48 results on '"Arvind Singh Mer"'
Search Results
2. Novel subtypes of NPM1-mutated AML with distinct outcome
- Author
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Arvind Singh Mer, Mark D. Minden, Benjamin Haibe-Kains, and Aaron D. Schimmer
- Subjects
acute myeloid leukemia ,biomarkers ,subtype ,machine learning ,systems biology ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Acute myeloid leukemia (AML) is heterogeneous with one common subtype recognized by the presence of recurrent mutation of nucleophosmin-1 (NPM1). Emerging evidence indicates that within NPM1 mutated AML there is variation in outcome which challenges how best to characterize and treat the individual patient. Our recent findings show that there are two distinct (primitive and committed) subtypes within NPM1 mutated AML patients. These subtypes exhibit specific molecular characteristics, disease differentiation states, patient survival, and differential drug responses.
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- 2021
- Full Text
- View/download PDF
3. Expression levels of long non-coding RNAs are prognostic for AML outcome
- Author
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Arvind Singh Mer, Johan Lindberg, Christer Nilsson, Daniel Klevebring, Mei Wang, Henrik Grönberg, Soren Lehmann, and Mattias Rantalainen
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lncRNA ,Acute myeloid leukemia ,Prognosis ,Molecular subtype ,Diseases of the blood and blood-forming organs ,RC633-647.5 ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Abstract Background Long non-coding RNA (lncRNA) expression has been implicated in a range of molecular mechanisms that are central in cancer. However, lncRNA expression has not yet been comprehensively characterized in acute myeloid leukemia (AML). Here, we assess to what extent lncRNA expression is prognostic of AML patient overall survival (OS) and determine if there are indications of lncRNA-based molecular subtypes of AML. Methods We performed RNA sequencing of 274 intensively treated AML patients in a Swedish cohort and quantified lncRNA expression. Univariate and multivariate time-to-event analysis was applied to determine association between individual lncRNAs with OS. Unsupervised statistical learning was applied to ascertain if lncRNA-based molecular subtypes exist and are prognostic. Results Thirty-three individual lncRNAs were found to be associated with OS (adjusted p value
- Published
- 2018
- Full Text
- View/download PDF
4. MicroRNAs modulate the dynamics of the NF-κB signaling pathway.
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Candida Vaz, Arvind Singh Mer, Alok Bhattacharya, and Ramakrishna Ramaswamy
- Subjects
Medicine ,Science - Abstract
NF-κB, a major transcription factor involved in mammalian inflammatory signaling, is primarily involved in regulation of response to inflammatory cytokines and pathogens. Its levels are tightly regulated since uncontrolled inflammatory response can cause serious diseases. Mathematical models have been useful in revealing the underlying mechanisms, the dynamics, and other aspects of regulation in NF-κB signaling. The recognition that miRNAs are important regulators of gene expression, and that a number of miRNAs target different components of the NF-κB network, motivate the incorporation of miRNA regulated steps in existing mathematical models to help understand the quantitative aspects of miRNA mediated regulation.In this study, two separate scenarios of miRNA regulation within an existing model are considered. In the first, miRNAs target adaptor proteins involved in the synthesis of IKK that serves as the NF-κB activator. In the second, miRNAs target different isoforms of IκB that act as NF-κB inhibitors. Simulations are carried out under two different conditions: when all three isoforms of IκB are present (wild type), and when only one isoform (IκBα) is present (knockout type). In both scenarios, oscillations in the NF-κB levels are observed and are found to be dependent on the levels of miRNAs.Computational modeling can provide fresh insights into intricate regulatory processes. The introduction of miRNAs affects the dynamics of the NF-κB signaling pathway in a manner that depends on the role of the target. This "fine-tuning" property of miRNAs helps to keep the system in check and prevents it from becoming uncontrolled. The results are consistent with earlier experimental findings.
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- 2011
- Full Text
- View/download PDF
5. PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis.
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Nikta Feizi, Sisira Kadambat Nair, Petr Smirnov, Gangesh Beri, Christopher Eeles, Parinaz Nasr Esfahani, Minoru Nakano, Denis Tkachuk, Anthony Mammoliti, Evgeniya Gorobets, Arvind Singh Mer, Eva Lin, Yihong Yu, Scott Martin, Marc Hafner, and Benjamin Haibe-Kains
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- 2022
- Full Text
- View/download PDF
6. Highlights from the 16th International Society for Computational Biology Student Council Symposium 2020 [version 1; peer review: not peer reviewed]
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Wim L. Cuypers, Handan Melike Dönertaş, Jasleen K. Grewal, Nazeefa Fatima, Chase Donnelly, Arvind Singh Mer, Spencer Krieger, Bart Cuypers, and Farzana Rahman
- Subjects
Editorial ,Articles ,Student Council Symposium ,SCS ,ISCB ,ISCBSC ,Virtual Seminar ,Networking ,ECR ,conference ,collaboration - Abstract
In this meeting overview, we summarise the scientific program and organisation of the 16th International Society for Computational Biology Student Council Symposium in 2020 (ISCB SCS2020). This symposium was the first virtual edition in an uninterrupted series of symposia that has been going on for 15 years, aiming to unite computational biology students and early career researchers across the globe.
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- 2021
- Full Text
- View/download PDF
7. Drug sensitivity prediction from cell line-based pharmacogenomics data: guidelines for developing machine learning models.
- Author
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Hossein Sharifi-Noghabi, Soheil Jahangiri-Tazehkand, Petr Smirnov, Casey Hon, Anthony Mammoliti, Sisira Kadambat Nair, Arvind Singh Mer, Martin Ester, and Benjamin Haibe-Kains
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- 2021
- Full Text
- View/download PDF
8. Supplementary Figure from Ontogeny and Vulnerabilities of Drug-Tolerant Persisters in HER2+ Breast Cancer
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Benjamin G. Neel, Alireza Khodadadi-Jamayran, Benjamin Haibe-Kains, Abhyudai Singh, Jason Moffat, Beatrix Ueberheide, Aristotelis Tsirigos, Jane A. Skok, Adriana Heguy, Peter Meyn, Sylvia Adams, Kwok-kin Wong, Jiehui Deng, Christos Sotiriou, David Venet, Kwan Ho Tang, Avantika Dhabaria, Allison M.L. Nixon, Kevin R. Brown, Arvind Singh Mer, Azin Sayad, Shaowen Jiang, Jayu Jen, and Chewei Anderson Chang
- Abstract
Supplementary Figure from Ontogeny and Vulnerabilities of Drug-Tolerant Persisters in HER2+ Breast Cancer
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- 2023
9. Data from Ontogeny and Vulnerabilities of Drug-Tolerant Persisters in HER2+ Breast Cancer
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Benjamin G. Neel, Alireza Khodadadi-Jamayran, Benjamin Haibe-Kains, Abhyudai Singh, Jason Moffat, Beatrix Ueberheide, Aristotelis Tsirigos, Jane A. Skok, Adriana Heguy, Peter Meyn, Sylvia Adams, Kwok-kin Wong, Jiehui Deng, Christos Sotiriou, David Venet, Kwan Ho Tang, Avantika Dhabaria, Allison M.L. Nixon, Kevin R. Brown, Arvind Singh Mer, Azin Sayad, Shaowen Jiang, Jayu Jen, and Chewei Anderson Chang
- Abstract
Resistance to targeted therapies is an important clinical problem in HER2-positive (HER2+) breast cancer. “Drug-tolerant persisters” (DTP), a subpopulation of cancer cells that survive via reversible, nongenetic mechanisms, are implicated in resistance to tyrosine kinase inhibitors (TKI) in other malignancies, but DTPs following HER2 TKI exposure have not been well characterized. We found that HER2 TKIs evoke DTPs with a luminal-like or a mesenchymal-like transcriptome. Lentiviral barcoding/single-cell RNA sequencing reveals that HER2+ breast cancer cells cycle stochastically through a “pre-DTP” state, characterized by a G0-like expression signature and enriched for diapause and/or senescence genes. Trajectory analysis/cell sorting shows that pre-DTPs preferentially yield DTPs upon HER2 TKI exposure. Cells with similar transcriptomes are present in HER2+ breast tumors and are associated with poor TKI response. Finally, biochemical experiments indicate that luminal-like DTPs survive via estrogen receptor–dependent induction of SGK3, leading to rewiring of the PI3K/AKT/mTORC1 pathway to enable AKT-independent mTORC1 activation.Significance:DTPs are implicated in resistance to anticancer therapies, but their ontogeny and vulnerabilities remain unclear. We find that HER2 TKI-DTPs emerge from stochastically arising primed cells (“pre-DTPs”) that engage either of two distinct transcriptional programs upon TKI exposure. Our results provide new insights into DTP ontogeny and potential therapeutic vulnerabilities.This article is highlighted in the In This Issue feature, p. 873
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- 2023
10. Supplementary Table from Ontogeny and Vulnerabilities of Drug-Tolerant Persisters in HER2+ Breast Cancer
- Author
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Benjamin G. Neel, Alireza Khodadadi-Jamayran, Benjamin Haibe-Kains, Abhyudai Singh, Jason Moffat, Beatrix Ueberheide, Aristotelis Tsirigos, Jane A. Skok, Adriana Heguy, Peter Meyn, Sylvia Adams, Kwok-kin Wong, Jiehui Deng, Christos Sotiriou, David Venet, Kwan Ho Tang, Avantika Dhabaria, Allison M.L. Nixon, Kevin R. Brown, Arvind Singh Mer, Azin Sayad, Shaowen Jiang, Jayu Jen, and Chewei Anderson Chang
- Abstract
Supplementary Table from Ontogeny and Vulnerabilities of Drug-Tolerant Persisters in HER2+ Breast Cancer
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- 2023
11. Supplementary Data Table S1, 3-4 from Organoid Cultures as Preclinical Models of Non–Small Cell Lung Cancer
- Author
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Ming-Sound Tsao, Nadeem Moghal, Geoffrey Liu, Benjamin Haibe-Kains, Nhu-An Pham, Laura Tamblyn, Yu-Hui Wang, Ming Li, Joshua C. Rosen, Arvind Singh Mer, Quan Li, Vibha Raghavan, Sebastiao N. Martins-Filho, Michael Cabanero, Hirotsugu Notsuda, Ni Liu, Christine Ng, Nikolina Radulovich, and Ruoshi Shi
- Abstract
Supplementary Data Table S1, 3-4
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- 2023
12. Data from Bimodal Gene Expression in Patients with Cancer Provides Interpretable Biomarkers for Drug Sensitivity
- Author
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Benjamin Haibe-Kains, Linda Z. Penn, Arvind Singh Mer, Petr Smirnov, Anthony Mammoliti, Bo Li, Sisira Kadambat Nair, and Wail Ba-Alawi
- Abstract
Identifying biomarkers predictive of cancer cell response to drug treatment constitutes one of the main challenges in precision oncology. Recent large-scale cancer pharmacogenomic studies have opened new avenues of research to develop predictive biomarkers by profiling thousands of human cancer cell lines at the molecular level and screening them with hundreds of approved drugs and experimental chemical compounds. Many studies have leveraged these data to build predictive models of response using various statistical and machine learning methods. However, a common pitfall to these methods is the lack of interpretability as to how they make predictions, hindering the clinical translation of these models. To alleviate this issue, we used the recent logic modeling approach to develop a new machine learning pipeline that explores the space of bimodally expressed genes in multiple large in vitro pharmacogenomic studies and builds multivariate, nonlinear, yet interpretable logic-based models predictive of drug response. The performance of this approach was showcased in a compendium of the three largest in vitro pharmacogenomic datasets to build robust and interpretable models for 101 drugs that span 17 drug classes with high validation rates in independent datasets. These results along with in vivo and clinical validation support a better translation of gene expression biomarkers between model systems using bimodal gene expression.Significance:A new machine learning pipeline exploits the bimodality of gene expression to provide a reliable set of candidate predictive biomarkers with a high potential for clinical translatability.
