1
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Wu G, Zaker A, Ebrahimi A, Tripathi S, Mer AS. Text-mining-based feature selection for anticancer drug response prediction. Bioinform Adv 2024; 4:vbae047. [PMID: 38606185 PMCID: PMC11009020 DOI: 10.1093/bioadv/vbae047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 03/09/2024] [Accepted: 03/22/2024] [Indexed: 04/13/2024]
Abstract
Motivation Predicting anticancer treatment response from baseline genomic data is a critical obstacle in personalized medicine. Machine learning methods are commonly used for predicting drug response from gene expression data. In the process of constructing these machine learning models, one of the most significant challenges is identifying appropriate features among a massive number of genes. Results In this study, we utilize features (genes) extracted using the text-mining of scientific literatures. Using two independent cancer pharmacogenomic datasets, we demonstrate that text-mining-based features outperform traditional feature selection techniques in machine learning tasks. In addition, our analysis reveals that text-mining feature-based machine learning models trained on in vitro data also perform well when predicting the response of in vivo cancer models. Our results demonstrate that text-mining-based feature selection is an easy to implement approach that is suitable for building machine learning models for anticancer drug response prediction. Availability and implementation https://github.com/merlab/text_features.
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Affiliation(s)
- Grace Wu
- Division of Engineering Science, University of Toronto, Toronto, M5S2E4, Canada
| | - Arvin Zaker
- Department of Biochemistry, Microbiology & Immunology, University of Ottawa, Ottawa, K1H8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, K1H8M5, Canada
| | - Amirhosein Ebrahimi
- Department of Biochemistry, Microbiology & Immunology, University of Ottawa, Ottawa, K1H8M5, Canada
| | - Shivanshi Tripathi
- Department of Biochemistry, Microbiology & Immunology, University of Ottawa, Ottawa, K1H8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, K1H8M5, Canada
| | - Arvind Singh Mer
- Department of Biochemistry, Microbiology & Immunology, University of Ottawa, Ottawa, K1H8M5, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, K1H8M5, Canada
- School of Electrical Engineering & Computer Science, University of Ottawa, Ottawa, K1N6N5, Canada
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2
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Audet-Delage Y, St-Louis C, Minarrieta L, McGuirk S, Kurreal I, Annis MG, Mer AS, Siegel PM, St-Pierre J. Spatiotemporal modeling of chemoresistance evolution in breast tumors uncovers dependencies on SLC38A7 and SLC46A1. Cell Rep 2023; 42:113191. [PMID: 37792528 DOI: 10.1016/j.celrep.2023.113191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 08/17/2023] [Accepted: 09/15/2023] [Indexed: 10/06/2023] Open
Abstract
In solid tumors, drug concentrations decrease with distance from blood vessels. However, cellular adaptations accompanying the gradated exposure of cancer cells to drugs are largely unknown. Here, we modeled the spatiotemporal changes promoting chemotherapy resistance in breast cancer. Using pairwise cell competition assays at each step during the acquisition of chemoresistance, we reveal an important priming phase that renders cancer cells previously exposed to sublethal drug concentrations refractory to dose escalation. Therapy-resistant cells throughout the concentration gradient display higher expression of the solute carriers SLC38A7 and SLC46A1 and elevated intracellular concentrations of their associated metabolites. Reduced levels of SLC38A7 and SLC46A1 diminish the proliferative potential of cancer cells, and elevated expression of these SLCs in breast tumors from patients correlates with reduced survival. Our work provides mechanistic evidence to support dose-intensive treatment modalities for patients with solid tumors and reveals two members of the SLC family as potential actionable targets.
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Affiliation(s)
- Yannick Audet-Delage
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Catherine St-Louis
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Lucía Minarrieta
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Shawn McGuirk
- Department of Biochemistry, McGill University, Montréal, QC H3G 1Y6, Canada; Goodman Cancer Institute, McGill University, Montréal, QC H3A 1A3, Canada
| | - Irwin Kurreal
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Matthew G Annis
- Goodman Cancer Institute, McGill University, Montréal, QC H3A 1A3, Canada; Department of Medicine, McGill University, Montréal, QC H4A 3J1, Canada
| | - Arvind Singh Mer
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Peter M Siegel
- Goodman Cancer Institute, McGill University, Montréal, QC H3A 1A3, Canada; Department of Medicine, McGill University, Montréal, QC H4A 3J1, Canada
| | - Julie St-Pierre
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada; Department of Biochemistry, McGill University, Montréal, QC H3G 1Y6, Canada.
