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Updated: Jan 11, 2026
Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
Published on: December 9, 2016
Mathematical strategies for predicting resistant subpopulations from scRNAseq data of a PANC-1 3D tissue model:
Aylin Caliskan1, Samantha A W Crouch1, Jesús Guillermo Nieves Pereira2,3
1Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, Würzburg 97074, Germany.
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 in silico analyses. Based on in silico analyses, we demonstrate the analytical value of our mathematical approach and identify candidate genes for further functional and therapeutic validation.
Insights
This study developed 3D pancreatic cancer models to investigate chemoresistance. Machine learning identified key genes associated with gemcitabine (GEM) resistance, offering new therapeutic targets for pancreatic ductal adenocarcinoma.
Area of Science:
- Oncology
- Biotechnology
- Computational Biology
Background:
- Pancreatic ductal adenocarcinoma (PDAC) presents significant treatment challenges due to inherent chemoresistance and aggressive progression.
- Traditional 2D cell cultures inadequately represent the complex tumor microenvironment and drug response, limiting therapeutic insights.
- Three-dimensional (3D) culture models offer a more physiologically relevant platform for studying PDAC, including therapy resistance.
Purpose of the Study:
- To establish and characterize 3D PANC-1 pancreatic cancer models using decellularized porcine jejunum.
- To investigate the drug response to gemcitabine (GEM) and TGFB1 stimulation in these 3D models.
- To identify molecular markers and mechanisms underlying chemoresistance using single-cell RNA sequencing (scRNA-seq) and a novel machine learning framework.
Main Methods:
- Development of 3D PANC-1 tissue models within a decellularized porcine jejunum scaffold.
- Treatment of models with GEM, TGFB1, or combination, followed by scRNA-seq.
- Analysis of scRNA-seq data using standard bioinformatics and a novel mutual-information-based machine learning framework (gSELECT).
- Application of a new mathematical approach to analyze gene expression differences between sensitive and resistant cell populations.
Main Results:
- 3D PANC-1 models exhibited intrinsic resistance to GEM, with approximately 75% cell survival.
- TGFB1 stimulation induced an invasive phenotype and enhanced drug resistance, characterized by epithelial-mesenchymal transition (EMT) transcriptional changes.
- Machine learning analysis identified mutual information (MI) genes distinguishing GEM-sensitive from GEM-resistant cells, with several validated by literature and survival data.
- The novel mathematical approach successfully predicted GEM-sensitive subpopulations within control cells.
Conclusions:
- Computational analysis of scRNA-seq data from 3D-cultured PANC-1 cells provides a valuable framework for understanding treatment responses.
- Identified MI genes hold potential relevance for therapeutic strategies and require further validation.
- The developed mathematical approach demonstrates analytical value in dissecting complex gene expression patterns and identifying therapeutic targets in cancer.
