Proteomic profiling of breast cancer cell lines and models


Marian Kalocsay, Matthew J. Berberich, Robert A. Everley, Maulik K. Nariya, Mirra Chung, Ben Gaudio, Chiara Victor, Gary A. Bradshaw, Marc Hafner, Peter K. Sorger, Caitlin E.Mills, and Kartik Subramanian. In Preparation. “Proteomic profiling of breast cancer cell lines and models”. Publisher's Version


We performed quantitative proteomics on 61 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 datasets to identify and characterize the subtypes of breast cancer and showed that they conform to known transcriptional subtypes, revealing that molecular subtypes are preserved even in under-sampled protein feature sets. All datasets are freely available as public resources on the LINCS portal. We anticipate that these datasets, either in isolation or in combination with complimentary measurements such as genomics, transcriptomics and phosphoproteomics, can be mined for the purpose of predicting drug response, informing cell line specific context in models of signalling pathways, and identifying markers of sensitivity or resistance to therapeutics.