Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China.
Department of Systems Biology, Columbia University, New York, New York, United States of America.
PLoS Comput Biol. 2020 Feb 26;16(2):e1007701. doi: 10.1371/journal.pcbi.1007701. eCollection 2020 Feb.
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.
肿瘤特异性基因组改变允许系统地鉴定促进肿瘤发生和肿瘤脆弱性的遗传相互作用，为个别患者的靶向治疗开发提供新策略。我们通过检查来自癌症基因组图谱的 14 种癌症类型的 6789 个肿瘤外显子中的超过 250 万个非同义体细胞突变，开发了一种基于个体网络共突变（INCM）的方法。我们的 INCM 分析显示，与通过 CRISPR-Cas9 筛选在 324 种癌细胞系中鉴定的人类泛癌必需基因相比，显著突变基因、经过实验验证的癌症基因、染色体调节因子和 DNA 损伤修复基因的遗传相互作用负担更高。我们发现，INCM 鉴定的癌症类型特异性遗传子网络中涉及的基因在已建立的癌症途径中显著富集，并且 INCM 推断的假定遗传相互作用与患者生存相关。通过分析癌症药物基因组学敏感性数据库中的药物药物基因组学概况，我们表明网络预测的假定遗传相互作用（例如，BRCA2-TP53）与多种治疗剂的敏感性/耐药性显著相关。我们通过实验验证了阿法替尼对 BT474（IC50=55.5 nM，BRCA2 和 TP53 共突变）的细胞毒性活性最强，与 MCF7（IC50=7.7 μM，BRCA2 和 TP53 均为野生型）和 MDA-MB-231（IC50=7.9 μM，BRCA2 野生型但 TP53 突变型）相比。最后，药物-靶标网络分析通过靶向肿瘤脆弱性揭示了几种潜在的可治疗遗传相互作用。这项研究提供了一种强大的基于网络的方法，用于鉴定针对肿瘤脆弱性的候选治疗途径，并为开发个性化癌症医学确定潜在的药物基因组学生物标志物。
