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

> **NIH NIH R21** · CALIFORNIA NORTHSTATE UNIVERSITY · 2020 · $68,738

## Abstract

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 ch...

## Key facts

- **NIH application ID:** 10016221
- **Project number:** 5R21CA231111-03
- **Recipient organization:** CALIFORNIA NORTHSTATE UNIVERSITY
- **Principal Investigator:** Yihui Shi
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $68,738
- **Award type:** 5
- **Project period:** 2019-09-11 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10016221

## Citation

> US National Institutes of Health, RePORTER application 10016221, Identification of Synthetic Lethal Partners of Cancer Germline Mutations using PanCancer Human Primary Tumor Data (5R21CA231111-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10016221. Licensed CC0.

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