Diversity Across the Pancreatic Ductal Adenocarcinoma Disease Spectrum Revealed by Network-Anchored Functional Genomics
Rose, J. L.; Srinivasan, S.; Yao, W.; Seth, S.; Peoples, M.; Machado, A.; Li, C.-Y.; Ho, I. L.; Lee, J. J.; Guerrero, P. A.; Kim, E.; Syed, M.; Daniele, J. R.; Deem, A.; Kim, M.; Bristow, C. A.; Koay, E. J.; Genovese, G.; Viale, A.; Heffernan, T. P.; Maitra, A.; Hart, T.; Carugo, A.; Draetta, G. F.
Show abstract
Cancers are highly complex ecosystems composed of molecularly distinct sub-populations of tumor cells, each exhibiting a unique spectrum of genetic features and phenotypes, and embedded within a complex organ context. To substantially improve clinical outcomes, there is a need to comprehensively define inter- and intra-tumor phenotypic diversity, as well as to understand the genetic dependencies that underlie discrete molecular subpopulations. To this end, we integrated CRISPR-based co-dependency annotations with a tissue-specific co-expression network developed from patient-derived models to establish CoDEX, a framework to quantitatively associate gene-cluster patterns with genetic vulnerabilities in pancreatic ductal adenocarcinoma (PDAC). Using CoDEX, we defined multiple prominent anticorrelated gene-cluster signatures and specific pathway dependencies, both across genetically distinct PDAC models and intratumorally at the single-cell level. Of these, one differential signature recapitulated the characteristics of classical and basal-like PDAC molecular subtypes on a continuous scale. Anchoring genetic dependencies identified through functional genomics within the gene-cluster signature defined fundamental vulnerabilities associated with transcriptomic signatures of PDAC subtypes. Subtype-associated dependencies were validated by feature-barcoded CRISPR knockout of prioritized basal-like-associated genetic vulnerabilities (SMAD4, ILK, and ZEB1) followed by scRNAseq in multiple PDAC models. Silencing of these genes resulted in a significant and directional clonal shift toward the classical-like signature of more indolent tumors. These results validate CoDEX as a novel, quantitative approach to identify specific genetic dependencies within defined molecular contexts that may guide clinical positioning of targeted therapeutics.
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