Prognostic DNA Methylation Biomarkers in Ovarian Cancer

• Published in: Clinical cancer research Vol. 12; no. 9; pp. 2788 - 2794
• Main Authors: WEI, Susan H, BALCH, Curtis, KARLAN, Beth Y, GIFFORD, Gillian, BROWN, Robert, KIM, Sun, HUANG, Tim H-M, NEPHEW, Kenneth P, PAIK, Henry H, KIM, Yoo-Sung, BALDWIN, Rae Lynn, LIYANARACHCHI, Sandya, LANG LI, ZAILONG WANG, WAN, Joseph C, DAVULURI, Ramana V
• Format: Journal Article
• Language: English
• Published: Philadelphia, PA American Association for Cancer Research 01.05.2006
• Subjects: Adenocarcinoma - genetics; Adenocarcinoma - pathology; Antineoplastic agents; Bioinformatics and Computational Molecular Biology; Biological and medical sciences; Biomarkers and intervention; Biomarkers, Tumor - analysis; Chromosome Mapping; DNA Methylation; DNA methylation/epigenetics; Female; Female genital diseases; Gynecologic cancers: ovarian; Gynecology. Andrology. Obstetrics; Humans; Medical sciences; Neoplasm Staging; Oligonucleotide Array Sequence Analysis; Ovarian Neoplasms - genetics; Ovarian Neoplasms - pathology; Pharmacology. Drug treatments; Prognosis; Reproducibility of Results; Tumors; Ovarian diseases; Prognosis; Ovary cancer; DNA; Biological marker; Malignant tumor; Methylation; Female genital diseases
• ISSN: 1078-0432; 1557-3265
• DOI: 10.1158/1078-0432.CCR-05-1551

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.