Predicting Drug Resistance in Cancer Genomes by DMA Methylation Profiling

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Ovarian cancer is the most lethal of all gynecological neoplasms. Although ovarian tumor resistance to chemotherapeutic drugs is a common problem, the underlying mechanisms of this multifactorial phenomenon remain poorly understood. While CpG island (CpGi) methylation likely plays a prominent role in the complexity of drug resistance in cancer, this has not been widely addressed in ovarian cancer, nor has the emerging phenomenon of acquired DNA methylation induced by cisplatin. We previously performed a genome-wide interrogation of CpGi loci in drug-resistant ovarian cancer and identified a subset of CpGi that were differentially hypermethylated in drug-resistant cell lines and relapse tumors and also were strongly correlated with shorter survival. Furthermore, within these loci, we identified conserved DNA sequences, characteristic of methylation-prone CpGi. We hypothesize that CpGi methylation is associated with cisplatin resistance. We will test this hypothesis by using models of ovarian cancer drug resistance and patient tumor samples. We will conduct computational modeling, using the drug-resistant, hypermethylated CpGi loci as our machine training set. An unsupervised learning scheme, specifically, a sequence clustering algorithm, will be used to classify CpGi sequences into groups of similar sequences. Patterns or subsequences in CpGi sequences will be used to further select methylation-prone CpGi sequences associated with drug-induced DNA methylation in ovarian cancer. The methylation-specific oligonucleotide (MSO) microarray method will be used to determine detailed methylation patterns of the CpGi loci in cisplatin-selected daughter ovarian cancer sublines, the drug-sensitive parental line, primary ovarian tumors taken before chemotherapy, and tumors obtained at relapse. Furthermore, the sequence prediction and MSO results will be evaluated directly, using a time-course methylation comparison between a drug-naive ovarian cancer cell line and the same cell line treated multiple times with cisplatin. We have designed this time-course experimental system to determine CpGi loci susceptible to drug-induced ate novo methylation and also to directly associate CpGi with the development of cisplatin resistance in ovarian cancer. To further confirm the roles of specific, implicated loci, we will use small interfering RNA technology to knock down such genes in drug sensitive cell lines and examine the subsequent effects on drug sensitivity. The results from the biological experiments will be analyzed using supervised or unsupervised learning methods. The computational analysis of experimental data will guide the reformulation of hypotheses. The entire procedure can be iterated to generate and refine mathematical models that predict drug-induced CpGi methylation and perhaps identify epigenetic relapse biomarkers in ovarian cancer.