Decipher the single-cell level responses to chemotherapy in pancreatic ductal adenocarcinoma by a cross-time context graph model

Gemcitabine is commonly used for pancreatic ductal adenocarcinoma (PDAC), one of the most lethal cancer types. However, the drug resistance is a critical challenge for improving the PDAC chemotherapy. Here, we applied single-cell RNA sequencing (scRNA-seq) on PDAC patient-derived xenograft (PDX) models to study the complex cellular responses related to the gemcitabine resistances. To reconstruct dynamic tumor cell responses from these static scRNA-seq snapshots, we proposed scConGraph, a scalable bi-layer graph model that can efficiently integrate cross-time context information. Based on scConGraph, we observed that stemness and endoplasmic reticulum stress contribute to intrinsic resistance. As for acquired resistance, cancer cells may resist or evade gemcitabine treatment by activating the cell cycle, entering quiescence, or inducing epithelial-mesenchymal transition. Notably, GDF15 exhibited recurrent and significant upregulations among acquired-resistance cell subpopulations. Experimental validation confirmed that inhibiting GDF15 sensitizes tumor cells to gemcitabine, suggesting a potential target for gemcitabine-induced chemoresistance.