics, Principal trees, pseudo-time, Diabetes, Dimensionality reduction, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computer Science Applications, Dynamical diseases phenotyping, Trajectory, Clinical data, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Data mining, AcademicSubjects/SCI02254, Data analysis, Health Informatics, Feature selection, Data type, Statistics - Applications, Clustering, 03 medical and health sciences, Data visualization, Diabetes Mellitus, Humans, Applications (stat.AP), Cluster analysis, 030304 developmental biology, [SDV.IB] Life Sciences [q-bio]/Bioengineering, business.industry, Research, Data structure, Missing data, Myocardial infarction, AcademicSubjects/SCI00960, Patient disease pathway, business, computer, 030217 neurology & neurosurgery
Abstract: Background Large observational clinical datasets are becoming increasingly available for mining associations between various disease traits and administered therapy. These datasets can be considered as representations of the landscape of all possible disease conditions, in which a concrete disease state develops through stereotypical routes, characterized by “points of no return" and “final states" (such as lethal or recovery states). Extracting this information directly from the data remains challenging, especially in the case of synchronic (with a short-term follow-up) observations. Results Here we suggest a semi-supervised methodology for the analysis of large clinical datasets, characterized by mixed data types and missing values, through modeling the geometrical data structure as a bouquet of bifurcating clinical trajectories. The methodology is based on application of elastic principal graphs, which can address simultaneously the tasks of dimensionality reduction, data visualization, clustering, feature selection, and quantifying the geodesic distances (pseudo-time) in partially ordered sequences of observations. The methodology allows a patient to be positioned on a particular clinical trajectory (pathological scenario) and the degree of progression along it to be characterized with a qualitative estimate of the uncertainty of the prognosis. We developed a tool ClinTrajan for clinical trajectory analysis implemented in the Python programming language. We test the methodology in 2 large publicly available datasets: myocardial infarction complications and readmission of diabetic patients data. Conclusions Our pseudo-time quantification-based approach makes it possible to apply the methods developed for dynamical disease phenotyping and illness trajectory analysis (diachronic data analysis) to synchronic observational data.
Published: 2020

28. Using a new diagnostic tool to predict lymph node metastasis in advanced epithelial ovarian cancer leads to simple lymphadenectomy decision rules: A multicentre study from the FRANCOGYN group

Author: Arnaud Fauconnier, Matthieu Mezzadri, Sofiane Bendifallah, Jean Louis Benifla, Roman Rouzier, Yohan Kerbage, Vincent Lavoué, Xavier Paoletti, Geoffroy Canlorbe, Camille Mimoun, Charles Coutant, Cyrille Huchon, Lobna Ouldamer, Adrien Crestani, Cyril Touboul, Thomas Gaillard, Yohann Dabi, Alexandre Bricou, Lise Lecointre, Risques cliniques et sécurité en santé des femmes et en santé périnatale (RISCQ), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Service de gynécologie et obstétrique [Hopital Lariboisière - Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Lariboisière-Fernand-Widal [APHP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université de Paris (UP), Département d'Oncologie Chirurgicale [Institut Curie], Institut Curie [Paris], Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Gynécologie-obstétrique et médecine de la reproduction - Maternité [CHU Tenon], CHU Tenon [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Service de gynécologie-obstétrique [Hôpital Jean Verdier], Hôpital Jean Verdier [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Sorbonne Paris Nord, Service de gynécologie et obstétrique [Créteil], CHI Créteil, Service de Gynécologie-Obstétrique, Maternité, Chirurgie Gynécologique [CHU Pitié-Salpêtrière], CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Clinique de Gynécologie [Lille] (Hôpital Jeanne de Flandre), Hôpital Jeanne de Flandre [Lille]-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Service de Gynécologie et Obstétrique [Rennes] = Gynaecology [Rennes], CHU Pontchaillou [Rennes], Hôpital Bretonneau, Centre Hospitalier Régional Universitaire de Tours (CHRU Tours), Département de Gynécologie-Obstétrique [CHU Strasbourg], CHU Strasbourg-Hôpital de Hautepierre [Strasbourg], Centre Régional de Lutte contre le cancer Georges-François Leclerc [Dijon] (UNICANCER/CRLCC-CGFL), UNICANCER, Service de gynécologie et obstétrique [CHI Poissy-Saint Germain], CHI Poissy-Saint-Germain, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris Cité (UPCité), Mines Paris - PSL (École nationale supérieure des mines de Paris), Service de Gynécologie-obstétrique et médecine de la reproduction - Maternité [CHU Tenon], and HAL UVSQ, Équipe
Subjects: Oncology, Epidemiology, medicine.medical_treatment, Cancer Treatment, Pelvic Lymphadenectomy, Lymph node metastasis, Carcinoma, Ovarian Epithelial, Logistic regression, Surgical planning, Metastasis, Mathematical and Statistical Techniques, 0302 clinical medicine, Positron Emission Tomography Computed Tomography, Basic Cancer Research, Medicine and Health Sciences, Blood and Lymphatic System Procedures, Medicine, Ovarian Neoplasms, 0303 health sciences, Multidisciplinary, Cancer Risk Factors, Statistics, Cytoreduction Surgical Procedures, Middle Aged, 3. Good health, Surgical Oncology, Lymphatic Metastasis, 030220 oncology & carcinogenesis, Physical Sciences, Cohort, Female, France, Anatomy, Research Article, Clinical Oncology, medicine.medical_specialty, Science, Clinical Decision-Making, Surgical and Invasive Medical Procedures, [SDV.CAN]Life Sciences [q-bio]/Cancer, [SDV.MHEP.GEO]Life Sciences [q-bio]/Human health and pathology/Gynecology and obstetrics, Research and Analysis Methods, Lymphatic System, 03 medical and health sciences, [SDV.CAN] Life Sciences [q-bio]/Cancer, Internal medicine, Humans, Statistical Methods, Aged, Retrospective Studies, 030304 developmental biology, Internet, business.industry, Biology and Life Sciences, Lymphadenectomy, Decision rule, medicine.disease, [SDV.MHEP.GEO] Life Sciences [q-bio]/Human health and pathology/Gynecology and obstetrics, Logistic Models, Medical Risk Factors, Lymph Node Excision, Lymph Nodes, Clinical Medicine, business, Ovarian cancer, Mathematics, Forecasting
Abstract: Objective The aim of this study was to develop a new diagnostic tool to predict lymph node metastasis (LNM) in patients with advanced epithelial ovarian cancer undergoing primary cytoreductive surgery. Materials and method The FRANCOGYN group’s multicenter retrospective ovarian cancer cohort furnished the patient population on which we developed a logistic regression model. The prediction model equation enabled us to create LNM risk groups with simple lymphadenectomy decision rules associated with a user-friendly free interactive web application called shinyLNM. Results 277 patients from the FRANCOGYN cohort were included; 115 with no LNM and 162 with LNM. Three variables were independently and significantly (p Conclusion This new tool may prove useful for improving surgical planning and provide useful information for patients.
Published: 2021

29. Determining the optimal number of independent components for reproducible transcriptomic data analysis

Author: Laura Cantini, Askhat Molkenov, Ulykbek Kairov, A. Greco, Andrei Zinovyev, Emmanuel Barillot, Urszula Czerwinska, Bodescot, Myriam, Laboratory of bioinformatics and computational systems biology [Astana, Kazakhstan] (Center for Life Sciences), Nazarbayev University [Kazakhstan]-National Laboratory Astana [Astana, Kazakhstan], Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), This study is supported by 'Analysis of cancer transcriptome data using Independent Component Analysis' project from the budget program 'Creation and development of genomic medicine in Kazakhstan' (0115RKO1931) from the Ministry of Education and Science of the Republic of Kazakhstan. This work was partly supported by ITMO Cancer within the framework of the Plan Cancer 2014–2019 and convention Biologie des Systèmes N°BIO2015–01 (M5 project) and MOSAIC project., and MINES ParisTech - École nationale supérieure des mines de Paris
Subjects: 0301 basic medicine, lcsh:QH426-470, lcsh:Biotechnology, Statistics as Topic, Independent component analysis, Biology, Blind signal separation, Stability (probability), Set (abstract data type), 03 medical and health sciences, 0302 clinical medicine, Dimension (vector space), Neoplasms, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], lcsh:TP248.13-248.65, Genetics, Range (statistics), 030304 developmental biology, Interpretability, Cancer, 0303 health sciences, business.industry, Gene Expression Profiling, Reproducibility of Results, Pattern recognition, Reproducibility, lcsh:Genetics, 030104 developmental biology, Ranking, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Artificial intelligence, business, Transcriptome, 030217 neurology & neurosurgery, Research Article, Biotechnology
Abstract: BackgroundIndependent Component Analysis (ICA) is a method that models gene expression data as an action of a set of statistically independent hidden factors. The output of ICA depends on a fundamental parameter: the number of components (factors) to compute. The optimal choice of this parameter, related to determining the effective data dimension, remains an open question in the application of blind source separation techniques to transcriptomic data.ResultsHere we address the question of optimizing the number of statistically independent components in the analysis of transcriptomic data for reproducibility of the components in multiple runs of ICA (within the same or within varying effective dimensions) and in multiple independent datasets. To this end, we introduce ranking of independent components based on their stability in multiple ICA computation runs and define a distinguished number of components (Most Stable Transcriptome Dimension, MSTD) corresponding to the point of the qualitative change of the stability profile. Based on a large body of data, we demonstrate that a sufficient number of dimensions is required for biological interpretability of the ICA decomposition and that the most stable components with ranks below MSTD have more chances to be reproduced in independent studies compared to the less stable ones. At the same time, we show that a transcriptomics dataset can be reduced to a relatively high number of dimensions without losing the interpretability of ICA, even though higher dimensions give rise to components driven by small gene sets.ConclusionsWe suggest a protocol of ICA application to transcriptomics data with a possibility of prioritizing components with respect to their reproducibility that strengthens the biological interpretation. Computing too few components (much less than MSTD) is not optimal for interpretability of the results. The components ranked within MSTD range have more chances to be reproduced in independent studies.
Published: 2017

30. Targeting CCR5 trafficking to inhibit HIV-1 infection

Author: Anne Brelot, Elaine Del Nery, Yuko Fukata, Gaelle Boncompain, Masaki Fukata, Isabelle Staropoli, Sarah Tessier, Floriane Herit, Pierre Gestraud, Florence Niedergang, Franck Perez, Aurianne Lescure, Université Paris sciences et lettres (PSL), Dynamique de l’organisation intra-cellulaire, Biologie Cellulaire et Cancer, Institut Curie [Paris]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Curie [Paris]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut Cochin (IC UM3 (UMR 8104 / U1016)), Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de criblage à haute teneur en biophénic [Institut Curie] (Biophenics High-Content Screening Laboratory), Institut Curie [Paris]-Université Paris sciences et lettres (PSL), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Pathogénie Virale - Viral Pathogenesis, Institut Pasteur [Paris] (IP)-Institut National de la Santé et de la Recherche Médicale (INSERM), Division of Membrane Physiology, Department of Molecular and Cellular Physiology [Okazaki], National Institute for Physiological Sciences-National Institutes of Natural Sciences [Tokyo] (NINS), We acknowledge the Cell and Tissue Imaging Facility (PICT-IBiSA), Institut Curie, a member of the French National Research Infrastructure, France-BioImaging (ANR10-INBS-04). This work was supported by grants from CNRS, INSERM, Université Paris Descartes, Agence Nationale de la Recherche (2011 BSV3 025 02), and Agence Nationale de Recherches sur le Sida et les Hépatites (ANRS, AO2012-2) to A.B., F.P., and F.N., and Fondation pour la Recherche Médicale (FRM DEQ20130326518) to F.N. This work has received support under the 'Investissements d’Avenir' program launched by the French government and implemented by ANR with the references ANR-10-LABX-62-IBEID and ANR-10-IDEX-0001-02 PSL, ANR-10-INBS-0004,France-BioImaging,Développment d'une infrastructure française distribuée coordonnée(2010), ANR-10-LABX-0062,IBEID,Integrative Biology of Emerging Infectious Diseases(2010), ANR-10-IDEX-0001,PSL,Paris Sciences et Lettres(2010), PSL Research University (PSL), Institut Curie-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Curie-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut Curie-PSL Research University Paris, Cancer et génôme: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, MINES ParisTech - École nationale supérieure des mines de Paris-Institut Curie-Institut National de la Santé et de la Recherche Médicale (INSERM), Pathogénie Virale, Institut Pasteur [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), ANR-10-INBS-04-01/10-INBS-0004,France-BioImaging,Développment d'une infrastructure française distribuée coordonnée(2010), ANR-10-LABX-62-IBEID,IBEID,Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases'(2010), ANR-10-IDEX-0001-02/10-IDEX-0001,PSL,Paris Sciences et Lettres(2010), Centre National de la Recherche Scientifique (CNRS)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Subjects: CCR1, Receptors, CXCR4, Receptors, CCR5, Cell, Receptors, CCR1, HIV Infections, CXCR4, Cell Line, 03 medical and health sciences, 0302 clinical medicine, Palmitoylation, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Cell Line, Tumor, medicine, Humans, Amino Acid Sequence, 030212 general & internal medicine, Receptor, Research Articles, Secretory pathway, 030304 developmental biology, 0303 health sciences, Secretory Pathway, Multidisciplinary, Chemistry, Macrophages, HEK 293 cells, SciAdv r-articles, virus diseases, Cell Biology, respiratory system, 3. Good health, Transport protein, Cell biology, Protein Transport, HEK293 Cells, medicine.anatomical_structure, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, HIV-1, human activities, HeLa Cells, Research Article
Abstract: The diversity of secretory routes enabled the identification of specific inhibitors of CCR5 transport and HIV-1 infection., Using a cell-based assay monitoring differential protein transport in the secretory pathway coupled to high-content screening, we have identified three molecules that specifically reduce the delivery of the major co-receptor for HIV-1, CCR5, to the plasma membrane. They have no effect on the closely related receptors CCR1 and CXCR4. These molecules are also potent in primary macrophages as they markedly decrease HIV entry. At the molecular level, two of these molecules inhibit the critical palmitoylation of CCR5 and thereby block CCR5 in the early secretory pathway. Our results open a clear therapeutics avenue based on trafficking control and demonstrate that preventing HIV infection can be performed at the level of its receptor delivery.
Published: 2019

