Improving ovarian cancer treatment decision using a novel risk predictive tool

As a major component of the tumor tissue, the tumor microenvironment (TME) has been proven to associate with tumor progression and immunotherapy. Ovarian cancer accounts for the highest mortality rate among gynecologic malignancies. Its clinical treatment decision is highly correlated with the prognosis, underscoring the need to evaluate the prognosis and choose the proper clinical treatment through TME information.

This study constructs a score with TME information obtained by the CIBERSORT algorithm, which classifies the patients into high and low TMEscore groups with quantified TME infiltration patterns through the PCA algorithm. TMEscore was constructed by TCGA cohort and validated in GEO cohort. Univariate and multivariate Cox proportional hazards model analyses were used to demonstrate prognostic value of TMEscore in overall and stratified analysis.

TMEscore is highly correlated with survival and high TMEscore group has a better prognosis. In order to improve treatment decision, the expression of immune checkpoints, immunophenoscore (IPS) and ESTIMATE score showed a high TMEscore have a better immune microenvironment and respond better to immune checkpoint inhibitors (ICIs). Meanwhile, the mutation landscape between TMEscore groups was profiled, and 13 genes were found mutated differently between the two groups. Among them, BRCA1 has more mutations in the high TMEscore group and speculated that high TMEscore patients might be a beneficiary population of PARP inhibitors combined with immunotherapy.

TMEscore based on TME with prognostic value and clinical value is proposed for the identification of targets treatment and immunotherapy strategies for ovarian cancer.