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3
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Ba-Alawi W, Kadambat Nair S, Li B, Mammoliti A, Smirnov P, Mer AS, Penn LZ, Haibe-Kains B. Bimodal gene expression in cancer patients provides interpretable biomarkers for drug sensitivity. Cancer Res 2022; 82:2378-2387. [PMID: 35536872 DOI: 10.1158/0008-5472.can-21-2395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 02/24/2022] [Accepted: 05/06/2022] [Indexed: 11/16/2022]
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 data sets 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.
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Affiliation(s)
| | | | - Bo Li
- University of Toronto, Toronto, Canada
| | | | | | | | - Linda Z Penn
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada
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4
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Chang CA, Jen J, Jiang S, Sayad A, Mer AS, Brown KR, Nixon AM, Dhabaria A, Tang KH, Venet D, Sotiriou C, Deng J, Wong KK, Adams S, Meyn P, Heguy A, Skok JA, Tsirigos A, Ueberheide B, Moffat J, Singh A, Haibe-Kains B, Khodadadi-Jamayran A, Neel BG. Ontogeny and Vulnerabilities of Drug-Tolerant Persisters in HER2+ Breast Cancer. Cancer Discov 2022; 12:1022-1045. [PMID: 34911733 PMCID: PMC8983469 DOI: 10.1158/2159-8290.cd-20-1265] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 10/14/2021] [Accepted: 12/10/2021] [Indexed: 11/16/2022]
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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Affiliation(s)
- Chewei Anderson Chang
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Jayu Jen
- Laura and Isaac Perlmutter Cancer Center, NYU Grossman School of Medicine, New York University Langone Health, New York, New York, USA
| | - Shaowen Jiang
- Applied Bioinformatics Laboratories, Office of Science and Research, NYU Grossman School of Medicine, New York University Langone Health, New York, New York, USA
| | - Azin Sayad
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Arvind Singh Mer
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Kevin R. Brown
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | | | - Avantika Dhabaria
- Proteomics Laboratory, Division of Advanced Research and Technology, NYU Grossman School of Medicine, New York University Langone Health, New York, New York, USA.,Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York University Langone Health, New York, New York, USA
| | - Kwan Ho Tang
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Laura and Isaac Perlmutter Cancer Center, NYU Grossman School of Medicine, New York University Langone Health, New York, New York, USA
| | - David Venet
- Breast Cancer Translational Research Laboratory, Institut Jules Bordet Brussels and Université Libre de Bruxelles (ULB), Belgium
| | - Christos Sotiriou
- Breast Cancer Translational Research Laboratory, Institut Jules Bordet Brussels and Université Libre de Bruxelles (ULB), Belgium.,Medical Oncology Department, Institut Jules Bordet Brussels and Université Libre de Bruxelles (ULB), Belgium
| | - Jiehue Deng
- Laura and Isaac Perlmutter Cancer Center, NYU Grossman School of Medicine, New York University Langone Health, New York, New York, USA.,Division of Hematology and Medical Oncology, Department of Medicine, Laura and Isaac Perlmutter Cancer Center, NYU Grossman School of Medicine, New York University Langone Health, New York, New York, USA
| | - Kwok-kin Wong
- Laura and Isaac Perlmutter Cancer Center, NYU Grossman School of Medicine, New York University Langone Health, New York, New York, USA.,Division of Hematology and Medical Oncology, Department of Medicine, Laura and Isaac Perlmutter Cancer Center, NYU Grossman School of Medicine, New York University Langone Health, New York, New York, USA
| | - Sylvia Adams
- Laura and Isaac Perlmutter Cancer Center, NYU Grossman School of Medicine, New York University Langone Health, New York, New York, USA.,Division of Hematology and Medical Oncology, Department of Medicine, Laura and Isaac Perlmutter Cancer Center, NYU Grossman School of Medicine, New York University Langone Health, New York, New York, USA
| | - Peter Meyn
- Genome Technology Center, Division of Advanced Research Technologies, NYU Grossman School of Medicine, New York University Langone Health, New York, New York, USA
| | - Adriana Heguy
- Genome Technology Center, Division of Advanced Research Technologies, NYU Grossman School of Medicine, New York University Langone Health, New York, New York, USA
| | - Jane A. Skok
