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Response and toxicity prediction by MALDI-TOF-MS serum peptide profiling in patients with non-small cell lung cancer.

Authors :
Rovithi M
Lind JS
Pham TV
Voortman J
Knol JC
Verheul HM
Smit EF
Jimenez CR
Source :
Proteomics. Clinical applications [Proteomics Clin Appl] 2016 Jul; Vol. 10 (7), pp. 743-9. Date of Electronic Publication: 2016 May 27.
Publication Year :
2016

Abstract

Purpose: We validated a previously reported proteomic signature, associated with treatment outcome, in an independent cohort of patients with non-small cell lung cancer (NSCLC). A novel peptide signature was developed to predict toxicity.<br />Experimental Design: Using automated magnetic C18 bead-assisted serum peptide capture coupled to MALDI-TOF MS, we conducted serum peptide profiling of 50 NCSLC patients participating in a phase II trial of erlotinib and sorafenib. On the obtained peptide mass profiles, we applied a previously described proteomic classification algorithm. Additionally, associations between observed side effects and peptide profiles were investigated.<br />Results: Application of the previously acquired algorithm successfully classified the new cohort of patients in groups significantly associated with the outcome. The "poor" group exhibited shorter median progression-free survival (PFS) and overall survival (OS) of 1.35 and 1.98 months (with p = 0.00677 and p = 0.00002, respectively) while the "good" group had significantly longer PFS and OS (10.63 and 14.4 months with p = 0.00142 and p = 0.00002, respectively), compared to average OS and PFS. Two specific peptides were detected in the sera of all patients that developed severe toxicity.<br />Conclusions and Clinical Relevance: Our results provide an algorithm that, following prospective validation in larger cohorts, could assist treatment selection of patients with NSCLC in the first line setting.<br /> (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Details

Language :
English
ISSN :
1862-8354
Volume :
10
Issue :
7
Database :
MEDLINE
Journal :
Proteomics. Clinical applications
Publication Type :
Academic Journal
Accession number :
27040893
Full Text :
https://doi.org/10.1002/prca.201600025