Abstract 4257: Integrated network analysis reveals potentially novel molecular pathways mechanism and therapeutic targets of pancreatic ductal adenocarcinoma

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal human cancers and shows insensitivity towards many chemotherapeutic drugs and target-based drugs. There is an urgently need to identify PDAC disease mechanism and detect novelty drug targets. With the large availability of protein interaction networks and microarray data supported, to identify the disease essential genes that have biological significance for potential drug targets is a challenge issue still. Methods: In this study, protein interaction network PathPPI, DrugBank targets and 374 transcriptome profiles from Gene Expression Omnibus (GEO) by Affymetrix HU 133 Plus 2.0 array test are collected. These microarray data includes 140 PDAC, 92 pancreatic cancer cell-line, 58 human normal pancreas tissues, and 72 patient original xenograft mouse. Based on these datasets, a novelty-integrated algorithm was developed for drug target prioritization by perturbed gene expression and network information. The integration network included data driven reconstruction network of gene regulatory (GRN) by Bayesian Network and protein interaction PathPPI network. The perturbed gene expression was refer to the differential expression genes of tumor verse adjacent normal. A novel hybrid method based on genetic algorithm (GA) is proposed to search the attractor with the maximum network perturbation for candidate drug targets in PDAC.These targets’ molecular mechanism was revealed further by network structure comparison of PDAC that is consistent with xerograph mouse model and cancer cells. Results: Highly accordance 56 genes with high perturbation score both in mRNAs and protein-protein network were identified and recommended as candidate drug targets for PDAC, including 16 novel targets, such as PSMD2, EPOR and PSMB9. Fifty-four essential genes shown strong concordance among tumor-model, patient drive xenograft mouse and pancreatic cancer cell-line by systematically comparison. We identified 1375 dysregulated genes that enriched in 25 pathways in PDAC by Gene Set Enrichment Analysis, among which 244 genes played as hubs in reconstructed PDAC regulatory network. Conclusion: In the study, we developed a global optimization-based inference of network perturbation to detect attractor for drug-target identification in PDAC tumors. The assembling Bayesian Network-based approach with protein-protein interaction provides a comprehensive information to observe energy transfer of gene perturbation in network to detect global optimum attractor for drug target selection. Citation Format: Lijun Cheng, Enze Liu, Li Lang, Xiaolin Cheng, Xiaotian Kong, Korc Murray. Integrated network analysis reveals potentially novel molecular pathways mechanism and therapeutic targets of pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4257.