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

使用 PanCancer 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: 10016221
• 负责人: Yihui Shi
• 金额: $ 6.87万
• 依托单位: CALIFORNIA NORTHSTATE UNIVERSITY
• 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 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10016221
• 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; computational pipelines; computerized tools; egg; gene discovery; genome sequencing; improved; in vivo; inhibitor/antagonist; knock-down; leukemia; loss of function mutation; malignant breast neoplasm; multiple datasets; 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 我们提出了一种新的计算方法来确定生殖系突变癌症的治疗靶点 使用来自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 数据这种方法将用于确定乳腺癌中生殖系突变的新治疗靶点， BRCA1、BRCA2和PALB2。生殖系突变增加癌症风险的基因被称为 癌症易感基因（CPG）。许多CPG是已知的，DNA的最新进展 测序有望发现更多的CPG。考虑到生殖细胞癌患者患癌风险增加， CPG突变，迫切需要确定新的治疗和化学预防策略， 这些突变。大多数这些突变是功能丧失的改变，不能直接用药。合成 致死性为鉴定这些突变的新治疗靶点的方法提供了基础。目前， 使用大规模功能筛选来识别合成致死（SL）伴侣，但会受到负面影响 由于细胞培养条件的人为性和具有特定突变的细胞系的有限可用性， 正确的癌症背景。我们建议挖掘患者肿瘤数据库，以确定生殖系SL合作伙伴 突变。我们的假设是，SL合作伙伴的种系突变将被选择性扩增或永远不会删除 并且在携带突变的原发性肿瘤样品中也过表达。以前，我们开发了一种小说 一种计算方法（Mining Synthetic Lethals，MiSL），分析原发性肿瘤数据以识别SL伴侣 特定肿瘤类型的体细胞突变。我们建议开发一个基于MiSL的计算管道， 鉴定与生殖系突变的遗传相互作用。在目标1中，我们将开发一个基于MiSL的计算 方法来鉴定癌症中生殖系突变的SL配偶体。该方法将应用于基因组和 来自多个大规模癌症基因组测序项目的转录组数据集和基因表达数据 用于GTEx（基因型-组织表达）的正常组织，以鉴定 三种众所周知的乳腺癌CPG，BRCA1，BRCA2和PALB2。在目标2中，我们将通过实验验证 在目标1中鉴定的每个种系突变的SL配偶体分两步进行。首先，在目标2a中，我们将验证 在同基因乳腺癌中使用具有诱导型shRNA的SL配偶体的遗传敲低的每个突变的SL配偶体 体外癌细胞系（+/-突变）。接下来，在目标2b中，我们将验证前三个突变-SL伙伴 使用遗传和药理学敲低在小鼠中的人乳腺癌细胞系异种移植物中的组合。 我们希望这项研究能够为乳腺癌的治疗和化学预防找到新的药物靶点。 癌长期目标是制定一种新的系统方法，以确定潜在的目标 用于治疗和化学预防具有癌症生殖系突变的患者的疗法。拟定研究 对PQ3有反应，并将阐明具有生殖系突变的肿瘤如何对靶向治疗产生反应， 生殖系突变和体细胞改变之间的遗传SL相互作用。