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1. Prognosis and chemosensitivity of non-colorectal alimentary tract cancers with microsatellite instability

Author: Claire Boyer, David Sefrioui, Romain Cohen, Romain Chautard, Marine Perrier, Hugo Lebrun, Gael Goujon, Vincent Hautefeuille, Marie Dior, Thomas Walter, Florence Mary, Silvain Manfredi, Francois-Xavier Caroli-Bosc, Baptiste Cervantes, Romain Coriat, Elise Deluche, Aziz Zaanan, Raphael Olivier, Olivier Bouché, Guillaume Piessen, Thierry Lecomte, Christophe Louvet, Pierre Michel, Thomas Aparicio, Thierry André, Julien Taieb, Violaine Randrian, David Tougeron, Centre hospitalier universitaire de Poitiers (CHU Poitiers), Communications Cellulaires et Différenciation (CCD), Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-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), Groupe innovation et ciblage cellulaire (GICC), EA 7501 [2018-...] (GICC EA 7501), Université de Tours (UT), AP-HP - Hôpital Bichat - Claude Bernard [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), CHU Amiens-Picardie, AP-HP - Hôpital Cochin Broca Hôtel Dieu [Paris], Université Paris Cité (UPCité), 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), Université Paris sciences et lettres (PSL), Hôpital Avicenne [AP-HP], Hémodynamique, Interaction Fibrose et Invasivité tumorales Hépatiques (HIFIH), Université d'Angers (UA), Hôpital Cochin [AP-HP], CHU Limoges, 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), Université de Reims Champagne-Ardenne (URCA), Centre Hospitalier Universitaire de Reims (CHU Reims), Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Cancer Heterogeneity, Plasticity and Resistance to Therapies - UMR 9020 - U 1277 (CANTHER), Institut Pasteur de Lille, and Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Hepatology, [SDV]Life Sciences [q-bio], Gastroenterology, Humans, Microsatellite Instability, Middle Aged, Prognosis, Colorectal Neoplasms, Colorectal Neoplasms, Hereditary Nonpolyposis, DNA Mismatch Repair
Abstract: Data on outcomes of microsatellite instable and/or mismatch repair deficient (dMMR/MSI) digestive non-colorectal tumors are limited.To evaluate overall survival (OS) of patients with dMMR/MSI digestive non-colorectal tumor.All consecutive patients with a dMMR/MSI digestive non-colorectal tumor were included in this French retrospective multicenter study.One hundred and sixteen patients were included with a mean age of 63.6 years and 32.6% with a Lynch syndrome. Most tumors were oesophago-gastric (54.3%) or small bowel (32.8%) adenocarcinomas and at a localized stage at diagnosis (86.7%). In patients with localized tumors and R0 resection, median OS was 134.0 ± 64.2 months. Median disease-free survival (DFS) was 100.3 ± 65.7 months. Considering oesophago-gastric tumors, median DFS was improved when chemotherapy was added to surgery (not reached versus 22.8 ± 10.0 months, p = 0.03). In patients with advanced tumors treated by chemotherapy, median OS was 14.2 ± 1.9 months and median progression-free survival was 7.4 ± 1.6 months.dMMR/MSI digestive non-colorectal tumors are mostly diagnosed at a non-metastatic stage with a good prognosis. Advanced dMMR/MSI digestive non-colorectal tumors have a poor prognosis with standard chemotherapy.
Published: 2023

2. Contribution of endocrine therapy in oestrogen receptor-positive pT1a-b breast cancer: Results of a retrospective study

Author: Gilles Houvenaeghel, Alexandre de Nonneville, Monique Cohen, Jean-Marc Classe, Fabien Reyal, Chafika Mazouni, Christelle Faure, Alejandra Martinez, Marie-Pierre Chauvet, Emile Daraï, Charles Coutant, Pierre-Emmanuel Colombo, Pierre Gimbergues, Anne-Sophie Azuar, Roman Rouzier, Christine Tunon de Lara, Patrice Crochet, Sandrine Rua, Anthony Gonçalves, 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), Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC), Sciences Economiques et Sociales de la Santé & Traitement de l'Information Médicale (SESSTIM - U1252 INSERM - Aix Marseille Univ - UMR 259 IRD), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Département de Chirurgie Oncologique [Institut Paoli-Calmettes, Marseille], Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC), Institut de Cancérologie de l'Ouest [Angers/Nantes] (UNICANCER/ICO), UNICANCER, Département de chirurgie, Institut Curie [Paris], CRLCC Jean Godinot, Residual Tumor & Response to Treatment Laboratory [Paris] (RT2Lab), Immunité et cancer (U932), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Gustave Roussy (IGR), Centre de Recherches en Cancérologie de Toulouse (CRCT), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre Régional de Lutte contre le Cancer Oscar Lambret [Lille] (UNICANCER/Lille), Université de Lille-UNICANCER, 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), Centre Régional de Lutte contre le cancer Georges-François Leclerc [Dijon] (UNICANCER/CRLCC-CGFL), Institut du Cancer de Montpellier (ICM), Centre Jean Perrin [Clermont-Ferrand] (UNICANCER/CJP), Hopital de Grasse, 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), Département d'oncologie chirurgicale, 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), Normandie Université (NU)-UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN), Department of Obstetrics and Gynecology, Assistance Publique H^opitaux de Marseille, La Conception Hospital, 13005 Marseille, Aix Marseille University, France, and Aix Marseille Université (AMU)
Subjects: Cancer Research, Receptors, Estrogen, Oncology, Chemotherapy, Adjuvant, Receptor, ErbB-2, [SDV]Life Sciences [q-bio], Humans, Female, Breast Neoplasms, Disease-Free Survival, Retrospective Studies
Abstract: Introduction: Few data have been reported regarding endocrine therapy (ET) in patients with small pT1a-b ER-postive breast cancer (BC). Thus, we conducted a study to detect possible survival improvements due to ET in such patients. Methods: Our retrospective observational study included 5545 patients with pT1a-b ERpositive BC treated in 15 French centres, excluding patients with HER2-positive status, neoadjuvant chemotherapy, ER-negative status, unknown pN status or in situ BC. We estimated disease-free survival (DFS), recurrence-free survival (RFS) and overall survival (OS) via univariate analysis and multivariate Cox regression. Results: Most patients (80.3%: 4453) received ET and-when compared to those without ET -experienced increases of 2.5% and 3.3% in DFS and 1.9% and 4.3% in RFS after 5 and 7 years of follow-up, respectively, with little difference in OS. In Cox regression analysis, no ET was significantly associated with decreased DFS (hazard ratio, HR = 1.275, p = 0.047, 95% CI[1.003-1.620]) but not OS or RFS in all patients, while in 2363 patients with pT1ab ER-positive grade 2-3 BC, no ET was significantly associated with decreased DFS (HR = 1.502, p = 0.049, 95% CI[1.001-2.252]), but not OS (HR = 1.361, p = 0.272). ET omission was not significantly associated with decreased survival in 3047 patients with pT1a-b ER-positive grade 1 BC. Conclusion: Our results indicate that while ET provided a beneficial impact on survival to patients with pT1a-bN0 ER-positive BC-and especially in those with grade 2-3 tumours-no such impact was observed in grade 1 tumours. Consequently, ET should be discussed with these patients, particularly in those with pT1a grade 1 tumours. 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Published: 2022

3. Curative high‐dose reirradiation for patients with recurrent head and neck squamous cell carcinoma using IMRT or proton therapy: Outcomes and analysis of patterns of failure

Author: Arnaud Beddok, Caroline Saint‐Martin, Samar Krhili, Catherine Ala Eddine, Laurence Champion, Anne Chilles, Farid Goudjil, Sofia Zefkili, Malika Amessis, Dominique Peurien, Olivier Choussy, Christophe le Tourneau, Remi Dendale, Irene Buvat, Gilles Créhange, Valentin Calugaru, Institut Curie [Paris], Laboratoire d'Imagerie Translationnelle en Oncologie (LITO ), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Curie Centre de Protonthérapie d'Orsay, Institut Curie - Saint Cloud (ICSC), 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), and Buvat, Irène
Subjects: Squamous Cell Carcinoma of Head and Neck, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, toxicity, Radiotherapy Dosage, [SDV.CAN]Life Sciences [q-bio]/Cancer, head and neck carcinoma, Re-Irradiation, Reirradiation head and neck carcinoma IMRT proton therapy toxicity, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, [SDV.CAN] Life Sciences [q-bio]/Cancer, Otorhinolaryngology, Head and Neck Neoplasms, Carcinoma, Squamous Cell, proton therapy, Humans, Radiotherapy, Intensity-Modulated, Neoplasm Recurrence, Local, IMRT, Reirradiation
Abstract: International audience; Purpose: To analyze outcomes of patients treated with curative reirradiation (reRT), with intensity-modulated radiation therapy (IMRT) or proton therapy (PT) for recurrent head and neck squamous cell carcinoma (HNSCC). Materials: Among the 55 patients reirradiated for head and neck cancer from 30/08/2012 to 08/04/2019, 23 had HNSCC and received IMRT (52.2%) or PT (47.8%) at a median maximum dose to the CTV of 66 Gy. Results: After a median follow-up of 41.3 months, 18 patients developed a locoregional recurrence (LR), of which eight (44.4%) occurred within the previously reirradiated volume. Two-year locoregional failure-free survival and overall survival were 18.3%[95%CI:7.1%-47.1%] and 42.5%[95%CI:26.2%-69.1%], respectively. Disease-free survival was significantly longer in the PT group (p = 0.031). Main late grade ≥2 toxicities were dysphagia and trismus. Conclusion: Curative reRT in HNSCC is possible for selected cases, but the LR rate in the irradiated field and the risk of toxicity grade ≥2 remain high.
Published: 2022

4. Identification of predictive criteria for pathogenic variants of primary bilateral macronodular adrenal hyperplasia (PBMAH) gene ARMC5 in 352 unselected patients

Author: Lucas Bouys, Anna Vaczlavik, Anne Jouinot, Patricia Vaduva, Stéphanie Espiard, Guillaume Assié, Rossella Libé, Karine Perlemoine, Bruno Ragazzon, Laurence Guignat, Lionel Groussin, Léopoldine Bricaire, Isadora Pontes Cavalcante, Fidéline Bonnet-Serrano, Hervé Lefebvre, Marie-Laure Raffin-Sanson, Nicolas Chevalier, Philippe Touraine, Christel Jublanc, Camille Vatier, Gérald Raverot, Magalie Haissaguerre, Luigi Maione, Matthias Kroiss, Martin Fassnacht, Sophie Christin-Maitre, Eric Pasmant, Françoise Borson-Chazot, Antoine Tabarin, Marie-Christine Vantyghem, Martin Reincke, Peter Kamenicky, Marie-Odile North, Jérôme Bertherat, 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 Paris Cité (UPCité), Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), 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), CHU Pontchaillou [Rennes], CHU Lille, Recherche translationnelle sur le diabète - U 1190 (RTD), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), CHU Rouen, Normandie Université (NU), Hôpital Ambroise Paré [AP-HP], Centre Hospitalier Universitaire de Nice (CHU Nice), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Sorbonne Université (SU), 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), Hospices Civils de Lyon (HCL), Hôpital Haut-Lévêque - CHU de Bordeaux (Centre médico chirurgical Magellan), Physiologie et physiopathologie endocriniennes (PHYSENDO), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, AP-HP Hôpital Bicêtre (Le Kremlin-Bicêtre), University Hospital of Würzburg, Klinikum der Universität [München], CHU Saint-Antoine [AP-HP], L B is recipient of research fellowships from the Cancer Research for Personalized Medicine (CARPEM) and the Fondation ARC pour la Recherche contre le Cancer, J B laboratory is supported by the Agence Nationale pour la Recherche grant ANR-18-CE14-0008-01 and the Fondation pour la Recherche Médicale (EQU201903007854). P T, C J, M F, S C M, F B C, M R, P C and J B clinical departments are part the European Reference Network on Rare Endocrine Conditions (Endo-ERN) – Project ID No 739572, and ANR-18-CE14-0008,STEROMICS,Steroïdogénomique de l'hypersécrétion des stéroïdes surrénaliens(2018)
Subjects: Armadillo Domain Proteins, Hyperplasia, Hydrocortisone, Endocrinology, Diabetes and Metabolism, [SDV]Life Sciences [q-bio], 71 ARMC5, General Medicine, genetic screening, PBMAH, Endocrinology, Primary Bilateral Macronodular Adrenal Hyperplasia, adrenal tumors, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, Adrenal Glands, Humans, Cushing syndrome, tumor suppressor gene, Retrospective Studies
Abstract: Objective Primary bilateral macronodular adrenal hyperplasia (PBMAH) is a heterogeneous disease characterized by adrenal macronodules and variable levels of cortisol excess, with not clearly established clinical diagnostic criteria. It can be caused by ARMC5 germline pathogenic variants. In this study, we aimed to identify predictive criteria for ARMC5 variants. Methods We included 352 consecutive index patients from 12 European centers, sequenced for germline ARMC5 alteration. Clinical, biological and imaging data were collected retrospectively. Results 52 patients (14.8%) carried ARMC5 germline pathogenic variants and showed a more distinct phenotype than non-mutated patients for cortisol excess (24-h urinary free cortisol 2.32 vs 1.11-fold ULN, respectively, P < 0.001) and adrenal morphology (maximal adrenal diameter 104 vs 83 mm, respectively, P < 0.001) and were more often surgically or medically treated (67.9 vs 36.8%, respectively, P < 0.001). ARMC5-mutated patients showed a constant, bilateral adrenal involvement and at least a possible autonomous cortisol secretion (defined by a plasma cortisol after 1 mg dexamethasone suppression above 50 nmol/L), while these criteria were not systematic in WT patients (78.3%). The association of these two criteria holds a 100% sensitivity and a 100% negative predictive value for ARMC5 pathogenic variant. Conclusion We report the largest series of index patients investigated for ARMC5 and confirm that ARMC5 pathogenic variants are associated with a more severe phenotype in most cases. To minimize negative ARMC5 screening, genotyping should be limited to clear bilateral adrenal involvement and autonomous cortisol secretion, with an optimum sensitivity for routine clinical practice. These findings will also help to better define PBMAH diagnostic criteria.
Published: 2023

5. Reactmine: a statistical search algorithm for inferring chemical reactions from time series data

Author: Martinelli, Julien, Grignard, Jeremy, Soliman, Sylvain, Ballesta, Annabelle, Fages, François, Aalto University, 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), 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 Institut de Recherches SERVIER (IRS)
Subjects: FOS: Computer and information sciences, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Statistics - Machine Learning, FOS: Biological sciences, Machine Learning (stat.ML), Quantitative Biology - Quantitative Methods, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Quantitative Methods (q-bio.QM)
Abstract: Inferring chemical reaction networks (CRN) from concentration time series is a challenge encouragedby the growing availability of quantitative temporal data at the cellular level. This motivates thedesign of algorithms to infer the preponderant reactions between the molecular species observed ina given biochemical process, and build CRN structure and kinetics models. Existing ODE-basedinference methods such as SINDy resort to least square regression combined with sparsity-enforcingpenalization, such as Lasso. However, we observe that these methods fail to learn sparse modelswhen the input time series are only available in wild type conditions, i.e. without the possibility toplay with combinations of zeroes in the initial conditions. We present a CRN inference algorithmwhich enforces sparsity by inferring reactions in a sequential fashion within a search tree of boundeddepth, ranking the inferred reaction candidates according to the variance of their kinetics on theirsupporting transitions, and re-optimizing the kinetic parameters of the CRN candidates on the wholetrace in a final pass. We show that Reactmine succeeds both on simulation data by retrievinghidden CRNs where SINDy fails, and on two real datasets, one of fluorescence videomicroscopyof cell cycle and circadian clock markers, the other one of biomedical measurements of systemiccircadian biomarkers possibly acting on clock gene expression in peripheral organs, by inferringpreponderant regulations in agreement with previous model-based analyses. The code is available athttps://gitlab.inria.fr/julmarti/crninf/ together with introductory notebooks.
Published: 2023

