Identification of Synthetic Lethal Partners of Cancer Germline Mutations using Pan-Cancer Human Primary Tumor Data

使用泛癌症人类原发性肿瘤数据鉴定癌症种系突变的合成致死伴侣

• 批准号: 9814587
• 负责人: Yihui Shi
• 金额: $ 13.36万
• 依托单位: SRI INTERNATIONAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-11 至 2019-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9814587
• 关键词: BRCA1 gene; BRCA2 gene; Breast Cancer cell line; CRISPR screen; Cell Culture Techniques; Cell Death; Cell Line; Cells; Chemoprevention; Chemopreventive Agent; Clinic; Computing Methodologies; DNA; DNA sequencing; Data; Data Analyses; Data Set; Databases; Dependence; Disease; Gene Expression; Gene Mutation; Genes; Genetic; Genomics; Genotype; Genotype-Tissue Expression Project; Germ Cells; Germ-Line Mutation; Human; In Vitro; Intercept; Lead; Malignant Neoplasms; Methodology; Methods; Mining; Mus; Mutation; Normal tissue morphology; Oncogenes; Operative Surgical Procedures; Other Genetics; Patients; Pharmacology; Prevention; Prevention strategy; Primary Neoplasm; RNA interference screen; Resistance; Sampling; Somatic Mutation; Susceptibility Gene; Testing; The Cancer Genome Atlas; Therapeutic; Tissues; Validation; Work; Xenograft procedure; base; cancer cell; cancer genome; cancer risk; cancer type; clinical practice; computerized tools; egg; gene discovery; genome sequencing; improved; in vivo; inhibitor/antagonist; knock-down; leukemia; loss of function mutation; malignant breast neoplasm; mutant; neoplastic cell; new therapeutic target; novel; novel therapeutics; offspring; overexpression; prophylactic; response; small hairpin RNA; sperm cell; targeted treatment; therapeutic target; transcriptomics; treatment strategy; tumor

PROJECT SUMMARY We propose a novel computational approach to identify therapeutic targets for cancers with germline mutations using integrative analysis of germline mutations and somatic alterations from pan-cancer primary human tumor data. This method will be used to identify new therapeutic targets in breast cancer for germline mutations in BRCA1, BRCA2, and PALB2. Genes in which germline mutations confer increased risks of cancer are called cancer predisposition genes (CPGs). Numerous CPGs are already known, and recent advances in DNA sequencing hold the promise of more CPG discoveries. Given the increased cancer risk in people with germline CPG mutations, there is an urgent need to identify new therapeutic and chemopreventive strategies specific to these mutations. Most of these mutations are loss-of-function alterations and not directly druggable. Synthetic lethality provides the basis for an approach to identify new therapeutic targets for these mutations. Currently, synthetic lethal (SL) partners are identified using large-scale functional screens, which are negatively impacted by the artificiality of the cell culture conditions and limited availability of cell lines with the specific mutations in the right cancer context. We propose to mine patient tumor databases to identify SL partners of germline mutations. Our hypothesis is that SL partners of a germline mutation will be selectively amplified or never deleted and also over-expressed in primary tumor samples harboring the mutation. Previously, we developed a novel computational method (Mining Synthetic Lethals, MiSL) that analyzes primary tumor data to identify SL partners of somatic mutations in specific tumor types. We propose to develop a computational pipeline based on MiSL to identify genetic interactions with germline mutations. In Aim 1, we will develop a MiSL-based computational method to identify SL partners of germline mutations in cancer. This method will be applied to genomic and transcriptomic datasets from multiple large-scale cancer genome sequencing projects and gene expression data for normal tissues from GTEx (Genotype-Tissue Expression) to identify SL partners of germline mutations in three well-known breast cancer CPGs, BRCA1, BRCA2, and PALB2. In Aim 2, we will experimentally validate the SL partners for each germline mutation identified in Aim 1 in two steps. First, in Aim 2a, we will validate the SL partners for each mutation using genetic knockdown of the SL partner with inducible shRNA in isogenic breast cancer cell lines (+/-mutation) in vitro. Next, in Aim 2b, we will validate the top three mutation-SL partner combinations in human breast cancer cell line xenografts in mice using genetic and pharmacologic knockdown. We expect the proposed study will identify novel druggable targets for treatment and chemoprevention in breast cancer. The long-term objective is to develop a new systematic methodology to identify potential targeted therapies for treatment and chemoprevention of patients with germline mutations in cancer. The proposed study responds to PQ3 and will elucidate how tumors with germline mutations respond to targeted therapies based on genetic SL interactions between germline mutations and somatic alterations.

项目摘要 我们提出了一种新型的计算方法，以鉴定具有种系突变的癌症的治疗靶标 利用泛 - 癌原发性人类肿瘤的种系突变和体细胞改变的综合分析 数据。此方法将用于鉴定乳腺癌中新的治疗靶标的用于种系突变 BRCA1，BRCA2和PALB2。种系突变会增加癌症风险的基因称为 癌症易感基因（CPGS）。已经知道了许多CPG，并且DNA的最新进展 排序具有更多CPG发现的希望。鉴于种系的癌症风险增加 CPG突变，迫切需要确定特定于 这些突变。这些突变中的大多数是功能丧失的改变，而不是直接吸毒。合成的 致死性为一种方法提供了确定这些突变的新治疗靶标的基础。现在， 使用大规模的功能屏幕确定合成致命（SL）伴侣，这些屏幕受到负面影响 通过细胞培养条件的人为性和细胞系的可用性有限 正确的癌症环境。我们建议挖掘患者肿瘤数据库以识别种系的SL伴侣 突变。我们的假设是种系突变的SL伴侣将被选择性扩增或从未删除 并在具有突变的原发性肿瘤样品中过表达。以前，我们开发了一本小说 计算方法（挖掘合成致死物，MISL）分析原发性肿瘤数据以识别SL伴侣 特定肿瘤类型中的体细胞突变。我们建议开发基于MISL的计算管道 确定与种系突变的遗传相互作用。在AIM 1中，我们将开发基于MISL的计算 鉴定癌症种系突变的SL伴侣的方法。该方法将应用于基因组和 来自多个大型癌症基因组测序项目和基因表达数据的转录组数据集 对于GTEX（基因型 - 组织表达）的正常组织，以鉴定在生殖线突变中的SL伴侣 三个著名的乳腺癌CPG，BRCA1，BRCA2和PALB2。在AIM 2中，我们将在实验中验证 AIM 1中每个种系突变的SL伴侣分为两个步骤。首先，在AIM 2A中，我们将验证 SL伴侣使用SL伴侣的遗传敲低的同源乳房中的诱导shRNA的遗传敲低 癌细胞系（+/-突变）体外。接下来，在AIM 2B中，我们将验证前三名突变SL合作伙伴 使用遗传和药理敲低的小鼠中人类乳腺癌细胞系异种移植的组合。 我们预计拟议的研究将确定乳房治疗和化学预防的新型可药物靶标 癌症。长期目标是开发一种新的系统方法，以识别潜在的目标 治疗和化学预防疗法的癌症患者。拟议的研究 对PQ3做出反应，并将阐明具有种系突变的肿瘤如何应对靶向疗法的反应 种系突变与体细胞改变之间的遗传SL相互作用。