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

Autor: Robert W. Sobol, Justin K. Huang, Trey Ideker, Jianfeng Li, Jia L. Xu, Ana Bojorquez-Gomez, Andrew M. Gross, James Jensen, Leonie Kollenstart, Rohith Srivas, Daniel Pekin, John Paul Shen, Haico van Attikum, Kristin Klepper, Vignesh Sivaganesh, Chih Cheng Yang, Su Ming Sun, Pedro Aza-Blanc, Huwate Yeerna, Katherine Licon
Jazyk: angličtina
Rok vydání: 2016
Předmět:
0301 basic medicine
Saccharomyces cerevisiae Proteins
Time Factors
Cell Survival
Gene regulatory network
Uterine Cervical Neoplasms
Antineoplastic Agents
Cell Cycle Proteins
Kaplan-Meier Estimate
Saccharomyces cerevisiae
Biology
medicine.disease_cause
Transfection
03 medical and health sciences
RNA interference
Gene Expression Regulation
Fungal
medicine
Biomarkers
Tumor
Humans
Gene Regulatory Networks
Genes
Tumor Suppressor
Genetic Predisposition to Disease
Molecular Targeted Therapy
Protein Interaction Maps
Precision Medicine
Molecular Biology
Gene
Cell Proliferation
Genetics
Mutation
Dose-Response Relationship
Drug
RecQ Helicases
Cancer
Cell Biology
medicine.disease
Phenotype
Gene Expression Regulation
Neoplastic
030104 developmental biology
Female
RNA Interference
Synthetic Lethal Mutations
Function (biology)
HeLa Cells
Signal Transduction
Zdroj: Molecular Cell, 63(3), 514-525
Popis: 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.
Databáze: OpenAIRE
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