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Mass spectrometry-based serum and plasma peptidome profiling for prediction of treatment outcome in patients with solid malignancies.
Labots, Mariette; Schütte, Lisette M; van der Mijn, Johannes C; Pham, Thang V; Jiménez, Connie R; Verheul, Henk M W.
Afiliación
  • Labots M; Department of Medical Oncology, VU University Medical Center, Amsterdam, The Netherlands.
  • Schütte LM; Department of Medical Oncology, VU University Medical Center, Amsterdam, The Netherlands.
  • van der Mijn JC; Department of Medical Oncology, VU University Medical Center, Amsterdam, The Netherlands.
  • Pham TV; Department of Medical Oncology, VU University Medical Center, Amsterdam, The Netherlands.
  • Jiménez CR; Department of Medical Oncology, VU University Medical Center, Amsterdam, The Netherlands.
  • Verheul HM; Department of Medical Oncology, VU University Medical Center, Amsterdam, The Netherlands h.verheul@vumc.nl.
Oncologist ; 19(10): 1028-39, 2014 Oct.
Article en En | MEDLINE | ID: mdl-25187478
ABSTRACT

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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Péptidos / Espectrometría de Masas / Carcinoma de Pulmón de Células no Pequeñas / Neoplasias Pulmonares / Neoplasias Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies / Systematic_reviews Límite: Humans Idioma: En Revista: Oncologist Asunto de la revista: NEOPLASIAS Año: 2014 Tipo del documento: Article País de afiliación: Países Bajos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Péptidos / Espectrometría de Masas / Carcinoma de Pulmón de Células no Pequeñas / Neoplasias Pulmonares / Neoplasias Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies / Systematic_reviews Límite: Humans Idioma: En Revista: Oncologist Asunto de la revista: NEOPLASIAS Año: 2014 Tipo del documento: Article País de afiliación: Países Bajos