6. Extended follow-up of a phase 2 trial of xevinapant plus chemoradiotherapy in high-risk locally advanced squamous cell carcinoma of the head and neck: a randomised clinical trial

Author: Yungan TAO, Xu-Shan Sun, Yoann Pointreau, Christophe Le Tourneau, Christian Sire, Marie-Christine Kaminsky, Alexandre Coutte, Marc Alfonsi, Benôit Calderon, Pierre Boisselier, Laurent Martin, Jessica Miroir, Jean-Francois Ramee, Jean-Pierre Delord, Florian Clatot, Frederic Rolland, Julie Villa, Nicolas Magne, Olgun Elicin, Elisabeta Gherga, France Nguyen, Cédrik Lafond, Guillaume Bera, Valentin Calugaru, Lionnel Geoffrois, Bruno Chauffert, Lars Damstrup, Philippa Crompton, Abdallah Ennaji, Kathrin Gollmer, Heidi Nauwelaerts, Jean Bourhis, Centre Hospitalier Universitaire de Nice (CHU Nice), Centre Hospitalier Régional Universitaire de Besançon (CHRU Besançon), Centre Jean Bernard [Institut Inter-régional de Cancérologie - Le Mans], 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 de Cancérologie de Lorraine - Alexis Vautrin [Nancy] (UNICANCER/ICL), UNICANCER, CHU Amiens-Picardie, CHirurgie, IMagerie et REgénération tissulaire de l’extrémité céphalique - Caractérisation morphologique et fonctionnelle - UR UPJV 7516 (CHIMERE), Université de Picardie Jules Verne (UPJV), Institut Sainte Catherine [Avignon], Institut du Cancer de Montpellier (ICM), Aix-Marseille Université - Faculté de pharmacie (AMU PHARM), Aix Marseille Université (AMU), Centre Jean Perrin [Clermont-Ferrand] (UNICANCER/CJP), Centre Hospitalier Départemental - Hôpital de La Roche-sur-Yon (CHD Vendée), 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-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Lutte Contre le Cancer Henri Becquerel Normandie Rouen (CLCC Henri Becquerel), Département d'oncologie médicale [Rouen], Institut de Cancérologie de l'Ouest [Angers/Nantes] (UNICANCER/ICO), and Centre Hospitalier Universitaire [Grenoble] (CHU)
Subjects: Xevinapant, Cancer Research, Efficacy, Survival, Oncology, Locally advanced squamous cell carcinoma of the head and neck, 610 Medicine & health, Chemoradiotherapy, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract: International audience; Introduction: We report long-term efficacy and overall survival (OS) results from a randomised, double-blind, phase 2 study (NCT02022098) investigating xevinapant plus standard-of-care chemoradiotherapy (CRT) vs. placebo plus CRT in 96 patients with unresected locally advanced squamous cell carcinoma of the head and neck (LA SCCHN).Methods: Patients were randomised 1:1 to xevinapant 200 mg/day (days 1-14 of a 21-day cycle for 3 cycles), or matched placebo, plus CRT (cisplatin 100 mg/m2 every 3 weeks for 3 cycles plus conventional fractionated high-dose intensity-modulated radiotherapy [70 Gy/35 F, 2 Gy/F, 5 days/week for 7 weeks]). Locoregional control, progression-free survival, and duration of response after 3 years, long-term safety, and 5-year OS were assessed.Results: The risk of locoregional failure was reduced by 54% for xevinapant plus CRT vs. placebo plus CRT but did not reach statistical significance (adjusted hazard ratio [HR] 0.46; 95% CI, 0.19-1.13; P = .0893). The risk of death or disease progression was reduced by 67% for xevinapant plus CRT (adjusted HR 0.33; 95% CI, 0.17-0.67; P = .0019). The risk of death was approximately halved in the xevinapant arm compared with placebo (adjusted HR 0.47; 95% CI, 0.27-0.84; P = .0101). OS was prolonged with xevinapant plus CRT vs. placebo plus CRT; median OS not reached (95% CI, 40.3-not evaluable) vs. 36.1 months (95% CI, 21.8-46.7). Incidence of late-onset grade ≥3 toxicities was similar across arms.Conclusions: In this randomised phase 2 study of 96 patients, xevinapant plus CRT demonstrated superior efficacy benefits, including markedly improved 5-year survival in patients with unresected LA SCCHN.
Published: 2023
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7. PointFISH -- learning point cloud representations for RNA localization patterns

Author: Imbert, Arthur, Mueller, Florian, Walter, Thomas, 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), Université Paris sciences et lettres (PSL), Institut Curie [Paris], Institut Pasteur [Paris] (IP), Imagerie et Modélisation - Imaging and Modeling, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), and ANR-19-CE12-0007,TRANSFACT,Caracterisation des foyers de traduction: de nouvelles structures qui organisement finement le cytoplasme(2019)
Subjects: FOS: Computer and information sciences, [SDV.BIO]Life Sciences [q-bio]/Biotechnology, Computer Vision and Pattern Recognition (cs.CV), smFISH RNA localization Point cloud Transfer learning Simulation Spatial transcriptomics, Computer Science - Computer Vision and Pattern Recognition, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], RNA localization, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Quantitative Biology - Quantitative Methods, smFISH, Transfer learning, Point cloud, Spatial transcriptomics, FOS: Biological sciences, Simulation, Quantitative Methods (q-bio.QM)
Abstract: Subcellular RNA localization is a critical mechanism for the spatial control of gene expression. Its mechanism and precise functional role is not yet very well understood. Single Molecule Fluorescence in Situ Hybridization (smFISH) images allow for the detection of individual RNA molecules with subcellular accuracy. In return, smFISH requires robust methods to quantify and classify RNA spatial distribution. Here, we present PointFISH, a novel computational approach for the recognition of RNA localization patterns. PointFISH is an attention-based network for computing continuous vector representations of RNA point clouds. Trained on simulations only, it can directly process extracted coordinates from experimental smFISH images. The resulting embedding allows scalable and flexible spatial transcriptomics analysis and matches performance of hand-crafted pipelines.
Published: 2023
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8. PointFISH: Learning Point Cloud Representations for RNA Localization Patterns

Author: Arthur Imbert, Florian Mueller, Thomas Walter, 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 Curie [Paris], Université Paris sciences et lettres (PSL), Imagerie et Modélisation - Imaging and Modeling, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), and ANR-19-CE12-0007,TRANSFACT,Caracterisation des foyers de traduction: de nouvelles structures qui organisement finement le cytoplasme(2019)
Subjects: Point cloud, Spatial transcriptomics, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, RNA localization, smFISH, Simulation, Transfer learning, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: Subcellular RNA localization is a critical mechanism for the spatial control of gene expression. Its mechanism and precise functional role is not yet very well understood. Single Molecule Fluorescence in Situ Hybridization (smFISH) images allow for the detection of individual RNA molecules with subcellular accuracy. In return, smFISH requires robust methods to quantify and classify RNA spatial distribution. Here, we present PointFISH, a novel computational approach for the recognition of RNA localization patterns. PointFISH is an attention-based network for computing continuous vector representations of RNA point clouds. Trained on simulations only, it can directly process extracted coordinates from experimental smFISH images. The resulting embedding allows scalable and flexible spatial transcriptomics analysis and matches performance of hand-crafted pipelines.
Published: 2023

9. HT-smFISH: a cost effective and flexible workflow for high-throughput single molecule RNA imaging

Author: Adham Safieddine, Emeline Coleno, Frederic Lionneton, Abdel-Meneem Traboulsi, Soha Salloum, Charles-Henri Lecellier, Thierry Gostan, Virginie Georget, Cédric Hassen-Khodja, Arthur Imbert, Florian Mueller, Thomas Walter, Marion Peter, Edouard Bertrand, Institut de Génétique Moléculaire de Montpellier (IGMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut de génétique humaine (IGH), BioCampus (BCM), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), 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], Imagerie et Modélisation - Imaging and Modeling, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), 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 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), and ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019)
Subjects: RNA metabolism, Molecular imaging probes, High-throughput screening HTS, [SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Molecular biology, [SDV.BC.BC]Life Sciences [q-bio]/Cellular Biology/Subcellular Processes [q-bio.SC], General Biochemistry, Genetics and Molecular Biology
Abstract: International audience; The ability to visualize RNA in its native subcellular environment by using single-molecule fluorescence in situ hybridization (smFISH) has reshaped our understanding of gene expression and cellular functions. A major hindrance of smFISH is the difficulty to perform systematic experiments in medium- or high-throughput formats, principally because of the high cost of generating the individual fluorescent probe sets. Here, we present high-throughput smFISH (HT-smFISH), a simple and cost-efficient method for imaging hundreds to thousands of single endogenous RNA molecules in 96-well plates. HT-smFISH uses RNA probes transcribed in vitro from a large pool of unlabeled oligonucleotides. This allows the generation of individual probes for many RNA species, replacing commercial DNA probe sets. HT-smFISH thus reduces costs per targeted RNA compared with many smFISH methods and is easily scalable and flexible in design. We provide a protocol that combines oligo pool design, probe set generation, optimized hybridization conditions and guidelines for image acquisition and analysis. The pipeline requires knowledge of standard molecular biology tools, cell culture and fluorescence microscopy. It is achievable in ~20 d. In brief, HT-smFISH is tailored for medium- to high-throughput screens that image RNAs at single-molecule sensitivity.
Published: 2023

10. KDM1A inactivation causes hereditary food-dependent Cushing syndrome

Author: Lucas Bouys, Guillaume Assié, Anna Vaczlavik, Florian Violon, Constantine A. Stratakis, Eric Letouzé, Maria Candida Barisson Villares Fragoso, Patricia Vaduva, Jérôme Bertherat, Marie-Odile North, Mathilde Sibony, Stéphanie Espiard, Rossella Libé, Magalie Haissaguerre, Roberta Armignacco, Bertrand Dousset, Laurence Guignat, Karine Perlemoine, Lionel Groussin, Anne Jouinot, Annabel Berthon, Christopher Ribes, Bruno Ragazzon, Martin Reincke, Fidéline Bonnet, Gaetan Giannone, Eric Pasmant, Igor Tauveron, Philippe Emy, Isadora Pontes Cavalcante, Maxime Barat, 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 Paris Cité (UPCité), Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), 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 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), CHU Pontchaillou [Rennes], CHU Lille, Centre Hospitalier Régional d'Orléans (CHRO), Université de Bordeaux (UB), CHU Bordeaux [Bordeaux], CHU Clermont-Ferrand, Universidade de São Paulo = University of São Paulo (USP), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), Agence Nationale de la Recherche, Fondation pour la Recherche Médicale, ANR-18-CE14-0008,STEROMICS,Steroïdogénomique de l'hypersécrétion des stéroïdes surrénaliens(2018), Admin, Oskar, and APPEL À PROJETS GÉNÉRIQUE 2018 - Steroïdogénomique de l'hypersécrétion des stéroïdes surrénaliens - - STEROMICS2018 - ANR-18-CE14-0008 - AAPG2018 - VALID
Subjects: medicine.medical_treatment, Adrenocortical tumors, ARMC5, Germline, Cushing syndrome, medicine, Humans, Allele, Cushing Syndrome, GIPR, Genetics (clinical), Exome sequencing, Armadillo Domain Proteins, Histone Demethylases, Hyperplasia, business.industry, Adrenalectomy, medicine.disease, Phenotype, KDM1A, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Macronodular Adrenal Hyperplasia, DNA methylation, Cancer research, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Ectopic expression, business
Abstract: International audience; PURPOSE: This study aimed to investigate the genetic cause of food-dependent Cushing syndrome (FDCS) observed in patients with primary bilateral macronodular adrenal hyperplasia (PBMAH) and adrenal ectopic expression of the glucose-dependent insulinotropic polypeptide receptor. Germline ARMC5 alterations have been reported in about 25% of PBMAH index cases but are absent in patients with FDCS. METHODS: A multiomics analysis of PBMAH tissues from 36 patients treated by adrenalectomy was performed (RNA sequencing, single-nucleotide variant array, methylome, miRNome, exome sequencing). RESULTS: The integrative analysis revealed 3 molecular groups with different clinical features, namely G1, comprising 16 patients with ARMC5 inactivating variants; G2, comprising 6 patients with FDCS with glucose-dependent insulinotropic polypeptide receptor ectopic expression; and G3, comprising 14 patients with a less severe phenotype. Exome sequencing revealed germline truncating variants of KDM1A in 5 G2 patients, constantly associated with a somatic loss of the KDM1A wild-type allele on 1p, leading to a loss of KDM1A expression both at messenger RNA and protein levels (P = 1.2 × 10(-12) and P < .01, respectively). Subsequently, KDM1A pathogenic variants were identified in 4 of 4 additional index cases with FDCS. CONCLUSION: KDM1A inactivation explains about 90% of FDCS PBMAH. Genetic screening for ARMC5 and KDM1A can now be offered for most PBMAH operated patients and their families, opening the way to earlier diagnosis and improved management.
Published: 2022

11. 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

12. A versatile and interoperable computational framework for the analysis and modeling of COVID-19 disease mechanisms

Author: Niarakis, Anna, Ostaszewski, Marek, Mazein, Alexander, Kuperstein, Inna, Kutmon, Martina, Gillespie, Marc, Funahashi, Akira, Acencio, Marcio, Hemedan, Ahmed, Aichem, Michael, Klein, Karsten, Czauderna, Tobias, Burtscher, Felicia, Yamada, Takahiro, Hiki, Yusuke, Hiroi, Noriko, Hu, Finterly, Pham, Nhung, Ehrhart, Friederike, Willighagen, Egon, Valdeolivas, Alberto, Dugourd, Aurelien, Messina, Francesco, Esteban-Medina, Marina, Pena-Chilet, Maria, Rian, Kinza, Soliman, Sylvain, Aghamiri, Sara, Puniya, Bhanwar, Naldi, Aurelien, Helikar, Tomas, Singh, Vidisha, Farinas Fernandez, Marco, Bermudez, Viviam, Tsirvouli, Eirini, Montagud, Arnau, Noel, Vincent, Ponce de Leon, Miguel, Maier, Dieter, Bauch, Angela, Gyori, Benjamin, Bachman, John, Luna, Agustin, Pinero, Janet, Furlong, Laura, Balaur, Irina, Rougny, Adrien, Jarosz, Yohan, Overall, Rupert, Phair, Robert, Perfetto, Livia, Matthews, Lisa, Rex, Devasahayam, Orlic-Milacic, Marija, Monraz Gomez, Luis, de Meulder, Bertrand, Ravel, Jean, Jassal, Bijay, Satagopam, Venkata, Wu, Guanming, Golebiewski, Martin, Gawron, Piotr, Calzone, Laurence, Beckmann, Jacques, Evelo, Chris, d'Eustachio, Peter, Schreiber, Falk, Saez-Rodriguez, Julio, Dopazo, Joaquin, Kuiper, Martin, Valencia, Alfonso, Wolkenhauer, Olaf, Kitano, Hiroaki, Barillot, Emmanuel, Auffray, Charles, Balling, Rudi, Schneider, Reinhard, 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), Laboratoire de recherche européen pour la polyarthrite rhumatoïde (GenHotel), Université d'Évry-Val-d'Essonne (UEVE)-Université Paris-Saclay, University of Luxembourg [Luxembourg], 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), Maastricht Centre for Systems Biology [Maastricht] (MaCSBio), Maastricht University [Maastricht], Ontario Institute for Cancer Research [Canada] (OICR), Ontario Institute for Cancer Research, Keio University, Luxembourg Centre For Systems Biomedicine (LCSB), University of Konstanz, Hochschule Mittweida - University of Applied Sciences, Kanagawa Institute of Technology, Heidelberg University Hospital [Heidelberg], National Institute for Infectious Diseases 'Lazzaro Spallanzani', Hospital Universitario Virgen del Rocío [Sevilla], Biomedicine Institute of Sevilla [Seville, Spain], University of Nebraska–Lincoln, University of Nebraska System, Norwegian University of Science and Technology [Trondheim] (NTNU), Norwegian University of Science and Technology (NTNU), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Harvard Medical School [Boston] (HMS), Universitat Pompeu Fabra [Barcelona] (UPF), National Institute of Advanced Industrial Science and Technology (AIST), Humboldt University Of Berlin, Integrative Bioinformatics Inc [Mountain View], Department of Informatics and System Sciences (Sapienza University of Rome), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA), New York University Langone Medical Center (NYU Langone Medical Center), NYU System (NYU), Yenepoya University, Janet Piñero, Laura I. Furlong: IMI2-JU grants, resources which are composed of financial contributions from the European Union’s Horizon 2020 Research and Innovation Programme and EFPIA [GA: 777365 eTRANSAFE], and the EU H2020 Programme [GA:964537 RISKHUNT3R], Project 001-P-001647—Valorisation of EGA for Industry and Society funded by the European Regional Development Fund (ERDF) and Generalitat de Catalunya, and Institute of Health Carlos III (project IMPaCT-Data, exp. IMP/00019), co-funded by the European Union, European Regional Development Fund (ERDF, 'A way to make Europe').
Subjects: SARS-CoV-2, disease maps, systems biology, dynamic models, systems medicine, large-scale community effort, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], mechanistic models
Abstract: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Community-driven and highly interdisciplinary, the project is collaborative and supports community standards, open access, and the FAIR data principles. The coordination of community work allowed for an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework links key molecules highlighted from broad omics data analysis and computational modeling to dysregulated pathways in a cell-, tissue- or patient-specific manner. We also employ text mining and AI-assisted analysis to identify potential drugs and drug targets and use topological analysis to reveal interesting structural features of the map. The proposed framework is versatile and expandable, offering a significant upgrade in the arsenal used to understand virus-host interactions and other complex pathologies.
Published: 2022

