Predictive modeling using a somatic mutational profile in ovarian high grade serous carcinoma

Abstract

Purpose: Recent high-throughput sequencing technology has identified numerous somatic mutations across the whole exome in a variety of cancers. In this study, we generate a predictive model employing the whole exome somatic mutational profile of ovarian high-grade serous carcinomas (Ov-HGSCs) obtained from The Cancer Genome Atlas data portal.

Methods: A total of 311 patients were included for modeling overall survival (OS) and 259 patients were included for modeling progression free survival (PFS) in an analysis of 509 genes. The model was validated with complete leave-one-out cross-validation involving re-selecting genes for each iteration of the cross-validation procedure. Cross-validated Kaplan-Meier curves were generated. Cross-validated time dependent receiver operating characteristic (ROC) curves were computed and the area under the curve (AUC) values were calculated from the ROC curves to estimate the predictive accuracy of the survival risk models.

Results: There was a significant difference in OS between the high-risk group (median, 28.1 months) and the low-risk group (median, 61.5 months) (permutated p-value <0.001). For PFS, there was also a significant difference in PFS between the high-risk group (10.9 months) and the low-risk group (22.3 months) (permutated p-value <0.001). Cross-validated AUC values were 0.807 for the OS and 0.747 for the PFS based on a defined landmark time t = 36 months. In comparisons between a predictive model containing only gene variables and a combined model containing both gene variables and clinical covariates, the predictive model containing gene variables without clinical covariates were effective and high AUC values for both OS and PFS were observed.

Conclusions: We designed a predictive model using a somatic mutation profile obtained from high-throughput genomic sequencing data in Ov-HGSC samples that may represent a new strategy for applying high-throughput sequencing data to clinical practice.

Figure 1. Workflow of prognostic model building using somatic mutation profile in ovarian high-grade serous carcinoma.

Figure 2. Cross-validated Kaplan-Meier curves of the prognostic models.

Model containing only gene variables for overall survival (A) and progression free survival (B). Combined model containing both clinicopathological covariates and gene variables for overall survival (C) and progression free survival (D).

Figure 3. Cross-validated time dependent receiver operating characteristic (ROC) curves and area under the curve (AUC).

ROC curve based on landmark time t = 36 months of the predictive model for overall survival (A) and progression free survival (B). Cross-validated time dependent AUC curves to compare the prognostic model containing only gene variables with the combined model containing both clinicopathological covariates and gene variables for overall survival (C) and progression free survival (D).