- Laura and Isaac Perlmutter Cancer Center, NYU Grossman School of Medicine, New York University Langone Health, New York, New York, USA.,Department of Pathology, NYU Grossman School of Medicine, New York University Langone Health, New York, New York, USA
| | - Aristotelis Tsirigos
- Laura and Isaac Perlmutter Cancer Center, NYU Grossman School of Medicine, New York University Langone Health, New York, New York, USA.,Applied Bioinformatics Laboratories, Office of Science and Research, NYU Grossman School of Medicine, New York University Langone Health, New York, New York, USA.,Department of Pathology, NYU Grossman School of Medicine, New York University Langone Health, New York, New York, USA
| | - Beatrix Ueberheide
- Proteomics Laboratory, Division of Advanced Research and Technology, NYU Grossman School of Medicine, New York University Langone Health, New York, New York, USA.,Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York University Langone Health, New York, New York, USA
| | - Jason Moffat
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Abhyudai Singh
- Department of Electrical and Computer Engineering, University of Delaware, Newark, Delaware, USA.,Department of Biomedical Engineering, University of Delaware, Newark, Delaware, USA
| | - Benjamin Haibe-Kains
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
| | - Alireza Khodadadi-Jamayran
- Applied Bioinformatics Laboratories, Office of Science and Research, NYU Grossman School of Medicine, New York University Langone Health, New York, New York, USA
| | - Benjamin G. Neel
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Laura and Isaac Perlmutter Cancer Center, NYU Grossman School of Medicine, New York University Langone Health, New York, New York, USA.,Division of Hematology and Medical Oncology, Department of Medicine, Laura and Isaac Perlmutter Cancer Center, NYU Grossman School of Medicine, New York University Langone Health, New York, New York, USA
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5
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Feizi N, Nair SK, Smirnov P, Beri G, Eeles C, Esfahani PN, Nakano M, Tkachuk D, Mammoliti A, Gorobets E, Mer AS, Lin E, Yu Y, Martin S, Hafner M, Haibe-Kains B. PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis. Nucleic Acids Res 2022; 50:D1348-D1357. [PMID: 34850112 PMCID: PMC8728279 DOI: 10.1093/nar/gkab1084] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 10/15/2021] [Accepted: 10/20/2021] [Indexed: 11/14/2022] Open
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.
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Affiliation(s)
- Nikta Feizi
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
| | - Sisira Kadambat Nair
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
| | - Petr Smirnov
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Gangesh Beri
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
| | - Christopher Eeles
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
| | - Parinaz Nasr Esfahani
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
| | - Minoru Nakano
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
| | - Denis Tkachuk
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
| | - Anthony Mammoliti
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Evgeniya Gorobets
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON M5S 3G5, Canada
| | - Arvind Singh Mer
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Eva Lin
- Department of Discovery Oncology, Genentech Inc, South San Francisco, CA 94080, USA
| | - Yihong Yu
- Department of Discovery Oncology, Genentech Inc, South San Francisco, CA 94080, USA
| | - Scott Martin
- Department of Discovery Oncology, Genentech Inc, South San Francisco, CA 94080, USA
| | - Marc Hafner
- Department of Oncology Bioinformatics, Genentech Inc, South San Francisco, CA 94080, USA
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5T 3A1, Canada
- Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON M5G 1M1, Canada
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6
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Ortmann J, Rampášek L, Tai E, Mer AS, Shi R, Stewart EL, Mascaux C, Fares A, Pham NA, Beri G, Eeles C, Tkachuk D, Ho C, Sakashita S, Weiss J, Jiang X, Liu G, Cescon DW, O'Brien CA, Guo S, Tsao MS, Haibe-Kains B, Goldenberg A. Assessing therapy response in patient-derived xenografts. Sci Transl Med 2021; 13:eabf4969. [PMID: 34788078 DOI: 10.1126/scitranslmed.abf4969] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Janosch Ortmann
- Département AOTI, Université du Québec à Montréal, Montréal, QC H2X3X2, Canada.,Group for Research in Decision Analysis (GERAD), Montreal, QC H3T1J4, Canada
