Proteomics of genetically engineered mouse mammary tumors identifies fatty acid metabolism members as potential predictive markers for cisplatin resistance.

In contrast to various signatures that predict the prognosis of breast cancer patients, markers that predict chemotherapy response are still elusive. To detect such predictive biomarkers, we investigated early changes in protein expression using two mouse models for distinct breast cancer subtypes who have a differential knock-out status for the breast cancer 1, early onset (Brca1) gene. The proteome of cisplatin-sensitive BRCA1-deficient mammary tumors was compared with that of cisplatin-resistant mammary tumors resembling pleomorphic invasive lobular carcinoma. The analyses were performed 24 h after administration of the maximum tolerable dose of cisplatin. At this time point, drug-sensitive BRCA1-deficient tumors showed DNA damage, but cells were largely viable. By applying paired statistics and quantitative filtering, we identified highly discriminatory markers for the sensitive and resistant model. Proteins up-regulated in the sensitive model are involved in centrosome organization, chromosome condensation, homology-directed DNA repair, and nucleotide metabolism. Major discriminatory markers that were up-regulated in the resistant model were predominantly involved in fatty acid metabolism, such as fatty-acid synthase. Specific inhibition of fatty-acid synthase sensitized resistant cells to cisplatin. Our data suggest that exploring the functional link between the DNA damage response and cancer metabolism shortly after the initial treatment may be a useful strategy to predict the efficacy of cisplatin.

Feasibility of label-free phosphoproteomics and application to base-line signaling of colorectal cancer cell lines.

Robust phosphopeptide enrichment methods with minimal fractionation are required to profile signaling network analysis in cancer cell lines and tissues. We assessed performance of single-shot LC-MS/MS label-free phosphoproteomics using TiOx-based phosphopeptide enrichment and report phosphopeptide identification reproducibility (75.8%), depth of identification (6014-6150 phosphopeptides) and reproducibility of label-free quantification (CV 17.8%). Subsequently, we have profiled the baseline global phosphorylation of 8 colorectal cancer (CRC) cell lines representing different CRC prognostic subtypes. Global single-shot phosphoproteomics can distinguish CRC subtypes previously identified by transcriptomics and identifies signaling proteins and processes associated with the CCS3 poor prognosis subtype. Data are available via ProteomeXchange with identifiers PXD001546 and PXD001550. BIOLOGICAL SIGNIFICANCE: Label-free single-shot phosphoproteomics is a mature workflow that can be used for global quantitative profiling of biological cell lines and tissues to map signaling networks in comparative analyses. Here we show the feasibility of label-free profiling of CRC cell lines at sample input levels compatible with clinical samples such as tumor biopsies. This article is part of a Special Issue entitled: HUPO 2014.