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- 2023
13. Supplementary Table from Bimodal Gene Expression in Patients with Cancer Provides Interpretable Biomarkers for Drug Sensitivity
- Author
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Benjamin Haibe-Kains, Linda Z. Penn, Arvind Singh Mer, Petr Smirnov, Anthony Mammoliti, Bo Li, Sisira Kadambat Nair, and Wail Ba-Alawi
- Abstract
Supplementary Table from Bimodal Gene Expression in Patients with Cancer Provides Interpretable Biomarkers for Drug Sensitivity
- Published
- 2023
14. Data from Organoid Cultures as Preclinical Models of Non–Small Cell Lung Cancer
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Ming-Sound Tsao, Nadeem Moghal, Geoffrey Liu, Benjamin Haibe-Kains, Nhu-An Pham, Laura Tamblyn, Yu-Hui Wang, Ming Li, Joshua C. Rosen, Arvind Singh Mer, Quan Li, Vibha Raghavan, Sebastiao N. Martins-Filho, Michael Cabanero, Hirotsugu Notsuda, Ni Liu, Christine Ng, Nikolina Radulovich, and Ruoshi Shi
- Abstract
Purpose:Non–small cell lung cancer (NSCLC) is the most common cause of cancer-related deaths worldwide. There is an unmet need to develop novel clinically relevant models of NSCLC to accelerate identification of drug targets and our understanding of the disease.Experimental Design:Thirty surgically resected NSCLC primary patient tissue and 35 previously established patient-derived xenograft (PDX) models were processed for organoid culture establishment. Organoids were histologically and molecularly characterized by cytology and histology, exome sequencing, and RNA-sequencing analysis. Tumorigenicity was assessed through subcutaneous injection of organoids in NOD/SCID mice. Organoids were subjected to drug testing using EGFR, FGFR, and MEK-targeted therapies.Results:We have identified cell culture conditions favoring the establishment of short-term and long-term expansion of NSCLC organoids derived from primary lung patient and PDX tumor tissue. The NSCLC organoids recapitulated the histology of the patient and PDX tumor. They also retained tumorigenicity, as evidenced by cytologic features of malignancy, xenograft formation, preservation of mutations, copy number aberrations, and gene expression profiles between the organoid and matched parental tumor tissue by whole-exome and RNA sequencing. NSCLC organoid models also preserved the sensitivity of the matched parental tumor to targeted therapeutics, and could be used to validate or discover biomarker–drug combinations.Conclusions:Our panel of NSCLC organoids closely recapitulates the genomics and biology of patient tumors, and is a potential platform for drug testing and biomarker validation.
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- 2023
15. Supplementary Data Figure S1-5, Table S2, 5-7 from Organoid Cultures as Preclinical Models of Non–Small Cell Lung Cancer
- Author
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Ming-Sound Tsao, Nadeem Moghal, Geoffrey Liu, Benjamin Haibe-Kains, Nhu-An Pham, Laura Tamblyn, Yu-Hui Wang, Ming Li, Joshua C. Rosen, Arvind Singh Mer, Quan Li, Vibha Raghavan, Sebastiao N. Martins-Filho, Michael Cabanero, Hirotsugu Notsuda, Ni Liu, Christine Ng, Nikolina Radulovich, and Ruoshi Shi
- Abstract
Supplementary Data Figure S1-5, Table S2, 5-7
- Published
- 2023
16. Supplementary Figure from Bimodal Gene Expression in Patients with Cancer Provides Interpretable Biomarkers for Drug Sensitivity
- Author
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Benjamin Haibe-Kains, Linda Z. Penn, Arvind Singh Mer, Petr Smirnov, Anthony Mammoliti, Bo Li, Sisira Kadambat Nair, and Wail Ba-Alawi
- Abstract
Supplementary Figure from Bimodal Gene Expression in Patients with Cancer Provides Interpretable Biomarkers for Drug Sensitivity
- Published
- 2023
17. Supplementary Data from Bimodal Gene Expression in Patients with Cancer Provides Interpretable Biomarkers for Drug Sensitivity
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Benjamin Haibe-Kains, Linda Z. Penn, Arvind Singh Mer, Petr Smirnov, Anthony Mammoliti, Bo Li, Sisira Kadambat Nair, and Wail Ba-Alawi
- Abstract
Supplementary Data from Bimodal Gene Expression in Patients with Cancer Provides Interpretable Biomarkers for Drug Sensitivity
- Published
- 2023
18. Machine Learning for Biomarker Discovery in Cancer Pharmacogenomics Data.
- Author
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Arvind Singh Mer, Petr Smirnov, and Benjamin Haibe-Kains
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- 2019
- Full Text
- View/download PDF
19. Orchestrating and sharing large multimodal data for transparent and reproducible research
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Eva Lin, Scott E. Martin, Yihong Yu, Sisira Kadambat Nair, Anthony Mammoliti, Christopher Eeles, Marc Hafner, Benjamin Haibe-Kains, Heewon Seo, Petr Smirnov, Chantal Ho, Arvind Singh Mer, Zhaleh Safikhani, Gangesh Beri, Rebecca Kusko, Minoru Nakano, and Ian Smith
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Data processing ,Open science ,Multidisciplinary ,Process (engineering) ,business.industry ,Computer science ,Multimodal data ,Science ,Interoperability ,General Physics and Astronomy ,Cloud computing ,General Chemistry ,Data science ,General Biochemistry, Genetics and Molecular Biology ,Article ,Identifier ,Software ,Human–computer interaction ,Computational platforms and environments ,Relevance (information retrieval) ,business ,Data objects - Abstract
Reproducibility is essential to open science, as there is limited relevance for findings that can not be reproduced by independent research groups, regardless of its validity. It is therefore crucial for scientists to describe their experiments in sufficient detail so they can be reproduced, scrutinized, challenged, and built upon. However, the intrinsic complexity and continuous growth of biomedical data makes it increasingly difficult to process, analyze, and share with the community in a FAIR (findable, accessible, interoperable, and reusable) manner. To overcome these issues, we created a cloud-based platform called ORCESTRA (orcestra.ca), which provides a flexible framework for the reproducible processing of multimodal biomedical data. It enables processing of clinical, genomic and perturbation profiles of cancer samples through automated processing pipelines that are user-customizable. ORCESTRA creates integrated and fully documented data objects with persistent identifiers (DOI) and manages multiple dataset versions, which can be shared for future studies., It is no secret that a significant part of scientific research is difficult to reproduce. Here, the authors present a cloud-computing platform called ORCESTRA that facilitates reproducible processing of multimodal biomedical data using customizable pipelines and well-documented data objects.
