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Predictive Modeling Using a Somatic Mutational Profile in Ovarian High Grade Serous Carcinoma

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  • Insuk Sohn
  • Chang Ohk Sung

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

Suggested Citation

  • Insuk Sohn & Chang Ohk Sung, 2013. "Predictive Modeling Using a Somatic Mutational Profile in Ovarian High Grade Serous Carcinoma," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-7, January.
    • Handle: RePEc:plo:pone00:0054089
    DOI: 10.1371/journal.pone.0054089
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