Proteomic Profiling Across Breast Cancer Cell Lines and Models

Authors

Marian Kalocsay
Matthew J Berberich
Robert A Everley
Maulik K Nariya
Mirra Chung
Benjamin Gaudio
Chiara Victor
Gary A Bradshaw
Robyn J Eisert
Marc Hafner
Peter K Sorger
Caitlin E Mills
Kartik Subramanian

Publication Date

8-4-2023

Journal

Scientific Data

Abstract

We performed quantitative proteomics on 60 human-derived breast cancer cell line models 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.

Keywords

Female, Humans, Breast Neoplasms, Cell Line, Cell Line, Tumor, Genomics, Proteomics, Reproducibility of Results

Comments

Data Citations

Supplementary Materials

PMID: 37542042