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Meta-analysis of transcriptome data identifies a novel 5-gene pancreatic adenocarcinoma classifier

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is largely incurable due to late diagnosis. Superior early detection biomarkers are critical to improving PDAC survival and risk stratification. EXPERIMENTAL DESIGN: Optimized meta-analysis of PDAC transcriptome datasets identified and validated key P...

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Autores principales: Bhasin, Manoj K., Ndebele, Kenneth, Bucur, Octavian, Yee, Eric U., Otu, Hasan H., Plati, Jessica, Bullock, Andrea, Gu, Xuesong, Castan, Eduardo, Zhang, Peng, Najarian, Robert, Muraru, Maria S., Miksad, Rebecca, Khosravi-Far, Roya, Libermann, Towia A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals LLC 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5029625/
https://www.ncbi.nlm.nih.gov/pubmed/26993610
http://dx.doi.org/10.18632/oncotarget.8139
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author Bhasin, Manoj K.
Ndebele, Kenneth
Bucur, Octavian
Yee, Eric U.
Otu, Hasan H.
Plati, Jessica
Bullock, Andrea
Gu, Xuesong
Castan, Eduardo
Zhang, Peng
Najarian, Robert
Muraru, Maria S.
Miksad, Rebecca
Khosravi-Far, Roya
Libermann, Towia A.
author_facet Bhasin, Manoj K.
Ndebele, Kenneth
Bucur, Octavian
Yee, Eric U.
Otu, Hasan H.
Plati, Jessica
Bullock, Andrea
Gu, Xuesong
Castan, Eduardo
Zhang, Peng
Najarian, Robert
Muraru, Maria S.
Miksad, Rebecca
Khosravi-Far, Roya
Libermann, Towia A.
author_sort Bhasin, Manoj K.
collection PubMed
description PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is largely incurable due to late diagnosis. Superior early detection biomarkers are critical to improving PDAC survival and risk stratification. EXPERIMENTAL DESIGN: Optimized meta-analysis of PDAC transcriptome datasets identified and validated key PDAC biomarkers. PDAC-specific expression of a 5-gene biomarker panel was measured by qRT-PCR in microdissected patient-derived FFPE tissues. Cell-based assays assessed impact of two of these biomarkers, TMPRSS4 and ECT2, on PDAC cells. RESULTS: A 5-gene PDAC classifier (TMPRSS4, AHNAK2, POSTN, ECT2, SERPINB5) achieved on average 95% sensitivity and 89% specificity in discriminating PDAC from non-tumor samples in four training sets and similar performance (sensitivity = 94%, specificity = 89.6%) in five independent validation datasets. This classifier accurately discriminated PDAC from chronic pancreatitis (AUC = 0.83), other cancers (AUC = 0.89), and non-tumor from PDAC precursors (AUC = 0.92) in three independent datasets. Importantly, the classifier distinguished PanIN from healthy pancreas in the PDX1-Cre;LSL-Kras(G12D) PDAC mouse model. Discriminatory expression of the PDAC classifier genes was confirmed in microdissected FFPE samples of PDAC and matched surrounding non-tumor pancreas or pancreatitis. Notably, knock-down of TMPRSS4 and ECT2 reduced PDAC soft agar growth and cell viability and TMPRSS4 knockdown also blocked PDAC migration and invasion. CONCLUSIONS: This study identified and validated a highly accurate 5-gene PDAC classifier for discriminating PDAC and early precursor lesions from non-malignant tissue that may facilitate early diagnosis and risk stratification upon validation in prospective clinical trials. Cell-based experiments of two overexpressed proteins encoded by the panel, TMPRSS4 and ECT2, suggest a causal link to PDAC development and progression, confirming them as potential therapeutic targets.
