Fig. 6
From: Personalized cancer therapy prioritization based on driver alteration co-occurrence patterns
Application of TCT4U to predict treatment outcome in a clinical cohort of HR+/HER2− metastatic breast cancer patients. a Driver co-occurrence (DCO) networks representing the oncogenic alterations and pairs of alterations that are overrepresented in patients that relapsed early (non-responders) or in patients that derived a durable clinical benefit (responders) from CDK4/6 inhibition combined with an aromatase inhibitor. The size of the nodes represents the average feature importance in the LOOCV. The color of the nodes represents the probability that alterations in a given gene are overrepresented in responders (red) or non-responders (blue). Previously known biomarkers detected in the cohort are annotated with diamond shapes. b The Kaplan-Meier analysis of progression-free survival (PFS). TCT4U high-confidence predictions are better able to discriminate between patients that would experience early and late relapse than known biomarkers, with a median time to progression of 5.4 and 13.5 months, respectively. c Summary plot of the SHAP values attributed to individual genes. Each point represents the contribution of a given feature to the prediction of response in a given patient. The color of the points indicates whether a given driver gene was altered or not in each patient. d SHAP interaction plot showing the positive effect of the co-alteration of FGF3, FGF4, and PAK1 with CCND1 (all in chr11q13-14). The size of the dots is proportional to the number of patients in each category