Project PXD008222

PRIDE Assigned Tags:
Biomedical Dataset



The Proteomic Landscape of Triple-Negative Breast Cancer


Triple-negative breast cancer is a heterogeneous disease characterized by poor clinical outcomes and a shortage of targeted treatment options. To discover molecular features of triple-negative breast cancer, we performed quantitative proteomics analysis of twenty human-derived breast cell lines and four primary breast tumors to a depth of more than 12,000 distinct proteins. We used this data to identify breast cancer subtypes at the protein level and demonstrate the precise quantification of biomarkers, signaling proteins, and biological pathways by mass spectrometry. We integrated proteomics data with exome sequence resources to identify genomic aberrations that affect protein expression. We performed a high-throughput drug screen to identify protein markers of drug sensitivity and understand the mechanisms of drug resistance. The genome and proteome provide complementary information that, when combined, yield a powerful engine for therapeutic discovery. This resource is available to the cancer research community to catalyze further analysis and investigation.

Sample Processing Protocol

Samples were lysed in denaturing buffer and centrifuged at 12,000 3 g for 10 min to pellet insoluble material. Protein extracts were reduced with 5 mM DTT at 55C and alkylated with 15 mM iodoacetamide at room temperature in the dark. Extracts from each sample (25 mg) were diluted and digested in solution overnight with either LysC (Wako Pure Chemicals Industries) or sequencing-grade trypsin (Promega). Peptides were desalted and fractionated on StageTips (Rappsilber et al., 2007) by basic reverse-phase using a stepwise gradient of increasing acetonitrile (5%, 10%, 15%, 25%, and 80%) in 0.1% NH4OH. The resulting fractions were analyzed by LCMS/MS. Peptide fractions were analyzed on an EASY-nLC-1000 (Thermo Scientific) coupled to a hybrid quadrupole-orbitrap Q-Exactive mass spectrometer (Thermo Scientific) configured for data-dependent acquisition.

Data Processing Protocol

Raw mass spectra were searched using Sequest (release 2012.01.0 of UW Sequest) against a concatenated forward and reverse version of the Uniprot human protein sequence database (v11/29/2012). Peptide spectral matches for all fractions corresponding to the same sample were filtered to reach a protein identification FDR of less than 1%, resulting in an aggregate peptide-level FDR of less than 0.1% for the entire dataset. Protein quantifications were calculated using the iBAQ approach (Schwanhäusser et al., 2011).


Martin Frejno, TUM
Judit Villén, University of Washington ( lab head )

Submission Date


Publication Date



Q Exactive


Not available


Not available

Experiment Type

Shotgun proteomics


    Lawrence RT, Perez EM, Hernández D, Miller CP, Haas KM, Irie HY, Lee SI, Blau CA, Villén J. The Proteomic Landscape of Triple-Negative Breast Cancer. Cell Rep. 2015 11(6):990 PubMed: 28843283