A classical epithelial state drives acute resistance to KRAS inhibition in pancreas cancer.

Intra-tumoral heterogeneity in pancreatic ductal adenocarcinoma (PDAC) is characterized by a balance between basal and classical epithelial cancer cell states, with basal dominance associating with chemoresistance and a dismal prognosis. Targeting oncogenic KRAS, the primary driver of pancreatic cancer, shows early promise in clinical trials but efficacy is limited by acquired resistance. Using genetically engineered mouse models and patient-derived xenografts, we find that basal PDAC cells are highly sensitive to KRAS inhibitors. Employing fluorescent and bioluminescent reporter systems, we longitudinally track cell-state dynamics in vivo and reveal a rapid, KRAS inhibitor-induced enrichment of the classical state. Lineage-tracing identifies these enriched classical PDAC cells to be a reservoir for disease relapse. Genetic ablation of the classical cell-state is synergistic with KRAS inhibition, providing a pre-clinical proof-of-concept for this therapeutic strategy. Our findings motivate combining classical-state directed therapies with KRAS inhibitors to deepen responses and counteract resistance in pancreatic cancer.

The therapeutic landscape for cells engineered with chimeric antigen receptors.

Despite the global rapid increase in the number of clinical trials employing chimeric antigen receptors (CARs), no comprehensive survey of their scope, targets and design exists. In this study, we present an interactive CAR clinical trial database, spanning 64 targets deployed in T cells (CAR-T), natural killer cells (CAR-NK) or mixtures (CAR-NK/T) from over 500 clinical trials in 20 countries, encompassing >20,000 patients. By combining these data with transcriptional and proteomic data, we create a 'targetable landscape' for CAR cell therapies based on 13,206 proteins and RNAs across 78 tissues, 124 cell types and 20 cancer types. These data suggest a landscape of over 100 single targets and over 100,000 target pairs using logical switches for CAR cell engineering. Our analysis of the CAR cellular therapeutic landscape may aid the design of future therapies, improve target-to-patient matching, and ultimately help inform our understanding of CAR therapy's safety and efficacy.