Improving Individualized Rhabdomyosarcoma Prognosis Predictions Using Somatic Molecular Biomarkers.

Purpose: Molecular markers 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.

Methods: Clinical variables and somatic mutation data for 20 genes from 641 patients with RMS 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 added TP53 and MYOD1 mutations to the BC predictors. The Gene Enhanced 6 (GE6) model further included NF1, MET, CDKN2A, and MYCN mutations, selected through least absolute shrinkage and selection operator regression. Model performance was assessed using likelihood ratio tests and optimism-adjusted, bootstrapped validation and calibration metrics.

Results: The GE6 model demonstrated superior predictive performance compared with the BC model (P < .001) and GE2 model (P < .001). The GE6 model achieved the highest discrimination with a time-dependent area under the receiver operating characteristic curve of 0.766. Mutations in TP53, MYOD1, CDKN2A, MET, and MYCN were 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% CI, 39 to 64) from BC but 76% (64 to 90) from GE6.

Conclusion: Incorporating molecular markers into RMS prognosis models improves prognosis predictions. Individualized prognosis predictions may suggest alternative treatment regimens compared with traditional risk-classification schemas. Improved clinical variables and external validation are required before implementing these models into clinical practice.