13. DNA methylation and hydroxymethylation characterize the identity of D1 and D2 striatal projection neurons

Author: Lucile Marion-Poll, Jean-Pierre Roussarie, Lieng Taing, Cloelia Dard-Dascot, Nicolas Servant, Yan Jaszczyszyn, Emmanuelle Jordi, Eskeatnaf Mulugeta, Denis Hervé, Déborah Bourc’his, Paul Greengard, Claude Thermes, Jean-Antoine Girault, Cell biology, Institut du Fer à Moulin (IFM - Inserm U1270 - SU), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), 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), Rockefeller University [New York], Unité de Recherche sur les Maladies Cardiovasculaires, du Métabolisme et de la Nutrition = Research Unit on Cardiovascular and Metabolic Diseases (ICAN), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Institut de Cardiométabolisme et Nutrition = Institute of Cardiometabolism and Nutrition [CHU Pitié Salpêtrière] (IHU ICAN), 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)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut de Biologie Intégrative de la Cellule (I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-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), and ANR-16-CE16-0018,EPITRACES,Traces épigénétiques neuronales des récompenses(2016)
Subjects: Neurons, Epigenomics, Interneurons, [SDV]Life Sciences [q-bio], Medicine (miscellaneous), DNA Methylation, General Agricultural and Biological Sciences, Corpus Striatum, General Biochemistry, Genetics and Molecular Biology
Abstract: Neuronal DNA modifications differ from those in other cells, including methylation outside CpG context and abundant 5-hydroxymethylation whose relevance for neuronal identities are unclear. Striatal projection neurons expressing D1 or D2 dopamine receptors allow addressing this question, as they share many characteristics but differ in their gene expression profiles, connections, and functional roles. We compare translating mRNAs and DNA modifications in these two populations. DNA methylation differences occur predominantly in large genomic clusters including differentially expressed genes, potentially important for D1 and D2 neurons. Decreased gene body methylation is associated with higher gene expression. Hydroxymethylation differences are more scattered and affect transcription factor binding sites, which can influence gene expression. We also find a strong genome-wide hydroxymethylation asymmetry between the two DNA strands, particularly pronounced at expressed genes and retrotransposons. These results identify novel properties of neuronal DNA modifications and unveil epigenetic characteristics of striatal projection neurons heterogeneity.
Published: 2022

14. Molecular Underpinnings and Environmental Drivers of Spontaneous Loss of Heterozygosity in Drosophila Intestinal Stem Cells

Author: Lara Al zouabi, Marine Stefanutti, Nick Riddiford, Natalia Rubanova, Mylène Bohec, Nicolas Servant, Allison Bardin, 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), Plateforme de génomique [Institut Curie], 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), and Bardin, Allison
Subjects: [SDV] Life Sciences [q-bio], [SDV]Life Sciences [q-bio]
Abstract: The genome stability of adult stem cells is of particular importance as these cells maintain long-term self-renewal capacity and can contribute extensively to adult tissues. During development and aging, genome mutation leading to loss of heterozygosity (LOH) can uncover recessive phenotypes and be propagated within tissue compartments. This phenomenon occurs in normal human tissues, and is prevalent in pathological genetic conditions and cancers. While previous studies in yeast have defined distinct DNA repair mechanisms that can promote LOH, the predominant pathways underlying LOH in complex somatic tissues of multicellular organisms arenot well understood. In addition, how environmental triggers such as pathogenic bacterial infection may impact LOH is unclear. Here, we investigate the mechanisms giving rise to LOH in adult intestinal stem cells in Drosophila. Our data indicate that infection with the enteric pathogenic bacteria, Erwinia carotovora carotovora 15 but not Pseudomonas entomophila increases LOH frequency. Using whole-genome sequencing of somatic LOH events, we demonstrate that they arise primarily via mitotic recombination. Molecular features of recombination sites and genetic evidence argue against formation via break-induced replication and instead support cross-over events arising from double Holliday junction-based repair. This study provides a mechanistic understanding of mitotic recombination in stem cells in vivo, an important mediator of LOH.
Published: 2022

15. Identification de régulateurs systémiques de l'horloge périphérique circadienne par apprentissage de modèles

Author: Sylvain Soliman, Annabelle Ballesta, François Fages, Xiao-Mei Li, Julien Martinelli, Michèle Teboul, Sandrine Dulong, Francis Lévi, Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Curie [Paris], 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), Université Paris-Saclay, Institut de Biologie Valrose (IBV), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Côte d'Azur (UCA)-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), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), 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 MINES ParisTech - École nationale supérieure des mines de Paris
Subjects: 0301 basic medicine, Statistics and Probability, AcademicSubjects/SCI01060, Systems biology, medicine.medical_treatment, [MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS], Population, Circadian clock, [SDV.BC.BC]Life Sciences [q-bio]/Cellular Biology/Subcellular Processes [q-bio.SC], Biology, Bioinformatics, Biochemistry, Mice, 03 medical and health sciences, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Circadian Clocks, Machine learning, [SDV.MHEP.PHY]Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO], medicine, Animals, Humans, Circadian rhythm, education, Molecular Biology, education.field_of_study, business.industry, Precision medicine, Chronotherapy (treatment scheduling), Circadian Rhythm, 3. Good health, Computer Science Applications, PER2, Computational Mathematics, 030104 developmental biology, Gene Expression Regulation, Computational Theory and Mathematics, Systems Biology and Networks, Personalized medicine, Network Inference, business, 030217 neurology & neurosurgery
Abstract: Motivation Personalized medicine aims at providing patient-tailored therapeutics based on multi-type data toward improved treatment outcomes. Chronotherapy that consists in adapting drug administration to the patient’s circadian rhythms may be improved by such approach. Recent clinical studies demonstrated large variability in patients’ circadian coordination and optimal drug timing. Consequently, new eHealth platforms allow the monitoring of circadian biomarkers in individual patients through wearable technologies (rest-activity, body temperature), blood or salivary samples (melatonin, cortisol) and daily questionnaires (food intake, symptoms). A current clinical challenge involves designing a methodology predicting from circadian biomarkers the patient peripheral circadian clocks and associated optimal drug timing. The mammalian circadian timing system being largely conserved between mouse and humans yet with phase opposition, the study was developed using available mouse datasets. Results We investigated at the molecular scale the influence of systemic regulators (e.g. temperature, hormones) on peripheral clocks, through a model learning approach involving systems biology models based on ordinary differential equations. Using as prior knowledge our existing circadian clock model, we derived an approximation for the action of systemic regulators on the expression of three core-clock genes: Bmal1, Per2 and Rev-Erbα. These time profiles were then fitted with a population of models, based on linear regression. Best models involved a modulation of either Bmal1 or Per2 transcription most likely by temperature or nutrient exposure cycles. This agreed with biological knowledge on temperature-dependent control of Per2 transcription. The strengths of systemic regulations were found to be significantly different according to mouse sex and genetic background. Availability and implementation https://gitlab.inria.fr/julmarti/model-learning-mb21eccb. Supplementary information Supplementary data are available at Bioinformatics online.
Published: 2021

16. Reactmine: a search algorithm for inferring chemical reaction networks from time series data

Author: Martinelli, Julien, Grignard, Jeremy, Soliman, Sylvain, Ballesta, Annabelle, Fages, François, Aalto University, 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), 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 Institut de Recherches SERVIER (IRS)
Subjects: [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Abstract: Inferring chemical reaction networks (CRN) from time series data is a challenge encouraged by the growing availability of quantitative temporal data at the cellular level. This motivates the design of algorithms to infer the preponderant reactions between the molecular species observed in a given biochemical process, and help to build CRN model structure and kinetics. Existing ODE-based inference methods such as SINDy resort to least square regression combined with sparsity-enforcing penalization, such as Lasso. However, when the input time series are only available in wild type conditions in which all reactions are present, we observe that current methods fail to learn sparse models. Results: We present Reactmine, a CRN learning algorithm which enforces sparsity by inferring reactions in a sequential fashion within a search tree of bounded depth, ranking the inferred reaction candidates according to the variance of their kinetics, and re-optimizing the CRN kinetic parameters on the whole trace in a final pass to rank the inferred CRN candidates. We first evaluate its performance on simulation data from a benchmark of hidden CRNs, together with algorithmic hyperparameter sensitivity analyses, and then on two sets of real experimental data: one from protein fluorescence videomicroscopy of cell cycle and circadian clock markers, and one from biomedical measurements of systemic circadian biomarkers possibly acting on clock gene expression in peripheral organs. We show that Reactmine succeeds both on simulation data by retrieving hidden CRNs where SINDy fails, and on the two real datasets by inferring reactions in agreement with previous studies.
Published: 2022

17. Dimeric G-quadruplex motifs determine a large fraction of strong replication origins in vertebrates

Author: Poulet-Benedetti, Jérémy, Tonnerre-Doncarli, Caroline, Valton, Anne-Laure, Laurent, Marc, Gérard, Marie, Barinova, Natalja, Parisis, Nikolaos, Massip, Florian, Picard, Franck, Prioleau, Marie-Noëlle, Institut Jacques Monod (IJM (UMR_7592)), Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), University of Massachusetts Medical School [Worcester] (UMASS), University of Massachusetts System (UMASS), Howard Hughes Medical Institute (HHMI), École des Mines de 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), Laboratoire de biologie et modélisation de la cellule (LBMC UMR 5239), École normale supérieure de Lyon (ENS de 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), massip, florian, and École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL)
Subjects: [SDV] Life Sciences [q-bio], [SDV]Life Sciences [q-bio]
Abstract: International audience; Abstract Replication of vertebrate genomes is tightly regulated to ensure accurate duplication, but our understanding of the interplay between genetic and epigenetic factors in this regulation remains incomplete. Here, we investigated the involvement of three elements enriched at gene promoters and replication origins: guanine-rich motifs potentially forming G-quadruplexes (pG4s), nucleosome-free regions (NFRs), and the histone variant H2A.Z, in the firing of origins of replication. We show that two pG4s on the same DNA strand (dimeric pG4s) are sufficient to induce assembly of an efficient minimal replication origin without inducing transcription, and that dimeric pG4s in replication origins are located in an NFR next to well-positioned nucleosomes enriched in H2A.Z. Thus, our data suggest a crucial role for dimeric pG4s in the organization and duplication of vertebrate genomes.
Published: 2022

18. Prediction of Treatment Response in Triple Negative Breast Cancer From Whole Slide Images

Author: Peter Naylor, Tristan Lazard, Guillaume Bataillon, Marick Laé, Anne Vincent-Salomon, Anne-Sophie Hamy, Fabien Reyal, Thomas Walter, Walter, Thomas, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Institut Q-LIFE - - Q-LIFE2017 - ANR-17-CONV-0005 - CONV - VALID, 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 Curie [Paris], Université Paris sciences et lettres (PSL), Génomique et Médecine Personnalisée du Cancer et des Maladies Neuropsychiatriques (GPMCND), Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Lutte Contre le Cancer Henri Becquerel Normandie Rouen (CLCC Henri Becquerel), 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), Residual Tumor & Response to Treatment Laboratory [Paris] (RT2Lab), Immunité et cancer (U932), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Ligue Contre le Cancer, ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), and ANR-17-CONV-0005,Q-LIFE,Institut Q-LIFE(2017)
Subjects: [SDV.MHEP.AHA] Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO], whole slide images, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, small sample size, treatment response, deep learning, [SDV.CAN]Life Sciences [q-bio]/Cancer, cross-validation, breast cancer, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, multiple instance learning, [SDV.CAN] Life Sciences [q-bio]/Cancer, [SDV.MHEP.AHA]Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO], digital pathology
Abstract: International audience; The automatic analysis of stained histological sections is becoming increasingly popular. Deep Learning is today the method of choice for the computational analysis of such data, and has shown spectacular results for large datasets for a large variety of cancer types and prediction tasks. On the other hand, many scientific questions relate to small, highly specific cohorts. Such cohorts pose serious challenges for Deep Learning, typically trained on large datasets. In this article, we propose a modification of the standard nested crossvalidation procedure for hyperparameter tuning and model selection, dedicated to the analysis of small cohorts. We also propose a new architecture for the particularly challenging question of treatment prediction, and apply this workflow to the prediction of response to neoadjuvant chemotherapy for Triple Negative Breast Cancer.
Published: 2022
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19. Deep Learning identifies new morphological patterns of Homologous Recombination Deficiency in luminal breast cancers from whole slide images

Author: Marc-Henri Stern, Etienne Decencière, Thomas Walter, François-Clément Bidard, Tristan Lazard, Guillaume Bataillon, Dominique Stoppa-Lyonnet, Tatiana Popova, Peter Naylor, Anne Vincent Salomon, 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), Unité de génétique et biologie des cancers (U830), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Curie - Saint Cloud (ICSC), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Centre de Morphologie Mathématique (CMM), Université Paris sciences et lettres (PSL), 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), ANR-17-CONV-0005,Q-LIFE,Institut Q-LIFE(2017), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Walter, Thomas, Institut Q-LIFE - - Q-LIFE2017 - ANR-17-CONV-0005 - CONV - VALID, and PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID
Subjects: Genome instability, homologous recombination deficiency (HRD), bias, whole slide images, Computational biology, Biology, breast cancer, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], visualization, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], Interpretability, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], business.industry, Deep learning, deep learning, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], prediction, artificial intelligence, molecular subtype, Phenotype, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], PARP inhibitor, Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], interpretability, Homologous recombination, business, Homologous Recombination Deficiency, computational pathology
Abstract: Homologous Recombination DNA-repair deficiency (HRD) is a well-recognized marker of platinum-salt and PARP inhibitor chemotherapies in ovarian and breast cancers (BC). Causing high genomic instability, HRD is currently determined by BRCA1/2 sequencing or by genomic signatures, but its morphological manifestation is not well understood. Deep Learning (DL) is a powerful machine learning technique that has been recently shown to be capable of predicting genomic signatures from stained tissue slides. However, DL is known to be sensitive to dataset biases and lacks interpretability. Here, we present and evaluate a strategy to control for biases in retrospective cohorts. We train a deep-learning model to predict the HRD in a controlled cohort with unprecedented accuracy (AUC: 0.86) and we develop a new visualization technique that allows for automatic extraction of new morphological features related to HRD. We analyze in detail the extracted morphological patterns that open new hypotheses on the phenotypic impact of HRD.
Published: 2022