- Published
- 2021
20. Biological and therapeutic implications of a unique subtype of NPM1 mutated AML
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Corey Nislow, Sören Lehmann, Alex Murison, Gary D. Bader, Mark Gower, Liran I. Shlush, Benjamin Haibe-Kains, Nergiz Dogan-Artun, Rose Hurren, John E. Dick, Veronique Voisin, Cynthia J. Guidos, Sisira Kadambat Nair, Seyed Ali Madani Tonekaboni, Mathieu Lupien, Emily Heath, Mark D. Minden, Aaron D. Schimmer, Laura García-Prat, Arvind Singh Mer, and Mattias Rantalainen
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0301 basic medicine ,Drug ,NPM1 ,Medicin och hälsovetenskap ,media_common.quotation_subject ,Science ,General Physics and Astronomy ,Disease ,Biology ,Medical and Health Sciences ,Article ,Acute myeloid leukaemia ,General Biochemistry, Genetics and Molecular Biology ,Immunophenotyping ,Prognostic markers ,03 medical and health sciences ,0302 clinical medicine ,hemic and lymphatic diseases ,Machine learning ,Cluster Analysis ,Humans ,Protein Kinase Inhibitors ,Epigenomics ,media_common ,Multidisciplinary ,Gene Expression Regulation, Leukemic ,Kinase ,Nuclear Proteins ,Reproducibility of Results ,Myeloid leukemia ,General Chemistry ,Survival Analysis ,Chromatin ,Leukemia, Myeloid, Acute ,Phenotype ,030104 developmental biology ,030220 oncology & carcinogenesis ,Mutation ,Cancer research ,Stem cell ,Nucleophosmin ,Biomarkers - Abstract
In acute myeloid leukemia (AML), molecular heterogeneity across patients constitutes a major challenge for prognosis and therapy. AML with NPM1 mutation is a distinct genetic entity in the revised World Health Organization classification. However, differing patterns of co-mutation and response to therapy within this group necessitate further stratification. Here we report two distinct subtypes within NPM1 mutated AML patients, which we label as primitive and committed based on the respective presence or absence of a stem cell signature. Using gene expression (RNA-seq), epigenomic (ATAC-seq) and immunophenotyping (CyToF) analysis, we associate each subtype with specific molecular characteristics, disease differentiation state and patient survival. Using ex vivo drug sensitivity profiling, we show a differential drug response of the subtypes to specific kinase inhibitors, irrespective of the FLT3-ITD status. Differential drug responses of the primitive and committed subtype are validated in an independent AML cohort. Our results highlight heterogeneity among NPM1 mutated AML patient samples based on stemness and suggest that the addition of kinase inhibitors to the treatment of cases with the primitive signature, lacking FLT3-ITD, could have therapeutic benefit., Molecular heterogeneity of acute myeloid leukaemia (AML) across patients is a major challenge for prognosis and therapy. Here, the authors show that NPM1 mutated AML is a heterogeneous class, consisting of two subtypes which exhibit distinct molecular characteristics, differentiation state, patient survival and drug response.
- Published
- 2021
21. PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis
- Author
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Arvind Singh Mer, Minoru Nakano, Parinaz Nasr Esfahani, Nikta Feizi, Gangesh Beri, Petr Smirnov, Anthony Mammoliti, Yihong Yu, Evgeniya Gorobets, Eva Lin, Marc Hafner, Scott E. Martin, Denis Tkachuk, Benjamin Haibe-Kains, Sisira Kadambat Nair, and Christopher Eeles
- Subjects
Profiling (computer programming) ,Standardization ,AcademicSubjects/SCI00010 ,Interface (computing) ,Genomics ,Transparency (human–computer interaction) ,Computational biology ,Biology ,Pharmacogenomic Testing ,Pharmacogenetics ,Pharmacogenomics ,Scalability ,Databases, Genetic ,Genetics ,Humans ,Database Issue ,Biomarker Analysis ,Software - Abstract
Cancer pharmacogenomics studies provide valuable insights into disease progression and associations between genomic features and drug response. PharmacoDB integrates multiple cancer pharmacogenomics datasets profiling approved and investigational drugs across cell lines from diverse tissue types. The web-application enables users to efficiently navigate across datasets, view and compare drug dose–response data for a specific drug-cell line pair. In the new version of PharmacoDB (version 2.0, https://pharmacodb.ca/), we present (i) new datasets such as NCI-60, the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) dataset, as well as updated data from the Genomics of Drug Sensitivity in Cancer (GDSC) and the Genentech Cell Line Screening Initiative (gCSI); (ii) implementation of FAIR data pipelines using ORCESTRA and PharmacoDI; (iii) enhancements to drug–response analysis such as tissue distribution of dose–response metrics and biomarker analysis; and (iv) improved connectivity to drug and cell line databases in the community. The web interface has been rewritten using a modern technology stack to ensure scalability and standardization to accommodate growing pharmacogenomics datasets. PharmacoDB 2.0 is a valuable tool for mining pharmacogenomics datasets, comparing and assessing drug–response phenotypes of cancer models., Graphical Abstract Graphical abstractPharmacoDB 2.0 : Improving scalability and transparency of in vitro pharmacogenomics analysis.
- Published
- 2021
22. PharmacoDB 2.0 : Improving scalability and transparency ofin vitropharmacogenomics analysis
- Author
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Marc Hafner, Sisira Kadambat Nair, Denis Tkachuk, Benjamin Haibe-Kains, Anthony Mammoliti, Eva Lin, Arvind Singh Mer, Nikta Feizi, Scott E. Martin, Minoru Nakano, Yihong Yu, Gangesh Beri, Parinaz Nasr Esfahani, Petr Smirnov, Evgeniya Gorobets, and Christopher Eeles
- Subjects
Profiling (computer programming) ,Standardization ,Computer science ,Pharmacogenomics ,Interface (computing) ,Scalability ,Genomics ,Computational biology ,Biomarker Analysis ,Biomarker discovery - Abstract
Cancer pharmacogenomics studies provide valuable insights into disease progression and associations between genomic features and drug response. PharmacoDB integrates multiple cancer pharmacogenomics datasets profiling approved and investigational drugs across cell lines from diverse tissue types. The web-application enables users to efficiently navigate across datasets, view and compare drug dose-response data for a specific drug-cell line pair. In the new version of PharmacoDB (version 2.0,https://pharmacodb.ca/), we present: (i) new datasets such as NCI-60, the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) dataset, as well as updated data from the Genomics of Drug Sensitivity in Cancer (GDSC) and the Genentech Cell Line Screening Initiative (gCSI); (ii) implementation of FAIR data pipelines using ORCESTRA and PharmacoDI; (iii) enhancements to drug response analysis such as tissue distribution of dose-response metrics and biomarker analysis; (iv) improved connectivity to drug and cell line databases in the community. The web interface has been rewritten using a modern technology stack to ensure scalability and standardization to accommodate growing pharmacogenomics datasets. PharmacoDB 2.0 is a valuable tool for mining pharmacogenomics datasets, comparing and assessing drug response phenotypes of cancer models.HIGHLIGHTSPharmacoDB 2.0 includes new and updated large pharmacogenomic datasets. The data processing for PharmacoDB is made fully reproducible through the use of the ORCESTRA platform and automated data ingestion pipelinesThe new release contains enriched annotations for drugs and cell lines via connectivity to external databases, as well as new analytical methods for tissue-specific and pan-cancer biomarker discoveryThe new version of PharmacoDB incorporates a scalable and reproducible framework that can accelerate the implementation of analytical pipelines including machine learning/AI for biomarker discovery in the future
- Published
- 2021
23. Proteogenomic Characterization of Pancreatic Ductal Adenocarcinoma
- Author
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Marcin J. Domagalski, Wen Jiang, Michael Smith, Li Ding, Michael Schnaubelt, Oxana Paklina, Gilbert S. Omenn, Magdalena Derejska, Karin D. Rodland, Johanna Gardner, Saravana M. Dhanasekaran, Pamela Grady, Pushpa Hariharan, David Mallery, Jesse Francis, Maciej Wiznerowicz, Eunkyung An, Nancy Roche, Ralph H. Hruban, Samuel H. Payne, Chen Huang, Olga Potapova, Gad Getz, Zhiao Shi, Shuai Guo, Oliver F. Bathe, Stacey Gabriel, Sandra Cottingham, Hui Zhang, Daniel Cui Zhou, Maureen Dyer, Houxiang Zhu, James Suh, Shuang Cai, Christopher R. Kinsinger, Felipe da Veiga Leprevost, Steven Chen, Chelsea J. Newton, Amanda G. Paulovich, Steven A. Carr, Melissa Borucki, Sandra Cerda, Troy Shelton, D. R. Mani, Tara Hiltke, Lijun Chen, Benjamin Haibe-Kains, Jiang Long, Ratna R. Thangudu, Arul M. Chinnaiyan, Mathangi Thiagarajan, Negin Vatanian, Peter Ronning, Thomas L. Bauer, Ki Sung Um, Christina Ayad, Seungyeul Yoo, Mitual Amin, Ruiyang Liu, Alicia Francis, Nikolay Gabrovski, Eric E. Schadt, Zhen Zhang, Alexey I. Nesvizhskii, Hariharan Easwaran, Huan Chen, Tao Liu, Elizabeth R. Duffy, Liwei Cao, Joshua M. Wang, Michael H.A. Roehrl, Antonio Colaprico, Ana I. Robles, Emily S. Boja, Rita Jui-Hsien Lu, Rodrigo Vargas Eguez, Yize Li, Jennifer M. Koziak, Wenke Liu, Weiming Yang, Arvind Singh Mer, Dana R. Valley, Sailaja Mareedu, Song Cao, Scott D. Jewell, William Bocik, Shilpi Singh, Yongchao Dou, Matthew A. Wyczalkowski, David Fenyö, Galen Hostetter, Liqun Qi, Wenyi Wang, Yvonne Shutack, Shirley Tsang, Karen A. Ketchum, Charles A. Goldthwaite, Katherine A. Hoadley, Richard D. Smith, Karsten Krug, Yuxing Liao, Nadezhda V. Terekhanova, Henry Rodriguez, Barbara Hindenach, Matthew J. Ellis, Yingwei Hu, Pei Wang, Daniel C. Rohrer, Sara R. Savage, Grace Zhao, Ludmila Danilova, Yige Wu, Parham Minoo, Jennifer M. Eschbacher, Nathan Edwards, T. Mamie Lih, Simina M. Boca, George D. Wilson, Alexey Shabunin, Bing Zhang, Michael A. Gillette, Brian J. Druker, David J. Clark, Jianbo Pan, Katarzyna Kusnierz, David Chesla, Ronald Matteotti, Corbin D. Jones, Michael J. Birrer, Lori J. Sokoll, Qing Kay Li, Mehdi Mesri, Peter B. McGarvey, Chet Birger, Barbara Pruetz, Daniel W. Chan, Bo Wen, Nicollette Maunganidze, and Jasmine Huang
- Subjects
Adult ,Male ,Pancreatic ductal adenocarcinoma ,Proteome ,Gene Dosage ,Biology ,Adenocarcinoma ,medicine.disease_cause ,General Biochemistry, Genetics and Molecular Biology ,Article ,Epigenesis, Genetic ,Substrate Specificity ,Cohort Studies ,medicine ,Humans ,Molecular Targeted Therapy ,Phosphorylation ,Aged ,Glycoproteins ,Proteogenomics ,Aged, 80 and over ,MicroRNA sequencing ,Genome, Human ,RNA ,Endothelial Cells ,Methylation ,Middle Aged ,Phosphoproteins ,Prognosis ,Pancreatic Neoplasms ,Phenotype ,Cancer research ,Female ,KRAS ,Signal transduction ,Carcinogenesis ,Transcriptome ,Glycolysis ,Protein Kinases ,Algorithms ,Carcinoma, Pancreatic Ductal - Abstract
Summary Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 normal pancreatic ductal tissues. Proteomic, phosphoproteomic, and glycoproteomic analyses were used to characterize proteins and their modifications. In addition, whole-genome sequencing, whole-exome sequencing, methylation, RNA sequencing (RNA-seq), and microRNA sequencing (miRNA-seq) were performed on the same tissues to facilitate an integrated proteogenomic analysis and determine the impact of genomic alterations on protein expression, signaling pathways, and post-translational modifications. To ensure robust downstream analyses, tumor neoplastic cellularity was assessed via multiple orthogonal strategies using molecular features and verified via pathological estimation of tumor cellularity based on histological review. This integrated proteogenomic characterization of PDAC will serve as a valuable resource for the community, paving the way for early detection and identification of novel therapeutic targets.
