Transfer Learning Reveals Cancer-Associated Fibroblasts Are Associated with Epithelial-Mesenchymal Transition and Inflammation in Cancer Cells in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by an immunosuppressive tumor microenvironment enriched with cancer-associated fibroblasts (CAF). This study used a convergence approach to identify tumor cell and CAF interactions through the integration of single-cell data from human tumors with human organoid coculture experiments. Analysis of a comprehensive atlas of PDAC single-cell RNA sequencing data indicated that CAF density is associated with increased inflammation and epithelial-mesenchymal transition (EMT) in epithelial cells. Transfer learning using transcriptional data from patient-derived organoid and CAF cocultures provided in silico validation of CAF induction of inflammatory and EMT epithelial cell states. Further experimental validation in cocultures demonstrated integrin beta 1 (ITGB1) and vascular endothelial factor A (VEGFA) interactions with neuropilin-1 mediating CAF-epithelial cell cross-talk. Together, this study introduces transfer learning from human single-cell data to organoid coculture analyses for experimental validation of discoveries of cell-cell cross-talk and identifies fibroblast-mediated regulation of EMT and inflammation. SIGNIFICANCE: Adaptation of transfer learning to relate human single-cell RNA sequencing data to organoid-CAF cocultures facilitates discovery of human pancreatic cancer intercellular interactions and uncovers cross-talk between CAFs and tumor cells through VEGFA and ITGB1.

Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R and GenePattern Notebook implementations of CoGAPS.

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. However, inferring biological processes from an NMF result still requires additional post hoc statistics and annotation for interpretation of learned features. Here, we introduce a suite of computational tools that implement NMF and provide methods for accurate and clear biological interpretation and analysis. A generalized discussion of NMF covering its benefits, limitations and open questions is followed by four procedures for the Bayesian NMF algorithm Coordinated Gene Activity across Pattern Subsets (CoGAPS). Each procedure will demonstrate NMF analysis to quantify cell state transitions in a public domain single-cell RNA-sequencing dataset. The first demonstrates PyCoGAPS, our new Python implementation that enhances runtime for large datasets, and the second allows its deployment in Docker. The third procedure steps through the same single-cell NMF analysis using our R CoGAPS interface. The fourth introduces a beginner-friendly CoGAPS platform using GenePattern Notebook, aimed at users with a working conceptual knowledge of data analysis but without a basic proficiency in the R or Python programming language. We also constructed a user-facing website to serve as a central repository for information and instructional materials about CoGAPS and its application programming interfaces. The expected timing to setup the packages and conduct a test run is around 15 min, and an additional 30 min to conduct analyses on a precomputed result. The expected runtime on the user's desired dataset can vary from hours to days depending on factors such as dataset size or input parameters.

Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces.

Recent advances in spatial transcriptomics (STs) enable gene expression measurements from a tissue sample while retaining its spatial context. This technology enables unprecedented in situ resolution of the regulatory pathways that underlie the heterogeneity in the tumor as well as the tumor microenvironment (TME). The direct characterization of cellular co-localization with spatial technologies facilities quantification of the molecular changes resulting from direct cell-cell interaction, as it occurs in tumor-immune interactions. We present SpaceMarkers, a bioinformatics algorithm to infer molecular changes from cell-cell interactions from latent space analysis of ST data. We apply this approach to infer the molecular changes from tumor-immune interactions in Visium spatial transcriptomics data of metastasis, invasive and precursor lesions, and immunotherapy treatment. Further transfer learning in matched scRNA-seq data enabled further quantification of the specific cell types in which SpaceMarkers are enriched. Altogether, SpaceMarkers can identify the location and context-specific molecular interactions within the TME from ST data.

Triple-negative breast cancer metastasis involves complex epithelial-mesenchymal transition dynamics and requires vimentin.

