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Introduction: Treatment selection tools are needed to enhance the efficacy of targeted treatment in patients with solid malignancies. Providing a readout of aberrant signaling pathways and proteolytic events, mass spectrometry-based (MS-based) peptidomics enables identification of predictive biomarkers, whereas the serum or plasma peptidome may provide easily accessible signatures associated with response to treatment. In this systematic review, we evaluate MS-based peptide profiling in blood for prompt clinical implementation., Methods: PubMed and Embase were searched for studies using a syntax based on the following hierarchy: (a) blood-based matrix-assisted or surface-enhanced laser desorption/ionization time-of-flight MS peptide profiling (b) in patients with solid malignancies (c) prior to initiation of any treatment modality, (d) with availability of outcome data., Results: Thirty-eight studies were eligible for review; the majority were performed in patients with non-small cell lung cancer (NSCLC). Median classification prediction accuracy was 80% (range: 66%-93%) in 11 models from 14 studies reporting an MS-based classification model. A pooled analysis of 9 NSCLC studies revealed clinically significant median progression-free survival in patients classified as "poor outcome" and "good outcome" of 2.0 ± 1.06 months and 4.6 ± 1.60 months, respectively; median overall survival was also clinically significant at 4.01 ± 1.60 months and 10.52 ± 3.49 months, respectively., Conclusion: Pretreatment MS-based serum and plasma peptidomics have shown promising results for prediction of treatment outcome in patients with solid tumors. Limited sample sizes and absence of signature validation in many studies have prohibited clinical implementation thus far. Our pooled analysis and recent results from the PROSE study indicate that this profiling approach enables treatment selection, but additional prospective studies are warranted., (©AlphaMed Press.)

The tyrosine kinase inhibitor sunitinib is an effective first-line treatment for patients with advanced renal cell carcinoma (RCC). Hypothesizing that a functional read-out by mass spectrometry-based (phospho, p-)proteomics will identify predictive biomarkers for treatment outcome of sunitinib, tumor tissues of 26 RCC patients were analyzed. Eight patients had primary resistant (RES) and 18 sensitive (SENS) RCC. A 78 phosphosite signature (p < 0.05, fold-change > 2) was identified; 22 p-sites were upregulated in RES (unique in RES: BCAR3, NOP58, EIF4A2, GDI1) and 56 in SENS (35 unique). EIF4A1/EIF4A2 were differentially expressed in RES at the (p-)proteome and, in an independent cohort, transcriptome level. Inferred kinase activity of MAPK3 (p = 0.026) and EGFR (p = 0.045) as determined by INKA was higher in SENS. Posttranslational modifications signature enrichment analysis showed that different p-site-centric signatures were enriched (p < 0.05), of which FGF1 and prolactin pathways in RES and, in SENS, vanadate and thrombin treatment pathways, were most significant. In conclusion, the RCC (phospho)proteome revealed differential p-sites and kinase activities associated with sunitinib resistance and sensitivity. Independent validation is warranted to develop an assay for upfront identification of patients who are intrinsically resistant to sunitinib. [ABSTRACT FROM AUTHOR]

