Abstract 4623: Meta-analysis of transcriptomic profiles identifies prognostic model for pancreatic ductal adenocarcinoma patients
Authors: Vandana Sandhu, Knut Jorgen Labori, Ayelet Borgida, Ilinca Lungu, John Bartlett, Sara Hafezi-Bakhtiari, Rob Denroche, Gun Ho Jang, Danielle Pasternack, Faridah Mbaabali, Matthew Watson, Julie Wilson, Elin H. Kure, Steven Gallinger, Benjamin Haibe-Kains
Journal: Cancer Research (2018)

Abstract Purpose The main objective was to develop a robust molecular predictor model with high prognostic value across multiple independent cohorts of pancreatic ductal adenocarcinoma (PDAC) patients using a novel meta-analysis framework. The median 5-year overall survival (OS) of PDAC patient is &amp;lt;8%. Only 10-20% of patients are eligible for surgery, and of these, more than half will die within a year of surgery. The identification of a biologically relevant molecular predictor model is urgently needed to define strategies that may help patient selection at high risk of early death to inform treatment decisions. Methods We developed the Pancreatic Cancer Overall Survival Predictor (PCOSP), a new prognostic model built from a unique set 89 PDAC patients whose gene expressions have been profiled using both microarray and sequencing platforms. We used the recent binary gene pair method to create gene expression barcodes robust to biases arising from heterogeneous profiling platforms and batch effects. Leveraging the largest compendium of PDAC transcriptomic datasets to date, including 1,198 patients, we show that PCOSP is a robust single-sample predictor of early death (≤1 yr) after surgery in a subset of 823 validation samples with available trancriptomics and survival data. Results The PCOSP model was strongly and significantly prognostic overall with a meta-estimate of the area under the ROC curve of 0.70 (P=1.9e-18) and hazard ratio of 1.95 (P=2.6e-16) for binary and survival predictions, respectively. The prognostic value of PCOSP was independent of clinicopathological parameters and molecular subtypes. The PCOSP model includes 2,619 gene pairs, with 1,070 unique genes. Over-representation analysis of these genes unveiled pathways associated with Hedgehog signalling, epithelial mesenchymal transition (EMT) and extracellular matrix (ECM) signalling at FDR&amp;lt;0.05. Conclusions This study reports a PCOSP model to predict post-operative OS independently of clinicopathological features. This may assist clinicians in making decisions that would ultimately improve the OS and facilitate decisions concerning a surgery-first versus a neoadjuvant approach. The functional analysis of the PCOSP genes may give further insight to tumor biology of short-term survival PDAC patients. Citation Format: Vandana Sandhu, Knut Jorgen Labori, Ayelet Borgida, Ilinca Lungu, John Bartlett, Sara Hafezi-Bakhtiari, Rob Denroche, Gun Ho Jang, Danielle Pasternack, Faridah Mbaabali, Matthew Watson, Julie Wilson, Elin H. Kure, Steven Gallinger, Benjamin Haibe-Kains. Meta-analysis of transcriptomic profiles identifies prognostic model for pancreatic ductal adenocarcinoma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4623.