Improving Individualized Rhabdomyosarcoma Prognosis Predictions Using Somatic Molecular Biomarkers

Abstract

AbstractPurposeMolecular markers, such asFOXO1fusion genes andTP53andMYOD1mutations, increasingly influence risk-stratified treatment selection for pediatric rhabdomyosarcoma (RMS). This study aims to integrate molecular and clinical data to produce individualized prognosis predictions that can further improve treatment selection.Patients and MethodsClinical variables and somatic mutation data for 20 genes from 641 RMS patients in the United Kingdom and the United States were used to develop three Cox proportional hazard models for predicting event-free survival (EFS). The ‘Baseline Clinical’ (BC) model included treatment location, age, fusion status, and risk group. The ‘Gene Enhanced 2’ (GE2) model addedTP53andMYOD1mutations to the BC predictors. The ‘Gene Enhanced 6’ (GE6) model further includedNF1,MET,CDKN2A, andMYCNmutations, selected through LASSO regression. Model performance was assessed using likelihood ratio (LR) tests and optimism-adjusted, bootstrapped validation and calibration metrics.ResultsThe GE6 model demonstrated superior predictive performance, offering 39% more predictive information than the BC model (LR p<0.001) and 15% more than the GE2 model (LR p<0.001). The GE6 model achieved the highest discrimination with a C-index of 0.7087, a Nagalkerke R2of 0.205, and appropriate calibration. Mutations inTP53,MYOD1,CDKN2A,MET, andMYCNwere associated with higher hazards, while NF1 mutation correlated with lower hazard. Individual prognosis predictions varied between models in ways that may suggest different treatments for the same patient. For example, the 5-year EFS for a 10-year-old patient with high-risk, fusion-negative,NF1-positive disease was 50.0% (95% confidence interval: 39-64%) from BC but 76% (64-90%) from GE6.ConclusionIncorporating molecular markers into RMS prognosis models improves prognosis predictions. Individualized prognosis predictions may suggest alternative treatment regimens compared to traditional risk-classification schemas. Improved clinical variables and external validation are required prior to implementing these models into clinical practice.