Predicting Non-Response to Cystic Fibrosis Modulators from Microbiota Using Standard and Custom Machine Learning

Abstract

National audienceAbstract Background: Predictive biomarkers for non-response to modulators in cystic fibrosis are needed. The microbiota is a promising candidate but challenging for standard machine learning. Results: Random Forests, Lasso, FLORAL, and Graph Neural Networks showed varying performance, with differences in taxa identification. Conclusions: Non-linear methods showed the best AUC and consistent results.Background Cystic fibrosis (CF) Cystic fibrosis (CF) results from CF transmembrane conductance regulator (CFTR) gene mutations. CFTR modulators have revolutionized patient outcomes, but response varies, highlighting the need for predictive biomarkers. The airway microbiota, key in disease progression, is a promising candidate. Microbiome-based outcome prediction is common but faces challenges like compositionality, phylogeny, high dimensionality, and sparsity. We applied standard random forests and lasso with various data transformations [1], FLORAL (pairwise log-ratio Lasso), and a Graph Neural Network (GNN) embedding microbiome counts in a phylogenetic tree to predict modulator nonresponse (no BMI z-score improvement after one year) from lung bacterial relative abundances. We assessed performance via ROC-AUC, PR-AUC, and interpretability.Results We analyzed 16S data from 73 pediatric patients (26% non-responders, 64 bacterial OTUs) The bestperforming models were GNN, Lasso regression, and Random Forest, with ROC-AUC above 0.65 and PR-AUC above 0.50. Random Forest and Lasso regression offered interpretability, while FLORAL identified distinct taxa. The GNN performed well; however, its interpretability was limited. Conclusion Different ML methods produced varying results with different advantages and disadvantages. In this study, the bacteria identified by the ML models aligned with CF microbiome literature. References [1] Karwowska Z, Aasmets O, Estonian Biobank RT, Kosciolek T, Org E. Effects of data transformationand model selection on feature importance in microbiome classification data. Microbiome. 2025</div