31. LOTUS: A single- and multitask machine learning algorithm for the prediction of cancer driver genes

Author: Jean-Philippe Vert, Véronique Stoven, Olivier Collier, Modélisation aléatoire de Paris X (MODAL'X), Université Paris Nanterre (UPN), Centre de Bioinformatique (CBIO), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Institut Curie [Paris], Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Brain team [Paris], Research at Google, This work was supported the European Research Council https://erc.europa.eu grant ERC-SMAC-280032 (OC, JPV), the Labex MME-DII https://labex-mme-dii.u-cergy.fr ANR11-LBX-0023-01 (OC), and Google LLC (JPV)., ANR-11-LABX-0023,MME-DII,Modèles Mathématiques et Economiques de la Dynamique, de l'Incertitude et des Interactions(2011), European Project: 280032,EC:FP7:ERC,ERC-2011-StG_20101014,SMAC(2012), MINES ParisTech - École nationale supérieure des mines de Paris, PSL Research University (PSL)-MINES ParisTech - École nationale supérieure des mines de Paris, Bodescot, Myriam, Modèles Mathématiques et Economiques de la Dynamique, de l'Incertitude et des Interactions - - MME-DII2011 - ANR-11-LABX-0023 - LABX - VALID, and Statistical machine learning for complex biological data - SMAC - - EC:FP7:ERC2012-02-01 - 2017-01-31 - 280032 - VALID
Subjects: 0301 basic medicine, Computer science, Lotus, Gene Identification and Analysis, Multi-task learning, Genetic Networks, Gene mutation, computer.software_genre, medicine.disease_cause, Machine Learning, Database and Informatics Methods, 0302 clinical medicine, Mathematical and Statistical Techniques, Neoplasms, Biology (General), ComputingMilieux_MISCELLANEOUS, 0303 health sciences, Mutation, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], biology, Ecology, Applied Mathematics, Simulation and Modeling, Statistics, Genomics, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computational Theory and Mathematics, 030220 oncology & carcinogenesis, Modeling and Simulation, Physical Sciences, Algorithms, Network Analysis, Research Article, Computer and Information Sciences, QH301-705.5, Gene prediction, [SDV.CAN]Life Sciences [q-bio]/Cancer, Machine learning, Research and Analysis Methods, Data type, Consistency (database systems), 03 medical and health sciences, Cellular and Molecular Neuroscience, Machine Learning Algorithms, Breast cancer, [SDV.CAN] Life Sciences [q-bio]/Cancer, Artificial Intelligence, medicine, Genetics, Humans, Statistical Methods, Gene Prediction, Molecular Biology, Gene, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, Models, Statistical, business.industry, Cancer, Computational Biology, Biology and Life Sciences, Oncogenes, biology.organism_classification, medicine.disease, Genome Analysis, 030104 developmental biology, Biological Databases, Apoptosis, Mutation Databases, Artificial intelligence, business, computer, 030217 neurology & neurosurgery, Software, Mathematics, Forecasting
Abstract: Cancer driver genes, i.e., oncogenes and tumor suppressor genes, are involved in the acquisition of important functions in tumors, providing a selective growth advantage, allowing uncontrolled proliferation and avoiding apoptosis. It is therefore important to identify these driver genes, both for the fundamental understanding of cancer and to help finding new therapeutic targets or biomarkers. Although the most frequently mutated driver genes have been identified, it is believed that many more remain to be discovered, particularly for driver genes specific to some cancer types. In this paper, we propose a new computational method called LOTUS to predict new driver genes. LOTUS is a machine-learning based approach which allows to integrate various types of data in a versatile manner, including information about gene mutations and protein-protein interactions. In addition, LOTUS can predict cancer driver genes in a pan-cancer setting as well as for specific cancer types, using a multitask learning strategy to share information across cancer types. We empirically show that LOTUS outperforms five other state-of-the-art driver gene prediction methods, both in terms of intrinsic consistency and prediction accuracy, and provide predictions of new cancer genes across many cancer types., Author summary Cancer development is driven by mutations and dysfunction of important, so-called cancer driver genes, that could be targeted by specific therapies. While a number of such cancer genes have already been identified, it is believed that many more remain to be discovered. To help prioritize experimental investigations of candidate genes, several computational methods have been proposed to rank promising candidates based on their mutations in large cohorts of cancer cases, or on their interactions with known driver genes in biological networks. We propose LOTUS, a new computational approach to identify genes with high oncogenic potential. LOTUS implements a machine learning approach to learn an oncogenic potential score from known driver genes, and brings two novelties compared to existing methods. First, it allows to easily combine heterogeneous sources of information into the scoring function, which we illustrate by learning a scoring function from both known mutations in large cancer cohorts and interactions in biological networks. Second, using a multitask learning strategy, it can predict different driver genes for different cancer types, while sharing information between them to improve the prediction for every type. We provide experimental results showing that LOTUS significantly outperforms several state-of-the-art cancer gene prediction software.
Published: 2019

32. Deconvolution of transcriptomes and miRNomes by independent component analysis provides insights into biological processes and clinical outcomes of melanoma patients

Author: Petr V. Nazarov, Andrei Zinovyev, Urszula Czerwinska, Arnaud Muller, Dorothee Nashan, Gunnar Dittmar, Francisco Azuaje, Anke Wienecke-Baldacchino, Stephanie Kreis, Quantitative Biology Unit [Strassen, Luxembourg], Luxembourg Institute of Health (LIH), Life Sciences Research Unit [Belvaux, Luxembourg] (LSRU), University of Luxembourg [Luxembourg], Epidemiology and Microbial Genomics Unit [Dudelange, Luxembourg], Department of Microbiology [Dudelange, Luxembourg], Laboratoire National de Santé [Luxembourg] (LNS)-Laboratoire National de Santé [Luxembourg] (LNS), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Recherches Interdisciplinaires [Paris] (CRI), Université Paris Descartes - Paris 5 (UPD5), Klinikum Dortmund GmbH [Dortmund, Allemagne], This work was supported by the Luxembourg Ministry of Higher Education and Research, a grant from the Luxembourg National Research Fund (C17/BM/11664971/DEMICS), the University of Luxembourg, IRP (R-AGR-0748-00) and by the Integrated Biobank of Luxembourg (IBBL) who funded the sequencing of clinical samples., Bodescot, Myriam, Mines Paris - PSL (École nationale supérieure des mines de Paris), and Luxembourg Ministry of Higher Educationand Research, the Luxembourg National Research Fund (C17/BM/11664971/DEMICS), the University of Luxembourg, IRP (R-AGR-0748-00) and by the Integrated Biobank of Luxembourg (IBBL) [sponsor]
Subjects: Male, lcsh:Internal medicine, lcsh:QH426-470, Computer science, [SDV.CAN]Life Sciences [q-bio]/Cancer, Computational biology, Deconvolution, Independent component analysis, Biochemistry, biophysics & molecular biology [F05] [Life sciences], Transcriptome, 03 medical and health sciences, 0302 clinical medicine, [SDV.CAN] Life Sciences [q-bio]/Cancer, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Databases, Genetic, Genetics, medicine, Humans, Biochimie, biophysique & biologie moléculaire [F05] [Sciences du vivant], lcsh:RC31-1245, Transcriptomics, Melanoma, Genetics (clinical), Survival analysis, 030304 developmental biology, Cancer, 0303 health sciences, Principal Component Analysis, Computational Biology, medicine.disease, Human genetics, 3. Good health, lcsh:Genetics, MicroRNAs, Technical Advance, 030220 oncology & carcinogenesis, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Female, DNA microarray
Abstract: Background The amount of publicly available cancer-related “omics” data is constantly growing and can potentially be used to gain insights into the tumour biology of new cancer patients, their diagnosis and suitable treatment options. However, the integration of different datasets is not straightforward and requires specialized approaches to deal with heterogeneity at technical and biological levels. Methods Here we present a method that can overcome technical biases, predict clinically relevant outcomes and identify tumour-related biological processes in patients using previously collected large discovery datasets. The approach is based on independent component analysis (ICA) – an unsupervised method of signal deconvolution. We developed parallel consensus ICA that robustly decomposes transcriptomics datasets into expression profiles with minimal mutual dependency. Results By applying the method to a small cohort of primary melanoma and control samples combined with a large discovery melanoma dataset, we demonstrate that our method distinguishes cell-type specific signals from technical biases and allows to predict clinically relevant patient characteristics. We showed the potential of the method to predict cancer subtypes and estimate the activity of key tumour-related processes such as immune response, angiogenesis and cell proliferation. ICA-based risk score was proposed and its connection to patient survival was validated with an independent cohort of patients. Additionally, through integration of components identified for mRNA and miRNA data, the proposed method helped deducing biological functions of miRNAs, which would otherwise not be possible. Conclusions We present a method that can be used to map new transcriptomic data from cancer patient samples onto large discovery datasets. The method corrects technical biases, helps characterizing activity of biological processes or cell types in the new samples and provides the prognosis of patient survival.
Published: 2019

33. Independent Component Analysis for Unraveling the Complexity of Cancer Omics Datasets

Author: Alexander N. Gorban, Askhat Molkenov, Ulykbek Kairov, Andrei Zinovyev, Laura Cantini, Nicolas Sompairac, Zhaxybay Zhumadilov, Urszula Czerwinska, Petr V. Nazarov, Anne Biton, François Radvanyi, Emmanuel Barillot, Université Paris sciences et lettres (PSL), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Paris Descartes - Paris 5 (UPD5), Luxembourg Institute of Health (LIH), Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Nazarbayev University [Kazakhstan], Compartimentation et dynamique cellulaires (CDC), Institut Curie [Paris]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), University of Leicester, Lobachevsky State University [Nizhni Novgorod], This work was partially supported by the grant research projects 'Pan-cancer deconvolution of omics data using Independent Component Analysis' (IRN: AP05135430) and 'Investigation of esophageal cancer tissue gene expression derived from Kazakhstan patients by next-generation sequencing technology' (IRN: AP05134722) of the Ministry of Education and Science of the Republic of Kazakhstan, by the Ministry of Science and Higher Education of the Russian Federation (Project No. 14.Y26.31.0022), the European Union’s Horizon 2020 program (grant No. 826121, iPC project), by the European IMI IMMUCAN project, and by Luxembourg National Research Fund (C17/BM/11664971/DEMICS)., PSL Research University (PSL), Cancer et génôme: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, MINES ParisTech - École nationale supérieure des mines de Paris-Institut Curie-Institut National de la Santé et de la Recherche Médicale (INSERM), École normale supérieure - Paris (ENS Paris)-École normale supérieure - Paris (ENS Paris)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut Curie-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut de biologie de l'ENS Paris (IBENS), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), and Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Databases, Factual, Computer science, dimension reduction, data analysis, [SDV.CAN]Life Sciences [q-bio]/Cancer, Review, computer.software_genre, Data type, Catalysis, Matrix decomposition, Non-negative matrix factorization, Inorganic Chemistry, Machine Learning, lcsh:Chemistry, 03 medical and health sciences, Matrix (mathematics), 0302 clinical medicine, Neoplasms, Humans, cancer, Physical and Theoretical Chemistry, Molecular Biology, lcsh:QH301-705.5, data integration, Spectroscopy, Data Curation, 030304 developmental biology, 0303 health sciences, Principal Component Analysis, Dimensionality reduction, Organic Chemistry, Computational Biology, General Medicine, Independent component analysis, Magnetic Resonance Imaging, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 3. Good health, Computer Science Applications, omics data, lcsh:Biology (General), lcsh:QD1-999, independent component analysis, Principal component analysis, Data mining, computer, 030217 neurology & neurosurgery, Algorithms, Data integration
Abstract: Independent component analysis (ICA) is a matrix factorization approach where the signalscaptured by each individual matrix factors are optimized to become as mutually independent aspossible. Initially suggested for solving source blind separation problems in various fields, ICA wasshown to be successful in analyzing functional magnetic resonance imaging (fMRI) and other typesof biomedical data. In the last twenty years, ICA became a part of the standard machine learningtoolbox, together with other matrix factorization methods such as principal component analysis (PCA)and non-negative matrix factorization (NMF). Here, we review a number of recent works where ICAwas shown to be a useful tool for unraveling the complexity of cancer biology from the analysis ofdifferent types of omics data, mainly collected for tumoral samples. Such works highlight the useof ICA in dimensionality reduction, deconvolution, data pre-processing, meta-analysis, and othersapplied to dierent data types (transcriptome, methylome, proteome, single-cell data). We particularlyfocus on the technical aspects of ICA application in omics studies such as using different protocols,determining the optimal number of components, assessing and improving reproducibility of the ICAresults, and comparison with other popular matrix factorization techniques. We discuss the emergingICA applications to the integrative analysis of multi-level omics datasets and introduce a conceptualview on ICA as a tool for defining functional subsystems of a complex biological system and theirinteractions under various conditions. Our review is accompanied by a Jupyter notebook whichillustrates the discussed concepts and provides a practical tool for applying ICA to the analysis ofcancer omics datasets.
Published: 2019