20. A comparison of Sars-Cov-2 vaccine platforms: the CoviCompare project

Author: Diana Molino, Christine Durier, Anne Radenne, Corinne Desaint, Jacques Ropers, Soizic Courcier, Louis Victorien Vieillard, Claire Rekacewicz, Beatrice Parfait, Victor Appay, Frédéric Batteux, Emmanuel Barillot, Michel Cogné, Béhazine Combadière, Christiane S. Eberhardt, Guy Gorochov, Philippe Hupé, Laetitia Ninove, Stéphane Paul, Isabelle Pellegrin, Sylvie van der Werf, Maeva Lefebvre, Elisabeth Botelho-Nevers, Inmaculada Ortega-Perez, Marie Jaspard, Samba Sow, Jean Daniel Lelièvre, Xavier de Lamballerie, Marie Paule Kieny, Eric Tartour, Odile Launay, CIC Cochin Pasteur (CIC 1417), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôtel-Dieu-Groupe hospitalier Broca-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), F-CRIN, Innovative clinical research network in vaccinology (I-REIVAC), Essais Thérapeutiques et Maladies Infectieuses, Université Paris-Sud - Paris 11 (UP11)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre d'investigation clinique Paris Est [CHU Pitié Salpêtrière] (CIC Paris-Est), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU), Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Composantes innées de la réponse immunitaire et différenciation (CIRID), Université Bordeaux Segalen - Bordeaux 2-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), Microenvironment and B-cells: Immunopathology,Cell Differentiation, and Cancer (MOBIDIC), Université de Rennes (UR)-Etablissement français du sang [Rennes] (EFS Bretagne)-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Pontchaillou [Rennes], Centre d'Immunologie et des Maladies Infectieuses (CIMI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Faculté de médecine [Genève], Biologie Cellulaire et Cancer, Institut Curie [Paris]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Unité des Virus Emergents (UVE), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Recherche pour le Développement (IRD), Physiologie cellulaire des régulations hormonale, nutritionnelles et pharmacologiques (UMR-S-530), Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre International de Recherche en Infectiologie (CIRI), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Génétique Moléculaire des Virus à ARN - Molecular Genetics of RNA Viruses (GMV-ARN (UMR_3569 / U-Pasteur_2)), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Centre d’Investigation Clinique de Nantes (CIC Nantes), Université de Nantes (UN)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre hospitalier universitaire de Nantes (CHU Nantes), Centre Hospitalier Universitaire de Saint-Etienne [CHU Saint-Etienne] (CHU ST-E), Agence Nationale de Recherches sur le Sida et les Hépatites Virales (ANRS), Alliance for International medical Action (ALIMA), Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre pour le Développement des Vaccins [Mali], 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), Biochimie des Lipides, Institut National de la Santé et de la Recherche Médicale (INSERM), 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 Paris Cité (UPCité), Ministere des Solidarites et de la Sante, Ministere de l'Enseignement Superieur, de la Recherche et de l'Innovation, Coalition for Epidemic Preparedness Innovations (CEPI), ANRS \Emerging Infectious Diseases, ANR-20-COV3-0004,CoMemRep,Caractérisation des complexes de réplication associés à la membrane du SARS CoV 2(2020), Unité de Recherche Clinique des hôpitaux Pitié-Salpêtrière – Charles Foix [CHU Pitié Salpêtrière] (URC PSL-CFX), CHU Charles Foix [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)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Direction de la Recherche Clinique et de l'Innovation [AP-HP] (DRCI), Centre International de Recherche en Infectiologie - UMR (CIRI), École normale supérieure - Lyon (ENS 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 Hospitalier Universitaire de Saint-Etienne (CHU de Saint-Etienne), Chard-Hutchinson, Xavier, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Etablissement français du sang [Rennes] (EFS Bretagne)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)
Subjects: [SDV] Life Sciences [q-bio], COVID-19 Vaccines, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, SARS-CoV-2, [SDV]Life Sciences [q-bio], Spike Glycoprotein, Coronavirus, COVID-19, Humans, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, General Medicine, General Biochemistry, Genetics and Molecular Biology, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2022

21. BIODICA: a computational environment for Independent Component Analysis of omics data

Author: Captier, Nicolas, Merlevede, Jane, Molkenov, Askhat, Seisenova, Ainur, Zhubanchaliyev, Altynbek, Nazarov, Petr V., Barillot, Emmanuel, Kairov, Ulykbek, Zinovyev, Andrei, 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), Laboratoire d'Imagerie Translationnelle en Oncologie (LITO ), 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), Nazarbayev University [Kazakhstan], Luxembourg Institute of Health (LIH), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), European Project: 826121,iPaediatricCurie, Captier, Nicolas, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, and European Union’s Horizon 2020 program (grant No. 826121, iPC project) - iPaediatricCurie - 826121 - INCOMING
Subjects: Statistics and Probability, Metadata, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computational Biology, Independent Components Analysis, Deconvolution, Biochemistry, Independent Component Analysis, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computational environment, Computer Science Applications, Computational Mathematics, Computational Theory and Mathematics, Bulk and single-cell, Omics analysis, Molecular Biology, Algorithms, Software
Abstract: Summary We developed BIODICA, an integrated computational environment for application of independent component analysis (ICA) to bulk and single-cell molecular profiles, interpretation of the results in terms of biological functions and correlation with metadata. The computational core is the novel Python package stabilized-ica which provides interface to several ICA algorithms, a stabilization procedure, meta-analysis and component interpretation tools. BIODICA is equipped with a user-friendly graphical user interface, allowing non-experienced users to perform the ICA-based omics data analysis. The results are provided in interactive ways, thus facilitating communication with biology experts. Availability and implementation BIODICA is implemented in Java, Python and JavaScript. The source code is freely available on GitHub under the MIT and the GNU LGPL licenses. BIODICA is supported on all major operating systems. URL: https://sysbio-curie.github.io/biodica-environment/.
Published: 2022

22. Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression

Author: Christophe Le Priol, Chloé-Agathe Azencott, Xavier Gidrol, Biomicrotechnologie et génomique fonctionnelle (BIOMICS ), Laboratoire de Biologie à Grande Échelle (BGE - UMR S1038), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA), Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), 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), Azencott, Chloé-Agathe, Biomicrotechnologie et génomique fonctionnelle (BIOMICS), Laboratoire Biosciences et bioingénierie pour la santé (BGE), Centre de Bioinformatique (CBIO), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)
Subjects: Cellular and Molecular Neuroscience, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Computational Theory and Mathematics, Ecology, Modeling and Simulation, Genetics, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Molecular Biology, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Ecology, Evolution, Behavior and Systematics, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract: The majority of gene expression studies focus on the search for genes whose mean expression is different between two or more populations of samples in the so-called “differential expression analysis” approach. However, a difference in variance in gene expression may also be biologically and physiologically relevant. In the classical statistical model used to analyze RNA-sequencing (RNA-seq) data, the dispersion, which defines the variance, is only considered as a parameter to be estimated prior to identifying a difference in mean expression between conditions of interest. Here, we propose to evaluate two recent methods, MDSeq and DiPhiSeq, which detect differences in both the mean and dispersion in RNA-seq data. We thoroughly investigated the performance of these methods on simulated datasets and characterized parameter settings to reliably detect genes with a differential expression dispersion. We applied both methods to The Cancer Genome Atlas datasets. Interestingly, among the genes with an increased expression dispersion in tumors and without a change in mean expression, we identified some key cellular functions, most of which were related to catabolism and were overrepresented in most of the analyzed cancers. In particular, our results highlight autophagy, whose role in cancerogenesis is context-dependent, illustrating the potential of the differential dispersion approach to gain new insights into biological processes.Author summaryGene expression is the process by which genetic information is translated into functional molecules. Transcription is the first step of this process, consisting of synthesizing messenger RNAs. During recent decades, genome-wide transcriptional profiling technologies have made it possible to assess the expression levels of thousands of genes in parallel in a variety of biological contexts. In statistical analyses, the expression of a gene is estimated by counting sequencing reads over a set of samples and is defined by two dimensions: mean and variance. The overwhelming majority of gene expression studies focus on identifying genes whose mean expression significantly changes when comparing samples of different conditions of interest to gain knowledge of biological processes. In this classical approach, the variance is usually considered only as a noise parameter to be estimated before assessing the mean expression. However, finely estimating the variance of expression may be biologically relevant since a modification of this parameter may reflect a change in gene expression regulation. Here, we propose to evaluate the performance of statistical methods that identify such differentially variant genes. We highlighted the potential of this approach by analyzing cancer datasets, thus identifying key cellular functions in tumor progression.
Published: 2023

23. Activité physique après diagnostic de cancer du sein et survie : revue de la littérature

Author: Claire Bonneau, Roman Rouzier, Eleonora Salakos, Louise Maumy, Grégoire Rocher, Cindy Neuzillet, Thuraya Al Mamari, Antoine Elies, Institut Curie - Saint Cloud (ICSC), Université de Versailles Saint-Quentin-en-Yvelines - UFR Sciences de la santé Simone Veil (UVSQ Santé), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), 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 CCSD, Accord Elsevier
Subjects: 0301 basic medicine, Cancer Research, Physical activity, Secondary prevention, [SDV.CAN]Life Sciences [q-bio]/Cancer, Hematology, General Medicine, 3. Good health, Activité physique, 03 medical and health sciences, Breast cancer, 030104 developmental biology, 0302 clinical medicine, Survie globale, Revue de la littérature, [SDV.CAN] Life Sciences [q-bio]/Cancer, Oncology, Recurrence, Prévention secondaire, 030220 oncology & carcinogenesis, Systematic review, Overall survival, Récidive, Radiology, Nuclear Medicine and imaging, Cancer du sein
Abstract: Introduction: The benefits of physical activity (PA) in breast cancer are currently recognized in primary prevention. The World Cancer Research Fund (WCRF) and then the National Cancer Institute (INCa) have reported conflicting results regarding the impact of post-diagnosis PA on breast cancer outcomes. The aim of this systematic review is to assess the association between PA after breast cancer diagnosis and overall mortality, specific mortality and risk of breast cancer recurrence in the literature. Methods: Randomized trials, prospective cohorts and meta-analyses studying post-diagnosis PA and overall mortality, breast cancer mortality or risk of recurrence after breast cancer published between January 1, 2014 and October 1, 2019 were included. The articles selected by the INCa report prior to 2014 were included in the literature review. Results: Eighteen articles have been selected. Studies unanimously concluded that overall mortality was reduced by post-diagnosis PA practice. For specific mortality, 5 meta-analyses showed a significant decrease in breast cancer mortality and 2 found a decrease in the risk of recurrence. Conclusion: Post-diagnosis PA reduces overall mortality and appears to impact specific breast cancer mortality and risk of recurrence. However, these results need to be confirmed by larger randomized trials., Introduction :Les bénéfices de l’activité physique (AP) dans le cancer du sein sont actuellement reconnus en prévention primaire. Le World Cancer Research Fund (WCRF), puis l’Institut national du cancer (INCa) ont rapporté des résultats contradictoires concernant l’impact de l’activité physique après diagnostic sur le pronostic du cancer du sein. L’objectif de cette revue de la littérature est d’évaluer l’association entre l’activité physique après le diagnostic de cancer du sein et la mortalité globale, la mortalité spécifique ainsi que le risque de récidive de cancer du sein dans la littérature.Méthodes :Les essais randomisés, cohortes prospectives et méta-analyses, étudiant l’activité physique postdiagnostic et la mortalité globale, la mortalité liée au cancer du sein ou le risque de récidive après cancer du sein, publiés entre le 1er janvier 2014 et le 1er octobre 2019, ont été inclus. Les articles sélectionnés, par le rapport de l’INCa antérieurs à 2014, ont été intégrés à la revue de la littérature.Résultats :Dix-huit articles ont été sélectionnés. Les études ont conclu de façon unanime à une diminution de la mortalité globale par la pratique d’une activité physique postdiagnostic. Pour la mortalité spécifique, 5 méta-analyses ont montré une diminution significative de la mortalité par cancer du sein et 2 ont retrouvé une diminution du risque de récidive.Conclusion :L’activité physique postdiagnostic d’un cancer du sein diminue la mortalité globale et semble impacter la mortalité spécifique par cancer du sein et le risque de récidive. Ces résultats nécessitent, cependant, d’être confirmés par de plus larges essais randomisés.
Published: 2020

24. The French Early Breast Cancer Cohort (FRESH): a resource for breast cancer research and evaluations of oncology practices based on the French National Healthcare System Database (SNDS)

Author: Elise Dumas, Lucie Laot, Florence Coussy, Beatriz Grandal Rejo, Eric Daoud, Enora Laas, Amyn Kassara, Alena Majdling, Rayan Kabirian, Floriane Jochum, Paul Gougis, Sophie Michel, Sophie Houzard, Christine Le Bihan-Benjamin, Philippe-Jean Bousquet, Judicaël Hotton, Chloé-Agathe Azencott, Fabien Reyal, Anne-Sophie Hamy, 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), Residual Tumor & Response to Treatment Laboratory [Paris] (RT2Lab), Immunité et cancer (U932), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), 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), Département de chirurgie, Institut Curie [Paris], Departement d'Oncologie médicale [Paris], Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Département d'Oncologie Médicale [Centre René-Huguenin, Saint-Cloud], Hôpital René HUGUENIN (Saint-Cloud), Département de Gynécologie-Obstétrique [CHU Strasbourg], CHU Strasbourg-Hôpital de Hautepierre [Strasbourg], Département de surveillance, d'enquête et d'évaluation des données [Boulogne Billancourt] (INCa), Institut national du cancer [Boulogne] (INCA), Sciences Economiques et Sociales de la Santé & Traitement de l'Information Médicale (SESSTIM - U1252 INSERM - Aix Marseille Univ - UMR 259 IRD), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), CRLCC Jean Godinot, ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Azencott, Chloé-Agathe, and PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID
Subjects: Cancer Research, breast cancer, Oncology, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], French database introduction, National Health Data System, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], nationwide population, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract: BackgroundBreast cancer (BC) is the most frequent cancer and the leading cause of cancer-related death in women. The French National Cancer Institute has created a national cancer cohort to promote cancer research and improve our understanding of cancer using the National Health Data System (SNDS). This cohort amalgamates all cancer sites, with no detailed separate data for early BC.ObjectivesWe describe the French Early Breast Cancer Cohort (FRESH).MethodsAll French women aged 18 years or over, with early-stage BC newly diagnosed between January 1, 2011 and December 31, 2017, treated by surgery and registered in the general health insurance coverage plan were included in the cohort. Patients with suspected locoregional or distant metastases at diagnosis were excluded. BC treatments (surgery, chemotherapy, targeted therapy, radiotherapy, endocrine therapy), and diagnostic procedures (biopsy, cytology, imaging) were extracted from hospital discharge reports, outpatient care notes or pharmacy drug delivery data. BC subtype was inferred from the treatments received.ResultsWe included 235,368 patients with early BC in the cohort (median age: 60 years). BC subtype distribution was as follows: luminal (80.2%), triple-negative (TNBC, 9.5%); HER2+ (10.3%), or unidentifiable (n=44,388, 18.9% of the cohort). Most patients underwent radiotherapy (n=200,685, 85.3%) and endocrine therapy (n=165,655, 70.4%), and 38.3% (n=90,252) received chemotherapy. Treatments and care pathways are described.ConclusionThe FRESH Cohort is an unprecedented population-based resource facilitating future large-scale real-life studies aiming to improve care pathways and quality of care for BC patients.
Published: 2022

