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Prognostic DNA Methylation Biomarkers in Ovarian Cancer

Authors' Affiliations: ¹ Human Cancer Genetics Program, Department of Molecular Virology, Immunology, and Medical Genetics, Comprehensive Cancer Center and ² Mathematical Bioscience Institute, The Ohio State University, Columbus, Ohio; ³ Medical Sciences, Department of Cellular and Integrative Physiology, Indiana University School of Medicine; ⁴ School of Informatics, Center for Bioinformatics and Genomics, Indiana University, Bloomington, Indiana; and ⁵ School of Information and Communication Engineering, Inha University, Incheon, South Korea; ⁶ Division of Gynecologic Oncology, Cedars-Sinai Medical Center, Department of Obstetrics and Gynecology, University of California at Los Angeles School of Medicine, Los Angeles, California; ⁷ Division of Biostatistics, Department of Medicine, Indiana University School of Medicine; ⁸ Indiana University Cancer Center, Indianapolis, Indiana; and ⁹ Cancer Research UK, Beatson Laboratories, University of Glasgow, Glasgow, Scotland, United Kingdom

Requests for reprints: Kenneth P. Nephew, Indiana University School of Medicine, Jordan Hall 303, 1001 East 3rd Street, Bloomington, IN 47405. Phone: 812-855-9445; Fax: 812-855-4436; E-mail: knephew{at}indiana.edu.

Purpose: Aberrant DNA methylation, now recognized as a contributing factor to neoplasia, often shows definitive gene/sequence preferences unique to specific cancer types. Correspondingly, distinct combinations of methylated loci can function as biomarkers for numerous clinical correlates of ovarian and other cancers.

Experimental Design: We used a microarray approach to identify methylated loci prognostic for reduced progression-free survival (PFS) in advanced ovarian cancer patients. Two data set classification algorithms, Significance Analysis of Microarray and Prediction Analysis of Microarray, successfully identified 220 candidate PFS-discriminatory methylated loci. Of those, 112 were found capable of predicting PFS with 95% accuracy, by Prediction Analysis of Microarray, using an independent set of 40 advanced ovarian tumors (from 20 short-PFS and 20 long-PFS patients, respectively). Additionally, we showed the use of these predictive loci using two bioinformatics machine-learning algorithms, Support Vector Machine and Multilayer Perceptron.

Conclusion: In this report, we show that highly prognostic DNA methylation biomarkers can be successfully identified and characterized, using previously unused, rigorous classifying algorithms. Such ovarian cancer biomarkers represent a promising approach for the assessment and management of this devastating disease.

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