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