34. Metabolic and signalling network maps integration: application to cross-talk studies and omics data analysis in cancer

Author: Nicolas Sompairac, Jennifer Modamio, Andrei Zinovyev, Emmanuel Barillot, Ronan M. T. Fleming, Inna Kuperstein, Institut Curie [Paris], Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Paris sciences et lettres (PSL), Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg [Luxembourg], This work has been supported by the COLOSYS grant ANR-15-CMED-0001-04, provided by the Agence Nationale de la Recherche under the frame of ERACoSysMed-1, the ERA-Net for Systems Medicine in clinical research and medical practice and by PRECISE H2020 EU grant. This work received support from MASTODON program by CNRS (project APLIGOOGLE)., ANR-15-CMED-0001,COLOSYS,A systems approach to preventing drug resistance in colon cancer(2015), European Project: 668858,H2020,H2020-PHC-2015-two-stage,PrECISE(2016), Bodescot, Myriam, A systems approach to preventing drug resistance in colon cancer - - COLOSYS2015 - ANR-15-CMED-0001 - ERACoSysMed - VALID, PERSONALIZED ENGINE FOR CANCER INTEGRATIVE STUDY AND EVALUATION - PrECISE - - H20202016-01-01 - 2018-12-31 - 668858 - VALID, and Mines Paris - PSL (École nationale supérieure des mines de Paris)
Subjects: Data Analysis, Computer science, Metabolic network, Multi-level omics data, Biochemistry, 0302 clinical medicine, Structural Biology, Neoplasms, lcsh:QH301-705.5, Cancer, 0303 health sciences, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Applied Mathematics, Systems Biology, Comprehensive map, Genomics, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 3. Good health, Computer Science Applications, Signalling, 030220 oncology & carcinogenesis, lcsh:R858-859.7, Female, DNA microarray, Metabolic Networks and Pathways, Data visualisation, Signal Transduction, Process (engineering), Systems biology, Context (language use), [SDV.CAN]Life Sciences [q-bio]/Cancer, Computational biology, lcsh:Computer applications to medicine. Medical informatics, 03 medical and health sciences, Data visualization, [SDV.CAN] Life Sciences [q-bio]/Cancer, Humans, Molecular Biology, 030304 developmental biology, business.industry, Research, Metabolism, Visualization, Metabolic pathway, lcsh:Biology (General), Networks, business, Software
Abstract: Background The interplay between metabolic processes and signalling pathways remains poorly understood. Global, detailed and comprehensive reconstructions of human metabolism and signalling pathways exist in the form of molecular maps, but they have never been integrated together. We aim at filling in this gap by integrating of both signalling and metabolic pathways allowing a visual exploration of multi-level omics data and study of cross-regulatory circuits between these processes in health and in disease. Results We combined two comprehensive manually curated network maps. Atlas of Cancer Signalling Network (ACSN), containing mechanisms frequently implicated in cancer; and ReconMap 2.0, a comprehensive reconstruction of human metabolic network. We linked ACSN and ReconMap 2.0 maps via common players and represented the two maps as interconnected layers using the NaviCell platform for maps exploration (https://navicell.curie.fr/pages/maps_ReconMap%202.html). In addition, proteins catalysing metabolic reactions in ReconMap 2.0 were not previously visually represented on the map canvas. This precluded visualisation of omics data in the context of ReconMap 2.0. We suggested a solution for displaying protein nodes on the ReconMap 2.0 map in the vicinity of the corresponding reaction or process nodes. This permits multi-omics data visualisation in the context of both map layers. Exploration and shuttling between the two map layers is possible using Google Maps-like features of NaviCell. The integrated networks ACSN-ReconMap 2.0 are accessible online and allows data visualisation through various modes such as markers, heat maps, bar-plots, glyphs and map staining. The integrated networks were applied for comparison of immunoreactive and proliferative ovarian cancer subtypes using transcriptomic, copy number and mutation multi-omics data. A certain number of metabolic and signalling processes specifically deregulated in each of the ovarian cancer sub-types were identified. Conclusions As knowledge evolves and new omics data becomes more heterogeneous, gathering together existing domains of biology under common platforms is essential. We believe that an integrated ACSN-ReconMap 2.0 networks will help in understanding various disease mechanisms and discovery of new interactions at the intersection of cell signalling and metabolism. In addition, the successful integration of metabolic and signalling networks allows broader systems biology approach application for data interpretation and retrieval of intervention points to tackle simultaneously the key players coordinating signalling and metabolism in human diseases. Electronic supplementary material The online version of this article (10.1186/s12859-019-2682-z) contains supplementary material, which is available to authorized users.
Published: 2019

35. Identification of microRNA clusters cooperatively acting on epithelial to mesenchymal transition in triple negative breast cancer

Author: Laura Cantini* 1, 2, 3, 4, 5, Gloria Bertoli* 6, Claudia Cava 6, Thierry Dubois 1, 7, Andrei Zinovyev 1, Michele Caselle 8, Isabella Castiglioni 6, Emmanuel Barillot 1, Loredana Martignetti 1, Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Mathématiques Blaise Pascal (LMBP), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Centre National de la Recherche Scientifique (CNRS), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Università degli studi di Torino (UNITO), Service de Bioinformatique (CURIE-BIOINFO), Institut Curie [Paris], Cantini, Laura, Institut de biologie de l'ENS Paris (IBENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Università degli studi di Torino = University of Turin (UNITO), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Epithelial-Mesenchymal Transition, Epithelial to Mesenchymal Transition, Molecular Networks (q-bio.MN), [SDV]Life Sciences [q-bio], Datasets as Topic, Triple Negative Breast Neoplasms, Computational biology, [MATH] Mathematics [math], Biology, medicine.disease_cause, Malignant transformation, Metastasis, 03 medical and health sciences, 0302 clinical medicine, Breast cancer, Cell Line, Tumor, microRNA, Breast Cancer, Genetics, medicine, Gene silencing, Humans, Quantitative Biology - Molecular Networks, Neoplasm Invasiveness, Epithelial–mesenchymal transition, Gene Silencing, [MATH]Mathematics [math], Triple-negative breast cancer, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, Cell Proliferation, 0303 health sciences, breast cancer, Cancer, Reproducibility of Results, Computational Biology, medicine.disease, Phenotype, [STAT] Statistics [stat], [SDV] Life Sciences [q-bio], [STAT]Statistics [stat], MicroRNAs, FOS: Biological sciences, 030220 oncology & carcinogenesis, Multigene Family, MicroRNA, Breast Cancer, Epithelial to Mesenchymal Transition, Carcinogenesis, 030217 neurology & neurosurgery
Abstract: MicroRNAs play important roles in many biological processes. Their aberrant expression can have oncogenic or tumor suppressor function directly participating to carcinogenesis, malignant transformation, invasiveness and metastasis. Indeed, miRNA profiles can distinguish not only between normal and cancerous tissue but they can also successfully classify different subtypes of a particular cancer. Here, we focus on a particular class of transcripts encoding polycistronic miRNA genes that yields multiple miRNA components. We describe clustered MiRNA Master Regulator Analysis (ClustMMRA), a fully redesigned release of the MMRA computational pipeline (MiRNA Master Regulator Analysis), developed to search for clustered miRNAs potentially driving cancer molecular subtyping. Genomically clustered miRNAs are frequently co-expressed to target different components of pro-tumorigenic signalling pathways. By applying ClustMMRA to breast cancer patient data, we identified key miRNA clusters driving the phenotype of different tumor subgroups. The pipeline was applied to two independent breast cancer datasets, providing statistically concordant results between the two analysis. We validated in cell lines the miR-199/miR-214 as a novel cluster of miRNAs promoting the triple negative subtype phenotype through its control of proliferation and EMT.
Published: 2019

36. EZHIP constrains Polycomb Repressive Complex 2 activity in germ cells

Author: Patricia Legoix, Michiel Vermeulen, Déborah Bourc'his, Seynabou Diop, Raphaël Margueron, Roberta Ragazzini, Maria-Elena Torres-Padilla, Dina Zielinski, Katia Ancelin, Maëlle Givelet, Nicolas Servant, Setareh Aflaki, Pierre Fouchet, Audrey Michaud, Raquel Pérez-Palacios, Baymaz Hi, Pwtc Jansen, Máté Borsos, Génétique et Biologie du Développement, Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université Paris sciences et lettres (PSL), Radboud Institute for Molecular Life Sciences [Nijmegen, the Netherlands], Radboud university [Nijmegen], Stabilité génétique, Cellules Souches et Radiations (SCSR (U_967)), Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institut de Radiobiologie Cellulaire et Moléculaire (IRCM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Helmholtz-Zentrum München (HZM), Ludwig-Maximilians-Universität München (LMU), Plateforme de séquençage ICGEX, Institut Curie [Paris], Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), MINES ParisTech - École nationale supérieure des mines de Paris, Radboud University [Nijmegen], Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Helmholtz Zentrum München = German Research Center for Environmental Health, Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), and Gestionnaire, Hal Sorbonne Université
Subjects: 0301 basic medicine, Male, Germline development, Regulator, General Physics and Astronomy, Histones, Mice, 0302 clinical medicine, Oogenesis, Transcription (biology), Gene expression, Testis, Sf9 Cells, lcsh:Science, Regulation of gene expression, Mice, Knockout, Oncogene Proteins, 0303 health sciences, Multidisciplinary, Proteomics and Chromatin Biology, Polycomb Repressive Complex 2, Spermatozoa, Recombinant Proteins, Chromatin, Cell biology, medicine.anatomical_structure, 030220 oncology & carcinogenesis, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Female, PRC2, Transcription, Adult, endocrine system, Gonad, Science, macromolecular substances, Biology, Development, Chromatin remodeling, General Biochemistry, Genetics and Molecular Biology, Article, 03 medical and health sciences, Cell Line, Tumor, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], medicine, Gene silencing, Animals, Humans, Epigenetics, Spermatogenesis, 030304 developmental biology, Ovum, Ovary, General Chemistry, Oocyte, Gene regulation, 030104 developmental biology, HEK293 Cells, Mutation, biology.protein, lcsh:Q, 030217 neurology & neurosurgery
Abstract: The Polycomb group of proteins is required for the proper orchestration of gene expression due to its role in maintaining transcriptional silencing. It is composed of several chromatin modifying complexes, including Polycomb Repressive Complex 2 (PRC2), which deposits H3K27me2/3. Here, we report the identification of a cofactor of PRC2, EZHIP (EZH1/2 Inhibitory Protein), expressed predominantly in the gonads. EZHIP limits the enzymatic activity of PRC2 and lessens the interaction between the core complex and its accessory subunits, but does not interfere with PRC2 recruitment to chromatin. Deletion of Ezhip in mice leads to a global increase in H3K27me2/3 deposition both during spermatogenesis and at late stages of oocyte maturation. This does not affect the initial number of follicles but is associated with a reduction of follicles in aging. Our results suggest that mature oocytes Ezhip−/− might not be fully functional and indicate that fertility is strongly impaired in Ezhip−/− females. Altogether, our study uncovers EZHIP as a regulator of chromatin landscape in gametes., Polycomb Repressive Complex 2 (PRC2) plays critical roles in transcriptional silencing during development. Here the authors identify EZHIP as a cofactor of PRC2 expressed predominantly in the gonads, finding that EZHIP limits the enzymatic activity of PRC2 in germ cells in mice.
Published: 2019