- Published
- 2021
24. Bimodal Gene Expression in Patients with Cancer Provides Interpretable Biomarkers for Drug Sensitivity
- Author
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Wail Ba-Alawi, Sisira Kadambat Nair, Bo Li, Anthony Mammoliti, Petr Smirnov, Arvind Singh Mer, Linda Z. Penn, and Benjamin Haibe-Kains
- Subjects
Cancer Research ,Oncology ,Pharmacogenetics ,Neoplasms ,Gene Expression ,Humans ,Precision Medicine ,Biomarkers - Abstract
Identifying biomarkers predictive of cancer cell response to drug treatment constitutes one of the main challenges in precision oncology. Recent large-scale cancer pharmacogenomic studies have opened new avenues of research to develop predictive biomarkers by profiling thousands of human cancer cell lines at the molecular level and screening them with hundreds of approved drugs and experimental chemical compounds. Many studies have leveraged these data to build predictive models of response using various statistical and machine learning methods. However, a common pitfall to these methods is the lack of interpretability as to how they make predictions, hindering the clinical translation of these models. To alleviate this issue, we used the recent logic modeling approach to develop a new machine learning pipeline that explores the space of bimodally expressed genes in multiple large in vitro pharmacogenomic studies and builds multivariate, nonlinear, yet interpretable logic-based models predictive of drug response. The performance of this approach was showcased in a compendium of the three largest in vitro pharmacogenomic datasets to build robust and interpretable models for 101 drugs that span 17 drug classes with high validation rates in independent datasets. These results along with in vivo and clinical validation support a better translation of gene expression biomarkers between model systems using bimodal gene expression. Significance: A new machine learning pipeline exploits the bimodality of gene expression to provide a reliable set of candidate predictive biomarkers with a high potential for clinical translatability.
- Published
- 2021
25. Novel subtypes of NPM1-mutated AML with distinct outcome
- Author
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Benjamin Haibe-Kains, Arvind Singh Mer, Mark D. Minden, and Aaron D. Schimmer
- Subjects
0303 health sciences ,Cancer Research ,NPM1 ,Myeloid leukemia ,Patient survival ,Disease ,Biology ,3. Good health ,03 medical and health sciences ,0302 clinical medicine ,hemic and lymphatic diseases ,Cancer research ,Author’s Views ,Molecular Medicine ,Recurrent mutation ,030304 developmental biology ,030215 immunology - Abstract
Acute myeloid leukemia (AML) is heterogeneous with one common subtype recognized by the presence of recurrent mutation of nucleophosmin-1 (NPM1). Emerging evidence indicates that within NPM1 mutated AML there is variation in outcome which challenges how best to characterize and treat the individual patient. Our recent findings show that there are two distinct (primitive and committed) subtypes within NPM1 mutated AML patients. These subtypes exhibit specific molecular characteristics, disease differentiation states, patient survival, and differential drug responses.
- Published
- 2021
26. Highlights from the 16th International Society for Computational Biology Student Council Symposium 2020
- Author
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Arvind Singh Mer, Spencer Krieger, Nazeefa Fatima, Jasleen K. Grewal, Farzana Rahman, Wim L. Cuypers, Bart Cuypers, Chase Donnelly, and Handan Melike Dönertaş
- Subjects
0301 basic medicine ,Engineering ,Globe ,Computational biology ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,Networking ,0302 clinical medicine ,medicine ,Humans ,Early career ,General Pharmacology, Toxicology and Pharmaceutics ,Students ,General Immunology and Microbiology ,business.industry ,ISCB ,Computational Biology ,General Medicine ,Articles ,Student Council Symposium ,collaboration ,Research Personnel ,ECR ,Virtual Seminar ,030104 developmental biology ,medicine.anatomical_structure ,Editorial ,ISCBSC ,business ,030217 neurology & neurosurgery ,SCS ,conference - Abstract
In this meeting overview, we summarise the scientific program and organisation of the 16th International Society for Computational Biology Student Council Symposium in 2020 (ISCB SCS2020). This symposium was the first virtual edition in an uninterrupted series of symposia that has been going on for 15 years, aiming to unite computational biology students and early career researchers across the globe.
- Published
- 2021
27. Integrative Pharmacogenomics Analysis of Patient-Derived Xenografts
- Author
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Anna Goldenberg, David Cescon, Wail Ba-alawi, Petr Smirnov, Janosch Ortmann, Ben Brew, Yi Xiao Wang, Arvind Singh Mer, Benjamin Haibe-Kains, and Ming-Sound Tsao
- Subjects
0301 basic medicine ,Cancer Research ,Computational biology ,Biology ,Tumor response ,Precision medicine ,Xenograft Model Antitumor Assays ,03 medical and health sciences ,030104 developmental biology ,0302 clinical medicine ,Oncology ,Pharmacogenetics ,In vivo ,Neoplasms ,030220 oncology & carcinogenesis ,Pharmacogenomics ,Drug response ,Animals ,Heterografts ,Humans ,Precision Medicine ,Biomarker discovery ,Pathway activity - Abstract
Identifying robust biomarkers of drug response constitutes a key challenge in precision medicine. Patient-derived tumor xenografts (PDX) have emerged as reliable preclinical models that more accurately recapitulate tumor response to chemo- and targeted therapies. However, the lack of computational tools makes it difficult to analyze high-throughput molecular and pharmacologic profiles of PDX. We have developed Xenograft Visualization & Analysis (Xeva), an open-source software package for in vivo pharmacogenomic datasets that allows for quantification of variability in gene expression and pathway activity across PDX passages. We found that only a few genes and pathways exhibited passage-specific alterations and were therefore not suitable for biomarker discovery. Using the largest PDX pharmacogenomic dataset to date, we identified 87 pathways that are significantly associated with response to 51 drugs (FDR < 0.05). We found novel biomarkers based on gene expressions, copy number aberrations, and mutations predictive of drug response (concordance index > 0.60; FDR < 0.05). Our study demonstrates that Xeva provides a flexible platform for integrative analysis of preclinical in vivo pharmacogenomics data to identify biomarkers predictive of drug response, representing a major step forward in precision oncology. Significance: A computational platform for PDX data analysis reveals consistent gene and pathway activity across passages and confirms drug response prediction biomarkers in PDX. See related commentary by Meehan, p. 4324
- Published
- 2019
28. Drug Sensitivity Prediction From Cell Line-Based Pharmacogenomics Data: Guidelines for Developing Machine Learning Models
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Casey Hon, Petr Smirnov, Benjamin Haibe-Kains, Anthony Mammoliti, Martin Ester, Arvind Singh Mer, Sisira Kadambat Nair, Hossein Sharifi-Noghabi, and Soheil Jahangiri-Tazehkand
- Subjects
Drug ,Future studies ,Computer science ,media_common.quotation_subject ,Datasets as Topic ,Review ,Machine learning ,computer.software_genre ,Machine Learning ,Omics data ,03 medical and health sciences ,0302 clinical medicine ,Cell Line, Tumor ,Humans ,Sensitivity (control systems) ,Set (psychology) ,Molecular Biology ,030304 developmental biology ,media_common ,0303 health sciences ,business.industry ,3. Good health ,Drug Resistance, Neoplasm ,Pharmacogenetics ,Precision oncology ,030220 oncology & carcinogenesis ,Pharmacogenomics ,Genomic Profile ,Artificial intelligence ,business ,computer ,Algorithms ,Information Systems - Abstract
The goal of precision oncology is to tailor treatment for patients individually using the genomic profile of their tumors. Pharmacogenomics datasets such as cancer cell lines are among the most valuable resources for drug sensitivity prediction, a crucial task of precision oncology. Machine learning methods have been employed to predict drug sensitivity based on the multiple omics data available for large panels of cancer cell lines. However, there are no comprehensive guidelines on how to properly train and validate such machine learning models for drug sensitivity prediction. In this paper, we introduce a set of guidelines for different aspects of training gene expression-based predictors using cell line datasets. These guidelines provide extensive analysis of the generalization of drug sensitivity predictors and challenge many current practices in the community including the choice of training dataset and measure of drug sensitivity. The application of these guidelines in future studies will enable the development of more robust preclinical biomarkers.