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spelling pubmed-50296252016-09-29 Meta-analysis of transcriptome data identifies a novel 5-gene pancreatic adenocarcinoma classifier Bhasin, Manoj K. Ndebele, Kenneth Bucur, Octavian Yee, Eric U. Otu, Hasan H. Plati, Jessica Bullock, Andrea Gu, Xuesong Castan, Eduardo Zhang, Peng Najarian, Robert Muraru, Maria S. Miksad, Rebecca Khosravi-Far, Roya Libermann, Towia A. Oncotarget Research Paper PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is largely incurable due to late diagnosis. Superior early detection biomarkers are critical to improving PDAC survival and risk stratification. EXPERIMENTAL DESIGN: Optimized meta-analysis of PDAC transcriptome datasets identified and validated key PDAC biomarkers. PDAC-specific expression of a 5-gene biomarker panel was measured by qRT-PCR in microdissected patient-derived FFPE tissues. Cell-based assays assessed impact of two of these biomarkers, TMPRSS4 and ECT2, on PDAC cells. RESULTS: A 5-gene PDAC classifier (TMPRSS4, AHNAK2, POSTN, ECT2, SERPINB5) achieved on average 95% sensitivity and 89% specificity in discriminating PDAC from non-tumor samples in four training sets and similar performance (sensitivity = 94%, specificity = 89.6%) in five independent validation datasets. This classifier accurately discriminated PDAC from chronic pancreatitis (AUC = 0.83), other cancers (AUC = 0.89), and non-tumor from PDAC precursors (AUC = 0.92) in three independent datasets. Importantly, the classifier distinguished PanIN from healthy pancreas in the PDX1-Cre;LSL-Kras(G12D) PDAC mouse model. Discriminatory expression of the PDAC classifier genes was confirmed in microdissected FFPE samples of PDAC and matched surrounding non-tumor pancreas or pancreatitis. Notably, knock-down of TMPRSS4 and ECT2 reduced PDAC soft agar growth and cell viability and TMPRSS4 knockdown also blocked PDAC migration and invasion. CONCLUSIONS: This study identified and validated a highly accurate 5-gene PDAC classifier for discriminating PDAC and early precursor lesions from non-malignant tissue that may facilitate early diagnosis and risk stratification upon validation in prospective clinical trials. Cell-based experiments of two overexpressed proteins encoded by the panel, TMPRSS4 and ECT2, suggest a causal link to PDAC development and progression, confirming them as potential therapeutic targets. Impact Journals LLC 2016-03-16 /pmc/articles/PMC5029625/ /pubmed/26993610 http://dx.doi.org/10.18632/oncotarget.8139 Text en Copyright: © 2016 Bhasin et al. http://creativecommons.org/licenses/by/2.5/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Paper
Bhasin, Manoj K.
Ndebele, Kenneth
Bucur, Octavian
Yee, Eric U.
Otu, Hasan H.
Plati, Jessica
Bullock, Andrea
Gu, Xuesong
Castan, Eduardo
Zhang, Peng
Najarian, Robert
Muraru, Maria S.
Miksad, Rebecca
Khosravi-Far, Roya
Libermann, Towia A.
Meta-analysis of transcriptome data identifies a novel 5-gene pancreatic adenocarcinoma classifier
title Meta-analysis of transcriptome data identifies a novel 5-gene pancreatic adenocarcinoma classifier
title_full Meta-analysis of transcriptome data identifies a novel 5-gene pancreatic adenocarcinoma classifier
title_fullStr Meta-analysis of transcriptome data identifies a novel 5-gene pancreatic adenocarcinoma classifier
title_full_unstemmed Meta-analysis of transcriptome data identifies a novel 5-gene pancreatic adenocarcinoma classifier
title_short Meta-analysis of transcriptome data identifies a novel 5-gene pancreatic adenocarcinoma classifier
title_sort meta-analysis of transcriptome data identifies a novel 5-gene pancreatic adenocarcinoma classifier
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5029625/
https://www.ncbi.nlm.nih.gov/pubmed/26993610
http://dx.doi.org/10.18632/oncotarget.8139
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