37. Synthesis of Boolean Networks from Biological Dynamical Constraints using Answer-Set Programming

Author: Stephanie Chevalier, Andrei Zinovyev, Christine Froidevaux, Loïc Paulevé, Bioinformatique (LRI) (BioInfo - LRI), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université Sciences et Technologies - Bordeaux 1-Université Bordeaux Segalen - Bordeaux 2, Cancer et génôme: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, MINES ParisTech - École nationale supérieure des mines de Paris-Institut Curie-Institut National de la Santé et de la Recherche Médicale (INSERM), ANR-16-CE12-0034,AlgoReCell,Computational Models and Algorithms for Predicting Cell Reprogramming Determinants with High Efficiency and High Fidelity(2016), CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Plafrim - GENCI, and ANR-16-CE12-0034,AlgoReCell,Modèles informatiques et algorithmes pour la prédiction de cibles de reprogrammation cellulaire avec haute fidélité et haute efficacité(2016)
Subjects: FOS: Computer and information sciences, Computer Science - Logic in Computer Science, Theoretical computer science, Dynamical systems theory, Computer Science - Artificial Intelligence, Computer science, Molecular Networks (q-bio.MN), attractors, 02 engineering and technology, Type (model theory), Domain (software engineering), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Set (abstract data type), 03 medical and health sciences, Answer set programming, Attractor, 0202 electrical engineering, electronic engineering, information engineering, discrete dynamical systems, Quantitative Biology - Molecular Networks, Boolean function, model synthesis, 030304 developmental biology, 0303 health sciences, Index Terms-model synthesis, [INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO], systems biology, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Logic in Computer Science (cs.LO), Artificial Intelligence (cs.AI), FOS: Biological sciences, 020201 artificial intelligence & image processing, Boolean satisfiability problem, reachability
Abstract: International audience; Boolean networks model finite discrete dynamical systems with complex behaviours. The state of each component is determined by a Boolean function of the state of (a subset of) the components of the network. This paper addresses the synthesis of these Boolean functions from constraints on their domain and emerging dynamical properties of the resulting network. The dynamical properties relate to the existence and absence of trajectories between partially observed configurations, and to the stable behaviours (fixpoints and cyclic attractors). The synthesis is expressed as a Boolean satisfiability problem relying on Answer-Set Programming with a parametrized complexity, and leads to a complete non-redundant characterization of the set of solutions. Considered constraints are particularly suited to address the synthesis of models of cellular differentiation processes, as illustrated on a case study. The scalability of the approach is demonstrated on random networks with scale-free structures up to 100 to 1,000 nodes depending on the type of constraints.
Published: 2019
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38. Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data

Author: Hélène Bonsang-Kitzis, Benjamin Sadacca, Judith Abecassis, Fabien Reyal, Cécile Laurent, Jean-Philippe Vert, Anne-Sophie Hamy, Bodescot, Myriam, Residual Tumor & Response to Treatment Laboratory [Paris] (RT2Lab), Translational Research Department [Paris], Immunité et cancer (U932), Université Paris Descartes - Paris 5 (UPD5)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Descartes - Paris 5 (UPD5)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Immunité et cancer (U932), Université Paris Descartes - Paris 5 (UPD5)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Descartes - Paris 5 (UPD5)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Bioinformatique (CBIO), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Mathématiques de Toulouse UMR5219 (IMT), Université Toulouse Capitole (UT Capitole), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Département de chirurgie, Institut Curie [Paris], Google Brain, Paris, Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), MINES ParisTech - École nationale supérieure des mines de Paris, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse 1 Capitole (UT1), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3)
Subjects: 0301 basic medicine, Epidemiology, Gene Identification and Analysis, Inference, Single sample, 0302 clinical medicine, Selective advantage, Medicine and Health Sciences, Exome, Exome sequencing, 0303 health sciences, Multidisciplinary, Cancer Risk Factors, Confounding, Squamous Cell Carcinomas, Prognosis, Head and Neck Tumors, 3. Good health, Oncology, 030220 oncology & carcinogenesis, Medicine, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Female, Algorithms, Research Article, DNA Copy Number Variations, Science, Genetic Causes of Cancer, Breast Neoplasms, [SDV.CAN]Life Sciences [q-bio]/Cancer, Computational biology, Biology, Research and Analysis Methods, Tumor heterogeneity, Carcinomas, Cancer prognosis, 03 medical and health sciences, Genetic Heterogeneity, Breast cancer, Head and Neck Squamous Cell Carcinoma, [SDV.CAN] Life Sciences [q-bio]/Cancer, Diagnostic Medicine, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Computational Techniques, Exome Sequencing, medicine, Genetics, Humans, Molecular Biology Techniques, Molecular Biology, Mutation Detection, 030304 developmental biology, Genetic heterogeneity, Genome, Human, Squamous Cell Carcinoma of Head and Neck, Computational Pipelines, Robustness (evolution), Biology and Life Sciences, Cancers and Neoplasms, Computational Biology, medicine.disease, Head and neck squamous-cell carcinoma, Human genetics, 030104 developmental biology, Head and Neck Cancers, Urinary Bladder Neoplasms, Medical Risk Factors, Mutation, Somatic Mutation, Cloning
Abstract: Tumors are made of evolving and heterogeneous populations of cells which arise from successive appearance and expansion of subclonal populations, following acquisition of mutations conferring them a selective advantage. Those subclonal populations can be sensitive or resistant to different treatments, and provide information about tumor aetiology and future evolution. Hence, it is important to be able to assess the level of heterogeneity of tumors with high reliability for clinical applications.In the past few years, a large number of methods have been proposed to estimate intra-tumor heterogeneity from whole exome sequencing (WES) data, but the accuracy and robustness of these methods on real data remains elusive. Here we systematically apply and compare 6 computational methods to estimate tumor heterogeneity on 1,697 WES samples from the cancer genome atlas (TCGA) covering 3 cancer types (breast invasive carcinoma, bladder urothelial carcinoma, and head and neck squamous cell carcinoma), and two distinct input mutation sets. We observe significant differences between the estimates produced by different methods, and identify several likely confounding factors in heterogeneity assessment for the different methods. We further show that the prognostic value of tumor heterogeneity for survival prediction is limited in those datasets, and find no evidence that it improves over prognosis based on other clinical variables.In conclusion, heterogeneity inference from WES data on a single sample, and its use in cancer prognosis, should be considered with caution. Other approaches to assess intra-tumoral heterogeneity such as those based on multiple samples may be preferable for clinical applications.
Published: 2019

39. Lineage tracing of Notch1-expressing cells in intestinal tumours reveals a distinct population of cancer stem cells

Author: Larissa Mourao, Naoual Menssouri, Guillaume Jacquemin, Silvia Fre, Nicolas Servant, Mathilde Huyghe, Wojciech J. Nawrocki, Biophysics Photosynthesis/Energy, LaserLaB - Energy, Génétique et Biologie du Développement, Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université Paris sciences et lettres (PSL), Sorbonne Université - Faculté de Médecine (SU FM), Sorbonne Université (SU), Vrije Universiteit Amsterdam [Amsterdam] (VU), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), MINES ParisTech - École nationale supérieure des mines de Paris, Centre de Bioinformatique (CBIO), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)
Subjects: 0301 basic medicine, lcsh:Medicine, Receptors, G-Protein-Coupled, Transcriptome, Mice, 0302 clinical medicine, Cell Self Renewal, Intestinal Mucosa, Receptor, Notch1, Receptor, lcsh:Science, 0303 health sciences, education.field_of_study, Multidisciplinary, LGR5, Cell Differentiation, Intestines, Cell Transformation, Neoplastic, 030220 oncology & carcinogenesis, Colonic Neoplasms, Neoplastic Stem Cells, Signal transduction, Stem cell, Signal Transduction, Transgene, Crypt, Population, Notch signaling pathway, Mice, Transgenic, [SDV.CAN]Life Sciences [q-bio]/Cancer, Biology, Article, 03 medical and health sciences, SDG 3 - Good Health and Well-being, Cancer stem cell, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Intestinal Neoplasms, Animals, Humans, Cell Lineage, education, Cell Proliferation, 030304 developmental biology, Gene Expression Profiling, lcsh:R, 030104 developmental biology, Cancer research, lcsh:Q, Biomarkers, 030217 neurology & neurosurgery
Abstract: Colon tumours are hierarchically organized and contain multipotent self-renewing cells, called Cancer Stem Cells (CSCs). We have previously shown that the Notch1 receptor is expressed in Intestinal Stem Cells (ISCs); given the critical role played by Notch signalling in promoting intestinal tumourigenesis, we explored Notch1 expression in tumours. Combining lineage tracing in two tumour models with transcriptomic analyses, we found that Notch1 + tumour cells are undifferentiated, proliferative and capable of indefinite self-renewal and of generating a heterogeneous clonal progeny. Molecularly, the transcriptional signature of Notch1+ tumour cells highly correlates with ISCs, suggestive of their origin from normal crypt cells. Surprisingly, Notch1+ expression labels a subset of CSCs that show reduced levels of Lgr5, a reported CSCs marker. The existence of distinct stem cell populations within intestinal tumours highlights the necessity of better understanding their hierarchy and behaviour, to identify the correct cellular targets for therapy.
Published: 2018
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40. The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks

Author: Andrei Zinovyev, Gautier Stoll, Pedro T. Monteiro, Sarah Cohen-Boulakia, Céline Hernandez, Laurence Calzone, Tomáš Helikar, Aurélien Naldi, Claudine Chaouiya, Nicolas Levy, Loïc Paulevé, Denis Thieffry, Institut de biologie de l'ENS Paris (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Bioinformatique (LRI) (BioInfo - LRI), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), École normale supérieure de Lyon (ENS de Lyon), Université Paris Descartes - Paris 5 (UPD5), Centre de Recherche des Cordeliers (CRC), Université Pierre et Marie Curie - Paris 6 (UPMC)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Santé et de la Recherche Médicale (INSERM), Université Pierre et Marie Curie - Paris 6 - UFR de Médecine Pierre et Marie Curie (UPMC), Université Pierre et Marie Curie - Paris 6 (UPMC), Plateforme de métabolomique, Direction de la recherche [Gustave Roussy], Institut Gustave Roussy (IGR)-Institut Gustave Roussy (IGR), Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento em Lisboa (INESC-ID), Instituto Superior Técnico, Universidade Técnica de Lisboa (IST)-Instituto de Engenharia de Sistemas e Computadores (INESC), Instituto Gulbenkian de Ciência [Oeiras] (IGC), Fundação Calouste Gulbenkian, University of Nebraska–Lincoln, University of Nebraska System, Institut Curie [Paris], Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Paris sciences et lettres (PSL), Lobachevsky State University [Nizhni Novgorod], Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria), ANR-16-CE12-0034,AlgoReCell,Modèles informatiques et algorithmes pour la prédiction de cibles de reprogrammation cellulaire avec haute fidélité et haute efficacité(2016), ANR-11-LABX-0045,DIGICOSME,Mondes numériques: Données, programmes et architectures distribués(2011), ANR-11-IDEX-0003,IPS,Idex Paris-Saclay(2011), Institut de biologie de l'ENS Paris (UMR 8197/1024) ( IBENS ), École normale supérieure - Paris ( ENS Paris ) -École normale supérieure - Paris ( ENS Paris ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Bioinformatique (LRI) ( BioInfo - LRI ), Laboratoire de Recherche en Informatique ( LRI ), Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ), École normale supérieure - Lyon ( ENS Lyon ), Computational systems biology and optimization ( Lifeware ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ), Université Paris Descartes - Paris 5 ( UPD5 ), Centre de Recherche des Cordeliers ( CRC ), Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Université Paris Descartes - Paris 5 ( UPD5 ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -École pratique des hautes études ( EPHE ) -Université Paris Diderot - Paris 7 ( UPD7 ), Institut National de la Santé et de la Recherche Médicale ( INSERM ), Université Pierre et Marie Curie - Paris 6 - UFR de Médecine Pierre et Marie Curie ( UPMC ), Université Pierre et Marie Curie - Paris 6 ( UPMC ), Institut Gustave Roussy ( IGR ), Instituto Superior Técnico, Universidade Técnica de Lisboa ( IST ), Instituto Gulbenkian de Ciência [Oeiras] ( IGC ), University of Nebraska-Lincoln, Institut Curie, Cancer et génôme: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, MINES ParisTech - École nationale supérieure des mines de Paris-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -INSTITUT CURIE, MINES ParisTech - École nationale supérieure des mines de Paris, ANR-16-CE12-0034,AlgoReCell,Computational Models and Algorithms for Predicting Cell Reprogramming Determinants with High Efficiency and High Fidelity ( 2016 ), ANR-11-IDEX-0003-02/11-LABX-0045,DIGICOSME,Mondes numériques: Données, programmes et architectures distribués ( 2011 ), ANR : IDEX PARIS SACLAY,ANR-11-IDEX-0003-02 IDEX Paris-Saclay, Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), École normale supérieure - Paris (ENS Paris)-École normale supérieure - Paris (ENS Paris)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Lyon (ENS Lyon), Université Paris Diderot - Paris 7 (UPD7)-École pratique des hautes études (EPHE)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Nebraska [Lincoln], MINES ParisTech - École nationale supérieure des mines de Paris-Institut Curie-Institut National de la Santé et de la Recherche Médicale (INSERM), ANR-16-CE12-0034,AlgoReCell,Computational Models and Algorithms for Predicting Cell Reprogramming Determinants with High Efficiency and High Fidelity(2016), ANR: 11-LABX-0045,DIGICOSME,Mondes numériques: Données, programmes et architectures distribués(2011), ANR: IDEX PARIS SACLAY,ANR-11-IDEX-0003-02 IDEX Paris-Saclay, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-École pratique des hautes études (EPHE), Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université Paris Diderot - Paris 7 (UPD7)-École pratique des hautes études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Subjects: Computer science, Physiology, 0206 medical engineering, 02 engineering and technology, Boolean networks, 03 medical and health sciences, Software, [ INFO.INFO-BI ] Computer Science [cs]/Bioinformatics [q-bio.QM], model analysis, Python programming language, computational systems biology, Methods, reproducibility, 030304 developmental biology, computer.programming_language, 0303 health sciences, business.industry, Python (programming language), Visualization, Workflow, Microsoft Windows, User interface, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Software engineering, business, computer, 020602 bioinformatics, Biological network
Abstract: Analysing models of biological networks typically relies on workflows in which different software tools with sensitive parameters are chained together, many times with additional manual steps. The accessibility and reproducibility of such workflows is challenging, as publications often overlook analysis details, and because some of these tools may be difficult to install, and/or have a steep learning curve. The CoLoMoTo Interactive Notebook provides a unified environment to edit, execute, share, and reproduce analyses of qualitative models of biological networks. This framework combines the power of different technologies to ensure repeatability and to reduce users’ learning curve of these technologies. The framework is distributed as a Docker image with the tools ready to be run without any installation step besides Docker, and is available on Linux, macOS, and Microsoft Windows. The embedded computational workflows are edited with a Jupyter web interface, enabling the inclusion of textual annotations, along with the explicit code to execute, as well as the visualisation of the results. The resulting notebook files can then be shared and re-executed in the same environment. To date, the CoLoMoTo Interactive Notebook provides access to software tools including GINsim, BioLQM, Pint, MaBoSS, and Cell Collective for the modelling and analysis of Boolean and multi-valued networks. More tools will be included in the future. We developed a Python interface for each of these tools to offer a seamless integration in the Jupyter web interface and ease the chaining of complementary analyses.
Published: 2018

