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

Authors

Manoj K. Bhasin, Harvard Medical School
Kenneth Ndebele, Harvard Medical School
Octavian Bucur, Harvard Medical School
Eric U. Yee, Harvard Medical School
Hasan H. Otu, University of Nebraska-LincolnFollow
Jessica Plati, Harvard Medical School
Andrea Bullock, Harvard Medical School
Xuesong Gu, Harvard Medical School
Eduardo Castan, University of Nebraska-Lincoln
Peng Zhang, Harvard Medical School
Robert Najarian, Harvard Medical School
Maria S. Muraru, Harvard Medical School
Rebecca Miksad, Harvard Medical School
Roya Khosravi-Far, Harvard Medical SchoolFollow
Towia A. Libermann, Harvard Medical SchoolFollow

Date of this Version

2016

Citation

Oncotarget, Vol. 7, No. 17

Comments

This document is published under a Creative Commons/Open Access license.

Abstract

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.