In situ multi-modal characterization of pancreatic cancer reveals tumor cell identity as a defining factor of the surrounding microenvironment. [RNA-Seq]

Pancreatic ductal adenocarcinoma is heterogeneous, with low tumor purity, a prominent microenvironment, and complex architecture, which preclude the identification of shared tumor-intrinsic and stromal biology within and across patients. We overcame these challenges by achieving necessary resolution and context through the application of complimentary genomics, pathology and machine learning approaches to characterize primary untreated tumors from 39 patients. We capture 340K spatial low-bulk and 530K spatial single-cell transcriptomes and observe a spectrum of classical-to-basal tumor subtypes present within all patients. We find each subtype has distinct regulators, stromal neighborhoods, microenvironment, extracellular matrix and histology corresponding to multiple immunosuppressive and therapy resistance mechanisms. We define key tumor heterogeneity features including the presence of mixed KRAS mutations and tertiary lymphoid structures; identifying biomarkers that distinguish the latter from lymph nodes. Lastly, leveraging patient, cell and mouse data, we determine which aspects of tumor biology are recapitulated in bulk datasets and reductionist models. Overall design: RNAseq characterization of Panc0203, Panc0403, Panc1, AsPC1, and PACADD-188 cells in normoxia vs hypoxia (1%O2) over the course of 14 days