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Mass spectrometry-based serum peptidome profiling accurately and reliably predicts outcomes of pemetrexed plus platinum chemotherapy in patients with advanced lung adenocarcinoma
BACKGROUND: Although pemetrexed plus cis/carboplatin has become the most effective chemotherapy regimen for patients with advanced lung adenocarcinoma, predictive biomarkers are not yet available, and new tools to identify chemosensitive patients who would likely benefit from this treatment are desp...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5464620/ https://www.ncbi.nlm.nih.gov/pubmed/28594947 http://dx.doi.org/10.1371/journal.pone.0179000 |
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author | Wang, Lin Tang, Chuanhao Xu, Bin Yang, Lin Qu, Lili Li, Liangliang Li, Xiaoyan Wang, Weixia Qin, Haifeng Gao, Hongjun He, Kun Liu, Xiaoqing |
author_facet | Wang, Lin Tang, Chuanhao Xu, Bin Yang, Lin Qu, Lili Li, Liangliang Li, Xiaoyan Wang, Weixia Qin, Haifeng Gao, Hongjun He, Kun Liu, Xiaoqing |
author_sort | Wang, Lin |
collection | PubMed |
description | BACKGROUND: Although pemetrexed plus cis/carboplatin has become the most effective chemotherapy regimen for patients with advanced lung adenocarcinoma, predictive biomarkers are not yet available, and new tools to identify chemosensitive patients who would likely benefit from this treatment are desperately needed. In this study, we constructed and validated predictive peptide models using the serum peptidome profiles of two datasets. METHODS: One hundred eighty-three patients treated with first-line platinum-based pemetrexed treatment for advanced lung adenocarcinoma were retrospectively enrolled and randomized into the training (n = 92) or validation (n = 91) set, and pre-treatment serum samples were analyzed using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and ClinProTools software. Serum peptidome profiles from the training set were used to identify potential predictive peptide biomarkers and construct a predictive peptide model for accurate group discrimination; which was then used to classify validation samples into “good” and “poor” outcome groups. The clinical outcomes of objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), and overall survival (OS) were analyzed based on the classification result. RESULTS: Eight potential peptide biomarkers were identified. A predictive peptide model based on four distinct m/z features (2,142.12, 3,316.19, 4,281.94, and 6,624.02 Da) was developed based on the clinical outcomes of training set patients after first-line pemetrexed plus platinum treatment. In the validation set, the good group had significantly higher ORR (49.1% vs. 8.3%, P <0.001) and DCR (96.4% vs. 47.2%, P <0.001), and longer PFS (7.3 months vs. 2.7 months, P <0.001) vs. the poor group. However, the model did not predict OS (13.6 months vs. 12.7 months, P = 0.0675). CONCLUSION: Our predictive peptide model could predict pemetrexed plus platinum treatment outcomes in patients with advanced lung adenocarcinoma and might thus facilitate appropriate patient selection. Further studies are needed to confirm these findings. |
format | Online Article Text |
id | pubmed-5464620 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-54646202017-06-22 Mass spectrometry-based serum peptidome profiling accurately and reliably predicts outcomes of pemetrexed plus platinum chemotherapy in patients with advanced lung adenocarcinoma Wang, Lin Tang, Chuanhao Xu, Bin Yang, Lin Qu, Lili Li, Liangliang Li, Xiaoyan Wang, Weixia Qin, Haifeng Gao, Hongjun He, Kun Liu, Xiaoqing PLoS One Research Article BACKGROUND: Although pemetrexed plus cis/carboplatin has become the most effective chemotherapy regimen for patients with advanced lung adenocarcinoma, predictive biomarkers are not yet available, and new tools to identify chemosensitive patients who would likely benefit from this treatment are desperately needed. In this study, we constructed and validated predictive peptide models using the serum peptidome profiles of two datasets. METHODS: One hundred eighty-three patients treated with first-line platinum-based pemetrexed treatment for advanced lung adenocarcinoma were retrospectively enrolled and randomized into the training (n = 92) or validation (n = 91) set, and pre-treatment serum samples were analyzed using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and ClinProTools software. Serum peptidome profiles from the training set were used to identify potential predictive peptide biomarkers and construct a predictive peptide model for accurate group discrimination; which was then used to classify validation samples into “good” and “poor” outcome groups. The clinical outcomes of objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), and overall survival (OS) were analyzed based on the classification result. RESULTS: Eight potential peptide biomarkers were identified. A predictive peptide model based on four distinct m/z features (2,142.12, 3,316.19, 4,281.94, and 6,624.02 Da) was developed based on the clinical outcomes of training set patients after first-line pemetrexed plus platinum treatment. In the validation set, the good group had significantly higher ORR (49.1% vs. 8.3%, P <0.001) and DCR (96.4% vs. 47.2%, P <0.001), and longer PFS (7.3 months vs. 2.7 months, P <0.001) vs. the poor group. However, the model did not predict OS (13.6 months vs. 12.7 months, P = 0.0675). CONCLUSION: Our predictive peptide model could predict pemetrexed plus platinum treatment outcomes in patients with advanced lung adenocarcinoma and might thus facilitate appropriate patient selection. Further studies are needed to confirm these findings. Public Library of Science 2017-06-08 /pmc/articles/PMC5464620/ /pubmed/28594947 http://dx.doi.org/10.1371/journal.pone.0179000 Text en © 2017 Wang et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Wang, Lin Tang, Chuanhao Xu, Bin Yang, Lin Qu, Lili Li, Liangliang Li, Xiaoyan Wang, Weixia Qin, Haifeng Gao, Hongjun He, Kun Liu, Xiaoqing Mass spectrometry-based serum peptidome profiling accurately and reliably predicts outcomes of pemetrexed plus platinum chemotherapy in patients with advanced lung adenocarcinoma |
title | Mass spectrometry-based serum peptidome profiling accurately and reliably predicts outcomes of pemetrexed plus platinum chemotherapy in patients with advanced lung adenocarcinoma |
title_full | Mass spectrometry-based serum peptidome profiling accurately and reliably predicts outcomes of pemetrexed plus platinum chemotherapy in patients with advanced lung adenocarcinoma |
title_fullStr | Mass spectrometry-based serum peptidome profiling accurately and reliably predicts outcomes of pemetrexed plus platinum chemotherapy in patients with advanced lung adenocarcinoma |
title_full_unstemmed | Mass spectrometry-based serum peptidome profiling accurately and reliably predicts outcomes of pemetrexed plus platinum chemotherapy in patients with advanced lung adenocarcinoma |
title_short | Mass spectrometry-based serum peptidome profiling accurately and reliably predicts outcomes of pemetrexed plus platinum chemotherapy in patients with advanced lung adenocarcinoma |
title_sort | mass spectrometry-based serum peptidome profiling accurately and reliably predicts outcomes of pemetrexed plus platinum chemotherapy in patients with advanced lung adenocarcinoma |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5464620/ https://www.ncbi.nlm.nih.gov/pubmed/28594947 http://dx.doi.org/10.1371/journal.pone.0179000 |
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