| | - Ladislav Rampášek
- Department of Computer Science, University of Toronto, Toronto, ON M5S2E4, Canada.,Vector Institute for Artificial Intelligence, Toronto, ON M5G1M1, Canada.,Hospital for Sick Children, Toronto, ON M5G1X8, Canada
| | - Elijah Tai
- Department of Computer Science, University of Toronto, Toronto, ON M5S2E4, Canada
| | - Arvind Singh Mer
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON M5G1L7, Canada
| | - Ruoshi Shi
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Erin L Stewart
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Celine Mascaux
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada.,Pulmonology Department, Hôpitaux Universitaires de Strasbourg, 67200 Strasbourg, France.,Laboratory of Molecular Mechanisms of the Stress Response and Pathologies, INSERM U1113, 3 Avenue Molière, 67200 Strasbourg, France
| | - Aline Fares
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Nhu-An Pham
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Gangesh Beri
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Christopher Eeles
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Denis Tkachuk
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Chantal Ho
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Shingo Sakashita
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Jessica Weiss
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Xiaoqian Jiang
- Crown Bioscience Taicang Inc., No.6 Beijing West Road, Taicang, Jiangsu 215400, P. R. China
| | - Geoffrey Liu
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - David W Cescon
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Catherine A O'Brien
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON M5G1L7, Canada.,Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S1A8, Canada.,Department of Physiology, University of Toronto, Toronto, ON M5G1L7, Canada.,Department of Surgery, Toronto General Hospital, Toronto, ON M5G2C4, Canada
| | - Sheng Guo
- Crown Bioscience Taicang Inc., No.6 Beijing West Road, Taicang, Jiangsu 215400, P. R. China
| | - Ming-Sound Tsao
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada
| | - Benjamin Haibe-Kains
- Department of Computer Science, University of Toronto, Toronto, ON M5S2E4, Canada.,Vector Institute for Artificial Intelligence, Toronto, ON M5G1M1, Canada.,Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G1L7, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON M5G1L7, Canada.,Ontario Institute for Cancer Research, Toronto, ON M5G1L7, Canada
| | - Anna Goldenberg
- Department of Computer Science, University of Toronto, Toronto, ON M5S2E4, Canada.,Vector Institute for Artificial Intelligence, Toronto, ON M5G1M1, Canada.,Hospital for Sick Children, Toronto, ON M5G1X8, Canada.,CIFAR, Toronto, ON M5G1M1, Canada
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7
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Mer AS, Minden MD, Haibe-Kains B, Schimmer AD. Novel subtypes of NPM1-mutated AML with distinct outcome. Mol Cell Oncol 2021; 8:1924600. [PMID: 34616866 DOI: 10.1080/23723556.2021.1924600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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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Affiliation(s)
- Arvind Singh Mer
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Mark D Minden
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Vector Institute, Toronto, Ontario, Canada
| | - Aaron D Schimmer
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Ontario Institute for Cancer Research, Toronto, Ontario, Canada
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8
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Cao L, Huang C, Cui Zhou D, Hu Y, Lih TM, Savage SR, Krug K, Clark DJ, Schnaubelt M, Chen L, da Veiga Leprevost F, Eguez RV, Yang W, Pan J, Wen B, Dou Y, Jiang W, Liao Y, Shi Z, Terekhanova NV, Cao S, Lu RJH, Li Y, Liu R, Zhu H, Ronning P, Wu Y, Wyczalkowski MA, Easwaran H, Danilova L, Mer AS, Yoo S, Wang JM, Liu W, Haibe-Kains B, Thiagarajan M, Jewell SD, Hostetter G, Newton CJ, Li QK, Roehrl MH, Fenyö D, Wang P, Nesvizhskii AI, Mani DR, Omenn GS, Boja ES, Mesri M, Robles AI, Rodriguez H, Bathe OF, Chan DW, Hruban RH, Ding L, Zhang B, Zhang H. Proteogenomic characterization of pancreatic ductal adenocarcinoma. Cell 2021; 184:5031-5052.e26. [PMID: 34534465 PMCID: PMC8654574 DOI: 10.1016/j.cell.2021.08.023] [Citation(s) in RCA: 205] [Impact Index Per Article: 68.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 03/19/2021] [Accepted: 08/18/2021] [Indexed: 02/07/2023]
Abstract
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.
