Large-scale CRISPR screens have identified essential genes across cancer cell lines, but links between tumor functional properties and specific dependencies require investigation to reveal the mechanisms underlying dependencies and broaden understanding of targeted therapy.

We selected 47 breast cancer cell lines from the Cancer Cell Line Encyclopedia (CCLE) with multi-omics data including gene dependency; somatic mutations; copy number alterations; and transcriptomic, proteomic, metabolomic, and methylation data. We established a dependency marker association (DMA) analytic pipeline by using linear regression modeling to assess associations between 3,874 representative gene dependencies and multi-omics markers. Additionally, we conducted non-negative matrix factorization clustering, to stratify breast cancer cell lines according to gene dependency features, and investigated cluster-specific DMAs.

We interpreted valuable DMAs according to two primary aspects. First, dependencies associated with gain-of-function alterations revealed addiction to lactate transporter SLC16A3, thus suggesting a promising therapeutic target. Second, dependencies associated with loss-of-function alterations included synthetic lethality (SL), collateral SL, and prioritized metabolic SL, encompassing paralog SL (e.g., IMPDH1 and IMPDH2), single pathway SL (e.g., GFPT1 and UAP1), and alternative pathway SL (e.g., GPI and PGD). DMA analysis of the two clusters with divergent dependency signatures demonstrated that cluster1 cell lines exhibited extensive metabolism with mitochondrial protein dependencies, whereas cluster2 displays enhanced cell signaling, and reliance on DNA replication and membrane organelle regulators.

We established a DMA analysis pipeline linking the gene dependencies of breast cancer cell lines to multi-omics characteristics, thus elucidating the underpinnings of tumor dependencies and offering a valuable resource for developing novel precision treatment strategies incorporating relevant markers.