Data Descriptor: Proteomic profiling across breast cancer cell lines and models

We performed quantitative proteomics of 60 human-derived breast cancer cell lines to a depth of ~13,000 proteins. The resulting high-throughput datasets were assessed for quality and reproducibility. We used the two omics datasets to identify and characterize the subtypes of breast cancer and showed that they conform with known transcriptional subtypes, revealing that molecular subtypes are preserved even in under-sampled molecular feature datasets. The datasets are made freely available as a public resource on the LINCS portal. We anticipate that these datasets, either in isolation or combination with measurements of complementary molecular features, can be mined for the purpose of predicting drug response, informing context in mathematical models of signaling pathways, inferring cell-type or subtype specific pathways activities of unperturbed cellular states, and identifying markers of sensitivity or resistance to therapeutics.