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Liu C, Zhao J, Lu W, Dai Y, Hockings J, Zhou Y, Nussinov R, Eng C, Cheng F. Individualized genetic network analysis reveals new therapeutic vulnerabilities in 6,700 cancer genomes. PLoS Comput Biol 2020; 16:e1007701. [PMID: 32101536 PMCID: PMC7062285 DOI: 10.1371/journal.pcbi.1007701] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 03/09/2020] [Accepted: 01/30/2020] [Indexed: 02/06/2023] Open
Abstract
Tumor-specific genomic alterations allow systematic identification of genetic interactions that promote tumorigenesis and tumor vulnerabilities, offering novel strategies for development of targeted therapies for individual patients. We develop an Individualized Network-based Co-Mutation (INCM) methodology by inspecting over 2.5 million nonsynonymous somatic mutations derived from 6,789 tumor exomes across 14 cancer types from The Cancer Genome Atlas. Our INCM analysis reveals a higher genetic interaction burden on the significantly mutated genes, experimentally validated cancer genes, chromosome regulatory factors, and DNA damage repair genes, as compared to human pan-cancer essential genes identified by CRISPR-Cas9 screenings on 324 cancer cell lines. We find that genes involved in the cancer type-specific genetic subnetworks identified by INCM are significantly enriched in established cancer pathways, and the INCM-inferred putative genetic interactions are correlated with patient survival. By analyzing drug pharmacogenomics profiles from the Genomics of Drug Sensitivity in Cancer database, we show that the network-predicted putative genetic interactions (e.g., BRCA2-TP53) are significantly correlated with sensitivity/resistance of multiple therapeutic agents. We experimentally validated that afatinib has the strongest cytotoxic activity on BT474 (IC50 = 55.5 nM, BRCA2 and TP53 co-mutant) compared to MCF7 (IC50 = 7.7 μM, both BRCA2 and TP53 wild type) and MDA-MB-231 (IC50 = 7.9 μM, BRCA2 wild type but TP53 mutant). Finally, drug-target network analysis reveals several potential druggable genetic interactions by targeting tumor vulnerabilities. This study offers a powerful network-based methodology for identification of candidate therapeutic pathways that target tumor vulnerabilities and prioritization of potential pharmacogenomics biomarkers for development of personalized cancer medicine. Recent efforts to map genetic interactions in tumor cells have suggested that tumor vulnerabilities can be exploited for development of novel targeted therapies. Tumor-specific genomic alterations derived from multi-center cancer genome projects allow identification of genetic interactions that promote tumor vulnerabilities, offering novel strategies for development of targeted cancer therapies. This study develops a novel Individualized Network-based Co-Mutation (termed INCM) methodology for quantifying the putative genetic interactions in cancer. Trained on over 2.5 million nonsynonymous somatic mutations derived from 6,789 tumor exomes across 14 cancer type, we found that genes identified in the cancer type-specific genetic subnetworks were significantly enriched in established cancer pathways. The network-predicted putative genetic interactions are correlated with patient survival. By analyzing drug pharmacogenomics profiles, we showed that the network-predicted putative genetic interactions (e.g., BRCA2-TP53) were significantly correlated with sensitivity/resistance of anticancer drugs (e.g., afatinib) and we experimentally validated it in breast cancer cell lines. Finally, drug-target network analysis reveals several potential druggable genetic interactions (e.g., PIK3CA-PTEN) by targeting tumor vulnerabilities. This study offers a generalizable network-based approach for comprehensive identification of candidate therapeutic pathways that target tumor vulnerabilities and prioritization of potential prognostic and pharmacogenomics biomarkers for development of personalized cancer medicine.
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Affiliation(s)
- Chuang Liu
- Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Junfei Zhao
- Department of Systems Biology, Columbia University, New York, New York, United States of America
- Department of Biomedical Informatics, Columbia University, New York, New York, United States of America
| | - Weiqiang Lu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
| | - Yao Dai
- Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Jennifer Hockings
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, Maryland, United States of America
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Charis Eng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
- Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
- * E-mail:
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