Abstract A058: Meta-analysis of single-cell RNA expression in genetically engineered mouse models of pancreatic ductal adenocarcinoma reveals inter-model heterogeneity

Background: Genetically engineered mouse models (GEMMs) are widely used in the study of pancreatic ductal adenocarcinoma (PDAC) because of their immune-competent tumor microenvironment (TME); however, the extent to which particular GEMMs recapitulate the tumor and TME observed in the patient population has not been systematically evaluated. In this study, we integrate single-cell RNA sequencing (sc-RNA-seq) data from multiple studies and multiple GEMM backgrounds to identify differences in the cellular compositions of popular PDAC GEMMs. Methods: A total of 49,191 cells were used from three studies, including normal mouse pancreas (N=2) and five different GEMM backgrounds (N=16). Data curation, integration, and analysis were based on the Seurat pipeline in R. SingelCellNet was used to train a random forest model on manually labeled human sc-RNA-seq data from 20 patients. To enable cross-species use, the classifier was trained using only genes with both human and mouse homologues. Cells classified as neoplastic were further clustered to quantify the number of classical and basal-like cells based on signature gene expression levels. The ratio of these subtypes in each GEMM and the relationship between the modified genes in each model were examined. Results: Ad-hoc clustering and a human-cell-trained single-cell classifier showed 79% agreement in an integrated data set of PDAC GEMMs. Cells identified by both methods as tumor (8,303 cells, 17% of total) were assessed for PDAC tumor subtype via subsequent clustering analysis (basal-like or classical). When comparing the ratio of differently subtyped tumor cells, we identified stark differences between GEMM genetic backgrounds. Among five different models, KIC (KrasLSL−G12D/+Ink4a/Arffl/flPtf1aCre/+), KPPCN (KrasLSL−G12D/+Trp53fl/flPdx1Cre/+Nsdhlfl/fl), and pdx1-KPC (KrasLSL−G12D/+Trp53LSL-R172H/+Pdx1Cre/+) exhibited a higher proportion of basal-like PDAC cells compared to KPfC/KPPC (KrasLSL−G12D/+Trp53fl/flPdx1Cre/+) and ptf1a-KPC (KrasLSL−G12D/+Trp53LSL-R172H/+Ptf1aCre/+). Interestingly, in the KIC model, which was harvested at early and late time points (40 or 60 days), classical PDAC was overrepresented in early models, and basal-like PDAC was more prevalent in the older tumors. While sample sizes are limited in this study, in Pdx1 driven models, we observed a bias towards basal-like phenotype in GEMMs using the Trp53LSL-R172H/+ method compared to those with Trp53fl/fl. Conclusions: In a comparison of public