Rxivist logo

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. ### Competing Interest Statement The authors have declared no competing interest.

Download data

  • Downloaded 218 times
  • Download rankings, all-time:
    • Site-wide: 77,563 out of 103,808
    • In cancer biology: 2,638 out of 3,707
  • Year to date:
    • Site-wide: 28,840 out of 103,808
  • Since beginning of last month:
    • Site-wide: 11,489 out of 103,808

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


PanLingua

Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News