25. Inhibition of the pseudokinase MLKL alters extracellular vesicle release and reduces tumor growth in glioblastoma

Author: Gwennan André-Grégoire, Clément Maghe, Tiphaine Douanne, Sara Rosińska, Fiorella Spinelli, An Thys, Kilian Trillet, Kathryn A. Jacobs, Cyndie Ballu, Aurélien Dupont, Anne-Marie Lyne, Florence M.G. Cavalli, Ignacio Busnelli, Vincent Hyenne, Jacky G. Goetz, Nicolas Bidère, Julie Gavard, Signaling in Oncogenesis, Angiogenesis and Permeability - SOAP (CRCI2NA / Eq 6), Centre de Recherche en Cancérologie et Immunologie Intégrée Nantes-Angers (CRCI2NA ), Université d'Angers (UA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Nantes Université - UFR de Médecine et des Techniques Médicales (Nantes Univ - UFR MEDECINE), Nantes Université - pôle Santé, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Santé, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Université d'Angers (UA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Nantes Université - UFR de Médecine et des Techniques Médicales (Nantes Univ - UFR MEDECINE), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ), Institut de Cancérologie de l'Ouest [Angers/Nantes] (UNICANCER/ICO), UNICANCER, Equipe Labellisée Ligue Contre le Cancer [Nantes] (ELLCC), Biosit : biologie, santé, innovation technologique (SFR UMS CNRS 3480 - INSERM 018), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Institut Curie - PSL Research University [Paris] (IC-PSLRU), 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), Immuno-Rhumatologie Moléculaire, Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Fédération de Médecine Translationnelle de Strasbourg (FMTS), Université de Strasbourg (UNISTRA), and Gavard, Julie
Subjects: PROGRAMMED NECROSIS, Science & Technology, Multidisciplinary, [SDV]Life Sciences [q-bio], TEMOZOLOMIDE, MICROENVIRONMENT, Multidisciplinary Sciences, [SDV] Life Sciences [q-bio], MIXED LINEAGE KINASE, DOMAIN-LIKE, Science & Technology - Other Topics, PROMOTE, TRAFFICKING, DOWNSTREAM, STEM-CELLS, MEDIATES NECROPTOSIS
Abstract: Extracellular vesicles (EVs) are lipid-based nanosized particles that convey biological material from donor to recipient cells. EVs play key roles in glioblastoma progression because glioblastoma stem-like cells (GSCs) release pro-oncogenic, pro-angiogenic, and pro-inflammatory EVs. However, the molecular basis of EV release remains poorly understood. Here, we report the identification of the pseudokinase MLKL, a crucial effector of cell death by necroptosis, as a regulator of the constitutive secretion of EVs in GSCs. We find that genetic, protein, and pharmacological targeting of MLKL alters intracellular trafficking and EV release, and reduces GSC expansion. Nevertheless, this function ascribed to MLKL appears independent of its role during necroptosis. In vivo, pharmacological inhibition of MLKL reduces the tumor burden and the level of plasmatic EVs. This work highlights the necroptosis-independent role of MLKL in vesicle release and suggests that interfering with EVs is a promising therapeutic option to sensitize glioblastoma cells. ispartof: ISCIENCE vol:25 issue:10 ispartof: location:United States status: published
Published: 2022

26. Inversion of a topological domain leads to restricted changes in its gene expression and affects inter-domain communication

Author: Rafael Galupa, Christel Picard, Nicolas Servant, Elphège P. Nora, Yinxiu Zhan, Joke G. van Bemmel, Fatima El Marjou, Colin Johanneau, Maud Borensztein, Katia Ancelin, Luca Giorgetti, Edith Heard, 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), 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), Friedrich Miescher Institute for Biomedical Research (FMI), Novartis Research Foundation, University of Basel (Unibas), Institut Curie [Paris], Collège de France - Chaire Epigénétique et mémoire cellulaire, Collège de France (CdF (institution)), and Borensztein, Maud
Subjects: Xist, CCCTC-Binding Factor, Genome, Communication, Gene Expression, Chromatin, Mice, X Chromosome Inactivation, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], [SDV.BDD] Life Sciences [q-bio]/Development Biology, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Animals, RNA, Long Noncoding, Genomic engineering, X-inactivation, TADs, [SDV.BDD]Life Sciences [q-bio]/Development Biology, Molecular Biology, Developmental Biology
Abstract: The interplay between the topological organization of the genome and the regulation of gene expression remains unclear. Depletion of molecular factors underlying genome topology, such as CTCF and cohesin, leads to modest alterations in gene expression, while genomic rearrangements involving boundaries of topologically associating domains (TADs) disrupt normal gene expression and can lead to pathological phenotypes. Here we inverted an almost entire TAD (245kb out of 300kb) within the X-inactivation centre (Xic), leaving its boundaries intact. This led to a significant rearrangement of topological contacts within the TAD, mostly in accordance to the orientation of underlying CTCF binding sites but suggesting heterogeneity in the “contact” potential of different CTCF sites. The inversion also led to increased contact insulation with the neighbouring TAD. Expression of most genes within the inverted TAD remained unaffected in mouse embryonic stem cells and during differentiation. Interestingly, expression in the neighbouring TAD of the noncoding transcript Xist, which controls X-chromosome inactivation, was ectopically upregulated. The same inversion in mouse embryos led to a bias in Xist expression, but X-inactivation choice ratios did not significantly deviate from wild type. Smaller deletions and inversions of specific clusters of CTCF sites within the TAD led to similar results: rearrangement of contacts, limited changes in local gene expression but significant changes in Xist expression. Our study suggests that the wiring of regulatory interactions within a TAD can influence the expression of genes in neighbouring TADs, highlighting the existence of mechanisms for inter-TAD communication.
Published: 2022

27. 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

28. Proteomic profiling of sweat in patients with cystic fibrosis provides new insights into epidermal homoeostasis

Author: Matthieu Cornet, Thao Nguyen‐Khoa, Mairead Kelly‐Aubert, Vincent Jung, Frédérique Chedevergne, Muriel Le Bourgeois, Laura Aoust, Kévin Roger, Chiara Ida Guerrera, Isabelle Sermet‐Gaudelus, NGUYEN KHOA, THAO, Institut Necker Enfants-Malades (INEM - UM 111 (UMR 8253 / U1151)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), 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), CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), National Cystic Fibrosis Reference Center [CHU Necker] (CNR - INSERM U1151), 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)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Structure Fédérative de Recherche Necker (SFR Necker - UMS 3633 / US24), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Service de Pneumologie Allergologie [CHU Necker], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Necker - Enfants Malades [AP-HP], European Reference Network on respiratory diseases (ERN LUNG), Université Paris Cité (UPCité), Imagine - Institut des maladies génétiques (IHU) (Imagine - U1163), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)
Subjects: [SDV] Life Sciences [q-bio], [SDV]Life Sciences [q-bio], General Medicine
Abstract: International audience; Background: A high proportion of patients with Cystic Fibrosis (CF) also present the rare skin disease aquagenic palmoplantar keratoderma. A possible link between this condition and absence of a functional CF Transmembrane conductance Regulator protein in the sweat acinus and collecting duct remains unknown. Methods: In-depth characterization of sweat proteome profiles was performed in 25 CF patients compared to 12 healthy controls. A 20 μL sweat sample was collected after pilocarpine iontophoresis and liquid chromatography tandem mass spectrometry (LC-MS/MS) proteomic analysis was performed. Results: Sweat proteome profile of CF patients was significantly different from that of healthy subjects with 57 differentially expressed proteins. Cystic Fibrosis sweat proteome was characterized by an increase in 25 proteins including proteases (Kallikrein 7 and 13, Phospholipase B domain containing 1, Cathepsin A L2 and B, Lysosomal Pro-X carboxypeptidase); proinflammatory proteins (Annexin A2, Chitinase-3-like protein 1); cytochrome c and transglutaminases. Thirty-two proteins were downregulated in CF sweat including proteases (Elastase 2), antioxidative protein FAM129 B; membrane-bound transporter SLC6A14 and regulator protein Sodiumhydrogen antiporter 3 regulator 1. Conclusion:This study is the first toreportin depth characterization of endogenous peptides in CFsweat and could help under stand the complex physiology of the sweat gland.The proteome profile highlights the unbalanced proteolytic and proinflammatory activity of sweatin CF.These results also suggest a efectin pathways involved in skin barrier integrity in CF patients.Sweat proteome profile could prove to be a useful tool in the context of personalized medicine in CF.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Published: 2022

29. Neural network for the prediction of treatment response in Triple Negative Breast Cancer *

Author: Peter Naylor, Tristan Lazard, Guillaume Bataillon, Marick Lae, Anne Vincent-Salomon, Anne-Sophie Hamy, Fabien Reyal, Thomas Walter, Walter, Thomas, 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), RIKEN Center for Advanced Intelligence Project [Tokyo] (RIKEN AIP), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), Institut Curie [Paris], 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-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Institut National de la Santé et de la Recherche Médicale (INSERM), Génomique et Médecine Personnalisée du Cancer et des Maladies Neuropsychiatriques (GPMCND), Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Lutte Contre le Cancer Henri Becquerel Normandie Rouen (CLCC Henri Becquerel), 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), Residual Tumor & Response to Treatment Laboratory (RT2Lab) - Translational Research Department, Institut Curie, Immunité et cancer (U932), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Département d'Oncologie Médicale [Institut Curie, Paris], and Université Paris sciences et lettres (PSL)
Subjects: [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract: The automatic analysis of stained histological sections is becoming increasingly popular. Deep Learning is today the method of choice for the computational analysis of such data, and has shown spectacular results for large datasets for a large variety of cancer types and prediction tasks. On the other hand, many scientific questions relate to small, highly specific cohorts. Such cohorts pose serious challenges for Deep Learning, typically trained on large datasets.In this article, we propose a modification of the standard nested cross-validation procedure for hyper-parameter tuning and model selection, dedicated to the analysis of small cohorts. We also propose a new architecture for the particularly challenging question of treatment prediction, and apply this workflow to the prediction of response to neoadjuvant chemotherapy for Triple Negative Breast Cancer.
Published: 2022

30. Second-Line Treatment and Prognostic Factors in Neuroendocrine Carcinoma

Author: Hadoux, Julien, Walter, Thomas, Kanaan, Christina, Hescot, Segolene, Hautefeuille, Vincent, Perrier, Marine, Tauveron, Igor, Laboureau, Sandrine, Do Cao, Christine, Pétorin, Caroline, Blanchet, Odile, Faron, Matthieu, Leteurtre, Emmanuelle, Rousselet, Marie-Christine, Joubert Zakeyh, Juliette, Marchal, Aude, Chatelain, Denis, Beaulaton, Clement, Hervieu, Valerie, Lombard-Bohas, Catherine, Ducreux, Michel, Scoazec, Jean Yves, Baudin, Eric, Département de médecine oncologique [Gustave Roussy], Institut Gustave Roussy (IGR), 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), Université Paris sciences et lettres (PSL), Institut Curie [Paris], CHU Amiens-Picardie, CHU Saint-Antoine [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Sorbonne Université - Faculté de Médecine (SU FM), Sorbonne Université (SU), Génétique, Reproduction et Développement (GReD), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA), Centre Hospitalier Universitaire d'Angers (CHU Angers), PRES Université Nantes Angers Le Mans (UNAM), CHU Lille, Service de Chirurgie Digestive et Hépatobiliaire [CHU Clermont-Ferrand], CHU Estaing [Clermont-Ferrand], CHU Clermont-Ferrand-CHU Clermont-Ferrand, Centre de Ressources Biologiques [CHU Angers], PRES Université Nantes Angers Le Mans (UNAM)-PRES Université Nantes Angers Le Mans (UNAM), 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, Cancer Heterogeneity, Plasticity and Resistance to Therapies - UMR 9020 - U 1277 (CANTHER), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Centre National de la Recherche Scientifique (CNRS), Hôpital universitaire Robert Debré [Reims], CHirurgie, IMagerie et REgénération tissulaire de l’extrémité céphalique - Caractérisation morphologique et fonctionnelle - UR UPJV 7516 (CHIMERE), Université de Picardie Jules Verne (UPJV), Hospices Civils de Lyon (HCL), Hôpital Edouard Herriot [CHU - HCL], UNICANCER, and Département de biologie et pathologie médicales [Gustave Roussy]
Subjects: [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract: International audience; Neuroendocrine carcinomas (NEC) are aggressive malignant diseases. Etoposide-based rechallenge (EBR) and the prognostic role of Rb status in second-line chemotherapy (2L) have not been studied. The objectives of this study were to report the results of 2L including EBR as well as prognostic factors in a national retrospective multicenter study. NEC patients treated in 2L and further, with tissue samples available were included. Rb status and morphological classification were reviewed centrally. Among the 121 NEC patients (40% female, median age 61) included, there were 73 small cell NEC (60%), 34 large cell NEC (28%) and 14 NEC (not otherwise specified, 12%). Primary sites were lung (39%), gastro-enteropancreatic (36%), other (13%) and unknown (12%). Median Ki-67 index was 80%. Median progression-free survival (PFS) and overall survival (OS) under 2L were 2.1 and 6.2 months, respectively. No difference was observed between patients who received an "Adenocarcinoma-like" or a "Neuroendocrine-like" 2L or according to the Rb status. Thoracic primary was the only adverse prognostic factor for OS. EBR, administered to 31 patients, resulted in a 62% disease control rate with a median PFS and OS of 3.2 and 11.7 months respectively. In the 94 patients with a relapse-free interval ≥q 3 months after first-line platinum-etoposide, median OS was 12 months in patients who received EBR as compared to 5.9 months in patients who did not (p=0.043). EBR could be the best 2L option for patient with initial response to first line platinum-etoposide lasting at least 3 months. Rb status does not provide prognostic information in this setting.
Published: 2022

31. Whole blood methylome-derived features to discriminate endocrine hypertension

Author: Roberta Armignacco, Parminder S. Reel, Smarti Reel, Anne Jouinot, Amandine Septier, Cassandra Gaspar, Karine Perlemoine, Casper K. Larsen, Lucas Bouys, Leah Braun, Anna Riester, Matthias Kroiss, Fidéline Bonnet-Serrano, Laurence Amar, Anne Blanchard, Anne-Paule Gimenez-Roqueplo, Aleksander Prejbisz, Andrzej Januszewicz, Piotr Dobrowolski, Eleanor Davies, Scott M. MacKenzie, Gian Paolo Rossi, Livia Lenzini, Filippo Ceccato, Carla Scaroni, Paolo Mulatero, Tracy A. Williams, Alessio Pecori, Silvia Monticone, Felix Beuschlein, Martin Reincke, Maria-Christina Zennaro, Jérôme Bertherat, Emily Jefferson, Guillaume Assié, 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 Paris Cité (UPCité), University of Dundee, 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), Plateforme Post-génomique de la Pitié-Salpêtrière (PASS-P3S), Unité Mixte de Service Production et Analyse de données en Sciences de la vie et en Santé (PASS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), 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 Paris Cité (UPCité), Ludwig-Maximilians University [Munich] (LMU), Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Institute of Cardiology (WARSAW - Cardiology), Institute of Cardiology, University of Glasgow, Università degli Studi di Padova = University of Padua (Unipd), Università degli studi di Torino = University of Turin (UNITO), Universität Zürich [Zürich] = University of Zurich (UZH), and BRUNEL, Nadège
Subjects: Circulating biomarker, Endocrine hypertension, Whole blood methylome, Adrenal Gland Neoplasms, Pheochromocytoma, [SDV.GEN.GH] Life Sciences [q-bio]/Genetics/Human genetics, DNA Methylation, [SDV.MHEP.CSC] Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Epigenome, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, Hypertension, Genetics, Humans, Molecular Biology, Genetics (clinical), Biomarkers, Developmental Biology
Abstract: Background Arterial hypertension represents a worldwide health burden and a major risk factor for cardiovascular morbidity and mortality. Hypertension can be primary (primary hypertension, PHT), or secondary to endocrine disorders (endocrine hypertension, EHT), such as Cushing's syndrome (CS), primary aldosteronism (PA), and pheochromocytoma/paraganglioma (PPGL). Diagnosis of EHT is currently based on hormone assays. Efficient detection remains challenging, but is crucial to properly orientate patients for diagnostic confirmation and specific treatment. More accurate biomarkers would help in the diagnostic pathway. We hypothesized that each type of endocrine hypertension could be associated with a specific blood DNA methylation signature, which could be used for disease discrimination. To identify such markers, we aimed at exploring the methylome profiles in a cohort of 255 patients with hypertension, either PHT (n = 42) or EHT (n = 213), and at identifying specific discriminating signatures using machine learning approaches. Results Unsupervised classification of samples showed discrimination of PHT from EHT. CS patients clustered separately from all other patients, whereas PA and PPGL showed an overall overlap. Global methylation was decreased in the CS group compared to PHT. Supervised comparison with PHT identified differentially methylated CpG sites for each type of endocrine hypertension, showing a diffuse genomic location. Among the most differentially methylated genes, FKBP5 was identified in the CS group. Using four different machine learning methods—Lasso (Least Absolute Shrinkage and Selection Operator), Logistic Regression, Random Forest, and Support Vector Machine—predictive models for each type of endocrine hypertension were built on training cohorts (80% of samples for each hypertension type) and estimated on validation cohorts (20% of samples for each hypertension type). Balanced accuracies ranged from 0.55 to 0.74 for predicting EHT, 0.85 to 0.95 for predicting CS, 0.66 to 0.88 for predicting PA, and 0.70 to 0.83 for predicting PPGL. Conclusions The blood DNA methylome can discriminate endocrine hypertension, with methylation signatures for each type of endocrine disorder.
Published: 2022