41. Assessing reproducibility of matrix factorization methods in independent transcriptomes

Author: François Radvanyi, Laura Cantini, Ulykbek Kairov, Emmanuel Barillot, Aurélien de Reyniès, Andrei Zinovyev, Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Bioinformatique (CBIO), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Nazarbayev University [Kazakhstan], Ligue Nationale Contre le Cancer - Paris, Ligue Nationnale Contre le Cancer, Compartimentation et dynamique cellulaires (CDC), Institut Curie [Paris]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU), Lobachevsky State University [Nizhni Novgorod], Institut de biologie de l'ENS Paris (IBENS), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Statistics and Probability, Computer science, Gene Expression, Breast Neoplasms, Computational biology, Biochemistry, Matrix decomposition, 03 medical and health sciences, 0302 clinical medicine, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], medicine, Tumor Microenvironment, Humans, Molecular Biology, Cancer, 030304 developmental biology, Protocol (science), 0303 health sciences, Reproducibility, Basis (linear algebra), Gene Expression Profiling, Matrix Factorization, Reproducibility of Results, medicine.disease, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Independent component analysis, Original Papers, Computer Science Applications, Gene expression profiling, Computational Mathematics, Computational Theory and Mathematics, Benchmark (computing), Ovarian cancer, Transcriptome, 030217 neurology & neurosurgery, Algorithms, Curse of dimensionality
Abstract: Motivation Matrix factorization (MF) methods are widely used in order to reduce dimensionality of transcriptomic datasets to the action of few hidden factors (metagenes). MF algorithms have never been compared based on the between-datasets reproducibility of their outputs in similar independent datasets. Lack of this knowledge might have a crucial impact when generalizing the predictions made in a study to others. Results We systematically test widely used MF methods on several transcriptomic datasets collected from the same cancer type (14 colorectal, 8 breast and 4 ovarian cancer transcriptomic datasets). Inspired by concepts of evolutionary bioinformatics, we design a novel framework based on Reciprocally Best Hit (RBH) graphs in order to benchmark the MF methods for their ability to produce generalizable components. We show that a particular protocol of application of independent component analysis (ICA), accompanied by a stabilization procedure, leads to a significant increase in the between-datasets reproducibility. Moreover, we show that the signals detected through this method are systematically more interpretable than those of other standard methods. We developed a user-friendly tool for performing the Stabilized ICA-based RBH meta-analysis. We apply this methodology to the study of colorectal cancer (CRC) for which 14 independent transcriptomic datasets can be collected. The resulting RBH graph maps the landscape of interconnected factors associated to biological processes or to technological artifacts. These factors can be used as clinical biomarkers or robust and tumor-type specific transcriptomic signatures of tumoral cells or tumoral microenvironment. Their intensities in different samples shed light on the mechanistic basis of CRC molecular subtyping. Availability and implementation The RBH construction tool is available from http://goo.gl/DzpwYp Supplementary information Supplementary data are available at Bioinformatics online.
Published: 2018

42. Effective normalization for copy number variation in Hi-C data

Author: Jean-Philippe Vert, Edith Heard, Nicolas Servant, Nelle Varoquaux, Emmanuel Barillot, Bodescot, Myriam, Institut Curie [Paris], Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Bioinformatique (CBIO), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Department of Statistics [Berkeley], University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), Berkeley Institute for Data Science (BIDS), Génétique et Biologie du Développement, Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Département de Mathématiques et Applications - ENS Paris (DMA), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), This work was supported by the Labex Deep, the Ligue Contre le Cancer, the European Research Coucil (SMAC-ERC-280032), the ERC Advanced Investigator award (ERC-250367), the ABS4NGS project (ANR-11-BINF-0001), the Gordon and Betty Moore Foundation (Grant GBMF3834) and the Alfred P. Sloan Foundation (Grant 2013-10-27)., Epigenèse et développement des mammifères, Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Collège de France - Chaire Epigénétique et mémoire cellulaire, Collège de France (CdF (institution)), Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), MINES ParisTech - École nationale supérieure des mines de Paris, University of California [Berkeley], University of California-University of California, École normale supérieure - Paris (ENS Paris), Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Centre National de la Recherche Scientifique (CNRS)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC), Chaire Epigénétique et mémoire cellulaire, Cancer et génôme: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, MINES ParisTech - École nationale supérieure des mines de Paris-Institut Curie-Institut National de la Santé et de la Recherche Médicale (INSERM), MINES ParisTech - École nationale supérieure des mines de Paris-PSL Research University (PSL), Institut Curie-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and École normale supérieure - Paris (ENS Paris)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0301 basic medicine, Normalization (statistics), Copy-number, DNA Copy Number Variations, Computer science, Sequencing data, [SDV.CAN]Life Sciences [q-bio]/Cancer, Biology, lcsh:Computer applications to medicine. Medical informatics, Genome, Biochemistry, Database normalization, Chromosome conformation capture, 03 medical and health sciences, 0302 clinical medicine, [SDV.CAN] Life Sciences [q-bio]/Cancer, Structural Biology, Hi-C, Neoplasms, Humans, Copy-number variation, lcsh:QH301-705.5, Molecular Biology, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, Cancer, Chromosome Aberrations, 0303 health sciences, Diploid genome, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Genome, Human, Applied Mathematics, Methodology Article, Chromosome Mapping, Computational Biology, Genomics, Simple extension, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computer Science Applications, Data set, Normalization, 030104 developmental biology, lcsh:Biology (General), 030220 oncology & carcinogenesis, lcsh:R858-859.7, Algorithm
Abstract: Background Normalization is essential to ensure accurate analysis and proper interpretation of sequencing data, and chromosome conformation capture data such as Hi-C have particular challenges. Although several methods have been proposed, the most widely used type of normalization of Hi-C data usually casts estimation of unwanted effects as a matrix balancing problem, relying on the assumption that all genomic regions interact equally with each other. Results In order to explore the effect of copy-number variations on Hi-C data normalization, we first propose a simulation model that predict the effects of large copy-number changes on a diploid Hi-C contact map. We then show that the standard approaches relying on equal visibility fail to correct for unwanted effects in the presence of copy-number variations. We thus propose a simple extension to matrix balancing methods that model these effects. Our approach can either retain the copy-number variation effects (LOIC) or remove them (CAIC). We show that this leads to better downstream analysis of the three-dimensional organization of rearranged genomes. Conclusions Taken together, our results highlight the importance of using dedicated methods for the analysis of Hi-C cancer data. Both CAIC and LOIC methods perform well on simulated and real Hi-C data sets, each fulfilling different needs. Electronic supplementary material The online version of this article (10.1186/s12859-018-2256-5) contains supplementary material, which is available to authorized users.
Published: 2018

