Decoding FGFR inhibitor sensitivity in cholangiocarcinoma with interpretable machine learning and cross-platform
Yading Xie1, Honglei Li1, Wujie Zhang1
1Department 2 of Hepatobiliary Surgery, Handan First Hospital, Handan, Hebei, China.
Frontiers in Pharmacology
|May 18, 2026
Summary
This study developed a reliable method to predict cholangiocarcinoma (CHOL) patient response to fibroblast growth factor receptor inhibitors (FGFRis) using transcriptomic data. The new approach improves biomarker discovery for personalized cancer treatment.
Area of Science:
- Genomics and Precision Medicine
- Cancer Biology and Therapeutics
Background:
- Fibroblast growth factor receptor inhibitors (FGFRis) show promise in treating cholangiocarcinoma (CHOL), but patient responses are inconsistent and not fully explained by known FGFR alterations.
- Current biomarker discovery methods for FGFRis are limited by variable drug sensitivity readouts and potential bias in pharmacogenomic modeling.
Purpose of the Study:
- To develop a robust and reliable method for predicting patient response to FGFR inhibitors (FGFRis) in cholangiocarcinoma (CHOL).
- To identify a transferable and interpretable transcriptomic signature associated with FGFRi sensitivity.
- To improve biomarker discovery for stratifying CHOL patients for targeted therapy.
Main Methods:
- Integrated large-scale pharmacogenomic screening with baseline transcriptomes to define a pathway-level FGFRi sensitivity phenotype.
- Developed a composite FGFRi score using normalized drug responses across an FGFR-targeting panel, assessing reliability via drug-drug concordance and split-half reproducibility.
- Trained transcriptome-based predictors using strict cross-validation with out-of-fold evaluation, quantifying performance by Spearman correlation and testing by permutation.
Main Results:
- A composite FGFRi score demonstrated higher stability and reliability (median ρ = 0.63) compared to single-drug readouts.
- Baseline transcriptomes accurately predicted FGFRi sensitivity, yielding a compact and interpretable signature.
- The derived signature mapped to tumor states in TCGA-CHOL, associated with FGFR-axis components, and showed consistent performance across external CHOL cohorts.
Conclusions:
- A multi-compound, pathway-level FGFRi phenotype combined with leakage-safe transcriptomic modeling identifies a transferable and interpretable FGFRi-associated program.
- This framework enhances reliability in biomarker discovery and supports practical, portable scoring for CHOL patient stratification.
- Prospective validation in FGFRi-treated CHOL cohorts is recommended.
