Figure 1: Combining germline and somatic/clinical features into a machine learning framework.

Germline and somatic features were assembled from the literature and computed from WES data for 7 ICB studies. Prior to training ICB response predictive models, features and clinical covariates were subjected to recursive feature elimination using the Cristescu study cohort³⁶. Models predicting ICB response were then trained on the selected features using three combined studies^33–35 as the training set. Performance of the trained model was evaluated separately on 3 independent cohorts^38–40. Models trained only on germline features, only on somatic features and the combination were compared. Feature contributions to the trained model were further investigated to develop biological hypotheses that were reproduced in the Liu cohort³⁷.