43. Computational pan-genomics : Status, promises and challenges

Author: Lodewyk F. A. Wessels, Jan O. Korbel, Jasmijn A. Baaijens, Francesca Chiaromonte, Gunnar W. Klau, Kai Ye, Alexander Schönhuth, Francesca D. Ciccarelli, Tobias Marschall, Robin Cijvat, Matthias Schlesner, Adam M. Novak, Eleazar Eskin, Ashley D. Sanders, Sven Rahmann, Carl Shneider, Wigard P. Kloosterman, Knut Reinert, Eric-Wubbo Lameijer, Sandra Smit, Benedict Paten, Mohammed El-Kebir, Valentina Boeva, Evan E. Eichler, Cornelia M. van Duijn, Can Alkan, Jeroen de Ridder, Benjamin Langmead, Dick de Ridder, Jiayin Wang, David Porubsky, Fabio Vandin, Erwin Datema, Ben Raphael, Paul Kersey, Nadia Pisanti, Corinna Ernst, Klaasjan G. Ouwens, Y. Zhang, Thomas Abeel, Erik Garrison, Veli Mäkinen, Paul I.W. de Bakker, Victor Guryev, Siavash Sheikhizadeh, Manja Marz, Marcel Martin, Ole Schulz-Trieglaff, Pieter B. Neerincx, Rayan Chikhi, Eric Rivals, John C. Mu, Raoul J. P. Bonnal, Bas E. Dutilh, Paul Medvedev, Louis Dijkstra, Pierre Peterlongo, Ali Ghaffaari, Daniel Valenzuela, Epidemiology, Gastroenterology & Hepatology, Erasmus MC other, Alkan, Can, Saarland University [Saarbrücken], Max Planck Institute for Informatics [Saarbrücken], Karlsruhe Institute of Technology (KIT), Broad Institute of MIT and Harvard (BROAD INSTITUTE), Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], Delft Bioinformatics Lab [Delft], Delft University of Technology (TU Delft), Computational Science Lab [Amsterdam], University of Amsterdam [Amsterdam] (UvA), Theoretical Biology & Bioinformatics [Utrecht], University Medical Center [Utrecht], Universidade Federal do Rio de Janeiro (UFRJ), European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, Section genetics [Utrecht], Center for Molecular medicine [Utrecht], University Medical Center [Utrecht]-University Medical Center [Utrecht], Helsinki Institute for Information Technology, Department of Computer Science [Helsinki], Falculty of Science [Helsinki], University of Helsinki-University of Helsinki, Howard Hughes Medical Institute [Santa Cruz] (HHMI), Center for Biomolecular Science & Engineering, European Research Institute for the Biology of Ageing [Groningen] (ERIBA), University Medical Center Groningen [Groningen] (UMCG), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Department of Computer Engineering (Bilkent-CS), Bilkent, Life Sciences [Amsterdam] (MAC4), Centrum Wiskunde & Informatica (CWI), Institut Cochin (IC UM3 (UMR 8104 / U1016)), Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), PSL Research University (PSL), Centre de Bioinformatique (CBIO), MINES ParisTech - École nationale supérieure des mines de Paris-PSL Research University (PSL), Cancer et génôme: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, MINES ParisTech - École nationale supérieure des mines de Paris-Institut Curie-Institut National de la Santé et de la Recherche Médicale (INSERM), Integrative Biology Program [Milano], Istituto Nazionale Genetica Molecolare [Milano] (INGM), Department of Statistics [Pennsylvania], Pennsylvania State University (Penn State), Penn State System-Penn State System, Bioinformatics and Sequence Analysis (BONSAI), Université de Lille, Sciences et Technologies-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189 (CRIStAL), Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Ecole Centrale de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Ecole Centrale de Lille-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Monet DB Solutions [Amsterdam], KeyGene [Wageningen], Department of Epidemiology [Rotterdam], Erasmus University Medical Center [Rotterdam] (Erasmus MC), Howard Hughes Medical Institute [Berkeley], University of California [Berkeley], University of California-University of California, Genome Informatics [Duisburg], Universität Duisburg-Essen [Essen], Departement of human genetics [Los Angeles], Computer Science Department [Los Angeles] (UCLA), University of California [Los Angeles] (UCLA), University of California-University of California-University of California [Los Angeles] (UCLA), The Wellcome Trust Sanger Institute [Cambridge], Equipe de recherche européenne en algorithmique et biologie formelle et expérimentale (ERABLE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Universiteit Leiden [Leiden], Department of Computer Science [Baltimore], Johns Hopkins University (JHU), Centre de Physique des Particules de Marseille (CPPM), Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Aix Marseille Université (AMU), University of Pennsylvania [Philadelphia], China Agricultural University (CAU), University of Groningen [Groningen], Department of Biological Psychology [Amsterdam], Vrije Universiteit Amsterdam [Amsterdam] (VU), Scalable, Optimized and Parallel Algorithms for Genomics (GenScale), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Dipartimento di Informatica [Pisa] (DI), University of Pisa - Università di Pisa, Faculty of Computer Science [Dortmund], Technische Universität Dortmund [Dortmund] (TU), Service de Pneumologie A [Paris], AP-HP - Hôpital Bichat - Claude Bernard [Paris], Department of Mathematics and Computer Science (Freie Universität Berlin), Freie Universität Berlin, Wageningen University and Research Centre [Wageningen] (WUR), Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Division of Theoretical Bioinformatics [Heidelberg], German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), Illumina Cambridge, Illumina, Terry Fox Laboratory, BC Cancer Agency (BCCRC)-British Columbia Cancer Agency Research Centre, Bioinformatics Group [Wageningen], Leiden Observatory [Leiden], Regeneron Pharmaceuticals [Tarrytown], Shaanxi University of Science and Technology, Netherlands Cancer Institute (NKI), Antoni van Leeuwenhoek Hospital, Universita degli Studi di Padova, The Netherlands Organization for Scientific Research (NWO) Vidi (639.072.309 to A.S., 864.14.004 to B.E.D.), CAPES/BRASIL (to B.E.D.), the Academy of Finland (284598 [CoECGR] to V.M. and D.V.), the Russian Scientific Foundation (14–11–00826 to L.D.), Institut de Biologie Computationnelle (ANR-11-BINF- 0002 to E.R.), and the French Colib’read project (ANR–12– BS02–0008 to E.R.). NSFC 31671372 (to K. Y.), the Dutch Graduate School for Experimental Plant Sciences (054EPS15 to S.S.), the EMGO Institute for Health and Care Research (EMGO+) to K.O., the National Human Genome Research Institute (1U54HG007990 [BD2K] to B.P. and A.M.N., 5U41HG007234 [GENCODE] to B.P.), the W. M. Keck Foundation (DT06172015 to B.P. and A.M.N.), the Simons Foundation (SFLIFE# 351901 to B.P. and A.M.N.), the ARCS Foundation (2014–15 ARCS fellowship to A.M.N.), Edward Schulak (Edward Schulak Fellowship in Genomics to A.M.N.), ANR-11-BINF-0002,IBC,Institut de Biologie Computationnelle de Montpellier(2011), ANR-12-BS02-0008,Colib'read,Méthodes d'extraction d'information biologique dans les données HTS non assemblées(2012), Karlsruhe Institute of Technology ( KIT ), Broad Institute of MIT and Harvard ( BROAD INSTITUTE ), Harvard Medical School [Boston] ( HMS ) -Massachusetts General Hospital [Boston] ( MGH ) -Massachusetts Institute of Technology ( MIT ), Delft University of Technology ( TU Delft ), University of Amsterdam [Amsterdam] ( UvA ), Universidade Federal do Rio de Janeiro ( UFRJ ), European Bioinformatics Institute [Hinxton] ( EMBL-EBI ), European Molecular Biology Laboratory [Hinxton], Department of Computer Science, Howard Hughes Medical Institute [Santa Cruz] ( HHMI ), European Research Institute for the Biology of Ageing [Groningen] ( ERIBA ), University Medical Center Groningen [Groningen] ( UMCG ), Institut de Biologie Computationnelle ( IBC ), Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ) -Institut National de la Recherche Agronomique ( INRA ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ), Méthodes et Algorithmes pour la Bioinformatique ( MAB ), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier ( LIRMM ), Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ), Department of Computer Engineering ( Bilkent-CS ), Life Sciences [Amsterdam] ( MAC4 ), Centrum Wiskunde & Informatica ( CWI ), Institut Cochin ( UM3 (UMR 8104 / U1016) ), Université Paris Descartes - Paris 5 ( UPD5 ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), PSL Research University ( PSL ), Centre de Bioinformatique ( CBIO ), MINES ParisTech - École nationale supérieure des mines de Paris-PSL Research University ( PSL ), MINES ParisTech - École nationale supérieure des mines de Paris-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -INSTITUT CURIE, Istituto Nazionale Genetica Molecolare [Milano] ( INGM ), PennState University [Pennsylvania] ( PSU ), Bioinformatics and Sequence Analysis ( BONSAI ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189 ( CRIStAL ), Ecole Centrale de Lille-Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut Mines-Télécom [Paris]-Université de Lille-Centre National de la Recherche Scientifique ( CNRS ) -Ecole Centrale de Lille-Institut Mines-Télécom [Paris]-Université de Lille-Centre National de la Recherche Scientifique ( CNRS ) -Centre National de la Recherche Scientifique ( CNRS ), Centre National de la Recherche Scientifique ( CNRS ), Erasmus University Medical Center [Rotterdam], Computer Science Department [Los Angeles] ( UCLA ), University of California at Los Angeles [Los Angeles] ( UCLA ) -University of California at Los Angeles [Los Angeles] ( UCLA ), Equipe de recherche européenne en algorithmique et biologie formelle et expérimentale ( ERABLE ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ), Johns Hopkins University ( JHU ), Centre de Physique des Particules de Marseille ( CPPM ), Centre National de la Recherche Scientifique ( CNRS ) -Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Aix Marseille Université ( AMU ), China Agricultural University ( CAU ), Vrije Universiteit Amsterdam [Amsterdam] ( VU ), Scalable, Optimized and Parallel Algorithms for Genomics ( GenScale ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -GESTION DES DONNÉES ET DE LA CONNAISSANCE ( IRISA_D7 ), Institut de Recherche en Informatique et Systèmes Aléatoires ( IRISA ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ) -Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ) -Institut de Recherche en Informatique et Systèmes Aléatoires ( IRISA ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ), Dipartimento di Informatica [Pisa] ( DI ), University of Pisa [Pisa], Technische Universität Dortmund [Dortmund] ( TU ), Department of Mathematics and Computer Science ( Freie Universität Berlin ), Freie Universität Berlin [Berlin], Wageningen University and Research Centre [Wageningen] ( WUR ), K.U.Leuven, German Cancer Research Center [Heidelberg] ( DKFZ ), Helmholtz-Gemeinschaft-Helmholtz-Gemeinschaft, BC Cancer Agency ( BCCRC ) -British Columbia Cancer Agency Research Centre, Netherlands Cancer Institute ( NKI ), ANR-11-BINF-0002,IBC,Institut de Biologie Computationnelle de Montpellier ( 2011 ), ANR-12-BS02-0008,Colib'read,Méthodes d'extraction d'information biologique dans les données HTS non assemblées ( 2012 ), Helsinki Institute for Information Technology (HIIT), Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Aalto University, Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Helsingin yliopisto = Helsingfors universitet = University of Helsinki, University of California [Santa Cruz] (UC Santa Cruz), University of California (UC), Bilkent University [Ankara], Université Paris sciences et lettres (PSL), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Department of Statistics [PennState], Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), University of Washington [Seattle], Howard Hughes Medical Institute (HHMI), Universität Duisburg-Essen = University of Duisburg-Essen [Essen], University of California (UC)-University of California (UC)-University of California [Los Angeles] (UCLA), University of California (UC)-University of California (UC), Universiteit Leiden, Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), University of Pennsylvania, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Department of Mathematics and Computer Science, Wageningen University and Research [Wageningen] (WUR), Università degli Studi di Padova = University of Padua (Unipd), ANR-11-BINF-0002,IBC,Institut de biologie Computationnelle(2011), Aalto University-University of Helsinki, University of California [Santa Cruz] (UCSC), University of California, Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), MINES ParisTech - École nationale supérieure des mines de Paris, Centre National de la Recherche Scientifique (CNRS)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Université de Lille-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Inria Lille - Nord Europe, Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), and Groningen Research Institute for Asthma and COPD (GRIAC)
Subjects: 0301 basic medicine, Data structures, Computer science, Medizin, 02 engineering and technology, computer.software_genre, Genome, Computational and Statistical Genetics, data structures, haplotypes, pan-genome, read mapping, sequence graph, Tumours of the digestive tract Radboud Institute for Molecular Life Sciences [Radboudumc 14], 0303 health sciences, education.field_of_study, Point (typography), Genomics, Papers, Data mining, Sequence graph, Computational problem, Construct (philosophy), Algorithms, Human, Information Systems, Bioinformatics, 0206 medical engineering, Population, Pan-genome, 03 medical and health sciences, [ INFO.INFO-BI ] Computer Science [cs]/Bioinformatics [q-bio.QM], Bioinformatica, Humans, Haplotypes, Read mapping, Computational Biology, Genome, Human, Software, Molecular Biology, education, Representation (mathematics), 030304 developmental biology, Computational genomics, Data structure, Data science, Human genetics, 030104 developmental biology, Paradigm shift, ddc:004, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], EPS, computer, 020602 bioinformatics
Abstract: Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case ofHomo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic datasets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension ofcomputational pan-genomics, a new sub-area of research in computational biology. In this paper, we generalize existing definitions and understand apan-genomeas any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies, and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains.
Published: 2018

44. Efficient multi-task chemogenomics for drug specificity prediction

Author: Chloé-Agathe Azencott, Véronique Stoven, Benoit Playe, Centre de Bioinformatique (CBIO), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)
Subjects: Support Vector Machine, Proteomes, Computer science, lcsh:Medicine, Bioinformatics, computer.software_genre, Biochemistry, 01 natural sciences, Machine Learning, chemistry.chemical_compound, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Medicine and Health Sciences, Drug Interactions, lcsh:Science, 0303 health sciences, Applied Mathematics, Simulation and Modeling, Genomics, Prognosis, Pharmaceutical Preparations, Physical Sciences, Proteome, Benchmark (computing), Algorithms, Research Article, Signal Transduction, Computer and Information Sciences, Transmembrane Receptors, Drug-Related Side Effects and Adverse Reactions, Protein family, Research and Analysis Methods, Machine learning, Kernel Methods, 03 medical and health sciences, Adverse Reactions, Artificial Intelligence, Support Vector Machines, Chemogenomics, Protein Interactions, Set (psychology), 030304 developmental biology, Pharmacology, business.industry, Ligand, lcsh:R, Biology and Life Sciences, Proteins, Cell Biology, 0104 chemical sciences, 010404 medicinal & biomolecular chemistry, Range (mathematics), ComputingMethodologies_PATTERNRECOGNITION, chemistry, lcsh:Q, Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], G Protein Coupled Receptors, business, computer, Mathematics
Abstract: Adverse drug reactions, also called side effects, range from mild to fatal clinical events and significantly affect the quality of care. Among other causes, side effects occur when drugs bind to proteins other than their intended target. As experimentally testing drug specificity against the entire proteome is out of reach, we investigate the application of chemogenomics approaches. We formulate the study of drug specificity as a problem of predicting interactions between drugs and proteins at the proteome scale. We build several benchmark datasets, and propose NN-MT, a multi-task Support Vector Machine (SVM) algorithm that is trained on a limited number of data points, in order to solve the computational issues or proteome-wide SVM for chemogenomics. We compare NN-MT to different state-of-the-art methods, and show that its prediction performances are similar or better, at an efficient calculation cost. Compared to its competitors, the proposed method is particularly efficient to predict (protein, ligand) interactions in the difficult double-orphan case, i.e. when no interactions are previously known for the protein nor for the ligand. The NN-MT algorithm appears to be a good default method providing state-of-the-art or better performances, in a wide range of prediction scenarii that are considered in the present study: proteome-wide prediction, protein family prediction, test (protein, ligand) pairs dissimilar to pairs in the train set, and orphan cases.
Published: 2018

