Improving Individualized Rhabdomyosarcoma Prognosis Predictions Using Somatic Molecular Biomarkers
This study investigates whether including additional biomarkers (measurable items that indicate what is happening in a patient’s body) allows oncologists to make a more accurate prognosis in patients with rhabdomyosarcoma. In this study, investigators used clinical features and mutation data for 20 genes from 641 patients with rhabdomyosarcoma to develop three 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 included the BC clinical characteristics and added mutations to the genes TP53 and MYOD1. The Gene Enhanced 6 (GE6) model added mutations to the genes NF1, MET, CDKN2A, and MYCN to those included in the GE2 model. The models were then compared to determine if adding the gene mutations increased the predictive efficacy.
After analysis, the GE6 model had the best predictability compared with the BC model and GE2 model. Mutations in TP53, MYOD1, CDKN2A, MET, and MYCN were associated with worse prognosis, whereas NF1 mutation correlated with better outcomes. The investigators found that personalized prognosis predictions may suggest alternative treatment regimens compared with current treatment plans. Additional studies need to be done; however, these results indicate that the new biomarkers may Improve prognosis and treatments in rhabdomyosarcoma. These genetic biomarkers may also be used as targets for precision therapies.
Read the full study in JCO Precision Oncology.