- Published
- 2021
- Full Text
- View/download PDF
29. KuLGaP: A Selective Measure for Assessing Therapy Response in Patient-Derived Xenografts
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Céline Mascaux, Catherine A. O’Brien, Jessica Weiss, Nhu-An Pham, Geoffrey Liu, Aline Fusco Fares, Denis Tkachuk, Chantal Ho, Benjamin Haibe-Kains, Gangesh Beri, Elijah Tai, Anna Goldenberg, Ruoshi Shi, Arvind Singh Mer, David W. Cescon, Ming-Sound Tsao, Sheng Guo, Ladislav Rampášek, Shingo Sakashita, Janosch Ortmann, Erin L. Stewart, Xiaoqian Jiang, and Christopher Eeles
- Subjects
Tumor size ,Computer science ,business.industry ,Treatment development ,Measure (physics) ,Experimental data ,Machine learning ,computer.software_genre ,Task (project management) ,Therapy response ,In patient ,Artificial intelligence ,business ,computer ,Human cancer - Abstract
Quantifying response to drug treatment in mouse models of human cancer is important for treatment development and assignment, and yet remains a challenging task. A preferred measure to quantify this response should take into account as much of the experimental data as possible, i.e. both tumor size over time and the variation among replicates. We propose a theoretically grounded measure, KuLGaP, to compute the difference between the treatment and control arms. KuLGaP is more selective than currently existing measures, reduces the risk of false positive calls and improves translation of the lab results to clinical practice.
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- 2020
30. Bimodality of gene expression in cancer patient tumors as interpretable biomarkers for drug sensitivity
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Linda Z. Penn, Benjamin Haibe-Kains, Bo Li, Arvind Singh Mer, Petr Smirnov, Sisira Kadambat Nair, Wail Ba-alawi, and Anthony Mammoliti
- Subjects
Drug ,Drug treatment ,Computer science ,media_common.quotation_subject ,Pharmacogenomics ,Biomarker (medicine) ,Profiling (information science) ,Computational biology ,Human cancer ,In vitro ,media_common - Abstract
Identifying biomarkers predictive of cancer cells’ response to drug treatment constitutes one of the main challenges in precision oncology. Recent large-scale cancer pharmacogenomic studies have boosted the research for finding predictive biomarkers by profiling thousands of human cancer cell lines at the molecular level and screening them with hundreds of approved drugs and experimental chemical compounds. Many studies have leveraged these data to build predictive models of response using various statistical and machine learning methods. However, a common challenge in these methods is the lack of interpretability as to how they make the predictions and which features were the most associated with response, hindering the clinical translation of these models. To alleviate this issue, we develop a new machine learning pipeline based on the recent LOBICO approach that explores the space of bimodally expressed genes in multiple largein vitropharmacogenomic studies and builds multivariate, nonlinear, yet interpretable logic-based models predictive of drug response. Using our method, we used a compendium of three of the largest pharmacogenomic data sets to build robust and interpretable models for 101 drugs that span 17 drug classes with high validation rate in independent datasets.
- Published
- 2020
31. Ontogeny and Vulnerabilities of Drug-Tolerant Persisters in HER2+ Breast Cancer
- Author
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Chewei Anderson Chang, Jayu Jen, Shaowen Jiang, Azin Sayad, Arvind Singh Mer, Kevin R. Brown, Allison M.L. Nixon, Avantika Dhabaria, Kwan Ho Tang, David Venet, Christos Sotiriou, Jiehui Deng, Kwok-kin Wong, Sylvia Adams, Peter Meyn, Adriana Heguy, Jane A. Skok, Aristotelis Tsirigos, Beatrix Ueberheide, Jason Moffat, Abhyudai Singh, Benjamin Haibe-Kains, Alireza Khodadadi-Jamayran, and Benjamin G. Neel
- Subjects
Receptor, ErbB-2 ,Breast Neoplasms ,mTORC1 ,Biology ,Lapatinib ,Article ,Metastasis ,Phosphatidylinositol 3-Kinases ,03 medical and health sciences ,0302 clinical medicine ,Breast cancer ,Cell Line, Tumor ,medicine ,Humans ,Protein kinase B ,PI3K/AKT/mTOR pathway ,030304 developmental biology ,0303 health sciences ,Cancer ,medicine.disease ,respiratory tract diseases ,3. Good health ,Oncology ,Drug Resistance, Neoplasm ,030220 oncology & carcinogenesis ,Cancer cell ,Cancer research ,Female ,Signal Transduction ,medicine.drug - Abstract
Resistance to targeted therapies is an important clinical problem in HER2-positive (HER2+) breast cancer. “Drug-tolerant persisters” (DTP), a subpopulation of cancer cells that survive via reversible, nongenetic mechanisms, are implicated in resistance to tyrosine kinase inhibitors (TKI) in other malignancies, but DTPs following HER2 TKI exposure have not been well characterized. We found that HER2 TKIs evoke DTPs with a luminal-like or a mesenchymal-like transcriptome. Lentiviral barcoding/single-cell RNA sequencing reveals that HER2+ breast cancer cells cycle stochastically through a “pre-DTP” state, characterized by a G0-like expression signature and enriched for diapause and/or senescence genes. Trajectory analysis/cell sorting shows that pre-DTPs preferentially yield DTPs upon HER2 TKI exposure. Cells with similar transcriptomes are present in HER2+ breast tumors and are associated with poor TKI response. Finally, biochemical experiments indicate that luminal-like DTPs survive via estrogen receptor–dependent induction of SGK3, leading to rewiring of the PI3K/AKT/mTORC1 pathway to enable AKT-independent mTORC1 activation. Significance: DTPs are implicated in resistance to anticancer therapies, but their ontogeny and vulnerabilities remain unclear. We find that HER2 TKI-DTPs emerge from stochastically arising primed cells (“pre-DTPs”) that engage either of two distinct transcriptional programs upon TKI exposure. Our results provide new insights into DTP ontogeny and potential therapeutic vulnerabilities. This article is highlighted in the In This Issue feature, p. 873
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- 2020
32. Integrative cancer pharmacogenomics to establish drug mechanism of action: drug repurposing
- Author
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Benjamin Haibe-Kains, Wail Ba-alawi, Arvind Singh Mer, Ian Smith, and El-Hachem N
- Subjects
0301 basic medicine ,Pharmacology ,Drug ,Computer science ,media_common.quotation_subject ,Drug Repositioning ,Computational Biology ,Cancer ,Bioinformatics ,medicine.disease ,03 medical and health sciences ,Drug repositioning ,030104 developmental biology ,Mechanism of action ,Pharmacogenetics ,Neoplasms ,Pharmacogenomics ,Drug Discovery ,Genetics ,medicine ,Humans ,Molecular Medicine ,medicine.symptom ,media_common - Published
- 2017
33. Validation of risk stratification models in acute myeloid leukemia using sequencing-based molecular profiling
- Author
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Sören Lehmann, Daniel Klevebring, Christer Nilsson, Johan Lindberg, Mei Wang, Henrik Grönberg, Mattias Rantalainen, and Arvind Singh Mer
- Subjects
Male ,0301 basic medicine ,Oncology ,Cancer Research ,DNA Mutational Analysis ,Bioinformatics ,Cohort Studies ,0302 clinical medicine ,Risk groups ,Bone Marrow ,hemic and lymphatic diseases ,RNA, Neoplasm ,Aged, 80 and over ,Myeloid leukemia ,DNA, Neoplasm ,Hematology ,Middle Aged ,Prognosis ,Leukemia, Myeloid, Acute ,Leukemia ,030220 oncology & carcinogenesis ,Risk stratification ,Cohort ,Female ,Original Article ,Adult ,medicine.medical_specialty ,Adolescent ,Models, Biological ,Risk Assessment ,DNA sequencing ,Young Adult ,03 medical and health sciences ,Internal medicine ,Cancer genome ,medicine ,Humans ,RNA, Messenger ,neoplasms ,Aged ,business.industry ,Gene Expression Profiling ,Sequence Analysis, DNA ,Microarray Analysis ,medicine.disease ,030104 developmental biology ,Karyotyping ,Transcriptome ,Risk classification ,business - Abstract
Risk stratification of acute myeloid leukemia (AML) patients needs improvement. Several AML risk classification models based on somatic mutations or gene-expression profiling have been proposed. However, systematic and independent validation of these models is required for future clinical implementation. We performed whole-transcriptome RNA-sequencing and panel-based deep DNA sequencing of 23 genes in 274 intensively treated AML patients (Clinseq-AML). We also utilized the The Cancer Genome Atlas (TCGA)-AML study (N=142) as a second validation cohort. We evaluated six previously proposed molecular-based models for AML risk stratification and two revised risk classification systems combining molecular- and clinical data. Risk groups stratified by five out of six models showed different overall survival in cytogenetic normal-AML patients in the Clinseq-AML cohort (P-value0.5). Risk classification systems integrating mutational or gene-expression data were found to add prognostic value to the current European Leukemia Net (ELN) risk classification. The prognostic value varied between models and across cohorts, highlighting the importance of independent validation to establish evidence of efficacy and general applicability. All but one model replicated in the Clinseq-AML cohort, indicating the potential for molecular-based AML risk models. Risk classification based on a combination of molecular and clinical data holds promise for improved AML patient stratification in the future.
