A CONTEXT-FORWARD IN VIVO FUNCTIONAL GENOMICS PLATFORM FOR TARGET DISCOVERY AND ESTABLISHING VULNERABILITY CONTEXT IN PANCREATIC CANCER

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a very poor patient prognosis (5-year survival of ≤ 7%). While transcriptional profiling has aided in the classification of this disease into at least two broader subtypes, this alone has so far been insufficient to inform on more nuanced patterns of oncogenic dependency. We hypothesized that a more comprehensive and granular characterization of PDAC disease diversity is required to establish relevant context for targeted therapy. To this end, we sought to establish an integrated platform to: i) more comprehensively characterize differential oncogenic signaling across our tumor models, and ii) establish a disease-specific co-expression network to delineate transcriptional signatures underlying PDAC diversity. Utilizing an in vivo functional genomics platform, we developed custom libraries to first characterize and then expand on PDAC surface protein dependencies in PDX cell lines. In parallel, leveraging our internally established set of over 50 PDX models, we generated a PDAC co-expression network (PCEN) of dynamically expressed genes to inform on transcriptional signatures underlying disease diversity. Upon integration, we identified CRISPR-defined dependencies anchored within prominent anti-correlating gene-cluster signatures, including one differential signature that perfectly recapitulated the characteristics of classical and basal-like PDAC molecular subtypes on a continuous scale. Additionally, through scRNAseq, this continuous scale was also observed intratumorally across PDX lines. Applying sgRNA direct-capture for targeted gene perturbation and sample multiplexing in scRNAseq, we validated PCEN-informed dependences (SMAD4, ZEB1, ILK) associated to the basal-like subtype intratumorally. Silencing these targets resulted in a significant and direction shift in the differential signature spectrum towards a more classical profile, coinciding with phenotypic response. These findings highlight this integrative network-anchored approach as a novel methodology for informing on gene dependency context in PDAC