Triple-negative breast cancer (TNBC) is an aggressive subtype associated with early metastatic recurrence and worse patient outcomes. TNBC tumors express molecular markers of the epithelial-mesenchymal transition (EMT), but its requirement during spontaneous TNBC metastasis in vivo remains incompletely understood. We demonstrated that spontaneous TNBC tumors from a genetically engineered mouse model (GEMM), multiple patient-derived xenografts, and archival patient samples exhibited large populations in vivo of hybrid E/M cells that lead invasion ex vivo while expressing both epithelial and mesenchymal characteristics. The mesenchymal marker vimentin promoted invasion and repressed metastatic outgrowth. We next tested the requirement for five EMT transcription factors and observed distinct patterns of utilization during invasion and colony formation. These differences suggested a sequential activation of multiple EMT molecular programs during the metastatic cascade. Consistent with this model, our longitudinal single-cell RNA analysis detected three different EMT-related molecular patterns. We observed cancer cells progressing from epithelial to hybrid E/M and strongly mesenchymal patterns during invasion and from epithelial to a hybrid E/M pattern during colony formation. We next investigated the relative epithelial versus mesenchymal state of cancer cells in both GEMM and patient metastases. In both contexts, we observed heterogeneity between and within metastases in the same individual. We observed a complex spectrum of epithelial, hybrid E/M, and mesenchymal cell states within metastases, suggesting that there are multiple successful molecular strategies for distant organ colonization. Together, our results demonstrate an important and complex role for EMT programs during TNBC metastasis.

DPP inhibition alters the CXCR3 axis and enhances NK and CD8+ T cell infiltration to improve anti-PD1 efficacy in murine models of pancreatic ductal adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is projected to be the second leading cause of cancer death in the USA by 2030. Immune checkpoint inhibitors fail to control most PDAC tumors because of PDAC's extensive immunosuppressive microenvironment and poor immune infiltration, a phenotype also seen in other non-inflamed (ie, 'cold') tumors. Identifying novel ways to enhance immunotherapy efficacy in PDAC is critical. Dipeptidyl peptidase (DPP) inhibition can enhance immunotherapy efficacy in other cancer types; however, the impact of DPP inhibition on PDAC tumors remains unexplored. METHODS: We examined the effects of an oral small molecule DPP inhibitor (BXCL701) on PDAC tumor growth using mT3-2D and Pan02 subcutaneous syngeneic murine models in C57BL/6 mice. We explored the effects of DPP inhibition on the tumor immune landscape using RNAseq, immunohistochemistry, cytokine evaluation and flow cytometry. We then tested if BXCL701 enhanced anti-programmed cell death protein 1 (anti-PD1) efficacy and performed immune cell depletion and rechallenged studies to explore the relevance of cytotoxic immune cells to combination treatment efficacy. RESULTS: In both murine models of PDAC, DPP inhibition enhanced NK and T cell immune infiltration and reduced tumor growth. DPP inhibition also enhanced the efficacy of anti-PD1. The efficacy of dual anti-PD1 and BXCL701 therapy was dependent on both CD8+ T cells and NK cells. Mice treated with this combination therapy developed antitumor immune memory that cleared some tumors after re-exposure. Lastly, we used The Cancer Genome Atlas (TCGA) to demonstrate that increased NK cell content, but not T cell content, in human PDAC tumors is correlated with longer overall survival. We propose that broad DPP inhibition enhances antitumor immune response via two mechanisms: (1) DPP4 inhibition increases tumor content of CXCL9/10, which recruits CXCR3+ NK and T cells, and (2) DPP8/9 inhibition activates the inflammasome, resulting in proinflammatory cytokine release and Th1 response, further enhancing the CXCL9/10-CXCR3 axis. CONCLUSIONS: These findings show that DPP inhibition with BXCL701 represents a pharmacologic strategy to increase the tumor microenvironment immune cell content to improve anti-PD1 efficacy in PDAC, suggesting BXCL701 can enhance immunotherapy efficacy in 'cold' tumor types. These findings also highlight the potential importance of NK cells along with T cells in regulating PDAC tumor growth.