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- 2017
34. Organoid Cultures as Preclinical Models of Non-Small Cell Lung Cancer
- Author
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Sebastiao N. Martins-Filho, Nadeem Moghal, Hirotsugu Notsuda, Nikolina Radulovich, Geoffrey Liu, Laura Tamblyn, Benjamin Haibe-Kains, Ming Li, Yuhui Wang, Michael Cabanero, Ruoshi Shi, Joshua C. Rosen, Christine Ng, Ming-Sound Tsao, Arvind Singh Mer, Vibha Raghavan, Nhu An Pham, Ni Liu, and Quan Li
- Subjects
0301 basic medicine ,Cancer Research ,Lung Neoplasms ,Mice, SCID ,Malignancy ,03 medical and health sciences ,Mice ,0302 clinical medicine ,Organ Culture Techniques ,Mice, Inbred NOD ,Carcinoma, Non-Small-Cell Lung ,medicine ,Organoid ,Carcinoma ,Biomarkers, Tumor ,Animals ,Humans ,Molecular Targeted Therapy ,Lung cancer ,Exome sequencing ,business.industry ,Histology ,medicine.disease ,Xenograft Model Antitumor Assays ,respiratory tract diseases ,3. Good health ,Organoids ,Disease Models, Animal ,030104 developmental biology ,Oncology ,Cell culture ,030220 oncology & carcinogenesis ,Mutation ,Cancer research ,Biomarker (medicine) ,business - Abstract
Purpose: Non–small cell lung cancer (NSCLC) is the most common cause of cancer-related deaths worldwide. There is an unmet need to develop novel clinically relevant models of NSCLC to accelerate identification of drug targets and our understanding of the disease. Experimental Design: Thirty surgically resected NSCLC primary patient tissue and 35 previously established patient-derived xenograft (PDX) models were processed for organoid culture establishment. Organoids were histologically and molecularly characterized by cytology and histology, exome sequencing, and RNA-sequencing analysis. Tumorigenicity was assessed through subcutaneous injection of organoids in NOD/SCID mice. Organoids were subjected to drug testing using EGFR, FGFR, and MEK-targeted therapies. Results: We have identified cell culture conditions favoring the establishment of short-term and long-term expansion of NSCLC organoids derived from primary lung patient and PDX tumor tissue. The NSCLC organoids recapitulated the histology of the patient and PDX tumor. They also retained tumorigenicity, as evidenced by cytologic features of malignancy, xenograft formation, preservation of mutations, copy number aberrations, and gene expression profiles between the organoid and matched parental tumor tissue by whole-exome and RNA sequencing. NSCLC organoid models also preserved the sensitivity of the matched parental tumor to targeted therapeutics, and could be used to validate or discover biomarker–drug combinations. Conclusions: Our panel of NSCLC organoids closely recapitulates the genomics and biology of patient tumors, and is a potential platform for drug testing and biomarker validation.
- Published
- 2019
35. Applications of Computational Systems Biology in Cancer Signaling Pathways
- Author
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Vandana Sandhu, Venkata Satya Kumar Manem, Arvind Singh Mer, Benjamin Haibe-Kains, and Elin H. Kure
- Subjects
Class (computer programming) ,Computer science ,Modelling biological systems ,medicine ,DECIPHER ,Cancer ,Single sample ,Cancer signaling ,Computational biology ,Set (psychology) ,Pathway analysis ,medicine.disease - Abstract
Computational systems biology approaches to decipher cancer signaling pathways have been proposed as an essential mode to gain insight into biology of cancer cells. Pathway analysis approaches are used to discern the biological processes underlying cancer development, as it reduces the complexity, and genomic disruptions are easier to interpret in terms of biological systems. A large number of bioinformatics’ tools have been developed for this purpose that can be distinctly divided based on methodology used including overrepresentation analysis, functional class scoring such as gene set enrichment analysis, single sample gene set enrichment analysis, and integrative multiple dataset-based approaches. The methodological challenges, limitations, and advantages of each approach are discussed, with the purpose of guiding the researchers to choose the appropriate pathway analysis method based on the available type of data and analysis tools. The various applications of pathway analytical approaches in cancer research include identifying cancer subtypes, identifying disease-associated pathways, and understanding tumor biology and biomarker identification.
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- 2019
36. Integrative Pharmacogenomics Analysis of Patient Derived Xenografts
- Author
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Anna Goldenberg, Ben Brew, Wail Ba-alawi, Petr Smirnov, Janosch Ortmann, Yi Xiao Wang, Ming-Sound Tsao, Benjamin Haibe-Kains, David Cescon, and Arvind Singh Mer
- Subjects
False discovery rate ,0303 health sciences ,Cancer ,Computational biology ,Biology ,medicine.disease ,Tumor response ,Precision medicine ,3. Good health ,03 medical and health sciences ,0302 clinical medicine ,In vivo ,030220 oncology & carcinogenesis ,Pharmacogenomics ,medicine ,Drug response ,Pathway activity ,030304 developmental biology - Abstract
One of the key challenges in cancer precision medicine is finding robust biomarkers of drug response. Patient-derived tumor xenografts (PDXs) have emerged as reliable preclinical models since they better recapitulate tumor response to chemo- and targeted therapies. However, the lack of standard tools poses a challenge in the analysis of PDXs with molecular and pharmacological profiles. Efficient storage, access and analysis is key to the realization of the full potential of PDX pharmacogenomic data. We have developed Xeva (XEnograft Visualization & Analysis), an open-source software package for processing, visualization and integrative analysis of a compendium ofin vivopharmacogenomic datasets. The Xeva package follows the PDX minimum information (PDX-MI) standards and can handle both replicate-based and 1×1×1 experimental designs. We used Xeva to characterize the variability of gene expression and pathway activity across passages. We found that only a few genes and pathways have passage specific alterations (median intraclass correlation of 0.53 for genes and positive enrichment score for 92.5% pathways). For example, activity of the mRNA 3’-end processing and elongation arrest and recovery pathways were strongly affected by model passaging (gene set enrichment analysis false discovery rate [FDR] 0.60; FDR < 0.05). Xeva provides a flexible platform for integrative analysis of preclinicalin vivopharmacogenomics data to identify biomarkers predictive of drug response, a major step toward precision oncology.
- Published
- 2018
37. Abstract PO-070: Bimodality of gene expression in cancer patient tumors as interpretable biomarkers for drug sensitivity
- Author
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Linda Z. Penn, Benjamin Haibe-Kains, Sisira Kadambat Nair, Bo Li, Petr Smirnov, Wail Ba-alawi, Arvind Singh Mer, and Anthony Mammoliti
- Subjects
Drug ,Cancer Research ,Computer science ,media_common.quotation_subject ,Cancer ,Computational biology ,medicine.disease ,Drug treatment ,Molecular level ,Oncology ,Pharmacogenomics ,medicine ,Human cancer ,Predictive biomarker ,media_common ,Interpretability - Abstract
Identifying biomarkers predictive of cancer cells’ response to drug treatment constitutes one of the main challenges in precision oncology. Recent large-scale cancer pharmacogenomic studies have boosted the research for finding predictive biomarkers by profiling thousands of human cancer cell lines at the molecular level and screening them with hundreds of approved drugs and experimental chemical compounds. Many studies have leveraged these data to build predictive models of response using various statistical and machine learning methods. However, a common challenge in these methods is the lack of interpretability as to how they make the predictions and which features were the most associated with response, hindering the clinical translation of these models. To alleviate this issue, we develop a new machine learning pipeline based on the recent LOBICO approach that explores the space of bimodally expressed genes in multiple large in vitro pharmacogenomic studies and builds multivariate, nonlinear, yet interpretable logic-based models predictive of drug response. Using our method, we used a compendium of three of the largest pharmacogenomic data sets to build robust and interpretable models for 101 drugs that span 17 drug classes with high validation rate in independent datasets. Citation Format: Wail Ba-alawi, Sisira Kadambat Nair, Bo Li, Anthony Mammoliti, Petr Smirnov, Arvind Singh Mer, Linda Penn, Benjamin Haibe-Kains. Bimodality of gene expression in cancer patient tumors as interpretable biomarkers for drug sensitivity [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-070.
- Published
- 2021
38. Abstract PO-052: Exploring patient derived xenografts based pharmacogenomic data for precision oncology
- Author
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Benjamin Haibe-Kains and Arvind Singh Mer
- Subjects
Oncology ,Cancer Research ,medicine.medical_specialty ,Precision oncology ,business.industry ,Pharmacogenomics ,Internal medicine ,medicine ,business - Abstract
Preclinical cancer models play a vital role in oncology research and precision medicine. Patient-derived tumor xenografts (PDXs) are used as reliable preclinical models for studying tumor biology and for testing anti-cancer therapies that are tailored according to genomic characteristics of tumors. Several academic groups, research institutes, and commercial organizations are generating and distributing PDX models. However the distributed nature of PDX model generation and lack of central repository make it challenging to find PDX models with specific characteristics. Furthermore this also hinders meta-analysis (across datasets) of PDX pharmacogenomic data. International consortia and catalogs of PDX models such as PDXNet, EurOPDX and PDXFinder are being developed to standardize PDX associated metadata and facilitate material sharing. Recently we have developed Xenograft Visualization & Analysis (Xeva), an open-source software package in R programming language. Xeva allows PDX growth curve visualization, different response metrics computation and biomarker discovery. Extending to this we have developed XevaDB, a database of PDX drug response and genomic profiles. XevaDB is the first resource to allow concurrent visualization of drug response and associated molecular data such as mutation and copy number alterations. Furthermore XevaDB enables exploration of the tumor growth curve of a PDX model, along with corresponding control. XevaDB contains PDXs from >600 individual patients, spanning across nine different tissue types and >70 drugs. Using XevaDB, we have performed meta-analysis of PDX pharmacogenomic data and have identified 90 pathways significantly associated with response to 53 drugs (FDR < 5%). Our results show that activity of the EGFR signaling pathway is significantly associated with erlotinib response in lung cancer PDXs. We have also found that in PDXs, response to binimetinib is associated with the MAP kinase activation pathway. XevaDB provides a comprehensive resource to search and explore PDX pharmacogenomic data. By combining drug response with genomic data of PDXs, XevaDB allows researchers to quickly find the model of interest and access the data to answer their biological questions. As PDXs based pharmacogenomic datasets continue to expand, XevaDB will facilitate easy access and analysis of this valuable data by the scientific community. Citation Format: Arvind Singh Mer, Benjamin Haibe-Kains. Exploring patient derived xenografts based pharmacogenomic data for precision oncology [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-052.
