A Network of Conserved Synthetic Lethal Interactions for Exploration of Precision Cancer Therapy.

An emerging therapeutic strategy for cancer is to induce selective lethality in a tumor by exploiting interactions between its driving mutations and specific drug targets. Here we use a multi-species approach to develop a resource of synthetic lethal interactions relevant to cancer therapy. First, we screen in yeast ∼169,000 potential interactions among orthologs of human tumor suppressor genes (TSG) and genes encoding drug targets across multiple genotoxic environments. Guided by the strongest signal, we evaluate thousands of TSG-drug combinations in HeLa cells, resulting in networks of conserved synthetic lethal interactions. Analysis of these networks reveals that interaction stability across environments and shared gene function increase the likelihood of observing an interaction in human cancer cells. Using these rules, we prioritize ∼10(5) human TSG-drug combinations for future follow-up. We validate interactions based on cell and/or patient survival, including topoisomerases with RAD17 and checkpoint kinases with BLM.
Mesh Terms:
Antineoplastic Agents, Biomarkers, Tumor, Cell Cycle Proteins, Cell Proliferation, Cell Survival, Dose-Response Relationship, Drug, Female, Gene Expression Regulation, Fungal, Gene Expression Regulation, Neoplastic, Gene Regulatory Networks, Genes, Tumor Suppressor, Genetic Predisposition to Disease, HeLa Cells, Humans, Kaplan-Meier Estimate, Molecular Targeted Therapy, Mutation, Phenotype, Precision Medicine, Protein Interaction Maps, RNA Interference, RecQ Helicases, Saccharomyces cerevisiae, Saccharomyces cerevisiae Proteins, Signal Transduction, Synthetic Lethal Mutations, Time Factors, Transfection, Uterine Cervical Neoplasms
Mol. Cell Aug. 04, 2016; 63(3);514-25 [PUBMED:27453043]
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