32. Exact solving and sensitivity analysis of stochastic continuous time Boolean models

Author: Mihály, Koltai, Vincent, Noel, Andrei, Zinovyev, Laurence, Calzone, Emmanuel, Barillot, 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), This work was part of the COLOSYS project, supported by Agence Nationale de la Recherche under the frame of ERACoSysMed-1, the ERA-Net for Systems Medicine in clinical research and medical practice., ANR-15-CMED-0001,COLOSYS,A systems approach to preventing drug resistance in colon cancer(2015), Bodescot, Myriam, A systems approach to preventing drug resistance in colon cancer - - COLOSYS2015 - ANR-15-CMED-0001 - ERACoSysMed - VALID, and Mines Paris - PSL (École nationale supérieure des mines de Paris)
Subjects: Boolean modeling, Stochastic Processes, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Exact method, Methodology Article, Asynchronous updating, lcsh:Computer applications to medicine. Medical informatics, Models, Biological, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Continuous time Markov chain, Stochastic model, lcsh:Biology (General), lcsh:R858-859.7, Steady state solution, Monte Carlo Method, lcsh:QH301-705.5
Abstract: International audience; Background: Solutions to stochastic Boolean models are usually estimated by Monte Carlo simulations, but as the state space of these models can be enormous, there is an inherent uncertainty about the accuracy of Monte Carlo estimates and whether simulations have reached all attractors. Moreover, these models have timescale parameters (transition rates) that the probability values of stationary solutions depend on in complex ways, raising the necessity of parameter sensitivity analysis. We address these two issues by an exact calculation method for this class of models. Results: We show that the stationary probability values of the attractors of stochastic (asynchronous) continuous time Boolean models can be exactly calculated. The calculation does not require Monte Carlo simulations, instead it uses graph theoretical and matrix calculation methods previously applied in the context of chemical kinetics. In this version of the asynchronous updating framework the states of a logical model define a continuous time Markov chain and for a given initial condition the stationary solution is fully defined by the right and left nullspace of the master equation's kinetic matrix. We use topological sorting of the state transition graph and the dependencies between the nullspaces and the kinetic matrix to derive the stationary solution without simulations. We apply this calculation to several published Boolean models to analyze the under-explored question of the effect of transition rates on the stationary solutions and show they can be sensitive to parameter changes. The analysis distinguishes processes robust or, alternatively, sensitive to parameter values, providing both methodological and biological insights. Conclusion: Up to an intermediate size (the biggest model analyzed is 23 nodes) stochastic Boolean models can be efficiently solved by an exact matrix method, without using Monte Carlo simulations. Sensitivity analysis with respect to the model's timescale parameters often reveals a small subset of all parameters that primarily determine the stationary probability of attractor states.
Published: 2020

33. 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

34. A review of the use of time‐varying covariates in the Fine‐Gray subdistribution hazard competing risk regression model

Author: Peter C. Austin, Aurélien Latouche, Jason P. Fine, University of Toronto, Sunnybrook Research Institute [Toronto] (SRI), Sunnybrook Health Sciences Centre, CEDRIC. Méthodes statistiques de data-mining et apprentissage (CEDRIC - MSDMA), Centre d'études et de recherche en informatique et communications (CEDRIC), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), 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), University of North Carolina [Chapel Hill] (UNC), and University of North Carolina System (UNC)
Subjects: Statistics and Probability, Time-varying covariate, Epidemiology, Competing risks, 01 natural sciences, survival analysis, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Covariate, Econometrics, Medicine, 030212 general & internal medicine, time-varying covariate, 0101 mathematics, Survival analysis, Research Articles, time‐varying covariate, competing risks, [STAT.AP]Statistics [stat]/Applications [stat.AP], Proportional hazards model, business.industry, Regression analysis, 3. Good health, Survival function, Subdistribution hazard, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, subdistribution hazard model, business, Research Article
Abstract: International audience; In survival analysis, time-varying covariates are covariates whose value can change during follow-up. Outcomes in medical research are frequently subject to competing risks (events precluding the occurrence of the primary outcome). We review the types of time-varying covariates and highlight the effect of their inclusion in the subdistribution hazard model. External time-dependent covariates are external to the subject, can effect the failure process, but are not otherwise involved in the failure mechanism. Internal time-varying covariates are measured on the subject, can effect the failure process directly, and may also be impacted by the failure mechanism. In the absence of competing risks, a consequence of including internal time-dependent covariates in the Cox model is that one cannot estimate the survival function or the effect of covariates on the survival function. In the presence of competing risks, the inclusion of internal time-varying covariates in a subdistribution hazard model results in the loss of the ability to estimate the cumulative incidence function (CIF) or the effect of covariates on the CIF. Furthermore, the definition of the risk set for the subdistribution hazard function can make defining internal time-varying covari-ates difficult or impossible. We conducted a review of the use of time-varying covariates in subdistribution hazard models in articles published in the medical literature in 2015 and in the first 5 months of 2019. Seven percent of articles published included a time-varying covariate. Several inappropriately described a time-varying covariate as having an association with the risk of the outcome.
Published: 2019

35. Evaluation of least significant changes of pulse contour analysis-derived parameters

Author: Eric Verchère, Matthieu Biais, Gregoire Cane, Hugues de Courson, Karine Nouette-Gaulain, Musa Sesay, Loic Ferrer, Service Anesthésie - Réanimation [Bordeaux], CHU Bordeaux [Bordeaux], 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 Maladies Rares: Génétique et Métabolisme (Bordeaux) (U1211 INSERM/MRGM), Université de Bordeaux (UB)-Groupe hospitalier Pellegrin-Institut National de la Santé et de la Recherche Médicale (INSERM), Biologie des maladies cardiovasculaires = Biology of Cardiovascular Diseases, Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), This study was supported from institutional and departmental sources., Boullé, Christelle, 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 Université de Bordeaux (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: medicine.medical_specialty, Fluid responsiveness, Cardiac index, Critical Care and Intensive Care Medicine, 03 medical and health sciences, 0302 clinical medicine, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, 030202 anesthesiology, Contour analysis, Internal medicine, Occlusion, Article CLINIQUE, Medicine, Pulse (signal processing), business.industry, Research, Pulse pressure variation, lcsh:Medical emergencies. Critical care. Intensive care. First aid, 030208 emergency & critical care medicine, Stroke volume, lcsh:RC86-88.9, Precision, Pulse pressure, Lung recruitment, [SDV.MHEP.CSC] Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Stroke volume variation, Cardiology, business, Pulse contour
Abstract: Background Many maneuvers assessing fluid responsiveness (minifluid challenge, lung recruitment maneuver, end-expiratory occlusion test, passive leg raising) are considered as positive when small variations in cardiac index, stroke volume index, stroke volume variation or pulse pressure variation occur. Pulse contour analysis allows continuous and real-time cardiac index, stroke volume, stroke volume variation and pulse pressure variation estimations. To use these maneuvers with pulse contour analysis, the knowledge of the minimal change that needs to be measured by a device to recognize a real change (least significant change) has to be studied. The aim of this study was to evaluate the least significant change of cardiac index, stroke volume index, stroke volume variation and pulse pressure variation obtained using pulse contour analysis (ProAQT®, Pulsion Medical System, Germany). Methods In this observational study, we included 50 mechanically ventilated patients undergoing neurosurgery in the operating room. Cardiac index, stroke volume index, pulse pressure variation and stroke volume variation obtained using ProAQT® (Pulsion Medical System, Germany) were recorded every 12 s during 15-min steady-state periods. Least significant changes were calculated every minute. Results Least significant changes statistically differed over time for cardiac index, stroke volume index, pulse pressure variation and stroke volume variation (p Conclusion To conclude, the present study suggests that pulse contour analysis is able to detect rapid and small changes in cardiac index and stroke volume index, but the interpretation of rapid and small changes of pulse pressure variation and stroke volume variation must be done with caution.
Published: 2019

36. FISH-quant v2: a scalable and modular tool for smFISH image analysis

Author: Arthur Imbert, Wei Ouyang, Adham Safieddine, Emeline Coleno, Christophe Zimmer, Edouard Bertrand, Thomas Walter, Florian Mueller, 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), Royal Institute of Technology [Stockholm] (KTH ), Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut de génétique humaine (IGH), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Imagerie et Modélisation - Imaging and Modeling, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-19-CE12-0007,TRANSFACT,Caracterisation des foyers de traduction: de nouvelles structures qui organisement finement le cytoplasme(2019), ANR-10-INBS-0004,France-BioImaging,Développment d'une infrastructure française distribuée coordonnée(2010), Walter, Thomas, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Caracterisation des foyers de traduction: de nouvelles structures qui organisement finement le cytoplasme - - TRANSFACT2019 - ANR-19-CE12-0007 - AAPG2019 - VALID, and Développment d'une infrastructure française distribuée coordonnée - - France-BioImaging2010 - ANR-10-INBS-0004 - INBS - VALID
Subjects: [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], RNA localization, smFISH, Single Molecule Imaging, [INFO.INFO-BT] Computer Science [cs]/Biotechnology, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], image analysis, image analysis RNA localization transcription smFISH, Nanotechnology, RNA, RNA, Messenger, [INFO.INFO-BT]Computer Science [cs]/Biotechnology, transcription, Molecular Biology, In Situ Hybridization, Fluorescence
Abstract: International audience; Regulation of RNA abundance and localization is a key step in gene expression control. Single-molecule RNA fluorescence in situ hybridization (smFISH) is a widely used single-cell-single-molecule imaging technique enabling quantitative studies of gene expression and its regulatory mechanisms. Today, these methods are applicable at a large scale, which in turn come with a need for adequate tools for data analysis and exploration. Here, we present FISH-quant v2, a highly modular tool accessible for both experts and non-experts. Our user-friendly package allows the user to segment nuclei and cells, detect isolated RNAs, decompose dense RNA clusters, quantify RNA localization patterns and visualize these results both at the single-cell level and variations within the cell population. This tool was validated and applied on large-scale smFISH image data sets, revealing diverse subcellular RNA localization patterns and a surprisingly high degree of cell-to-cell heterogeneity.
Published: 2021

37. Diagnostic chest X-rays and breast cancer risk among women with a hereditary predisposition to breast cancer unexplained by a BRCA1 or BRCA2 mutation

Author: Michel Longy, Christine Maugard, Elisabeth Luporsi, Capucine Delnatte, Séverine Eon-Marchais, Marion Gauthier-Villars, Juana Beauvallet, Noura Mebirouk, Muriel Belotti, Alain Lortholary, Sophie Lejeune-Dumoulin, Christine Lasset, Isabelle Mortemousque, Yves-Jean Bignon, François Eisinger, Sylvie Mazoyer, Valérie Bonadona, Jean-Marc Limacher, Anne Tardivon, Paul Gesta, Sandra Fert-Ferrer, Fabienne Lesueur, Dorothée Le Gal, Jean Chiesa, Florent Soubrier, Pascaline Berthet, Laurence Faivre, Bruno Buecher, Nadine Andrieu, Juliette Coignard, Julie Tinat, Thierry Frebourg, Isabelle Coupier, Sophie Giraud, Laure Barjhoux, Catherine Noguès, Séverine Audebert-Bellanger, Claude Adenis-Lavignasse, Chrystelle Colas, Maximiliano Ribeiro Guerra, Laurence Venat-Bouvet, Eve Cavaciuti, Anne Floquet, Pascal Pujol, Francesca Damiola, Anne Fajac, Jean-Pierre Fricker, Hélène Dreyfus, Dominique Stoppa-Lyonnet, Marie-Gabrielle Dondon, Laurence Gladieff, Olivier Caron, 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 [Paris], Université Paris sciences et lettres (PSL), Federal University of Juiz de Fora (UFIJ), Institut Gustave Roussy (IGR), AP-HP. Université Paris Saclay, Hôpital Arnaud de Villeneuve [CHRU Montpellier], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), 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 Catherine-de-Sienne [Nantes] (CCS), Centre Paul Strauss, CRLCC Paul Strauss, Centre Hospitalier Georges Renon [Niort] (CH Georges Renon Niort), Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC), Sciences Economiques et Sociales de la Santé & Traitement de l'Information Médicale (SESSTIM - U1252 INSERM - Aix Marseille Univ - UMR 259 IRD), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Dijon, Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon), Centre Régional de Lutte contre le cancer Georges-François Leclerc [Dijon] (UNICANCER/CRLCC-CGFL), UNICANCER, Centre Régional de Lutte contre le Cancer François Baclesse [Caen] (UNICANCER/CRLC), Normandie Université (NU)-UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN), Centre hospitalier régional Metz-Thionville (CHR Metz-Thionville), Hôpital de Mercy, Centre René Gauducheau, CRLCC René Gauducheau, Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-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), Centre Léon Bérard [Lyon], Les Hôpitaux Universitaires de Strasbourg (HUS), Institut Bergonié [Bordeaux], Imagerie Moléculaire et Stratégies Théranostiques (IMoST), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Clermont Auvergne (UCA), Centre Jean Perrin [Clermont-Ferrand] (UNICANCER/CJP), Ramsay Générale de Santé - Hôpital Privé La Louvière, Hôpital Dupuytren [CHU Limoges], Institut Sainte Catherine [Avignon], hôpital couple-enfant [CHU Grenoble Alpes], 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), Hôpital Bretonneau, Centre Hospitalier Régional Universitaire de Tours (CHRU Tours), Hôpital Morvan [Brest], CHU Tenon [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Hospices Civils de Lyon (HCL), Groupement Hospitalier Lyon-Est (GHE), Clinique de Génétique médicale Guy Fontaine [CHRU LIlle], Hôpital pasteur [Colmar], Hôpital Universitaire Carémeau [Nîmes] (CHU Nîmes), Centre Hospitalier Universitaire de Nîmes (CHU Nîmes), CHU de Bordeaux Pellegrin [Bordeaux], Centre Hospitalier Métropole Savoie [Chambéry], Hôpital René HUGUENIN (Saint-Cloud), CHU Rouen, Normandie Université (NU), Hôpital Edouard Herriot [CHU - HCL], Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center (CRNL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Unité de génétique et biologie des cancers (U830), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Paris Descartes - Paris 5 (UPD5), MINES ParisTech - École nationale supérieure des mines de Paris, Institut de recherche en cancérologie de Montpellier (IRCM - U896 Inserm - UM1), CRLCC Val d'Aurelle - Paul Lamarque-Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Montpellier 1 (UM1), 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), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre de recherche en neurosciences de Lyon (CRNL), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), 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, 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 Montpellier 1 (UM1)-CRLCC Val d'Aurelle - Paul Lamarque-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)
Subjects: Adult, Risk, Oncology, medicine.medical_specialty, DNA Repair, DNA repair, [SDV]Life Sciences [q-bio], Genes, BRCA2, Genes, BRCA1, Breast Neoplasms, X-ray exposure, Logistic regression, Young Adult, 03 medical and health sciences, 0302 clinical medicine, Breast cancer, Risk Factors, Surgical oncology, Internal medicine, medicine, Genetic predisposition, Humans, Genetic Predisposition to Disease, 030212 general & internal medicine, Risk factor, RC254-282, High-risk population, Pregnancy, DNA repair genes, business.industry, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Cancer, Middle Aged, medicine.disease, 3. Good health, Radiography, [SDV] Life Sciences [q-bio], Low dose, 030220 oncology & carcinogenesis, Mutation, Female, business, Research Article
Abstract: Background Diagnostic ionizing radiation is a risk factor for breast cancer (BC). BC risk increases with increased dose to the chest and decreases with increased age at exposure, with possible effect modification related to familial or genetic predisposition. While chest X-rays increase the BC risk of BRCA1/2 mutation carriers compared to non-carriers, little is known for women with a hereditary predisposition to BC but who tested negative for a BRCA1 or BRCA2 (BRCA1/2) mutation. Methods We evaluated the effect of chest X-rays from diagnostic medical procedures in a dataset composed of 1552 BC cases identified through French family cancer clinics and 1363 unrelated controls. Participants reported their history of X-ray exposures in a detailed questionnaire and were tested for 113 DNA repair genes. Logistic regression and multinomial logistic regression models were used to assess the association with BC. Results Chest X-ray exposure doubled BC risk. A 3% increased BC risk per additional exposure was observed. Being 20 years old or younger at first exposure or being exposed before first full-term pregnancy did not seem to modify this risk. Birth after 1960 or carrying a rare likely deleterious coding variant in a DNA repair gene other than BRCA1/2 modified the effect of chest X-ray exposure. Conclusion Ever/never chest X-ray exposure increases BC risk 2-fold regardless of age at first exposure and, by up to 5-fold when carrying 3 or more rare variants in a DNA repair gene. Further studies are needed to evaluate other DNA repair genes or variants to identify those which could modify radiation sensitivity. Identification of subpopulations that are more or less susceptible to ionizing radiation is important and potentially clinically relevant.
Published: 2021