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Affiliation(s)
- Liwei Cao
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Chen Huang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Daniel Cui Zhou
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Yingwei Hu
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - T Mamie Lih
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Sara R Savage
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Karsten Krug
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - David J Clark
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Michael Schnaubelt
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Lijun Chen
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | | | | | - Weiming Yang
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Jianbo Pan
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Bo Wen
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yongchao Dou
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Wen Jiang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yuxing Liao
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Zhiao Shi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Nadezhda V Terekhanova
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Song Cao
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Rita Jui-Hsien Lu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Yize Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Ruiyang Liu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Houxiang Zhu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Peter Ronning
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Yige Wu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Matthew A Wyczalkowski
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Hariharan Easwaran
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Ludmila Danilova
- Department of Oncology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Arvind Singh Mer
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Seungyeul Yoo
- Sema4, a Mount Sinai venture, Stamford, CT 06902, USA
| | - Joshua M Wang
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Wenke Liu
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Mathangi Thiagarajan
- Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Scott D Jewell
- Van Andel Research Institute, Grand Rapids, MI 49503, USA
| | | | | | - Qing Kay Li
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Michael H Roehrl
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - David Fenyö
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Pei Wang
- Sema4, a Mount Sinai venture, Stamford, CT 06902, USA
| | | | - D R Mani
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Emily S Boja
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Mehdi Mesri
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Oliver F Bathe
- Departments of Surgery and Oncology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Daniel W Chan
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA; The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Ralph H Hruban
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA; The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA; The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Li Ding
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA.
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA; The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA.
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9
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Sharifi-Noghabi H, Jahangiri-Tazehkand S, Smirnov P, Hon C, Mammoliti A, Nair SK, Mer AS, Ester M, Haibe-Kains B. Drug sensitivity prediction from cell line-based pharmacogenomics data: guidelines for developing machine learning models. Brief Bioinform 2021; 22:6348324. [PMID: 34382071 DOI: 10.1093/bib/bbab294] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 06/29/2021] [Accepted: 07/10/2021] [Indexed: 11/13/2022] Open
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.
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Affiliation(s)
- Hossein Sharifi-Noghabi
- School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada.,Vancouver Prostate Center, Vancouver, British Columbia, Canada.,Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Soheil Jahangiri-Tazehkand
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,University of Toronto, Toronto, Ontario, Canada
| | - Petr Smirnov
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,University of Toronto, Toronto, Ontario, Canada
| | - Casey Hon
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,University of Toronto, Toronto, Ontario, Canada
| | - Anthony Mammoliti
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,University of Toronto, Toronto, Ontario, Canada
| | | | - Arvind Singh Mer
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,University of Toronto, Toronto, Ontario, Canada
| | - Martin Ester
- School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada.,Vancouver Prostate Center, Vancouver, British Columbia, Canada
| | - Benjamin Haibe-Kains
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,University of Toronto, Toronto, Ontario, Canada
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10
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Cuypers WL, Dönertaş HM, Grewal JK, Fatima N, Donnelly C, Mer AS, Krieger S, Cuypers B, Rahman F. Highlights from the 16th International Society for Computational Biology Student Council Symposium 2020. F1000Res 2021; 10. [PMID: 34136128 PMCID: PMC8182693 DOI: 10.12688/f1000research.53408.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/25/2021] [Indexed: 11/20/2022] Open
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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Affiliation(s)
- Wim L Cuypers
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium.,Tropical Bacteriology Unit, Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium
| | - Handan Melike Dönertaş
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, UK
| | - Jasleen K Grewal
- Canada's Michael Smith Genome Sciences Centre, BC Cancer Research Institute, Vancouver, British Columbia, Canada
| | | | - Chase Donnelly
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Arvind Singh Mer
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Spencer Krieger
- Department of Computer Science, University of Arizona, Tucson, Arizona, USA
| | - Bart Cuypers
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium.,Molecular Parasitology Unit, Department of Biomedical Sciences, Institute of Tropical Medicine, Antwerp, Belgium
| | - Farzana Rahman
- School of Human and Life Sciences, Canterbury Christ Church University, Canterbury, UK
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11
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Ba-alawi W, Nair SK, Li B, Mammoliti A, Smirnov P, Mer AS, Penn L, Haibe-Kains B. Abstract PO-070: Bimodality of gene expression in cancer patient tumors as interpretable biomarkers for drug sensitivity. Clin Cancer Res 2021. [DOI: 10.1158/1557-3265.adi21-po-070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
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.
