Effects of Cancer Presence and Therapy on the Platelet Proteome.

Platelets are involved in tumor angiogenesis and cancer progression. Previous studies indicated that cancer could affect platelet content. In the current study, we investigated whether cancer-associated proteins can be discerned in the platelets of cancer patients, and whether antitumor treatment may affect the platelet proteome. Platelets were isolated from nine patients with different cancer types and ten healthy volunteers. From three patients, platelets were isolated before and after the start of antitumor treatment. Mass spectrometry-based proteomics of gel-fractionated platelet proteins were used to compare patients versus controls and before and after treatment initiation. A total of 4059 proteins were detected, of which 50 were significantly more abundant in patients, and 36 more in healthy volunteers. Eight of these proteins overlapped with our previous cancer platelet proteomics study. From these data, we selected potential biomarkers of cancer including six upregulated proteins (RNF213, CTSG, PGLYRP1, RPL8, S100A8, S100A9) and two downregulated proteins (GPX1, TNS1). Antitumor treatment resulted in increased levels of 432 proteins and decreased levels of 189 proteins. In conclusion, the platelet proteome may be affected in cancer patients and platelets are a potential source of cancer biomarkers. In addition, we found in a small group of patients that anticancer treatment significantly changes the platelet proteome.

Proteome analysis of non-small cell lung cancer cell line secretomes and patient sputum reveals biofluid biomarker candidates for cisplatin response prediction.

Molecular markers are urgently needed to select non-small cell lung cancer (NSCLC) patients most likely to benefit from platinum-based chemotherapies. Of particular interest are proteins that can be found in biofluids like sputum for non-invasive detection. Therefore, we profiled the secretomes of 6 NSCLC cell lines with varying IC50-values for cisplatin, using label-free GeLC-MS/MS-based proteomics. Out of a total dataset of 2610 proteins, 304 proteins showed significant differences in expression levels between cisplatin sensitive and insensitive cell lines. Functional data mining revealed that the secretion of typically extracellular factors was associated with a higher sensitivity towards cisplatin, while cisplatin insensitivity correlated with increased secretion of theoretically intra-cellular proteins. Stringent statistical analysis and quantitative filtering yielded 58 biomarker candidates, 34 of which could be detected in clinical biofluids of lung cancer patients such as sputum using label-free LC-MS/MS-based proteomics. To assess performance of these biofluid biomarker candidates, we correlated protein expression with patient survival using a publically available clinical gene expression data set (GSE14814). We thus identified 3 top candidates with potential predictive value in determining cisplatin response (UGGT1, COL6A1 and MAP4) for future development as non-invasive biomarkers to guide treatment decisions. SIGNIFICANCE: Platinum-based chemotherapies are still the standard of care for NSCLC and other lung cancer types in the clinic today. However, due to chemoresistance, many patients suffer from the toxic side effects of these treatments without gaining any benefit in terms of survival. To date, no molecular biomarkers are available to predict clinical outcome of platinum-based chemotherapy. Because proteins present the functional read-out of genetic, epigenetic and translational events in the cell, a protein test is likely to be particularly suitable for response prediction. Of high relevance are proteins that are shed or secreted from cells, for example at primary tumor sites, and can be found in easily accessible biofluids like sputum for non-invasive detection. Here, we report the proteome profiling of the conditioned media (secretomes) of a panel of NSCLC cell lines in relation to cisplatin IC50 values, as a pre-clinical model, and of patient sputum as a clinical, lung cancer relevant biofluid. Using this approach in conjunction with exploration of the predictive potential in a transcriptome lung cancer patient dataset, we reveal biofluid biomarker candidates that, with further validation, may be used for non-invasive cisplatin response prediction in the future.

Peptide-mediated 'miniprep' isolation of extracellular vesicles is suitable for high-throughput proteomics.

Extracellular vesicles (EVs) are cell-secreted membrane vesicles enclosed by a lipid bilayer derived from endosomes or from the plasma membrane. Since EVs are released into body fluids, and their cargo includes tissue-specific and disease-related molecules, they represent a rich source for disease biomarkers. However, standard ultracentrifugation methods for EV isolation are laborious, time-consuming, and require high inputs. Ghosh and co-workers recently described an isolation method utilizing Heat Shock Protein (HSP)-binding peptide Vn96 to aggregate HSP-decorated EVs, which can be performed at small 'miniprep' scale. Based on microscopic, immunoblot, and RNA sequencing analyses this method compared well with ultracentrifugation-mediated EV isolation, but a detailed proteomic comparison was lacking. Therefore, we compared both methods using label-free proteomics of replicate EV isolations from HT-29 cell-conditioned medium. Despite a 30-fold different scale (ultracentrifugation: 60 ml/Vn96-mediated aggregation: 2 ml) both methods yielded comparable numbers of identified proteins (3115/3085), with similar reproducibility of identification (72.5%/75.5%) and spectral count-based quantification (average CV: 31%/27%). EV fractions obtained with either method contained established EV markers and proteins linked to vesicle-related gene ontologies. Thus, Vn96 peptide-mediated aggregation is an advantageous, simple and rapid approach for EV isolation from small biological samples, enabling high-throughput analysis in a biomarker discovery setting.

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.

Whole gel processing procedure for GeLC-MS/MS based proteomics.

BACKGROUND: SDS-PAGE followed by in-gel digestion (IGD) is a popular workflow in mass spectrometry-based proteomics. In GeLC-MS/MS, a protein lysate of a biological sample is separated by SDS-PAGE and each gel lane is sliced in 5-20 slices which, after IGD, are analyzed by LC-MS/MS. The database search results for all slices of a biological sample are combined yielding global protein identification and quantification for each sample. In large scale GeLC-MS/MS experiments the manual processing steps including washing, reduction and alkylation become a bottleneck. Here we introduce the whole gel (WG) procedure where, prior to gel slice cutting, the processing steps are carried out on the whole gel. RESULTS: In two independent experiments human HCT116 cell lysate and mouse tumor tissue lysate were separated by 1D SDS PAGE. In a back to back comparison of the IGD procedure and the WG procedure, both protein identification (>80% overlap) and label-free protein quantitation (R2=0.94) are highly similar between procedures. Triplicate analysis of the WG procedure of both HCT116 cell lysate and formalin-fixed paraffin embedded (FFPE) tumor tissue showed identification reproducibility of >88% with a CV<20% on protein quantitation. CONCLUSIONS: The whole gel procedure allows for reproducible large-scale differential GeLC-MS/MS experiments, without a prohibitive amount of manual processing and with similar performance as conventional in-gel digestion. This procedure will especially enable clinical proteomics for which GeLC-MS/MS is a popular workflow and sample numbers are relatively high.