Fig. 3: MuTATE identifies CIC and NOTCH1, ATRX, and NF1 as key markers of heightened LGG severity, enhancing risk classification and potentially reshaping clinical decision-making.

The MuTATE-generated multi-endpoint decision tree stratifies LGG patients first by IDH1 and 1p19q status (top section, aligned with expert subtypes), and then by CIC, NOTCH1, NF1, and ATRX mutation status (middle section, representing MuTATE-defined final subtypes). To interpret this figure, follow each branching path from the expert subtype (top) to the MuTATE-defined subgroups (middle). Each node summarizes how the presence or absence of specific mutations alters risk across endpoints. Icons and color gradients reflect increasing clinical severity and inform potential clinical actions (bottom). Each node includes the number of patients (N, %) and subtype-specific percentages for key clinical endpoints: mortality, progression, new tumor events, and neoplasm status. Color shading reflects estimated clinical severity, with darker shades indicating higher-risk subtypes (defined by rates of death, progression, new tumor events, and neoplasm status). Summary statistics (% of patients with each clinical outcome) are provided for each subtype to illustrate clinical heterogeneity. Statistical associations between MuTATE subtypes and clinical outcomes were evaluated using logistic and Cox regression models and are reported in Fig. S4 and Supplementary Data 4–6. These results demonstrate MuTATE’s ability to replicate expert-defined classifications (e.g., IDH1-1p19q) while revealing more granular, higher-risk subgroups that were not captured by existing clinical models. The bottom section of the figure summarizes potential implications for clinical decision-making based on MuTATE-defined subtypes, including options for tailored monitoring, therapeutic escalation, or intensified surveillance. Notably, CIC and NOTCH1 variants stratified a higher-risk group within the traditionally low-risk IDH-mutant population, while NF1 and ATRX variants flagged more aggressive disease courses—highlighting MuTATE’s potential to inform post-resection therapeutic decisions and targeted surveillance strategies.