Research Papers:

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

Manoj K. Bhasin, Kenneth Ndebele, Octavian Bucur, Eric U. Yee, Hasan H. Otu, Jessica Plati, Andrea Bullock, Xuesong Gu, Eduardo Castan, Peng Zhang, Robert Najarian, Maria S. Muraru, Rebecca Miksad, Roya Khosravi-Far and Towia A. Libermann _

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Oncotarget. 2016; 7:23263-23281. https://doi.org/10.18632/oncotarget.8139

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Manoj K. Bhasin1, Kenneth Ndebele2, Octavian Bucur2,3, Eric U. Yee2, Hasan H. Otu4, Jessica Plati2, Andrea Bullock5, Xuesong Gu1, Eduardo Castan1, Peng Zhang1, Robert Najarian2, Maria S. Muraru2, Rebecca Miksad5,*, Roya Khosravi-Far2,* and Towia A. Libermann1,*

1 Department of Medicine, BIDMC Genomics, Proteomics, Bioinformatics and Systems Biology Center, Harvard Medical School and Beth Israel Deaconess Medical Center, Boston, MA, USA

2 Department of Pathology, Harvard Medical School and Beth Israel Deaconess Medical Center, Boston, MA, USA

3 Department of Molecular Cell Biology, Institute of Biochemistry of the Romanian Academy, Bucharest, Romania

4 Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA

5 Division of Hematology and Oncology, Harvard Medical School and Beth Israel Deaconess Medical Center, Boston, MA, USA

* These authors have contributed equally and should be considered senior authors

Correspondence to:

Towia A. Libermann, email:

Roya Khosravi-Far, email:

Keywords: pancreatic cancer, biomarkers, transcriptome, bioinformatics, meta-analysis

Received: February 05, 2016 Accepted: February 28, 2016 Published: March 16, 2016


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-KrasG12D 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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