Background Mass spectrometry based phosphoproteomics emerged as advantageous approach for the analysis of tyrosine phosphorylation on proteins and tyrosine kinase signaling. Immunoaffinity purification is required for comprehensive analysis. Here we compared the performance of two antibodies for label-free phosphotyrosine-based phosphoproteomics. Methods Phosphopeptide immunoprecipitation of six technical replicates corresponding to 10 mg protein from HCT116 cells was performed using agarose bead-coupled phosphotyrosine antibodies P-Tyr-1000 (N = 3) and 4G10 (N = 3). NanoLC–MS/MS was performed using a Q Exactive mass spectrometer. For relative quantitation of protein phosphorylation, spectral counts of phosphoproteins and ion intensities of phosphopeptides were determined using MaxQuant. Results From the 3 samples incubated with P-Tyr-1000 a total of 689 phosphopeptides were identified with 60% ID reproducibility. The phosphopeptide capture using 4G10 resulted in a total of 421 at 46% ID reproducibility. The P-Tyr-1000 was applied to EGFR mutated U87 cells. Erlotinib reduced EGFR phosphorylation with 59% at y978, y1125, y1138, y1172, and y1197. EGFR inhibition was accompanied by enhanced phosphorylation of FYN, MET, PTK2, DYRK1A, MAPK1 and EPHA2. Conclusion The P-Tyr-1000 phosphotyrosine antibody performs superiorly when compared to 4G10 antibody for label-free phosphotyrosine-based phosphoproteomics. This workflow allows evaluation of drug target phosphorylation and may give insights in the pharmacodynamic effects of tyrosine kinase inhibitors. Clinical significance In the past decade multiple tyrosine kinase inhibitors (TKIs) have been implemented in standard treatment regimens for patients with cancer. Unfortunately the majority of patients develops resistance to these drugs. Reliable tools for analysis of pharmacodynamic effects and drug resistance mechanisms are therefore warranted. Phosphoproteomic analyses have meanwhile emerged as a sophisticated approach for the determination of protein phosphorylation. These analyses rely on antibodies for enrichment of tyrosine-phosphorylated peptides. Here we compared two commercially available phosphotyrosine antibodies and show that P-Tyr-1000 yields 64% more phosphopeptides than 4G10 antibody, while including almost all 4G10 captured phosphopeptides. The workflow can be reproducibly performed at intermediate protein input levels of 10 mg. Furthermore, application of the P-Tyr-1000 antibody in a standardized phosphoproteomics workflow allows relative quantitation of drug target inhibition and provides insights in alternative signaling pathways in cancer cells. This article is part of a Special Issue entitled: HUPO 2014. [ABSTRACT FROM AUTHOR]

Kinases are key enzymes involved in deregulated signal transduction associated with cancer development and progression. The advent of personalized medicine drives the development of new diagnostic tools for patient stratification and therapy selection Ginsburg and Willard (Transl Res 154:277-287, ). Since deregulation of kinase-mediated signal transduction is implied in tumorigenesis, the analysis of all kinases (the kinome) active in a particular tumor may yield tumor-specific information on aberrant cell signalling pathways. Tumor tissue kinase activity profiles may correlate with response to therapy and therefore may be used for future therapy selection. In this Trend paper we describe peptide array and mass spectrometry-based technologies and new developments for kinome profiling, and we present an outlook towards future implementation of therapy selection based on kinome profiling in clinical practice. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

Identification of predictive biomarkers for targeted therapies requires information on drug exposure at the target site as well as its effect on the signaling context of a tumor. To obtain more insight in the clinical mechanism of action of protein kinase inhibitors (PKIs), we studied tumor drug concentrations of protein kinase inhibitors (PKIs) and their effect on the tyrosine-(pTyr)-phosphoproteome in patients with advanced cancer. Tumor biopsies were obtained from 31 patients with advanced cancer before and after 2 weeks of treatment with sorafenib (SOR), erlotinib (ERL), dasatinib (DAS), vemurafenib (VEM), sunitinib (SUN) or everolimus (EVE). Tumor concentrations were determined by LC-MS/MS. pTyr-phosphoproteomics was performed by pTyr-immunoprecipitation followed by LC-MS/MS. Median tumor concentrations were 2–10 µM for SOR, ERL, DAS, SUN, EVE and >1 mM for VEM. These were 2–178 × higher than median plasma concentrations. Unsupervised hierarchical clustering of pTyr-phosphopeptide intensities revealed patient-specific clustering of pre- and on-treatment profiles. Drug-specific alterations of peptide phosphorylation was demonstrated by marginal overlap of robustly up- and downregulated phosphopeptides. These findings demonstrate that tumor drug concentrations are higher than anticipated and result in drug specific alterations of the phosphoproteome. Further development of phosphoproteomics-based personalized medicine is warranted. [ABSTRACT FROM AUTHOR]