Individualized Genetic Network Analysis Reveals New Therapeutic Vulnerabilities in Cancer
56 Pages Posted: 3 Oct 2018More...
Background: Tumor-specific genomic alterations derived from multi-center cancer genome projects allow systematic identification of genetic interactions that promote tumor vulnerabilities, offering novel strategies for development of targeted therapies for individual tumors.
Methods: In this study, we develop an Individualized Network-based Co-Mutation (INCM) methodology by inspecting over 2.5 million nonsynonymous somatic mutations and RNA-Seq profiles derived from 6,789 tumor exomes across 14 cancer types from The Cancer Genome Atlas (TCGA).
Findings: Our INCM analysis reveals a higher genetic interaction burden on the significantly mutated genes in cancer populations, experimentally validated cancer genes, chromosome regulatory factors, and DNA repair genes, as compared to human essential genes identified by CRISPR-Cas9 screenings of cancer cell lines. We find that genes identified in the cancer type-specific genetic subnetworks by INCM are significantly enriched in established cancer pathways, and the INCM-predicted putative genetic interactions (e.g., BCL2L1-HRAS and BACH2-KRAS) are correlated with patient survival. By analyzing drug pharmacogenomics profiles across over 1,000 cancer cell lines from the Genomics of Drug Sensitivity in Cancer (GDSC) database, we show that the network-predicted putative genetic interactions (e.g., BRCA2-TP53 and KRAS-TP53) are significantly correlated with sensitivity/resistance of multiple therapeutic agents in cancer cell lines. Finally, drug-target network analysis reveals multiple potentially druggable genetic interactions (e.g., CETN2-CDK4 and PIK3CA-PTEN) by targeting tumor vulnerabilities. In summary, this study offers a powerful network-based methodology for comprehensive identification of candidate therapeutic pathways that target tumor vulnerabilities and prioritization of potential pharmacogenomics biomarkers for development of personalized cancer medicine.
Funding: This work was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under Award Number K99HL138272 and R00HL138272 to F.C. This work has been also funded in whole or in part with Federal funds from the Frederick National Laboratory for Cancer Research, National Institutes of Health, under contract HHSN261200800001E. This research was supported (in part) by the Intramural Research Program of NIH, Frederick National Lab, Center for Cancer Research.
Declaration of Interest: The authors declare no competing financial interest.
Keywords: tumor vulnerability; genetic interaction; pharmacogenomics; somatic mutations; network analysis; personalized cancer medicine
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