There are no clear candidates for taxane/platinum pharmacogenetic markers. This study highlights the need for validation of putative genetic markers in large, well-defined clinical sample sets.

Aberrant epigenetic regulation, such as CpG island methylation and associated transcriptional silencing of genes, has been implicated in a variety of human diseases, including cancer. Methylation of genes involved in apoptosis, including the DNA mismatch repair (MMR) gene hMLH1, can occur in tumor models of resistance to chemotherapeutic drugs. However, the relevance for acquired resistance to chemotherapy of patients' tumors remains unsubstantiated. Plasma DNA from cancer patients, including those with ovarian cancer, often contains identical DNA changes as the tumor and provides a means to monitor CpG island methylation changes. We have examined plasma DNA of patients with epithelial ovarian cancer enrolled in the SCOTROC1 Phase III clinical trial for methylation of the hMLH1 CpG island before carboplatin/taxoid chemotherapy and at relapse. Methylation of hMLH1 is increased at relapse, and 25% (34 of 138) of relapse samples have hMLH1 methylation that is not detected in matched prechemotherapy plasma samples. Furthermore, hMLH1 methylation is significantly associated with increased microsatellite instability in plasma DNA at relapse, providing an independent measure of function of the MMR pathway. Acquisition of hMLH1 methylation in plasma DNA at relapse predicts poor overall survival of patients, independent from time to progression and age (hazard ratio, 1.99; 95% confidence interval, 1.20 -3.30; P ‫؍‬ 0.007). These data support the clinical relevance of acquired hMLH1 methylation and concomitant loss of DNA MMR after chemotherapy of ovarian cancer patients. DNA methylation changes in plasma provide the potential to define patterns of methylation during therapy and identify those patient populations who would be suitable for novel epigenetic therapies.

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.