Abstract LB-239: Co-expression analysis reveals tumor biology and prognostic biomarkers in pancreatic ductal adenocarcinoma
Authors: Giulia Mantini, Andrea Valles, Tessa Le Large, Mjriam Capula, Niccola Funel, Thang Pham, Sander Piersma, Geert Kazemier, Maarten Bijlsma, Elisa Giovannetti, Connie Jimenez
Journal: Cancer Research (2020)

Abstract Despite extensive biological and clinical studies, including comprehensive profiling using genomics and transcriptomics efforts, pancreatic ductal adenocarcinoma (PDAC) remains a devastating disease, with poor survival and no effective therapies to date. Development of new tools and experimental methods are driven by the urge of identification of better sensitive and specific biomarkers for this disease. In recent years, multiple statistical methods and freely available tools have been developed and can extrapolate important features in high-throughput data, e.g. pinpointing genes associated with clinical parameters such as cancer status or patient survival. In this context, network topology based on co-expression has extensively been used to identify subsets of gene-disease relations. Correlation networks are emerging as a powerful approach to infer tumor biology from -omics data and to prioritize candidate genes as biomarkers or drug targets. In this study, we applied a weighted co-expression analysis to the proteome of 20 surgically resected PDAC specimens. Next, modules were associated to clinical end points to underline prognostic markers.This co-expression analysis yielded 12 consensus modules, subsequently analyzed by gene set enrichment analysis (GSEA) to characterize the associated biology. The modules covered a wide range of biological terms, and the most frequently occurring terms were those implicated in metabolic processes in the context of mitochondrial compartment. More specifically, five modules consisted predominantly of immune system and defense and four modules were associated with epithelial to mesenchymal transition processes. Notably, one module enriched for metabolic processes and epithelial-mesenchymal-transition (EMT) was significantly associated with overall survival (p=0.01) and disease-free survival (p = 0.03). The prognostic value of three proteins (SPTBN1, KHSRP and PYGL) belonging to this module was confirmed using immunohistochemistry in a cohort of 82 resected patients. Risk score evaluation of the prognostic signature confirmed the association to OS in multivariate analysis. Finally, immunofluorescence confirmed co-expression of SPTBN1 and KHSRP in Hs766t PDAC cells. Taken together, our results demonstrate that an EMT - metabolic module is associated with prognosis after surgical resection of PDAC patients and the module's proteins SPTBN1, KHSRP and PYGL are potential biomarkers for prognosis. Our results also show that co-expression networks are able to extrapolate tumor-specific biology as well as biological mechanisms empowering prognostic marker discovery, even with a limited number of samples. Citation Format: Giulia Mantini, Andrea Valles, Tessa Le Large, Mjriam Capula, Niccola Funel, Thang Pham, Sander Piersma, Geert Kazemier, Maarten Bijlsma, Elisa Giovannetti, Connie Jimenez. Co-expression analysis reveals tumor biology and prognostic biomarkers in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-239.