
==== Front
medRxiv
MEDRXIV
medRxiv
Cold Spring Harbor Laboratory

10.1101/2024.09.04.24313032
preprint
1
Article
Improving Individualized Rhabdomyosarcoma Prognosis Predictions Using Somatic Molecular Biomarkers
Zobeck Mark http://orcid.org/0000-0003-0997-906X

Khan Javed
Venkatramani Rajkumar http://orcid.org/0000-0002-4785-106X

Okcu M. Fatih
Scheurer Michael E. http://orcid.org/0000-0002-8379-6088

Lupo Philip J. http://orcid.org/0000-0003-0978-5863

05 9 2024
2024.09.04.24313032https://creativecommons.org/licenses/by-nc/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator.
http://medrxiv.org/lookup/doi/10.1101/2024.09.04.24313032
nihpp-2024.09.04.24313032.pdf
Abstract

Purpose

Molecular markers, such as FOXO1 fusion genes and TP53 and MYOD1 mutations, 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 Methods

Clinical 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 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 LASSO regression. Model performance was assessed using likelihood ratio (LR) tests and optimism-adjusted, bootstrapped validation and calibration metrics.

Results

The 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 R 2 of 0.205, and appropriate calibration. 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% confidence interval: 39-64%) from BC but 76% (64-90%) from GE6.

Conclusion

Incorporating 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.
==== Body
pmc