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Affiliation(s)
| | | | - Bo Li
- 2University of Toronto, Toronto, Canada
| | | | | | | | - Linda Penn
- 1Princess Margaret Cancer Centre, Toronto, Canada,
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12
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Mer AS, Haibe-Kains B. Abstract PO-052: Exploring patient derived xenografts based pharmacogenomic data for precision oncology. Clin Cancer Res 2021. [DOI: 10.1158/1557-3265.adi21-po-052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
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.
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13
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Rehman SK, Haynes J, Collignon E, Brown KR, Wang Y, Nixon AML, Bruce JP, Wintersinger JA, Singh Mer A, Lo EBL, Leung C, Lima-Fernandes E, Pedley NM, Soares F, McGibbon S, He HH, Pollet A, Pugh TJ, Haibe-Kains B, Morris Q, Ramalho-Santos M, Goyal S, Moffat J, O'Brien CA. Colorectal Cancer Cells Enter a Diapause-like DTP State to Survive Chemotherapy. Cell 2021; 184:226-242.e21. [PMID: 33417860 PMCID: PMC8437243 DOI: 10.1016/j.cell.2020.11.018] [Citation(s) in RCA: 212] [Impact Index Per Article: 70.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 08/25/2020] [Accepted: 11/10/2020] [Indexed: 12/22/2022]
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.
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Affiliation(s)
- Sumaiyah K Rehman
- Princess Margaret Cancer Center, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Jennifer Haynes
- Princess Margaret Cancer Center, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Evelyne Collignon
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5T 3L9, Canada
| | - Kevin R Brown
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Yadong Wang
- Princess Margaret Cancer Center, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Allison M L Nixon
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Jeffrey P Bruce
- Princess Margaret Cancer Center, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Jeffrey A Wintersinger
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5T 3A1, Canada; Vector Institute, Toronto, ON M5G 1M1, Canada
| | - Arvind Singh Mer
- Princess Margaret Cancer Center, University Health Network, Toronto, ON M5G 1L7, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Edwyn B L Lo
- Princess Margaret Cancer Center, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Cherry Leung
- Princess Margaret Cancer Center, University Health Network, Toronto, ON M5G 1L7, Canada
| | | | - Nicholas M Pedley
- Princess Margaret Cancer Center, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Fraser Soares
- Princess Margaret Cancer Center, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Sophie McGibbon
- Department of Physics, University of Toronto, Toronto, ON M5S 1A7, Canada
| | - Housheng Hansen He
- Princess Margaret Cancer Center, University Health Network, Toronto, ON M5G 1L7, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Aaron Pollet
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5T 3L9, Canada
| | - Trevor J Pugh
- Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada; Clinical Genomics Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Center, University Health Network, Toronto, ON M5G 1L7, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5T 3A1, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Quaid Morris
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5T 3A1, Canada; Vector Institute, Toronto, ON M5G 1M1, Canada; Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Miguel Ramalho-Santos
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5T 3L9, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada.
| | - Sidhartha Goyal
- Department of Physics, University of Toronto, Toronto, ON M5S 1A7, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON M5S 3E1, Canada.
| | - Jason Moffat
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON M5S 3E1, Canada.
| | - Catherine A O'Brien
- Princess Margaret Cancer Center, University Health Network, Toronto, ON M5G 1L7, Canada; Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada; Department of Surgery, University Health Network, Toronto, ON M5G 1L7, Canada.
