Integrating large-scale in vitro functional genomic screen and multi-omics data to identify novel breast cancer targets.

Purpose: Our goal is to leverage publicly available whole transcriptome and genome-wide CRISPR-Cas9 screen data to identify and prioritize novel breast cancer therapeutic targets.

Methods: We used DepMap dependency scores > 0.5 to identify genes that are potential therapeutic targets in 48 breast cancer cell lines. We removed genes that were pan-essential or were not expressed in TCGA breast cancer cohort. Genes were prioritized based on druggability using the Drug-Gene Interaction Database. Targets were defined separately for ER+, HER2+, and TNBC. A broader list of genes with dependency score > 0.25 were used to assess the associations between dependency scores and mutations and copy number variations (CNV) to identify potential synthetic lethal relationships and to map survival critical genes into biological pathways.

Results: 66, 53, and 29 genes were prioritized as targets in ER+, HER2+, and TNBC, respectively. These included known actionable targets and many novel targets. ER+ included FOXA1, GATA3, LDB1, TRPS1, NAMPT, WDR26, and ZNF217; HER2+ cancers included STX4, HECTD1, and TBL1XR1; and TNBC included GFPT1 and GPX4. Synthetic lethal associations revealed 5 and 19 significant associations between potential survival critical genes and mutations in HER2+ and TNBC, respectively. For example, PIK3CA mutation increased dependency on NDUFS3 in HER2+ cancers, and CNTRL mutation increased dependency on electron transport chain (ETC) genes in TNBC. 329, 747, and 622 CNVs showed synthetic lethal association in ER+, HER2+, and TNBC, respectively.

Conclusion: We provide a genome-wide drug target prioritization list for breast cancer derived from integrated large-scale omics data.