- Published
- 2021
39. Abstract PR-07: ORCESTRA: A platform for orchestrating and sharing high-throughput multimodal data analyses
- Author
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Zhaleh Safikhani, Sisira Kadambat Nair, Benjamin Haibe-Kains, Minoru Nakano, Anthony Mammoliti, Arvind Singh Mer, Gangesh Beri, Chantal Ho, and Petr Smirnov
- Subjects
Cancer Research ,business.industry ,Computer science ,Dashboard (business) ,Frame (networking) ,Cloud computing ,Pipeline (software) ,Data science ,Data type ,Bioconductor ,Oncology ,Orchestration (computing) ,User interface ,business - Abstract
Reproducibility is essential to Open Science. If a finding cannot be reproduced by independent research groups its relevance is extremely limited, regardless of its validity. It is therefore crucial for scientists to describe their experiments in sufficient detail so they can be reproduced, challenged, and built upon. However, due to recent technological advances in the biological and computational sciences, experimental protocols, data analysis and interpretation have become increasingly complex. This has made reproducing research findings more challenging, with some researchers going as far as suggesting that the biomedical sciences are experiencing a "reproducibility crisis". In order to overcome these issues we developed ORCESTRA, a cloud-based platform that provides a transparent, reproducible and flexible computational framework for processing and sharing high-throughput multimodal biomedical data. The platform enables processing of genomic and pharmacological profiles of cancer samples through the use of automated processing pipelines executed by Pachyderm, a data versioning and orchestration tool. ORCESTRA creates an integrated and fully documented data object known as a PharmacoSet (PSet) for future analyses using the Bioconductor PharmacoGx package. A PSet includes cell line and drug annotations, along with molecular and pharmacological data from the largest studies and consortia. Our platform is currently being expanded to additional data types, which includes toxicogenomics, xenographic pharmacogenomic data, radiomics, and clinical genomic data. The automated pipelines can be accessed via a web interface (www.orcestra.ca). Users can view and download existing dataset or request a new one by selecting pipeline parameters. The web application provides features to improve user experience, and to accommodate different scenarios for ORCESTRA deployment. They include a personal account to save PSets, a dashboard to check the status of a requested pipeline, email notification upon the pipeline completion, handling pipeline requests while the Pachyderm cluster is offline, and “manual push” of the pipeline requests once the cluster becomes online. Funding: This project is supported by CIHR, under the frame of ERA PerMed. Citation Format: Anthony Mammoliti, Petr Smirnov, Minoru Nakano, Zhaleh Safikhani, Sisira Nair, Arvind Singh Mer, Chantal Ho, Gangesh Beri, Benjamin Haibe-Kains. ORCESTRA: A platform for orchestrating and sharing high-throughput multimodal data analyses [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PR-07.
- Published
- 2021
40. Colorectal Cancer Cells Enter a Diapause-like DTP State to Survive Chemotherapy
- Author
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Aaron Pollet, Arvind Singh Mer, Miguel Ramalho-Santos, Catherine A. O’Brien, Sidhartha Goyal, Allison M.L. Nixon, Sumaiyah K. Rehman, Benjamin Haibe-Kains, Jason Moffat, Jeff Wintersinger, Kevin R. Brown, Evelyne Lima-Fernandes, Sophie McGibbon, Yadong Wang, Jeff Bruce, Cherry Leung, Nicholas M. Pedley, Quaid Morris, Edwyn B.L. Lo, Fraser Soares, Jennifer Haynes, Housheng Hansen He, Evelyne Collignon, and Trevor J. Pugh
- Subjects
0303 health sciences ,Chemotherapy ,Mechanism (biology) ,Colorectal cancer ,medicine.medical_treatment ,Autophagy ,Diapause ,Biology ,medicine.disease ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,0302 clinical medicine ,Cancer cell ,medicine ,Cancer research ,030217 neurology & neurosurgery ,PI3K/AKT/mTOR pathway ,030304 developmental biology - Abstract
Cancer cells enter a reversible drug-tolerant persister (DTP) state to evade death from chemotherapy and targeted agents. It is increasingly appreciated that DTPs are important drivers of therapy failure and tumor relapse. We combined cellular barcoding and mathematical modeling in patient-derived colorectal cancer models to identify and characterize DTPs in response to chemotherapy. Barcode analysis revealed no loss of clonal complexity of tumors that entered the DTP state and recurred following treatment cessation. Our data fit a mathematical model where all cancer cells, and not a small subpopulation, possess an equipotent capacity to become DTPs. Mechanistically, we determined that DTPs display remarkable transcriptional and functional similarities to diapause, a reversible state of suspended embryonic development triggered by unfavorable environmental conditions. Our study provides insight into how cancer cells use a developmentally conserved mechanism to drive the DTP state, pointing to novel therapeutic opportunities to target DTPs.
- Published
- 2021
41. Disruption of the anaphase-promoting complex confers resistance to TTK inhibitors in triple-negative breast cancer
- Author
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David W. Cescon, Wail Ba-alawi, Tak W. Mak, Jennifer Silvester, M. J. Elliott, Zhaleh Safikhani, M. H. Duncan, Benjamin Haibe-Kains, Kelsie L. Thu, Antonia Concetta Elia, Arvind Singh Mer, and Petr Smirnov
- Subjects
0301 basic medicine ,Genome instability ,Somatic cell ,Regulator ,Mitosis ,Cell Cycle Proteins ,Triple Negative Breast Neoplasms ,Protein Serine-Threonine Kinases ,Biology ,Anaphase-Promoting Complex-Cyclosome ,Genomic Instability ,03 medical and health sciences ,Cell Line, Tumor ,Humans ,Protein Kinase Inhibitors ,Triple-negative breast cancer ,Multidisciplinary ,Protein-Tyrosine Kinases ,Spindle checkpoint ,Pyrimidines ,030104 developmental biology ,PNAS Plus ,Drug Resistance, Neoplasm ,Cancer research ,Pyrazoles ,Female ,Anaphase-promoting complex - Abstract
TTK protein kinase (TTK), also known as Monopolar spindle 1 (MPS1), is a key regulator of the spindle assembly checkpoint (SAC), which functions to maintain genomic integrity. TTK has emerged as a promising therapeutic target in human cancers, including triple-negative breast cancer (TNBC). Several TTK inhibitors (TTKis) are being evaluated in clinical trials, and an understanding of the mechanisms mediating TTKi sensitivity and resistance could inform the successful development of this class of agents. We evaluated the cellular effects of the potent clinical TTKi CFI-402257 in TNBC models. CFI-402257 induced apoptosis and potentiated aneuploidy in TNBC lines by accelerating progression through mitosis and inducing mitotic segregation errors. We used genome-wide CRISPR/Cas9 screens in multiple TNBC cell lines to identify mechanisms of resistance to CFI-402257. Our functional genomic screens identified members of the anaphase-promoting complex/cyclosome (APC/C) complex, which promotes mitotic progression following inactivation of the SAC. Several screen candidates were validated to confer resistance to CFI-402257 and other TTKis using CRISPR/Cas9 and siRNA methods. These findings extend the observation that impairment of the APC/C enables cells to tolerate genomic instability caused by SAC inactivation, and support the notion that a measure of APC/C function could predict the response to TTK inhibition. Indeed, an APC/C gene expression signature is significantly associated with CFI-402257 response in breast and lung adenocarcinoma cell line panels. This expression signature, along with somatic alterations in genes involved in mitotic progression, represent potential biomarkers that could be evaluated in ongoing clinical trials of CFI-402257 or other TTKis.
- Published
- 2018
42. OA08.01 Organoid Cultures as Novel Preclinical Models of Non-Small Cell Lung Cancer
- Author
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Sebastiao N. Martins-Filho, Nadeem Moghal, Hirotsugu Notsuda, M. Tsao, Michael Cabanero, Quan Li, Arvind Singh Mer, Ni Liu, Nikolina Radulovich, Benjamin Haibe-Kains, Ruoshi Shi, Christine Ng, G. Liu, Nhu-An Pham, and Vibha Raghavan
- Subjects
Pulmonary and Respiratory Medicine ,Oncology ,business.industry ,medicine ,Organoid ,Cancer research ,Non small cell ,Lung cancer ,medicine.disease ,business - Published
- 2019
43. Software for the integration of multi-omics experiments in Bioconductor
- Author
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Aedín C. Culhane, Levi Waldron, Vincent J. Carey, Rimsha Azhar, Arvind Singh Mer, Kasper D. Hansen, Phil Chapman, Angela Re, Tiffany Chan, Sean Davis, Carmen Rodríguez, Martin Morgan, Lucas Schiffer, Marcel Ramos, Azfar Basunia, Hanish Kodali, David Gomez-Cabrero, Marie Stephie Louis, Benjamin Haibe-Kains, and Markus Riester
- Subjects
0301 basic medicine ,Cancer Research ,Computer science ,Datasets as Topic ,Genomics ,Bioinformatics ,External Data Representation ,computer.software_genre ,Data type ,Article ,Bioconductor ,03 medical and health sciences ,0302 clinical medicine ,Software ,Neoplasms ,Humans ,Representation (mathematics) ,030304 developmental biology ,0303 health sciences ,disease ,business.industry ,Genome, Human ,Computational Biology ,bioinformatics ,Omics ,Visualization ,omics ,030104 developmental biology ,Oncology ,030220 oncology & carcinogenesis ,Scalability ,Data mining ,business ,computer ,030217 neurology & neurosurgery ,Data integration - Abstract
Multiomics experiments are increasingly commonplace in biomedical research and add layers of complexity to experimental design, data integration, and analysis. R and Bioconductor provide a generic framework for statistical analysis and visualization, as well as specialized data classes for a variety of high-throughput data types, but methods are lacking for integrative analysis of multiomics experiments. The MultiAssayExperiment software package, implemented in R and leveraging Bioconductor software and design principles, provides for the coordinated representation of, storage of, and operation on multiple diverse genomics data. We provide the unrestricted multiple ‘omics data for each cancer tissue in The Cancer Genome Atlas as ready-to-analyze MultiAssayExperiment objects and demonstrate in these and other datasets how the software simplifies data representation, statistical analysis, and visualization. The MultiAssayExperiment Bioconductor package reduces major obstacles to efficient, scalable, and reproducible statistical analysis of multiomics data and enhances data science applications of multiple omics datasets. Cancer Res; 77(21); e39–42. ©2017 AACR.