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14
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Shi R, Radulovich N, Ng C, Liu N, Notsuda H, Cabanero M, Martins-Filho SN, Raghavan V, Li Q, Mer AS, Rosen JC, Li M, Wang YH, Tamblyn L, Pham NA, Haibe-Kains B, Liu G, Moghal N, Tsao MS. Organoid Cultures as Preclinical Models of Non-Small Cell Lung Cancer. Clin Cancer Res 2019; 26:1162-1174. [PMID: 31694835 DOI: 10.1158/1078-0432.ccr-19-1376] [Citation(s) in RCA: 135] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2019] [Revised: 09/19/2019] [Accepted: 10/30/2019] [Indexed: 11/16/2022]
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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Affiliation(s)
- Ruoshi Shi
- University Health Network, Ontario Cancer Institute/Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Nikolina Radulovich
- University Health Network, Ontario Cancer Institute/Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Christine Ng
- University Health Network, Ontario Cancer Institute/Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Ni Liu
- University Health Network, Ontario Cancer Institute/Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Hirotsugu Notsuda
- University Health Network, Ontario Cancer Institute/Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Michael Cabanero
- University Health Network, Ontario Cancer Institute/Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Sebastiao N Martins-Filho
- University Health Network, Ontario Cancer Institute/Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Vibha Raghavan
- University Health Network, Ontario Cancer Institute/Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Quan Li
- University Health Network, Ontario Cancer Institute/Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Arvind Singh Mer
- University Health Network, Ontario Cancer Institute/Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Joshua C Rosen
- University Health Network, Ontario Cancer Institute/Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Ming Li
- University Health Network, Ontario Cancer Institute/Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Yu-Hui Wang
- University Health Network, Ontario Cancer Institute/Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Laura Tamblyn
- University Health Network, Ontario Cancer Institute/Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Nhu-An Pham
- University Health Network, Ontario Cancer Institute/Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Benjamin Haibe-Kains
- University Health Network, Ontario Cancer Institute/Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
| | - Geoffrey Liu
- University Health Network, Ontario Cancer Institute/Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
| | - Nadeem Moghal
- University Health Network, Ontario Cancer Institute/Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Ming-Sound Tsao
- University Health Network, Ontario Cancer Institute/Princess Margaret Cancer Centre, Toronto, Ontario, Canada. .,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
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Mer AS, Ba-alawi W, Smirnov P, Wang YX, Brew B, Tsao MS, Cescon D, Goldenberg A, Haibe-Kains B. Abstract 3378: Systematic pharmacogenomic analysis of large patient derived xenografts data. Cancer Res 2019. [DOI: 10.1158/1538-7445.am2019-3378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
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] <5%). We then leveraged our platform to link the drug response and the pathways whose activity is consistent across passages by mining the Novartis PDX Encyclopedia (PDXE) data containing 1,075 PDXs spanning 5 tissue types and 62 anticancer drugs. We identified 87 pathways significantly associated with response to 51 drugs (FDR < 5%), including associations such as erlotinib response and signaling by EGFR in cancer pathways and MAP kinase activation in TLR cascade and binimetinib response. Among the significant pathway-drug associations, we found novel biomarkers based on gene expressions, Copy Number Aberrations (CNAs) and mutations predictive of drug response (concordance index > 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.
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Affiliation(s)
- Arvind Singh Mer
- 1Princess Margaret Cancer Centre University Health Network, Toronto, Ontario, Canada
| | - Wail Ba-alawi
- 1Princess Margaret Cancer Centre University Health Network, Toronto, Ontario, Canada
| | - Petr Smirnov
- 1Princess Margaret Cancer Centre University Health Network, Toronto, Ontario, Canada
| | - Yi Xiao Wang
- 1Princess Margaret Cancer Centre University Health Network, Toronto, Ontario, Canada
| | - Ben Brew
- 2SickKids Research Institute, Toronto, Ontario, Canada
| | - Ming-Sound Tsao
- 1Princess Margaret Cancer Centre University Health Network, Toronto, Ontario, Canada
| | - David Cescon
- 3University Health Network, Toronto, Ontario, Canada
| | | | - Benjamin Haibe-Kains
- 1Princess Margaret Cancer Centre University Health Network, Toronto, Ontario, Canada
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Mer AS, Lindberg J, Nilsson C, Klevebring D, Wang M, Grönberg H, Lehmann S, Rantalainen M. Expression levels of long non-coding RNAs are prognostic for AML outcome. J Hematol Oncol 2018; 11:52. [PMID: 29625580 PMCID: PMC5889529 DOI: 10.1186/s13045-018-0596-2] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Accepted: 03/21/2018] [Indexed: 01/08/2023] Open
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 < 0.05). We established four distinct molecular subtypes based on lncRNA expression using a consensus clustering approach. LncRNA-based subtypes were found to stratify patients into groups with prognostic information (p value < 0.05). Subsequently, lncRNA expression-based subtypes were validated in an independent patient cohort (TCGA-AML). LncRNA subtypes could not be directly explained by any of the recurrent cytogenetic or mutational aberrations, although associations with some of the established genetic and clinical factors were found, including mutations in NPM1, TP53, and FLT3. Conclusion LncRNA expression-based four subtypes, discovered in this study, are reproducible and can effectively stratify AML patients. LncRNA expression profiling can provide valuable information for improved risk stratification of AML patients. Electronic supplementary material The online version of this article (10.1186/s13045-018-0596-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Arvind Singh Mer
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Vag 12A, SE-17177, Stockholm, Sweden
| | - Johan Lindberg
- Department of Medical Epidemiology and Biostatistics, Science for Life Laboratory, Karolinska Institutet, Nobels Vag 12A, SE-17177, Stockholm, Sweden
| | - Christer Nilsson
- Hematology Centre, Karolinska University Hospital and Karolinska Institute, Huddinge, Stockholm, Sweden
| | - Daniel Klevebring
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Vag 12A, SE-17177, Stockholm, Sweden
| | - Mei Wang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Vag 12A, SE-17177, Stockholm, Sweden
| | - Henrik Grönberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Vag 12A, SE-17177, Stockholm, Sweden
| | - Soren Lehmann
- Hematology Centre, Karolinska University Hospital and Karolinska Institute, Huddinge, Stockholm, Sweden.,Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Mattias Rantalainen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Vag 12A, SE-17177, Stockholm, Sweden.
