Transfer Learning Reveals Cancer-Associated Fibroblasts Are Associated with Epithelial-Mesenchymal Transition and Inflammation in Cancer Cells in Pancreatic Ductal Adenocarcinoma.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 38587552.
- Also identified by DOI 10.1158/0008-5472.CAN-23-1660 and PMC identifier 11065624.
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Abstract
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. 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.
Medical subject headings
- Epithelial-Mesenchymal Transition
- Carcinoma, Pancreatic Ductal
- Cancer-Associated Fibroblasts
- Pancreatic Neoplasms
- Inflammation
- Tumor Microenvironment
- Integrin beta1
- Coculture Techniques
- Single-Cell Analysis