Prognostic DNA methylation biomarkers in ovarian cancer

Susan H. Wei, Curtis Balch, Henry H. Paik, Yoo Sung Kim, Rae Lynn Baldwin, Sandya Liyanarachchi, Lang Li, Zailong Wang, Joseph C. Wan, Ramana V. Davuluri, Beth Y. Karlan, Gillian Gifford, Robert Brown, Sun Kim, Tim H.M. Huang, Kenneth P. Nephew

Research output: Contribution to journalArticlepeer-review

135 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)2788-2794
Number of pages7
JournalClinical Cancer Research
Issue number9
StatePublished - May 1 2006
Externally publishedYes

ASJC Scopus subject areas

  • General Medicine


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