Pancreatic Ductal Adenocarcinoma (PDAC) is an incurable disease characterized by poor survival, a dense desmoplastic stroma, and frequent activating mutations in KRAS (<90%) with a relatively flat mutation landscape. Despite recent advances in RNA sequencing analysis that enabled the characterization of PDAC into at least two tumor subtypes, this alone is insufficient to define more specific patterns of oncogenic dependency. As a result, there is currently no approach for stratifying these KRAS-driven tumors for targeted therapy. To inform on the more nuanced oncogenic dependencies we investigated how the deregulation of proteins localized to the extracellular face of the cell, or the “surfaceome”, plays a role in tumor maintenance and progression by utilizing an in vivo pooled screening platform in patient-derived models. A PDAC prioritized surfaceome gene set was established based on aberrant transcription in microdissected and bulk tumor datasets, functional amplification observations via the TCGA dataset, and KRAS dependency via SILAC screening in KRASG12D inducible mouse lines. Refining this initial gene set for functional relevance, through parallel loss of function shRNA screens in orthotopic and 2D conditions, we were able to successfully define an in vivo enriched PDAC functional surfaceome. To interconnect each protein of the functional surface gene set, we utilized a protein-protein interaction dataset (STRING) and built a custom CRISPR sgRNA library for the purpose of informing the downstream oncogenic PDAC dependencies. Starting from oncogenic signaling at the cell surface, we established a set of connected oncogenic modules to functionally fingerprint each PDAC model both in vivo and in vitro. Our custom algorithm leveraging essentiality to define distinct oncogenic routes has allowed insight into the shared core oncogenic dependencies within PDAC (KRAS, MYC, etc), as well as model-specific central dependencies. Notably, many of the more central model-specific nodes, like AKT, EGFR, and UBC, were druggable targets. These unique centralities have been confirmed through differential sensitivity to MK-2206, Gifitinib/Erlotinib and AUY922 respectively. Importantly, by driving the defined network from periphery to center, we associated the unique pivotal dependencies to larger functional module sets to be leveraged as potential biomarkers. The functional modules suggested by the network and the associated vulnerability match the transcriptionally defined PDAC subtypes. To validate these module-based biomarker sets, we are currently leveraging a larger PDAC xenograft cohort, as well as patient data, to quantify the predictive capacity of the differentially regulated modules in the context of variable drug dependencies.

Citation Format: Johnathon L. Rose, Alessandro Carugo, Wantong Yao, Sahil Seth, Sanjana Srinivasan, Michael Peoples, Traver Hart, Timothy Heffernan, Giulio Draetta. Reverse functional genomics: Identifying differentially regulated functional networks as predictive biomarkers of pivotal vulnerabilities in pancreatic cancer [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 392.