- Published
- 2017
44. Abstract 3378: Systematic pharmacogenomic analysis of large patient derived xenografts data
- Author
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Petr Smirnov, David W. Cescon, Ben Brew, Ming-Sound Tsao, Anna Goldenberg, Benjamin Haibe-Kains, Yi Xiao Wang, Arvind Singh Mer, and Wail Ba-alawi
- Subjects
False discovery rate ,Cancer Research ,Cancer ,Pharmacogenomic Analysis ,Computational biology ,Biology ,medicine.disease ,Precision medicine ,Tumor response ,Oncology ,Pharmacogenomics ,medicine ,Drug response ,Pathway activity - Abstract
One of the key challenges in cancer precision medicine is finding robust biomarkers of drug response. Patient-derived tumor xenografts (PDXs) have emerged as reliable preclinical models since they better recapitulate tumor response to chemo- and targeted therapies. However, the lack of standard tools poses a challenge in the analysis of PDXs with molecular and pharmacological profiles. Efficient storage, access and analysis is key to the realization of the full potential of PDX pharmacogenomic data. To address this, we have developed Xeva (XEnograft Visualization & Analysis), an open-source software package for processing, visualization and integrative analysis of a compendium of in vivo pharmacogenomic datasets. The Xeva package follows the PDX minimum information (PDX-MI) standards and can handle both replicate-based and 1x1x1 experimental designs. We used Xeva to characterize the variability of gene expression and pathway activity across passages. We found that only a few genes and pathways have passage specific alterations (median intraclass correlation of 0.53 for genes and positive enrichment score for 92.5% pathways). For example, activity of the mRNA 3'-end processing and elongation arrest and recovery pathways were strongly affected by model passaging (gene set enrichment analysis false discovery rate [FDR] 0.60; FDR < 0.05). Xeva provides a flexible platform for integrative analysis of preclinical in vivo pharmacogenomics data to identify biomarkers predictive of drug response, a major step toward precision oncology. Citation Format: Arvind Singh Mer, Wail Ba-alawi, Petr Smirnov, Yi Xiao Wang, Ben Brew, Ming-Sound Tsao, David Cescon, Anna Goldenberg, Benjamin Haibe-Kains. Systematic pharmacogenomic analysis of large patient derived xenografts data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3378.
- Published
- 2019
45. Study design requirements for RNA sequencing-based breast cancer diagnostics
- Author
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Daniel Klevebring, Arvind Singh Mer, Henrik Grönberg, and Mattias Rantalainen
- Subjects
0301 basic medicine ,Breast Neoplasms ,Receptors, Cell Surface ,Bioinformatics ,Article ,03 medical and health sciences ,Breast cancer ,Databases, Genetic ,medicine ,Humans ,Multidisciplinary ,Sequence Analysis, RNA ,business.industry ,RNA ,Cancer ,medicine.disease ,Subtyping ,Cancer treatment ,030104 developmental biology ,Research Design ,Sample size determination ,Sample Size ,Biomarker (medicine) ,Female ,business ,Subtype classification - Abstract
Sequencing-based molecular characterization of tumors provides information required for individualized cancer treatment. There are well-defined molecular subtypes of breast cancer that provide improved prognostication compared to routine biomarkers. However, molecular subtyping is not yet implemented in routine breast cancer care. Clinical translation is dependent on subtype prediction models providing high sensitivity and specificity. In this study we evaluate sample size and RNA-sequencing read requirements for breast cancer subtyping to facilitate rational design of translational studies. We applied subsampling to ascertain the effect of training sample size and the number of RNA sequencing reads on classification accuracy of molecular subtype and routine biomarker prediction models (unsupervised and supervised). Subtype classification accuracy improved with increasing sample size up to N = 750 (accuracy = 0.93), although with a modest improvement beyond N = 350 (accuracy = 0.92). Prediction of routine biomarkers achieved accuracy of 0.94 (ER) and 0.92 (Her2) at N = 200. Subtype classification improved with RNA-sequencing library size up to 5 million reads. Development of molecular subtyping models for cancer diagnostics requires well-designed studies. Sample size and the number of RNA sequencing reads directly influence accuracy of molecular subtyping. Results in this study provide key information for rational design of translational studies aiming to bring sequencing-based diagnostics to the clinic.
- Published
- 2016
46. CellWhere: graphical display of interaction networks organized on subcellular localizations
- Author
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Stephanie Duguez, William Duddy, Lu Zhu, Arvind Singh Mer, Idonnya Aghoghogbe, Thomas Voit, Apostolos Malatras, Matthew Thorley, Gillian Butler-Browne, Centre de recherche en myologie, Université Pierre et Marie Curie - Paris 6 (UPMC)-Association française contre les myopathies (AFM-Téléthon)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Bioinformatics Department, Universität Bielefeld = Bielefeld University, Institute of Orthopaedics and Musculoskeletal Science, University College of London [London] (UCL), Department of Medical Epidemiology and Biostatistics (MEB), and Karolinska Institutet [Stockholm]
- Subjects
Internet ,Intracellular Space ,Proteins ,Context (language use) ,Computational biology ,Biology ,JavaScript library ,Bioinformatics ,Subcellular localization ,Interactome ,Protein–protein interaction ,Upload ,Genes ,[SDV.MHEP.RSOA]Life Sciences [q-bio]/Human health and pathology/Rhumatology and musculoskeletal system ,Interaction network ,Protein Interaction Mapping ,Computer Graphics ,Genetics ,Web Server issue ,Relevance (information retrieval) ,[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM] ,Software - Abstract
International audience; Given a query list of genes or proteins, CellWhere produces an interactive graphical display that mimics the structure of a cell, showing the local interaction network organized into subcellular locations. This user-friendly tool helps in the formulation of mechanistic hypotheses by enabling the experimental biologist to explore simultaneously two elements of functional context: (i) protein subcellular localization and (ii) protein–protein interactions or gene functional associations. Subcellular localization terms are obtained from public sources (the Gene Ontology and UniProt—together containing several thousand such terms) then mapped onto a smaller number of CellWhere localizations. These localizations include all major cell compartments, but the user may modify the mapping as desired. Protein–protein interaction listings, and their associated evidence strength scores, are obtained from the Mentha interactome server, or power-users may upload a pre-made network produced using some other interactomics tool. The Cytoscape.js JavaScript library is used in producing the graphical display. Importantly, for a protein that has been observed at multiple subcellular locations, users may prioritize the visual display of locations that are of special relevance to their research domain. CellWhere is at http://cellwhere-myology.rhcloud.com.
- Published
- 2015
47. PRC1-Mediated Gene Silencing in Pluripotent ES Cells: Function and Evolution
- Author
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Nancy Mah, Xiaoli Li, Daniela Zdzieblo, Matthias Becker, Miguel A. Andrade-Navarro, Arvind Singh Mer, and Albrecht M. Müller
- Subjects
Genetics ,Cell type ,medicine.anatomical_structure ,Cell division ,Cellular differentiation ,Rex1 ,medicine ,Embryoid body ,Biology ,Embryonic stem cell ,Cell potency ,Germ cell ,Cell biology - Abstract
Pluripotency is a remarkable property, which is only transiently present during development. It is functionally defined by the capacity of a cell to differentiate into all cell lineages of an organism (cell types of the three embryonic germ layers, i.e., ecto-, endo-, and mesoderm, and the germ cell lineage) and to generate pluripotent daughter cells. It seems obvious that these special features of pluripotent cells must be reflected in molecular mechanisms regulating gene expression and chromatin structure. However, defining what are the mechanisms that control pluripotency and how are the unique features of pluripotent cells established, regulated, and maintained on the molecular level is a matter of intense research. Polycomb repressive complexes (PRCs) are key epigenetic regulators of development and cell specification. Here we summarize and discuss recent data on the role of PRC1 for the establishment and maintenance of embryonic stem (ES) cell pluripotency with special emphasis on the evolution of mammalian orthologs of PRC1 components.
- Published
- 2014
48. A novel approach for protein subcellular location prediction using amino acid exposure
- Author
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Arvind Singh Mer and Miguel A. Andrade-Navarro
- Subjects
Nucleocytoplasmic Transport Proteins ,Support Vector Machine ,Amino Acid Motifs ,Peptide ,Biology ,Biochemistry ,Homology (biology) ,Predictive Value of Tests ,Structural Biology ,Humans ,Protein function prediction ,Amino Acid Sequence ,Databases, Protein ,Molecular Biology ,Peptide sequence ,chemistry.chemical_classification ,Principal Component Analysis ,Methodology Article ,Applied Mathematics ,Computational Biology ,Proteins ,food and beverages ,Computer Science Applications ,Amino acid ,chemistry ,Neural Networks, Computer ,DNA microarray ,Threading (protein sequence) ,Algorithms ,Subcellular Fractions - Abstract
Background Proteins perform their functions in associated cellular locations. Therefore, the study of protein function can be facilitated by predictions of protein location. Protein location can be predicted either from the sequence of a protein alone by identification of targeting peptide sequences and motifs, or by homology to proteins of known location. A third approach, which is complementary, exploits the differences in amino acid composition of proteins associated to different cellular locations, and can be useful if motif and homology information are missing. Here we expand this approach taking into account amino acid composition at different levels of amino acid exposure. Results Our method has two stages. For stage one, we trained multiple Support Vector Machines (SVMs) to score eukaryotic protein sequences for membership to each of three categories: nuclear, cytoplasmic and extracellular, plus extra category nucleocytoplasmic, accounting for the fact that a large number of proteins shuttles between those two locations. In stage two we use an artificial neural network (ANN) to propose a category from the scores given to the four locations in stage one. The method reaches an accuracy of 68% when using as input 3D-derived values of amino acid exposure. Calibration of the method using predicted values of amino acid exposure allows classifying proteins without 3D-information with an accuracy of 62% and discerning proteins in different locations even if they shared high levels of identity. Conclusions In this study we explored the relationship between residue exposure and protein subcellular location. We developed a new algorithm for subcellular location prediction that uses residue exposure signatures. Our algorithm uses a novel approach to address the multiclass classification problem. The algorithm is implemented as web server 'NYCE’ and can be accessed at http://cbdm.mdc-berlin.de/~amer/nyce.
- Published
- 2013
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