Mathematical strategies for predicting resistant subpopulations from scRNAseq data of a PANC-1 3D tissue model: Insight into gemcitabine resistance and TGFB1-induced invasion and EMT.

Background: Pancreatic ductal adenocarcinoma is characterized by high levels of chemoresistance and aggressive progression of the disease, which is a major challenge for effective treatment. Conventional 2D cultures capture therapy resistance only to a limited extent, whereas 3D cultures may better reflect relevant conditions. Methods: We established 3D PANC-1 tissue models based on a decellularized porcine jejunum with niche-specific drug response to gemcitabine (GEM) treatment. TGFB1 induced invasion and further drug resistance. Thus, we performed scRNA-seq after treatment with GEM, TGFB1 stimulation, or both. Data were analyzed using standard approaches and a novel mutual-information-based machine learning framework (gSELECT). Candidate genes were further evaluated through enrichment and survival analyses.Additionally, we present a novel mathematical approach as proof of concept for analyzing differences in gene expression between groups seemingly similar with respect to a projection such as t-SNE or UMAP (e.g., GEM-treated and untreated cells). For this, we stratified control cells by similarity to GEM-treated survivors, yielding predicted-resistant and predicted-sensitive subgroups for downstream analysis. Results: Pre-analysis using machine learning and comparative analyses of single-cell RNA sequencing data showed only minor differences in gene expression in response to GEM treatment, whereas TGFB1 induced an invasive phenotype characterized by EMT-related transcriptional changes, including downregulation of cytokeratins.Laboratory experiments showed that ∼75 % of PANC-1 cells survived GEM in 3D, indicating intrinsic resistance. Our mathematical approach using machine learning predicted a GEM-sensitive subpopulation consistent with these findings. Comparative analyses revealed mutual information (MI) genes distinguishing sensitive from resistant cells, several of which were supported by literature and survival data. Conclusion: Computational analysis of scRNA-seq data from 3D-cultured PANC-1 cells provides a useful framework for studying treatment effects. The potential relevance of the identified MI genes is supported by further