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.
A germline mutation is a change in the body's reproductive cells? (egg or sperm) DNA that becomes part of every cell in the body of the offspring. Genes in which germline mutations confer increased risks of cancer are called cancer predisposition genes (CPGs). We will develop a computational method to study CPG germline mutations in relation to other genetic changes in breast cancer, so we can identify new ways to intercept the disease and devise new strategies for treatment and prevention.