38. 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

39. 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

40. 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

41. 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

42. A new hybrid record linkage process to make epidemiological databases interoperable: application to the GEMO and GENEPSO studies involving BRCA1 and BRCA2 mutation carriers

Author: Jiao, Yue, Lesueur, Fabienne, Azencott, Chloé-Agathe, Laurent, Maïté, Mebirouk, Noura, Laborde, Lilian, Beauvallet, Juana, Dondon, Marie-Gabrielle, Eon-Marchais, Séverine, Laugé, Anthony, Noguès, Catherine, Andrieu, Nadine, Stoppa-Lyonnet, Dominique, Caputo, Sandrine, Boutry-Kryza, Nadia, Calender, Alain, Giraud, Sophie, Léone, Mélanie, Bressac- de Paillerets, Brigitte, Caron, Olivier, Guillaud-Bataille, Marine, Bignon, Yves-Jean, Uhrhammer, Nancy, Bonadona, Valérie, Lasset, Christine, Berthet, Pascaline, Castera, Laurent, Vaur, Dominique, Bourdon, Violaine, Noguchi, Tetsuro, Popovici, Cornel, Remenieras, Audrey, Sobol, Hagay, Coupier, Isabelle, Harmand, Pierre-Olivier, Pujol, Pascal, Vilquin, Paul, Dumont, Aurélie, Révillion, Françoise, Muller, Danièle, Barouk-Simonet, Emmanuelle, Bonnet, Françoise, Bubien, Virginie, Longy, Michel, Sevenet, Nicolas, Gladieff, Laurence, Guimbaud, Rosine, Feillel, Viviane, Toulas, Christine, Dreyfus, Hélène, Leroux, Dominique, Peysselon, Magalie, Rebischung, Christine, Baurand, Amandine, Bertolone, Geoffrey, Coron, Fanny, Faivre, Laurence, Goussot, Vincent, Jacquot, Caroline, Sawka, Caroline, Kientz, Caroline, Lebrun, Marine, Prieur, Fabienne, Fert-Ferrer, Sandra, Mari, Véronique, Venat-Bouvet, Laurence, Bézieau, Stéphane, Delnatte, Capucine, Mortemousque, Isabelle, Coulet, Florence, Soubrier, Florent, Warcoin, Mathilde, Bronner, Myriam, Lizard, Sarab, Sokolowska, Johanna, Collonge-Rame, Marie-Agnès, Damette, Alexandre, Gesta, Paul, Lallaoui, Hakima, Chiesa, Jean, Molina-Gomes, Denise, Ingster, Olivier, Manouvrier-Hanu, Sylvie, Lejeune, Sophie, Pontois, Pauline, Lyonnet, Dominique Stoppa, Gauthier-Villars, Marion, Buecher, Bruno, Mouret-Fourme, Emmanuelle, Fricker, Jean-Pierre, Luporsi, Elisabeth, Frenay, Marc, Eisinger, Francois, Moretta, Jessica, Dugast, Catherine, Colas, Chrystelle, Lortholary, Alain, Vennin, Philippe, Adenis, Claude, Nguyen, Tan Dat, Rossi, Annick, Tinat, Julie, Tennevet, Isabelle, Limacher, Jean-Marc, Maugard, Christine, Bignon, Jean-Yves, Demange, Liliane, Cohen-Haguenauer, Odile, Gilbert, Brigitte, Zattara-Cannoni, Hélène, Institut Curie [Paris], Université Paris sciences et lettres (PSL), 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), Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC), Unité de génétique et biologie des cancers (U830), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Paris Descartes - Paris 5 (UPD5), Aix Marseille Université (AMU), Sciences Economiques et Sociales de la Santé & Traitement de l'Information Médicale (SESSTIM - U1252 INSERM - Aix Marseille Univ - UMR 259 IRD), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Paris (UP), Nadia Boutry-Kryza, Alain Calender, Sophie Giraud, Mélanie Léone, Brigitte Bressac-de-Paillerets, Olivier Caron, Marine Guillaud-Bataille, Yves-Jean Bignon, Nancy Uhrhammer, Valérie Bonadona, Christine Lasset, Pascaline Berthet, Laurent Castera, Dominique Vaur, Violaine Bourdon, Catherine Noguès, Tetsuro Noguchi, Cornel Popovici, Audrey Remenieras, Hagay Sobol, Isabelle Coupier, Pierre-Olivier Harmand, Pascal Pujol, Paul Vilquin, Aurélie Dumont, Françoise Révillion, Danièle Muller, Emmanuelle Barouk-Simonet, Françoise Bonnet, Virginie Bubien, Michel Longy, Nicolas Sévenet, Laurence Gladieff, Rosine Guimbaud, Viviane Feillel, Christine Toulas, Hélène Dreyfus, Dominique Leroux, Magalie Peysselon, Christine Rebischung, Amandine Baurand, Geoffrey Bertolone, Fanny Coron, Laurence Faivre, Vincent Goussot, Caroline Jacquot, Caroline Sawka, Caroline Kientz, Marine Lebrun, Fabienne Prieur, Sandra Fert-Ferrer, Véronique Mari, Laurence Vénat-Bouvet, Stéphane Bézieau, Capucine Delnatte, Isabelle Mortemousque, Florence Coulet, Florent Soubrier, Mathilde Warcoin, Myriam Bronner, Sarab Lizard, Johanna Sokolowska, Marie-Agnès Collonge-Rame, Alexandre Damette, Paul Gesta, Hakima Lallaoui, Jean Chiesa, Denise Molina-Gomes, Olivier Ingster, Sylvie Manouvrier-Hanu, Sophie Lejeune, Catherine Noguès, Lilian Laborde, Pauline Pontois, Dominique Stoppa-Lyonnet, Marion Gauthier-Villars, Bruno Buecher, Olivier Caron, Emmanuelle Mouret-Fourme, Jean-Pierre Fricker, Christine Lasset, Valérie Bonadona, Pascaline Berthet, Laurence Faivre, Elisabeth Luporsi, Marc Frénay, Laurence Gladieff, Paul Gesta, Hagay Sobol, François Eisinger, Jessica Moretta, Michel Longy, Catherine Dugast, Chrystelle Colas, Florent Soubrier, Isabelle Coupier, Pascal Pujol, Alain Lortholary, Philippe Vennin, Claude Adenis, Tan Dat Nguyen, Capucine Delnatte, Annick Rossi, Julie Tinat, Isabelle Tennevet, Jean-Marc Limacher, Christine Maugard, Yves-Jean Bignon, Liliane Demange, Hélène Dreyfus, Odile Cohen-Haguenauer, Brigitte Gilbert, Dominique Leroux, Hélène Zattara-Cannoni, Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris Cité (UPCité), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Génétique et évolution des maladies infectieuses (GEMI), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Malbec, Odile, and Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC)
Subjects: Risk, Medicine (General), Databases, Factual, Epidemiology, Computer science, [SDV]Life Sciences [q-bio], Breast Neoplasms, Health Informatics, computer.software_genre, Cohort Studies, 03 medical and health sciences, Record linkage, R5-920, 0302 clinical medicine, Humans, Genetic Predisposition to Disease, Prospective Studies, 030212 general & internal medicine, AdaBoost, Supervised machine learning, BRCA2 Protein, Linkage (software), [SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, Database, BRCA1 Protein, Random forest, Support vector machine, Identifier, [SDV] Life Sciences [q-bio], Identification (information), Probabilistic linkage, Hybrid process, 030220 oncology & carcinogenesis, Mutation, Mutation (genetic algorithm), Female, computer, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, Research Article
Abstract: Background Linking independent sources of data describing the same individuals enable innovative epidemiological and health studies but require a robust record linkage approach. We describe a hybrid record linkage process to link databases from two independent ongoing French national studies, GEMO (Genetic Modifiers of BRCA1 and BRCA2), which focuses on the identification of genetic factors modifying cancer risk of BRCA1 and BRCA2 mutation carriers, and GENEPSO (prospective cohort of BRCAx mutation carriers), which focuses on environmental and lifestyle risk factors. Methods To identify as many as possible of the individuals participating in the two studies but not registered by a shared identifier, we combined probabilistic record linkage (PRL) and supervised machine learning (ML). This approach (named “PRL + ML”) combined together the candidate matches identified by both approaches. We built the ML model using the gold standard on a first version of the two databases as a training dataset. This gold standard was obtained from PRL-derived matches verified by an exhaustive manual review. Results The Random Forest (RF) algorithm showed a highest recall (0.985) among six widely used ML algorithms: RF, Bagged trees, AdaBoost, Support Vector Machine, Neural Network. Therefore, RF was selected to build the ML model since our goal was to identify the maximum number of true matches. Our combined linkage PRL + ML showed a higher recall (range 0.988–0.992) than either PRL (range 0.916–0.991) or ML (0.981) alone. It identified 1995 individuals participating in both GEMO (6375 participants) and GENEPSO (4925 participants). Conclusions Our hybrid linkage process represents an efficient tool for linking GEMO and GENEPSO. It may be generalizable to other epidemiological studies involving other databases and registries.
Published: 2021

43. Spatial transcriptomics for respiratory research and medicine

Author: Arturo Londoño-Vallejo, Juliette Soulier, Thomas Walter, Florian Mueller, Sandra Curras-Alonso, Charles Fouillade, Dynamique de l'information génétique : bases fondamentales et cancer (DIG CANCER), Centre National de la Recherche Scientifique (CNRS)-Institut Curie [Paris]-Sorbonne Université (SU), 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), 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), 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), We acknowledge financial support from Agence National de la Recherche (ANR-14-CE36-0008-02 program), the Comprehensive Cancer Center 'SIRIC' program of Institut Curie (Grant INCa-DGOS-4654) as well as the grants ANR-10-EQPX-03 (Equipex), ANR-10-INBS-09-08 (France Génomique Consortium) from the Agence Nationale de la Recherche (Investissements d'Avenir program) for the NGS core facility of Institut Curie., ANR-14-CE36-0008,FLASH-RT,FLASH, a novel radiotherapy modality Understanding physical interaction and biological effect of FLASH radiotherapy on haematopoiesis and leukaemia(2014), ANR-10-EQPX-0003,ICGex,Equipement de biologie intégrative du cancer pour une médecine personnalisée(2010), ANR-10-INBS-0009,France-Génomique,Organisation et montée en puissance d'une Infrastructure Nationale de Génomique(2010), Institut Curie [Paris]-Sorbonne Université (SU)-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), 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: Pulmonary and Respiratory Medicine, Lung, business.industry, Gene Expression Profiling, Computational biology, Respiratory physiology, [SDV.BC]Life Sciences [q-bio]/Cellular Biology, respiratory system, Cellular level, respiratory tract diseases, 3. Good health, Transcriptome, medicine.anatomical_structure, medicine, [SDV.MHEP.PHY]Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO], Humans, Single-Cell Analysis, Respiratory system, business, ComputingMilieux_MISCELLANEOUS
Abstract: In combination with scRNA-seq, spatial transcriptomics have the potential to lead to an unprecedented view of lung architecture at the single cell level, providing original information on lung physiology and physiopathologyhttps://bit.ly/3uDYir0
Published: 2021

44. FISH-quant v2: a scalable and modular analysis tool for smFISH image analysis

Author: Edouard Bertrand, Christophe Zimmer, Thomas Walter, Arthur Imbert, Adham Safieddine, Wei Ouyang, Emeline Coleno, Florian Mueller, Institut de génétique humaine (IGH), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Royal Institute of Technology [Stockholm] (KTH ), Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Imagerie et Modélisation - Imaging and Modeling, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), 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), Université Paris sciences et lettres (PSL), Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), ANR-19-CE12-0007,TRANSFACT,Caracterisation des foyers de traduction: de nouvelles structures qui organisement finement le cytoplasme(2019), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), 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), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Biologie mitochondriale – Mitochondrial biology, and Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPC)
Subjects: Messenger RNA, RNA localization, business.industry, Computer science, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], RNA, Computational biology, Modular design, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Image (mathematics), [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Scalability, Gene expression, %22">Fish , [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business
Abstract: Regulation of RNA abundance and localization is a key step in gene expression control. Single-molecule RNA fluorescence in-situ hybridization (smFISH) is a widely used single-cell-single-molecule imaging technique enabling a quantitative understanding of gene expression and its regulatory mechanisms. Recent progress in experimental techniques provides larger data-sets, requiring adequate tools for data analysis and exploration. Here, we present FISH-quant v2, a highly modular analysis tool accessible both for non-experts and experts, which we validated and applied on large-scale smFISH image datasets. Our package allows the user to detect isolated and clustered mRNA spots, segment nuclei and cells, quantify RNA localization patterns and visualize these results at the single-cell level.
Published: 2021

45. TUMOSPEC: A Nation-Wide Study of Hereditary Breast and Ovarian Cancer Families with a Predicted Pathogenic Variant Identified through Multigene Panel Testing

