Fig. 2. Strain-resolution gut microbial signatures outperform clinical predictors and cross-validate across tumor histology types.
a, Schematic of the supervised ML framework. Input features (clinical, microbiome or combined) and the target variables (RvsP or PFS12) were split into five folds (four training folds, one testing fold). The process was repeated 20 times per iteration, with the AUC score used to select the best hyperparameters. CV, cross-validation. b, AUC scores for the best iteration of RvsP classifiers for each feature set combination during 20 times repeated fivefold cross-validation (100 folds each): clinical (yellow), microbiome (blue) and combined (green), at different taxonomic resolutions. Data represent the mean (circle) and s.d. (error bars) over the 100 folds. The linear model (line of best fit) for the AUC score and taxonomic rank of microbiome-only feature sets (with shaded 95% confidence interval) is superimposed. Kendall τ and P values for the association between the AUC score and taxonomic rank of microbiome-only feature sets are indicated. The Mann–Whitney U test P value for comparing the AUCs of specific pairwise feature sets (depicted by calipers) is also indicated. c, ROC curves for the strain–RvsP classifiers retrained using leave-one-histology-cohort-out cross-validation. Model training and testing were repeated 100 times, with predictions averaged to account for model stochasticity.