Prognostic DNA methylation biomarkers in ovarian cancer.
OBJECTIVE
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.


METHODS
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.


CONCLUSIONS
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.
Wei
Susan H
SH
Human Cancer Genetics Program, Department of Molecular Virology, Immunology, and Medical Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA.
Balch
Curtis
C
Paik
Henry H
HH
Kim
Yoo-Sung
YS
Baldwin
Rae Lynn
RL
Liyanarachchi
Sandya
S
Li
Lang
L
Wang
Zailong
Z
Wan
Joseph C
JC
Davuluri
Ramana V
RV
Karlan
Beth Y
BY
Gifford
Gillian
G
Brown
Robert
R
Kim
Sun
S
Huang
Tim H-M
TH
Nephew
Kenneth P
KP