Somatic mutation of targeted sequencing identifies risk stratification in advanced ovarian clear cell carcinoma

Background: Ovarian clear cell carcinoma (OCCC) is a unique subtype of epithelial ovarian cancer. Advanced OCCC display a poor prognosis. Therefore, we aimed to make risk stratification for precise medicine.

Methods: We performed a large next generation sequencing (NGS) gene panel on 44 patients with OCCC in FIGO stage II-IV. Then, by machine learning algorithms, including extreme gradient boosting (XGBoost), random survival forest (RSF), and Cox regression, we screened for feature genes associated with prognosis and constructed a 5-gene panel for risk stratification. The prediction efficacy of the 5-gene panel was compared with FIGO stage and residual disease by receiver operating characteristic curve and decision curve analysis.

Results: The feature mutated genes related to prognosis, selected by machine learning algorithms, include MUC16, ATM, NOTCH3, KMT2A, and CTNNA1. The 5-gene panel can effectively distinguish the prognosis, as well as platinum response, of advanced OCCC in both internal and external cohorts, with the predictive capability superior to FIGO stage and residual disease.

Conclusions: Mutations in genes, including MUC16, ATM, NOTCH3, KMT2A, and CTNNA1, were associated with the poor prognosis of advanced OCCC. The risk stratification according to these genes demonstrated acceptable prediction power of prognosis and platinum response, suggesting the potential to be a novel target for precision medicine.