45. Classification of gene signatures for their information value and functional redundancy

Author: Emmanuel Barillot, Laura Cantini, Laurence Calzone, Nils Blüthgen, Andrei Zinovyev, Mattias Rydenfelt, Loredana Martignetti, Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Service de Bioinformatique (CURIE-BIOINFO), Institut Curie [Paris], Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Cantini, Laura, Institut de biologie de l'ENS Paris (IBENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Systems biology, [SDV]Life Sciences [q-bio], Computational biology, [MATH] Mathematics [math], Biology, computer.software_genre, Article, 03 medical and health sciences, 0302 clinical medicine, Redundancy (information theory), Reference gene, [MATH]Mathematics [math], lcsh:QH301-705.5, Gene, 030304 developmental biology, Statistical hypothesis testing, 0303 health sciences, Information value, Functional redundancy, Cancer type, [STAT] Statistics [stat], [SDV] Life Sciences [q-bio], [STAT]Statistics [stat], lcsh:Biology (General), 030220 oncology & carcinogenesis, Data mining, computer
Abstract: Gene signatures are more and more used to interpret results of omics data analyses but suffer from compositional (large overlap) and functional (correlated read-outs) redundancy. Moreover, many gene signatures rarely come out as significant in statistical tests. Based on pan-cancer data analysis, we construct a restricted set of 962 signatures defined as informative and demonstrate that they have a higher probability to appear enriched in comparative cancer studies. We show that the majority of informative signatures conserve their weights for the genes composing the signature (eigengenes) from one cancer type to another. We finally construct InfoSigMap, an interactive online map of these signatures and their cross-correlations. This map highlights the structure of compositional and functional redundancies between informative signatures, and it charts the territories of biological functions. InfoSigMap can be used to visualize the results of omics data analyses and suggests a rearrangement of existing gene sets., Data-driven signature classification An informative collection of gene signatures for transcriptomic data analysis is constructed. The number of transcriptomic signatures grows fast and their collections are highly redundant that hampers omics data analyses interpretation. A computational biology team from Institut Curie led by Andrei Zinovyev selected a collection of 962 gene signatures shown to be informative for cancer studies and reflecting mechanisms of cancer progression. The signatures were filtered from a large compendium without requiring any manual curation by experts through a large-scale unbiased analysis of pancancer data. They have much higher chance to obtain significant enrichment scores in a comparative trancriptomic study. The authors integrated the 962 signatures into InfoSigMap, a new data visualization resource for the interpretation of the results of omics data analyses, which facilitates getting an insight into the mechanisms driving cancer.
Published: 2017

46. TIPIN depletion leads to apoptosis in breast cancer cells

Author: Elie Saliba, Stéphane Koundrioukoff, Gordon C. Tucker, David Gentien, Leanne De Koning, Sergio Roman-Roman, Thierry Dubois, Stéphane Depil, Didier Meseure, Didier Decaudin, André Nicolas, Mengliang Ye, Michelle Debatisse, Guillem Rigaill, Amélie Brisson, Alain Pierré, Francisco Cruzalegui, Bruno Tesson, Céline Baldeyron, Fariba Nemati, Elise Martel, Institut Curie [Paris], Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Laboratory of Preclinical Investigation, Department of Translational Research, Institut Curie Research Center, Université Pierre et Marie Curie - Paris 6 (UPMC), Dynamique de l'information génétique : bases fondamentales et cancer (DIG CANCER), Centre National de la Recherche Scientifique (CNRS)-Institut Curie [Paris]-Sorbonne Université (SU), Institut des Sciences des Plantes de Paris-Saclay (IPS2 (UMR_9213 / UMR_1403)), Institut National de la Recherche Agronomique (INRA)-Université Paris-Sud - Paris 11 (UP11)-Université Paris Diderot - Paris 7 (UPD7)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), SERVIER, Mines Paris - PSL (École nationale supérieure des mines de Paris), Institut Curie [Paris]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and ProdInra, Migration
Subjects: Cancer Research, [SDV]Life Sciences [q-bio], TIPIN, Estrogen receptor, Cell Cycle Proteins, Triple Negative Breast Neoplasms, Apoptosis, Mice, 0302 clinical medicine, Tumor Cells, Cultured, [SDV.BV] Life Sciences [q-bio]/Vegetal Biology, RNA, Small Interfering, Research Articles, Triple-negative breast cancer, 0303 health sciences, Nuclear Proteins, General Medicine, Replicative stress, 3. Good health, DNA-Binding Proteins, Gene Expression Regulation, Neoplastic, [SDV] Life Sciences [q-bio], Oncology, 030220 oncology & carcinogenesis, Molecular Medicine, Female, DNA Replication, Therapeutic target, Mice, Nude, Biology, 03 medical and health sciences, Replication fork protection complex, Breast cancer, Genetics, medicine, Animals, Humans, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Replication protein A, 030304 developmental biology, Cancer, medicine.disease, Xenograft Model Antitumor Assays, Molecular biology, Proliferating cell nuclear antigen, Tissue Array Analysis, Cancer research, biology.protein, Basal-like, Carrier Proteins, Gene Deletion
Abstract: International audience; Triple-negative breast cancer (TNBC) is the breast cancer subgroup with the most aggressive clinical behavior. Alternatives to conventional chemotherapy are required to improve the survival of TNBC patients. Gene-expression analyses for different breast cancer subtypes revealed significant overexpression of the Timeless-interacting protein (TIPIN), which is involved in the stability of DNA replication forks, in the highly proliferative associated TNBC samples. Immunohistochemistry analysis showed higher expression of TIPIN in the most proliferative and aggressive breast cancer subtypes including TNBC, and no TIPIN expression in healthy breast tissues. The depletion of TIPIN by RNA interference impairs the proliferation of both human breast cancer and non-tumorigenic cell lines. However, this effect may be specifically associated with apoptosis in breast cancer cells. TIPIN silencing results in higher levels of single-stranded DNA (ssDNA), indicative of replicative stress (RS), in TNBC compared to non-tumorigenic cells. Upon TIPIN depletion, the speed of DNA replication fork was significantly decreased in all BC cells. However, TIPIN-depleted TNBC cells are unable to fire additional replication origins in response to RS and therefore undergo apoptosis. TIPIN knockdown in TNBC cells decreases tumorigenicity in vitro and delays tumor growth in vivo. Our findings suggest that TIPIN is important for the maintenance of DNA replication and represents a potential treatment target for the worst prognosis associated breast cancers, such as TNBC.
Published: 2015

47. Biological and Clinical Relevance of Associated Genomic Alterations in MYD88 L265P and non-L265P-Mutated Diffuse Large B-Cell Lymphoma: Analysis of 361 Cases

Author: Thierry Jo Molina, Fabrice Jardin, Marc André, Richard Delarue, Sylvain Mareschal, Philippe Bertrand, Thierry Fest, Corinne Haioun, Pierre-Julien Viailly, Martin Figeac, Philippe Ruminy, Dominique Penther, Vinciane Marchand, Sydney Dubois, Frederic Peyrade, Jean-Michel Picquenot, Karen Leroy, Jean-Philippe Jais, Christiane Copie-Bergman, Bruno Tesson, Catherine Maingonnat, Bettina Fabiani, Elodie Bohers, Fabienne Desmots, Hervé Tilly, Nicolas Ketterer, Thierry Lamy, Pauline Peyrouze, Gilles Salles, Groupe d'étude des proliférations lymphoïdes (GPL), Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Génomique et Médecine Personnalisée du Cancer et des Maladies Neuropsychiatriques (GPMCND), Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), Centre de Recherche des Cordeliers (CRC), Université Pierre et Marie Curie - Paris 6 (UPMC)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Res Ctr, Institut Curie, Institut Curie [Paris], Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Lille 2 - Faculté de Médecine, Plateforme de génomique fonctionnelle et structurelle [Lille], Institut pour la recherche sur le cancer de Lille [Lille] (IRCL)-Université de Lille, Droit et Santé, CHU Pontchaillou [Rennes], Microenvironment, Cell Differentiation, Immunology and Cancer (MICMAC), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Etablissement français du sang [Rennes] (EFS Bretagne), Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Service d’immunologie, de thérapie cellulaire et d’hématopoïèse, Hôpital Pontchaillou, Service d'hématologie clinique, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Henri Mondor-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Centre d'Investigation Clinique [Rennes] (CIC), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Hôpital Pontchaillou-Institut National de la Santé et de la Recherche Médicale (INSERM), Département de pathologie [Mondor], Service de Pathologie [CHU Saint-Antoine], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Saint-Antoine [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU), Service d'immuno-hématologie pédiatrique [CHU Necker], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Necker - Enfants Malades [AP-HP], Centre de Lutte contre le Cancer Antoine Lacassagne [Nice] (UNICANCER/CAL), UNICANCER-Université Côte d'Azur (UCA), Cliniques Universitaires UCL Mont-Godinne, Department of Oncology, Lausanne Hospital, Lausanne Hospital, Institut Mondor de Recherche Biomédicale (IMRB), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-IFR10, Service d’Hématologie [Centre Hospitalier Lyon Sud - HCL], Centre Hospitalier Lyon Sud [CHU - HCL] (CHLS), Hospices Civils de Lyon (HCL)-Hospices Civils de Lyon (HCL), Centre de Recherche en Cancérologie de Lyon (UNICANCER/CRCL), Centre Léon Bérard [Lyon]-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Service d'anatomie pathologique [CHU Necker], Service d'informatique médicale et biostatistiques [CHU Necker], Institut Carnot Lymphome (CALYM), Faculté de Médecine Henri Warembourg - Université de Lille, Interactions cellulaires et moléculaires (ICM), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS), Microenvironnement et cancer (MiCa), Service d'Hématologie, Immunologie et de Thérapie Cellulaire (HITC), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Hôpital Pontchaillou-CHU Pontchaillou [Rennes], Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Service d'Anatomie et cytologie pathologiques [CHU Saint-Antoine], CHU Saint-Antoine [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Département de Pathologie [CHU Necker], Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Groupe d'étude des proliférations lymphoïdes ( GPL ), Université de Rouen Normandie ( UNIROUEN ), Normandie Université ( NU ) -Normandie Université ( NU ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes ( LITIS ), Université Le Havre Normandie ( ULH ), Normandie Université ( NU ) -Normandie Université ( NU ) -Université de Rouen Normandie ( UNIROUEN ), Normandie Université ( NU ) -Institut national des sciences appliquées Rouen Normandie ( INSA Rouen Normandie ), Normandie Université ( NU ), HEMATOLOGIE, CRLCC Henri Becquerel, Centre de Recherche des Cordeliers ( CRC ), Université Paris Diderot - Paris 7 ( UPD7 ) -École pratique des hautes études ( EPHE ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Université Paris Descartes - Paris 5 ( UPD5 ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Dept Translat Res, Breast Canc Biol Grp, Cancer et génôme: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, MINES ParisTech - École nationale supérieure des mines de Paris-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -INSTITUT CURIE, Institut pour la recherche sur le cancer de Lille [Lille] ( IRCL ) -Université de Lille, Droit et Santé, Microenvironment, Cell Differentiation, Immunology and Cancer ( MICMAC ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Etablissement français du sang [Rennes] ( EFS Bretagne ), Assistance publique - Hôpitaux de Paris (AP-HP)-Hôpital Henri Mondor-Université Paris-Est Créteil Val-de-Marne - Paris 12 ( UPEC UP12 ), Centre d'Investigation Clinique [Rennes] ( CIC ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Hôpital Pontchaillou-Institut National de la Santé et de la Recherche Médicale ( INSERM ), Assistance publique - Hôpitaux de Paris (AP-HP)-CHU Saint-Antoine [APHP], Assistance publique - Hôpitaux de Paris (AP-HP)-CHU Necker - Enfants Malades [AP-HP], Centre Antoine Lacassagne, CRLCC Antoine Lacassagne, Institut Mondor de Recherche Biomédicale ( IMRB ), Institut National de la Santé et de la Recherche Médicale ( INSERM ) -IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 ( UPEC UP12 ), Centre Hospitalier Lyon Sud [CHU - HCL] ( CHLS ), Hospices Civils de Lyon ( HCL ) -Hospices Civils de Lyon ( HCL ), Centre de Recherche en Cancérologie de Lyon ( CRCL ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Centre Léon Bérard [Lyon]-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Université Pierre et Marie Curie - Paris 6 (UPMC)-École Pratique des Hautes Études (EPHE), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Université de Rennes (UR)-Hôpital Pontchaillou-Institut National de la Santé et de la Recherche Médicale (INSERM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut Carnot CALYM [Pierre-Benite], Service d'anatomie et cytologie pathologiques [CHU Saint-Antoine], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-CHU Saint-Antoine [AP-HP], Université Côte d'Azur (UCA)-UNICANCER, Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-CHU Necker - Enfants Malades [AP-HP], Centre National de la Recherche Scientifique (CNRS)-IFR140-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-Hôpital Henri Mondor-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-Sorbonne Université (SU)-CHU Saint-Antoine [APHP], Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Université Le Havre Normandie (ULH), Normandie Université (NU), MINES ParisTech - École nationale supérieure des mines de Paris, Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Rennes (UR)-Centre National de la Recherche Scientifique (CNRS), Université de Rennes (UR)-Hôpital Pontchaillou-CHU Pontchaillou [Rennes], Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), Université Paris Diderot - Paris 7 (UPD7)-École pratique des hautes études (EPHE)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), MINES ParisTech - École nationale supérieure des mines de Paris-Institut Curie-Institut National de la Santé et de la Recherche Médicale (INSERM), Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-CHU Saint-Antoine [APHP], Centre de Recherche en Cancérologie de Lyon (CRCL), Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Université de Lyon-Centre Léon Bérard [Lyon]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Male, Oncology, 0301 basic medicine, Cancer Research, medicine.medical_treatment, [SDV]Life Sciences [q-bio], Bioinformatics, medicine.disease_cause, Biochemistry, [ SDV.CAN ] Life Sciences [q-bio]/Cancer, Targeted therapy, 0302 clinical medicine, CDKN2A, hemic and lymphatic diseases, Copy-number variation, Aged, 80 and over, Genetics, 0303 health sciences, Mutation, NF-kappa B, High-Throughput Nucleotide Sequencing, hemic and immune systems, Genomics, Hematology, Middle Aged, Prognosis, 3. Good health, 030220 oncology & carcinogenesis, Female, Lymphoma, Large B-Cell, Diffuse, Signal Transduction, Adult, medicine.medical_specialty, Adolescent, DNA Copy Number Variations, Immunology, [SDV.CAN]Life Sciences [q-bio]/Cancer, Biology, Genetic Heterogeneity, 03 medical and health sciences, Internal medicine, medicine, Humans, Aged, 030304 developmental biology, Genome, Human, Genetic heterogeneity, Germinal center, Cancer, Cell Biology, CD79B, medicine.disease, Lymphoma, Gene expression profiling, 030104 developmental biology, Myeloid Differentiation Factor 88, Diffuse large B-cell lymphoma, 030215 immunology
Abstract: Introduction: MYD88 mutations, notably the recurrent gain-of-function L265P variant, are a distinguishing feature of Activated B-Cell like (ABC) Diffuse Large B Cell Lymphoma (DLBCL), leading to constitutive NFkB pathway activation. The frequency of MYD88 mutations in DLBCL and other hematologic malignancies is well described; however, there has not yet been a large-scale study of a MYD88 mutated patient cohort with additional Next Generation Sequencing (NGS), copy number variation (CNV), and gene expression data, in order to thoroughly characterize the associated genomic profiles of these patients. The aims of our study were to compare the L265P and non-L265P mutations in terms of pathological and genetic features, to better detail the genomic background associated with MYD88 mutations in order to delineate patients potentially sensitive to targeted therapies, and to define the prognostic value of MYD88 mutations according to different genomic contexts. Methods: A cohort of 361 DLBCL patients (94 MYD88 mutant and 267 MYD88 wild-type) was selected among the prospective, multicenter and randomized LNH-03B and LNH09-7B (NCT01195714) LYSA trials, as well as among patients sequenced at our institution as part of routine procedure. Cell of origin (COO) classification was obtained with HGU133+2.0 Affymetrix GeneChip arrays for 214 patients, with RT-MLPA for 77 patients1 and with Hans immunohistochemistry (IHC) method for 49 patients. All cases were submitted to next generation sequencing (NGS) focusing on 34 genes (Lymphopanel2) in order to analyze associated mutations and copy number variations (CNVs), as well as IHC, FISH, and clinical and prognostic analyses. Results: Importantly, we highlighted different genomic profiles for MYD88 L265P and MYD88 non-L265P mutant DLBCL, shedding light on their divergent backgrounds. Clustering analysis segregated subgroups according to associated genetic alterations among patients with either MYD88 L265P or non-L265P mutations. As such, clustering separated MYD88 L265P mutated DLBCL with associated PIM1 (52%), CD79B (52%), KMT2D (42%), and PRDM1 (32%) mutations, as well as MYD88 L265P mutated DLBCL with CDKN2A/B (67%/50%), PRDM1 (57%) and TNFAIP3 (52%) CNVs. We showed that associated CD79B and MYD88 L265P mutations act synergistically to increase NFkB pathway activation, although the majority of ABC MYD88 L265P mutant cases harbor downstream NFkB alterations, which can potentially predict BTK inhibitor resistance. Of note, although the MYD88 L265P variant was not an independent prognostic factor in ABC DLBCL, associated CD79B mutations significantly improved the survival of MYD88 L265P mutant ABC DLBCL in our cohort both in OS (p=0.02) and PFS (p=0.01), whereas the association of CARD11 or TNFAIP3 alterations did not impact survival. Interestingly, MYD88 mutant DLBCL cases were significantly more likely to experience central nervous system (CNS) relapse than MYD88 WT cases (p=0.02), as were MYD88 L265P mutant cases specifically (p=0.03). This result still tended toward statistical significance when considering only ABC patients (7 of 11 ABC CNS-relapsing cases were MYD88 mutant, p=0.1) but would have to be confirmed in a larger cohort. Conclusions: This study highlights the relative heterogeneity of MYD88 mutant DLBCBL, adding to the field's knowledge of the distinct genetic backgrounds of these subgroups. Our data highlights the theranostic and prognostic relevance of examining MYD88 and associated genomic alterations, emphasizing the usefulness of genomic profiling to best stratify patients for targeted therapy. 1. Mareschal S, Ruminy P, Bagacean C, et al. Accurate Classification of Germinal Center B-Cell-Like/Activated B-Cell-Like Diffuse Large B-Cell Lymphoma Using a Simple and Rapid Reverse Transcriptase-Multiplex Ligation-Dependent Probe Amplification Assay: A CALYM Study. The Journal of molecular diagnostics : JMD. 2015;17(3):273-283. 2. Dubois S, Viailly P-J, Mareschal S, et al. Next Generation Sequencing in Diffuse Large B Cell Lymphoma Highlights Molecular Divergence and Therapeutic Opportunities: a LYSA Study. Clinical cancer research : an official journal of the American Association for Cancer Research. 2016;22(12):2919-2928. Disclosures Salles: Novartis: Consultancy, Honoraria; Mundipharma: Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Gilead: Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Roche/Genentech: Consultancy, Honoraria, Research Funding.
Published: 2017

48. Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation

Author: Boryana Petrova, Jean-Karim Hériché, Christine A. Orengo, M. Julia Roberti, José M. Fernández, Priit Adler, Christian H. Haering, Thomas Walter, Ian Morilla, Juan A. G. Ranea, Jon G. Lees, M. Julius Hossain, Jaak Vilo, Martin Krallinger, Alfonso Valencia, Jan Ellenberg, European Molecular Biology Laboratory [Heidelberg] (EMBL), Laboratoire d'Annecy de Physique des Particules (LAPP), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Bioinformatique (CBIO), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Biochimica e Biologia Molecolare 'E.Quagliariello', Università degli studi di Bari Aldo Moro (UNIBA), Graduate School of Business, Curtin University [Perth], Planning and Transport Research Centre (PATREC)-Planning and Transport Research Centre (PATREC), Institut de Physique du Globe de Paris (IPGP), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-IPG PARIS-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Centre National de la Recherche Scientifique (CNRS), Núcleo de Bioinformática Estrutural (NBI), and Embrapa Informática Agropecuária
Subjects: Mitosis, [SDV.BC]Life Sciences [q-bio]/Cellular Biology, Computational biology, Biology, Genome, Chromosomes, Mitotic chromosome condensation, 03 medical and health sciences, 0302 clinical medicine, RNA interference, Chromosome Segregation, Humans, RNA, Small Interfering, Molecular Biology, Gene, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, Genetics, 0303 health sciences, Biological data, Gene knockdown, Microscopy, Confocal, Systems Biology, Computational Biology, Computational gene, Articles, Cell Biology, Prognosis, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Phenotype, RNA Interference, Human genome, Software, 030217 neurology & neurosurgery, HeLa Cells
Abstract: A gene function prediction method suitable for the design of targeted RNAi libraries is described and used to predict chromosome condensation genes. Systematic experimental validation of candidate genes in a focused RNAi screen by automated microscopy and quantitative image analysis reveals many new chromosome condensation factors., The advent of genome-wide RNA interference (RNAi)–based screens puts us in the position to identify genes for all functions human cells carry out. However, for many functions, assay complexity and cost make genome-scale knockdown experiments impossible. Methods to predict genes required for cell functions are therefore needed to focus RNAi screens from the whole genome on the most likely candidates. Although different bioinformatics tools for gene function prediction exist, they lack experimental validation and are therefore rarely used by experimentalists. To address this, we developed an effective computational gene selection strategy that represents public data about genes as graphs and then analyzes these graphs using kernels on graph nodes to predict functional relationships. To demonstrate its performance, we predicted human genes required for a poorly understood cellular function—mitotic chromosome condensation—and experimentally validated the top 100 candidates with a focused RNAi screen by automated microscopy. Quantitative analysis of the images demonstrated that the candidates were indeed strongly enriched in condensation genes, including the discovery of several new factors. By combining bioinformatics prediction with experimental validation, our study shows that kernels on graph nodes are powerful tools to integrate public biological data and predict genes involved in cellular functions of interest.
Published: 2014

49. A bagging SVM to learn from positive and unlabeled examples

Author: Fantine Mordelet, Jean-Philippe Vert, Duke University [Durham], Centre de Bioinformatique (CBIO), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)
Subjects: FOS: Computer and information sciences, Transduction (machine learning), PU learning, Property (programming), Computer science, SVM, Bootstrap aggregating, Machine Learning (stat.ML), 02 engineering and technology, Semi-supervised learning, Machine learning, computer.software_genre, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Set (abstract data type), 03 medical and health sciences, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Statistics - Machine Learning, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Artificial Intelligence, Bagging, 0202 electrical engineering, electronic engineering, information engineering, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, 0303 health sciences, Training set, business.industry, Pattern recognition, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Support vector machine, ComputingMethodologies_PATTERNRECOGNITION, Binary classification, Signal Processing, Unsupervised learning, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, business, computer, Software
Abstract: We consider the problem of learning a binary classifier from a training set of positive and unlabeled examples, both in the inductive and in the transductive setting. This problem, often referred to as PU learning, differs from the standard supervised classification problem by the lack of negative examples in the training set. It corresponds to an ubiquitous situation in many applications such as information retrieval or gene ranking, when we have identified a set of data of interest sharing a particular property, and we wish to automatically retrieve additional data sharing the same property among a large and easily available pool of unlabeled data. We propose a new method for PU learning with a conceptually simple implementation based on bootstrap aggregating (bagging) techniques: the algorithm iteratively trains many binary classifiers to discriminate the known positive examples from random subsamples of the unlabeled set, and averages their predictions. We show theoretically and experimentally that the method can match and even outperform the performance of state-of-the-art methods for PU learning, particularly when the number of positive examples is limited and the fraction of negatives among the unlabeled examples is small. The proposed method can also run considerably faster than state-of-the-art methods, particularly when the set of unlabeled examples is large.
Published: 2014

50. Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks

Author: Lages, José, Shepelyansky, Dima L., Zinovyev, Andrei, Univers, Transport, Interfaces, Nanostructures, Atmosphère et environnement, Molécules (UMR 6213) (UTINAM), Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS), Information et Chaos Quantiques (LPT), Laboratoire de Physique Théorique (LPT), Institut de Recherche sur les Systèmes Atomiques et Moléculaires Complexes (IRSAMC), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche sur les Systèmes Atomiques et Moléculaires Complexes (IRSAMC), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), CNRS MASTODONS 2016 project APLIGOOGLE, Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), MINES ParisTech - École nationale supérieure des mines de Paris, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)
Subjects: Proteomics, 0301 basic medicine, Theoretical computer science, Computer science, Gene Expression, lcsh:Medicine, Bioinformatics, Biochemistry, 0302 clinical medicine, Neoplasms, Macromolecular Structure Analysis, Centrality, Gene Regulatory Networks, Spectral analysis, Databases, Protein, lcsh:Science, 0303 health sciences, Multidisciplinary, Google matrix, Markov chains, Applied Mathematics, Simulation and Modeling, Transcriptional Control, Neoplasm Proteins, Causality, Crosstalk (biology), cnacer networks, Physical Sciences, Protein Interaction Networks, Network Analysis, Algorithms, Research Article, Signal Transduction, Computer and Information Sciences, Protein Structure, Systems biology, [SDV.CAN]Life Sciences [q-bio]/Cancer, Research and Analysis Methods, Models, Biological, [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], Cell Line, 03 medical and health sciences, DNA-binding proteins, Genetics, Humans, Gene Regulation, Molecular Biology, 030304 developmental biology, Stochastic Processes, [SDV.GEN]Life Sciences [q-bio]/Genetics, Models, Genetic, Markov chain, Causal relations, lcsh:R, Biology and Life Sciences, Proteins, Computational Biology, Quantum chaos, Signaling Networks, Regulatory Proteins, Search Engine, Algebra, 030104 developmental biology, Linear Algebra, directed networks, Quantum Theory, lcsh:Q, Eigenvectors, Protein Structure Networks, K562 Cells, Mathematics, 030217 neurology & neurosurgery, Biological network, Transcription Factors
Abstract: Signaling pathways represent parts of the global biological network which connects them into a seamless whole through complex direct and indirect (hidden) crosstalk whose structure can change during normal development or in a pathological conditions such as cancer. Advanced methods for characterizing the structure of the global directed causal network can shed light on the mechanisms of global cell reprogramming changing the distribution of possible signaling flows. We suggest a methodology, called Googlomics, for the analysis of the structure of directed biological networks using spectral analysis of their Google matrix. This approach uses parallels with quantum scattering theory, developed for processes in nuclear and mesoscopic physics and quantum chaos. We introduce the notion of reduced Google matrix in the context of the regulatory biological networks and demonstrate how its computation allows inferring hidden causal relations between the members of a signaling pathway or a functionally related group of genes. We investigate how the structure of hidden causal relations can be reprogrammed as the result of changes in the transcriptional network layer during cancerogenesis. The suggested Googlomics approach can be useful in various contexts for characterizing non-intuitive changes in the wiring of complex and large causal biological networks.
Published: 2016
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