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El-Hachem N, Ba-Alawi W, Smith I, Mer AS, Haibe-Kains B. Integrative cancer pharmacogenomics to establish drug mechanism of action: drug repurposing. Pharmacogenomics 2017; 18:1469-1472. [PMID: 29057710 DOI: 10.2217/pgs-2017-0132] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Affiliation(s)
- Nehme El-Hachem
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Wail Ba-Alawi
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Ian Smith
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Arvind Singh Mer
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Ontario Institute of Cancer Research, Toronto, Ontario, Canada
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Mer AS, Klevebring D, Grönberg H, Rantalainen M. Study design requirements for RNA sequencing-based breast cancer diagnostics. Sci Rep 2016; 6:20200. [PMID: 26830453 PMCID: PMC4735337 DOI: 10.1038/srep20200] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2015] [Accepted: 12/23/2015] [Indexed: 11/30/2022] Open
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.
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Affiliation(s)
- Arvind Singh Mer
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Nobels Väg 12A, SE-17177, Stockholm, Sweden
| | - Daniel Klevebring
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Nobels Väg 12A, SE-17177, Stockholm, Sweden
| | - Henrik Grönberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Nobels Väg 12A, SE-17177, Stockholm, Sweden
| | - Mattias Rantalainen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Nobels Väg 12A, SE-17177, Stockholm, Sweden
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Gebhardt ML, Mer AS, Andrade-Navarro MA. mBISON: Finding miRNA target over-representation in gene lists from ChIP-sequencing data. BMC Res Notes 2015; 8:157. [PMID: 25889572 PMCID: PMC4404576 DOI: 10.1186/s13104-015-1118-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2014] [Accepted: 04/01/2015] [Indexed: 11/10/2022] Open
Abstract
Background Over-representation of predicted miRNA targets in sets of genes regulated by a given transcription factor (e.g. as defined by ChIP-sequencing experiments) helps to identify biologically relevant miRNA targets and is useful to get insight into post-transcriptional regulation. Findings To facilitate the application of this approach we have created the mBISON web-application. mBISON calculates the significance of over-representation of miRNA targets in a given non-ranked gene set. The gene set can be specified either by a list of genes or by one or more ChIP-seq datasets followed by a user-defined peak-gene association procedure. mBISON is based on predictions from TargetScan and uses a randomization step to calculate False-Discovery-Rates for each miRNA, including a correction for gene set specific properties such as 3’UTR length. The tool can be accessed from the following web-resource: http://cbdm.mdc-berlin.de/~mgebhardt/cgi-bin/mbison/home. Conclusion mBISON is a web-application that helps to extract functional information about miRNAs from gene lists, which is in contrast to comparable applications easy to use by everyone and can be applied on ChIP-seq data directly.
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Affiliation(s)
| | - Arvind Singh Mer
- Max Delbrück Center for Molecular Medicine, Berlin, 13125, Germany. .,Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
| | - Miguel Angel Andrade-Navarro
- Max Delbrück Center for Molecular Medicine, Berlin, 13125, Germany. .,Institute of Molecular Biology, Mainz, 55128, Germany. .,Faculty of Biology, Johannes-Gutenberg University of Mainz, Mainz, 55128, Germany.
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Abstract
BACKGROUND 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. METHODOLOGY/PRINCIPAL FINDINGS 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. CONCLUSIONS/SIGNIFICANCE 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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Affiliation(s)
- Candida Vaz
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
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