SynthesisThe oncologist2014
Mass spectrometry-based serum and plasma peptidome profiling for prediction of treatment outcome in patients with solid malignancies.
Synthesis in The oncologist, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The liver matrisome - looking beyond collagens.JHEP reports : innovation in hepatology · 2020Review
- Proteomics-based insights into mitogen-activated protein kinase inhibitor resistance of cerebral melanoma metastases.Clinical proteomics · 2018Article
- Evaluation of a tyrosine kinase peptide microarray for tyrosine kinase inhibitor therapy selection in cancer.Experimental & molecular medicine · 2016Article
- Evaluation of potential circulating biomarkers for prediction of response to chemoradiation in patients with glioblastoma.Journal of neuro-oncology · 2016Article
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Authors and funding
6 authors.
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Abstract
introductionTreatment 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.
methodsPubMed 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.
resultsThirty-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.
conclusionPretreatment 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.
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