Author: Celine Garrec, Monique Mozelle-Nivoix, Jérôme Lemonnier, Etienne Rouleau, Tan Dat Nguyen, Dominique Vaur, Elodie Lacaze, Sarah Bonnet-Boissinot, Sophie Nambot, Jean Chiesa, Sophie Giraud, Dominique Stoppa-Lyonnet, Michel Longy, Alain Lortholary, Jean-Marc Rey, Olivier Caron, Christine Lasset, Claude Houdayer, Christine Maugard, Virginie Bubien, Véronique Mari, Marie-Emmanuelle Morin-Meschin, Nadine Andrieu, Nancy Uhrhammer, Louise Crivelli, Hélène Dreyfus, Vincent Goussot, Stephanie Chieze-Valéro, Nicolas Sevenet, Lisa Golmard, Séverine Eon-Marchais, Mathilde Martinez, Isabelle Coupier, Marie-Gabrielle Dondon, Odile Béra, Paul Vilquin, Capucine Delnatte, Odile Cohen-Haguenauer, Cornel Popovici, Christine Toulas, Laurence Gladieff, Marie-Agnès Collonge-Rame, Chrystelle Colas, Paul Gesta, Catherine Noguès, Fanny Brayotel, Audrey Mailliez, Fabienne Lesueur, Julie Tinat, Marie Bidart, Yves-Jean Bignon, Florence Coulet, Nadia Boutry-Kryza, Fabienne Prieur, Juana Beauvallet, Philippe Denizeau, 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], Institut Gustave Roussy (IGR), Génétique (Biologie pathologie), Département de biologie et pathologie médicales [Gustave Roussy], Institut Gustave Roussy (IGR)-Institut Gustave Roussy (IGR), Hôtel-Dieu de Nantes, CHU Rouen, Normandie Université (NU), Institut Claudius Regaud, 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), Institut Jean Godinot [Reims], UNICANCER, Centre Eugène Marquis (CRLCC), Institut de Cancérologie de Strasbourg Europe (ICANS), Centre Paul Strauss, CRLCC Paul Strauss, Institut Bergonié [Bordeaux], Actions for OnCogenesis understanding and Target Identification in ONcology (ACTION), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Bordeaux Segalen - Bordeaux 2-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER, Centre Hospitalier Georges Renon [Niort] (CH Georges Renon Niort), Genetic and Immunology Medical Institute (GIMI), Centre Hospitalier Régional Universitaire de Besançon (CHRU Besançon)-Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon)-Centre Régional de Lutte contre le cancer Georges-François Leclerc [Dijon] (UNICANCER/CRLCC-CGFL), UNICANCER-UNICANCER-Etablissement français du sang [Bourgogne-Franche-Comté] (EFS [Bourgogne-Franche-Comté]), Centre Régional de Lutte contre le cancer Georges-François Leclerc [Dijon] (UNICANCER/CRLCC-CGFL), Centre de Lutte contre le Cancer Antoine Lacassagne [Nice] (UNICANCER/CAL), UNICANCER-Université Côte d'Azur (UCA), Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC), CHU de Saint-Étienne Hôpital Nord [Saint Etienne], Hôpital Nord (Saint Etienne), Institut de Cancérologie de l'Ouest [Angers/Nantes] (UNICANCER/ICO), CHU Bordeaux [Bordeaux], Centre Catherine-de-Sienne [Nantes] (CCS), Hôpital privé du Confluent [Nantes], Institut Sainte Catherine [Avignon], hôpital couple-enfant [CHU Grenoble Alpes], Centre Hospitalier Universitaire [Grenoble] (CHU), Centre Hospitalier Régional Universitaire de Besançon (CHRU Besançon), Centre Hospitalier de Troyes, Hospices Civils de Lyon (HCL), Groupement Hospitalier Lyon-Est (GHE), Hôpital Universitaire Carémeau [Nîmes] (CHU Nîmes), Centre Hospitalier Universitaire de Nîmes (CHU Nîmes), Hôpital Sud [CHU Rennes], CHU Pontchaillou [Rennes], Imagerie Moléculaire et Stratégies Théranostiques (IMoST), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Clermont Auvergne (UCA), Centre Jean Perrin [Clermont-Ferrand] (UNICANCER/CJP), Immunologie humaine, physiopathologie & immunothérapie (HIPI (UMR_S_976 / U976)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), Hopital Saint-Louis [AP-HP] (AP-HP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre Régional de Lutte contre le Cancer Oscar Lambret [Lille] (UNICANCER/Lille), Université Lille Nord de France (COMUE)-UNICANCER, Hôpital Arnaud de Villeneuve [CHRU Montpellier], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), 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), Groupe Hospitalier du Havre, CHU de la Martinique [Fort de France], CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), CRLCC René Gauducheau, Génomique et Médecine Personnalisée du Cancer et des Maladies Neuropsychiatriques (GPMCND), Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-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), Centre Léon Bérard [Lyon], Sciences Economiques et Sociales de la Santé & Traitement de l'Information Médicale (SESSTIM - U1252 INSERM - Aix Marseille Univ - UMR 259 IRD), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Unité de génétique et biologie des cancers (U830), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Régional de Lutte contre le Cancer François Baclesse [Caen] (UNICANCER/CRLC), UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU), Centre médical de Bligny, Onco-génétique, Département de médecine oncologique [Gustave Roussy], COLO, Mouniati, 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 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), UNICANCER-UNICANCER-Université Bordeaux Segalen - Bordeaux 2-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de médecine génomique et d’immunothérapie (Genomic and Immunotherapy Medical Institute) (institut GIMI), UNICANCER-UNICANCER-Etablissement français du sang [Bourgogne-Franche-Comté] (EFS [Bourgogne-Franche-Comté])-FHU TRANSLAD (CHU de Dijon), Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Université de Lille-UNICANCER, 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), Normandie Université (NU)-UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN), 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), UNICANCER-UNICANCER-Etablissement français du sang [Bourgogne-Franche-Comté] (EFS BFC)-FHU TRANSLAD (CHU de Dijon), and Centre Hospitalier Universitaire de Saint-Etienne [CHU Saint-Etienne] (CHU ST-E)
Subjects: 0301 basic medicine, Cancer Research, medicine.medical_specialty, Sequencing data, [SDV.CAN]Life Sciences [q-bio]/Cancer, Context (language use), [SDV.GEN.GH] Life Sciences [q-bio]/Genetics/Human genetics, [SDV.MHEP.GEO]Life Sciences [q-bio]/Human health and pathology/Gynecology and obstetrics, 030105 genetics & heredity, Article, 03 medical and health sciences, 0302 clinical medicine, [SDV.CAN] Life Sciences [q-bio]/Cancer, Care plan, Internal medicine, Epidemiology, medicine, RC254-282, Genetic testing, medicine.diagnostic_test, business.industry, Cancer, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, medicine.disease, 3. Good health, genetic predisposition to breast and ovarian cancer, [SDV.MHEP.GEO] Life Sciences [q-bio]/Human health and pathology/Gynecology and obstetrics, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, Oncology, 030220 oncology & carcinogenesis, multigene panels, Cancer risk, Ovarian cancer, business, cancer risk estimate, penetrance of pathogenic variants
Abstract: Simple Summary TUMOSPEC was designed for estimating the risk of cancer for carriers of a predicted pathogenic variant (PPV) in a gene usually tested in a hereditary breast and ovarian cancer context. Index cases are enrolled consecutively among patients who undergo genetic testing as part of their care plan in France. First- and second-degree relatives and cousins of PPV carriers are invited to participate whether they are affected by cancer or not, and are tested for the familial PPV. Genetic, clinical, family and epidemiological data are centralized at the coordinating centre. The three-year feasibility study included 4431 prospective index cases, with 19.1% of them carrying a PPV. This showed that the study logistics are well adapted to clinical and laboratory constraints, and collaboration between partners (clinicians, biologists, coordinating centre and participants) is smooth. Hence, TUMOSPEC is being pursued, with the aim of optimizing clinical management guidelines specific to each gene. Abstract Assessment of age-dependent cancer risk for carriers of a predicted pathogenic variant (PPV) is often hampered by biases in data collection, with a frequent under-representation of cancer-free PPV carriers. TUMOSPEC was designed to estimate the cumulative risk of cancer for carriers of a PPV in a gene that is usually tested in a hereditary breast and ovarian cancer context. Index cases are enrolled consecutively among patients who undergo genetic testing as part of their care plan in France. First- and second-degree relatives and cousins of PPV carriers are invited to participate whether they are affected by cancer or not, and genotyped for the familial PPV. Clinical, family and epidemiological data are collected, and all data including sequencing data are centralized at the coordinating centre. The three-year feasibility study included 4431 prospective index cases, with 19.1% of them carrying a PPV. When invited by the coordinating centre, 65.3% of the relatives of index cases (5.7 relatives per family, on average) accepted the invitation to participate. The study logistics were well adapted to clinical and laboratory constraints, and collaboration between partners (clinicians, biologists, coordinating centre and participants) was smooth. Hence, TUMOSPEC is being pursued, with the aim of optimizing clinical management guidelines specific to each gene.
Published: 2021

46. Role of DNA Repair Variants and Diagnostic Radiology Exams in Differentiated Thyroid Cancer Risk: A Pooled Analysis of Two Case–Control Studies

Author: Carole Rubino, Pascal Guénel, Emilie Cordina-Duverger, Françoise Borson-Chazot, Vincent Souchard, Pierre Laurent-Puig, Florent de Vathaire, Thérèse Truong, Julie Guibon, Anne-Valérie Guizard, Mojgan Karimi, Elizabeth Adjadj, Catherine Ory, Hélène Blanché, Jean-Baptiste Cazier, Sylvie Chevillard, Martin Schlumberger, Constance Xhaard, Claire Schvartz, Yan Ren, Fabienne Lesueur, Jean-François Deleuze, Anne Boland, Mohammed Zakarya Zemmache, Sandra Canale, Eugènia Mariné Barjoan, Claire Mulot, Monia Zidane, Université Paris-Sud - Paris 11 (UP11), 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, 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), 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), 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 ), Université Paris-Saclay, Centre National de Recherche en Génomique Humaine (CNRGH), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Fondation Jean Dausset - Centre d’Etudes du Polymorphisme Humain [Paris] (CEPH), Laboratory of Excellence GENMED (Medical Genomics), Laboratoire de Cancérologie Expérimentale (LCE), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Institut de Biologie François JACOB (JACOB), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institut de Radiobiologie Cellulaire et Moléculaire (IRCM), Service de Radobiologie Expérimentale et Innovation Technologique (SREIT), Institut de Génomique d'Evry (IG), Université Paris-Saclay-Institut de Biologie François JACOB (JACOB), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), 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), Institut Jean Godinot [Reims], UNICANCER, Centre Hospitalier Universitaire de Nice (CHU Nice), Centre Régional de Lutte contre le Cancer François Baclesse [Caen] (UNICANCER/CRLC), Normandie Université (NU)-UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN), 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), Epigenetec, Centre de Ressources Biologiques (CRB), Université Sorbonne Paris Cité (USPC), Université Paris Descartes - Paris 5 (UPD5), University of Birmingham [Birmingham], FDV received the following grants as principal investigator of Young-Thyr study: Fondation de France (Grant number 65391), L'Alliance pour les sciences de la vie et de la santé, « AVIESAN » (Grants number ENV201416 and 2007005070), the Ligue Nationale Contre le Cancer (LNCC) (Grants number EPDAC6036, EPDTP6004, R10127LL).TT received the following grants for CATHY 470 and Young-Thyr studies genotyping Institut National du Cancer (Grant number 9533), the ARC foundation (Grant number PGA120150202302) MZ performed this work during her PhD funded by Ecole des Hautes Etudes en Santé Publique (EHESP) and the ARC Foundation (Grant number ARCDOC42020070002532)., Université Paris Sud Orsay, Inserm, U900, Institut Curie, Université PSL, Mines ParisTech, 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 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 Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU), CHU Caen, Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN)-Université de Caen Normandie (UNICAEN), 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), UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU)-UNICANCER, and BOZEC, Erwan
Subjects: 0301 basic medicine, Adult, Male, medicine.medical_specialty, Neoplasms, Radiation-Induced, Adolescent, DNA Repair, Epidemiology, DNA repair, Thyroid Gland, Single-nucleotide polymorphism, Logistic regression, Polymorphism, Single Nucleotide, Risk Assessment, 03 medical and health sciences, Young Adult, 0302 clinical medicine, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, medicine, Genetic predisposition, Biomarkers, Tumor, Humans, Genetic Predisposition to Disease, Thyroid Neoplasms, Child, Thyroid cancer, Aged, Aged, 80 and over, business.industry, Thyroid, Case-control study, Dose-Response Relationship, Radiation, Middle Aged, medicine.disease, Confidence interval, [SDV.MHEP.CSC] Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, 3. Good health, 030104 developmental biology, medicine.anatomical_structure, Oncology, 030220 oncology & carcinogenesis, Case-Control Studies, Female, Radiology, France, business
Abstract: Background: Given the increased use and diversity of diagnostic procedures, it is important to understand genetic susceptibility to radiation-induced thyroid cancer. Methods: On the basis of self-declared diagnostic radiology examination records in addition to existing literature, we estimated the radiation dose delivered to the thyroid gland from diagnostic procedures during childhood and adulthood in two case–control studies conducted in France. A total of 1,071 differentiated thyroid cancer (DTC) cases and 1,188 controls from the combined studies were genotyped using a custom-made Illumina OncoArray DNA chip. We focused our analysis on variants in genes involved in DNA damage response and repair pathways, representing a total of 5,817 SNPs in 571 genes. We estimated the OR per milli-Gray (OR/mGy) of the radiation dose delivered to the thyroid gland using conditional logistic regression. We then used an unconditional logistic regression model to assess the association between DNA repair gene variants and DTC risk. We performed a meta-analysis of the two studies. Results: The OR/mGy was 1.02 (95% confidence interval, 1.00–1.03). We found significant associations between DTC and rs7164173 in CHD2 (P = 5.79 × 10−5), rs6067822 in NFATc2 (P = 9.26 × 10−5), rs1059394 and rs699517 both in ENOSF1/THYS, rs12702628 in RPA3, and an interaction between rs7068306 in MGMT and thyroid radiation doses (P = 3.40 × 10−4). Conclusions: Our results suggest a role for variants in CDH2, NFATc2, ENOSF1/THYS, RPA3, and MGMT in DTC risk. Impact: CDH2, NFATc2, ENOSF1/THYS, and RPA3 have not previously been shown to be associated with DTC risk.
Published: 2021

47. 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

48. 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

49. Has tumor doubling time in breast cancer changed over the past 80 years? A systematic review

Author: Delphine Hequet, Claire Bonneau, Xavier Paoletti, Meryl Dahan, Roman Rouzier, 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 de Versailles Saint-Quentin-en-Yvelines - UFR Sciences de la santé Simone Veil (UVSQ Santé), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), MD was supported by the Malakoff Mederic group., HAL UVSQ, Équipe, 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: Oncology, Cancer Research, medicine.medical_specialty, Time Factors, Overdiagnosis, Receptor, ErbB-2, [SDV]Life Sciences [q-bio], Reviews, Breast Neoplasms, Triple Negative Breast Neoplasms, Review, 03 medical and health sciences, 0302 clinical medicine, Qualitative analysis, Breast cancer, breast cancer, Internal medicine, Medicine, Doubling time, Humans, Mass Screening, Radiology, Nuclear Medicine and imaging, Tumor growth, 030212 general & internal medicine, Prospective cohort study, RC254-282, Cell Proliferation, Cancer Biology, molecular subtypes, Tumor size, business.industry, screening, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, medicine.disease, 3. Good health, Tumor Burden, [SDV] Life Sciences [q-bio], Ki-67 Antigen, 030220 oncology & carcinogenesis, tumor growth rate, Regression Analysis, Female, Electronic database, tumor doubling time, business
Abstract: Over the past century, epidemiologic changes and implementation of screening may have had an impact on tumor doubling time in breast cancer. Our study was designed to evaluate changes in tumor doubling time in breast cancer over the past 80 years. A systematic review of published literature and meta‐regression analysis was performed. An online electronic database search was undertaken using the PubMed platform from inception until June 2020. All studies that measured tumor doubling time in breast cancer were included. A total of 151 publications were retrieved. Among them, 16 full‐text articles were included in the qualitative analysis. An exponential growth model was used for quantitative characterization of tumor growth rate. Tumor doubling time has remained stable over the past 80 years. Recent studies have not only identified “fast growing tumor” (grade 3, human epidermal growth factor receptor 2‐positive, triple‐negative, or tumor with an elevated Ki‐67) but also “inactive breast cancer” feeding the ongoing debate of overdiagnosis due to screening programs. The stability of tumor doubling time over the past 80 years, despite increasing and changing risk factors, supports the validity for our screening guidelines. Prospective studies based on more precise measurement of tumor size and adjustment for tumor characteristics are necessary to more clearly characterize the prognostic and predictive impact of tumor doubling time in breast cancer., The stability of tumor doubling time over the past 80 years, despite increasing and changing risk factors, supports the validity of our screening guidelines. Recent studies have not only identified “fast growing tumor” but also “inactive breast cancer” feeding the ongoing debate of overdiagnosis due to screening programs.
Published: 2021

50. 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

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