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

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.

[1]  T. Stankovic,et al.  Mutations associated with variant phenotypes in ataxia-telangiectasia. , 1996, American journal of human genetics.

[2]  B. Ponder,et al.  Involvement of Brca2 in DNA repair. , 1998, Molecular cell.

[3]  G E Tomlinson,et al.  BRCA2 is required for ionizing radiation-induced assembly of Rad51 complex in vivo. , 1999, Cancer research.

[4]  T. Lumley,et al.  Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker , 2000, Biometrics.

[5]  G. Iliakis,et al.  Deficiency of human BRCA2 leads to impaired homologous recombination but maintains normal nonhomologous end joining , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[6]  M. Jasin,et al.  BRCA2 is required for homology-directed repair of chromosomal breaks. , 2001, Molecular cell.

[7]  Francisco Azuaje,et al.  Genomic data sampling and its effect on classification performance assessment , 2003, BMC Bioinformatics.

[8]  M. Radmacher,et al.  Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. , 2003, Journal of the National Cancer Institute.

[9]  M. Bookman.  Developmental chemotherapy and management of recurrent ovarian cancer. , 2003, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[10]  Edward R. Dougherty,et al.  Is cross-validation valid for small-sample microarray classification? , 2004, Bioinform..

[11]  P. Heagerty,et al.  Survival Model Predictive Accuracy and ROC Curves , 2005, Biometrics.

[12]  W. Foulkes.  BRCA1 and BRCA2: Chemosensitivity, Treatment Outcomes and Prognosis , 2005, Familial Cancer.

[13]  Annette M. Molinaro,et al.  Prediction error estimation: a comparison of resampling methods , 2005, Bioinform..

[14]  Estimation of prediction error variances via Monte Carlo sampling methods using different formulations of the prediction error variance , 2009, Genetics Selection Evolution.

[15]  Daniel Birnbaum,et al.  Gene expression profiling and prediction of clinical outcome in ovarian cancer. , 2009, Critical reviews in oncology/hematology.

[16]  Robert S Mannel,et al.  Phase II evaluation of pemetrexed in the treatment of recurrent or persistent platinum-resistant ovarian or primary peritoneal carcinoma: a study of the Gynecologic Oncology Group. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[17]  David D. L. Bowtell,et al.  The genesis and evolution of high-grade serous ovarian cancer , 2010, Nature Reviews Cancer.

[18]  Igor Jurisica,et al.  Optimized application of penalized regression methods to diverse genomic data , 2011, Bioinform..

[19]  Benjamin J. Raphael,et al.  Integrated Genomic Analyses of Ovarian Carcinoma , 2011, Nature.

[20]  D. Bodurka,et al.  Improved survival in non-Ashkenazi Jewish ovarian cancer patients with BRCA1 and BRCA2 gene mutations. , 2011, Gynecologic oncology.

[21]  Rochelle L. Garcia,et al.  Secondary somatic mutations restoring BRCA1/2 predict chemotherapy resistance in hereditary ovarian carcinomas. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[22]  K. Offit,et al.  Survival in epithelial ovarian cancer: a multivariate analysis incorporating BRCA mutation status and platinum sensitivity. , 2011, Annals of oncology : official journal of the European Society for Medical Oncology.

[23]  Richard M. Simon,et al.  Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data , 2011, Briefings Bioinform..

[24]  K. Hess,et al.  Association of BRCA1 and BRCA2 mutations with survival, chemotherapy sensitivity, and gene mutator phenotype in patients with ovarian cancer. , 2011, JAMA.

[25]  I. Sohn,et al.  Somatic hypermutation and outcomes of platinum based chemotherapy in patients with high grade serous ovarian cancer. , 2012, Gynecologic oncology.