The molecular prognostic score, a classifier for risk stratification of high-grade serous ovarian cancer

Abstract

The clinicopathological parameters such as residual tumor, grade, FIGO score are often used to predict the survival of ovarian cancer patients, but the 5-year survival of high grade serous ovarian cancer (HGSOC) still remains around 30%. In recent years, a gene expression based molecular prognostic score (mPS) was developed that showed improved prognosis in several cancers including ovarian cancer. The feature extraction using LASSO-Cox regression was applied on the training data with 10-fold cross validation to obtain 20 predictor genes along with the coefficients to derive mPS. The mPS based prognosis of HGSOC patients was validated using the log-rank test and receiver operator characteristic curve. The AUC of 20 gene-based mPS in predicting the 5-year overall survival was around 0.7 in both the training (n=491) and test datasets (n=491). It was also validated across HGSOC patients (n=7542), data collected from the Ovarian Tumor Tissue Analysis (OTTA) consortium. The mPS showed significant impact (adjusted HR = 6.1, 95% CI of HR= 3.65-10.3; p <0.001) on prognosis of HGSOC. The performance of mPS for the prognosis of survival of HGSOC was substantially better than conventional parameters: FIGO (adjusted HR=1.1, 95% CI=0.97-1.2, p=0.121), residual disease (adjusted HR=1.3, 95% CI= 1.13-1.4, p<0.001), and age (adjusted HR=1.2, 95% CI= 0.98-1.6, p=0.08). It was found that focal-adhesion, Wnt and Notch signaling pathways were significantly (p<0.001) upregulated, whereas antigen processing and presentation (p<0.001) was downregulated in high risk HGSOC cohorts based on mPS stratification. The molecular prognostic score derived from 20-gene signature is found to be the novel robust prognostic marker of HGSOC. It could potentially be harnessed in clinical settings to determine the overall survival of ovarian cancer. The high risk HGSOC patients based on mPS stratification could be benefited from alternative therapies targeting Wnt/ Notch signaling pathways and also immune evasion.