Proteomic Dynamics of Breast Cancer Cell Lines Identifies Potential Therapeutic Protein Targets

Treatment and relevant targets for breast cancer (BC) remain limited, especially for triple-negative BC (TNBC). We identified 6091 proteins of 76 human BC cell lines using data-independent acquisition (DIA). Integrating our proteomic findings with prior multi-omics datasets, we found that including proteomics data improved drug sensitivity predictions and provided insights into the mechanisms of action. We subsequently profiled the proteomic changes in nine cell lines (five TNBC and four non-TNBC) treated with EGFR/AKT/mTOR inhibitors. In TNBC, metabolism pathways were dysregulated after EGFR/mTOR inhibitor treatment, while RNA modification and cell cycle pathways were affected by AKT inhibitor. This systematic multi-omics and in-depth analysis of the proteome of BC cells can help prioritize potential therapeutic targets and provide insights into adaptive resistance in TNBC. [Display omitted] •Proteomic analysis of 76 breast cancer cell lines and 9 lines with perturbation.•Machine learning-based multi-omics predicts breast cancer cells’ sensitivity to 90 drugs.•Longitudinal proteomic profiling provides insights into drug resistance. Here we present a deep proteome resource of 76 human breast cancer cell lines. Each cell line included four replicates. Then we built machine learning models using these multi-omics data to predict BC cells’ sensitivity to 90 drugs. We further performed longitudinal proteomic profiling of 9 cell lines perturbed by three drugs. Together, this study presents the so far most comprehensive proteomic investigation of BC cell lines and uncovered novel molecular insights for TNBC cells.