Integrated analysis of DNA methylation and gene expression reveals specific signaling pathways associated with platinum resistance in ovarian cancer

<p>Abstract</p> <p>Background</p> <p>Cisplatin and carboplatin are the primary first-line therapies for the treatment of ovarian cancer. However, resistance to these platinum-based drugs occurs in the large majority of initially responsive tumors, resulting in fully chemoresistant, fatal disease. Although the precise mechanism(s) underlying the development of platinum resistance in late-stage ovarian cancer patients currently remains unknown, CpG-island (CGI) methylation, a phenomenon strongly associated with aberrant gene silencing and ovarian tumorigenesis, may contribute to this devastating condition.</p> <p>Methods</p> <p>To model the onset of drug resistance, and investigate DNA methylation and gene expression alterations associated with platinum resistance, we treated clonally derived, drug-sensitive A2780 epithelial ovarian cancer cells with increasing concentrations of cisplatin. After several cycles of drug selection, the isogenic drug-sensitive and -resistant pairs were subjected to global CGI methylation and mRNA expression microarray analyses. To identify chemoresistance-associated, biological pathways likely impacted by DNA methylation, promoter CGI methylation and mRNA expression profiles were integrated and subjected to pathway enrichment analysis.</p> <p>Results</p> <p>Promoter CGI methylation revealed a positive association (Spearman correlation of 0.99) between the total number of hypermethylated CGIs and GI₅₀values (<it>i.e</it>., increased drug resistance) following successive cisplatin treatment cycles. In accord with that result, chemoresistance was reversible by DNA methylation inhibitors. Pathway enrichment analysis revealed hypermethylation-mediated repression of cell adhesion and tight junction pathways and hypomethylation-mediated activation of the cell growth-promoting pathways PI3K/Akt, TGF-beta, and cell cycle progression, which may contribute to the onset of chemoresistance in ovarian cancer cells.</p> <p>Conclusion</p> <p>Selective epigenetic disruption of distinct biological pathways was observed during development of platinum resistance in ovarian cancer. Integrated analysis of DNA methylation and gene expression may allow for the identification of new therapeutic targets and/or biomarkers prognostic of disease response. Finally, our results suggest that epigenetic therapies may facilitate the prevention or reversal of transcriptional repression responsible for chemoresistance and the restoration of sensitivity to platinum-based chemotherapeutics.</p

Methylation Linear Discriminant Analysis (MLDA) for identifying differentially methylated CpG islands-0

Unmethylated spots.<p><b>Copyright information:</b></p><p>Taken from "Methylation Linear Discriminant Analysis (MLDA) for identifying differentially methylated CpG islands"</p><p>http://www.biomedcentral.com/1471-2105/9/337</p><p>BMC Bioinformatics 2008;9():337-337.</p><p>Published online 8 Aug 2008</p><p>PMCID:PMC2529322.</p><p></p

Methylation linear discriminant analysis (MLDA) for identifying differentially methylated CpG islands

&lt;b&gt;Background:&lt;/b&gt; Hypermethylation of promoter CpG islands is strongly correlated to transcriptional gene silencing and epigenetic maintenance of the silenced state. As well as its role in tumor development, CpG island methylation contributes to the acquisition of resistance to chemotherapy. Differential Methylation Hybridisation (DMH) is one technique used for genomewide DNA methylation analysis. The study of such microarray data sets should ideally account for the specific biological features of DNA methylation and the non-symmetrical distribution of the ratios of unmethylated and methylated sequences hybridised on the array. We have therefore developed a novel algorithm tailored to this type of data, Methylation Linear Discriminant Analysis (MLDA). &lt;b&gt;Results:&lt;/b&gt; MLDA was programmed in R (version 2.7.0) and the package is available at CRAN [1]. This approach utilizes linear regression models of non-normalised hybridisation data to define methylation status. Log-transformed signal intensities of unmethylated controls on the microarray are used as a reference. The signal intensities of DNA samples digested with methylation sensitive restriction enzymes and mock digested are then transformed to the likelihood of a locus being methylated using this reference. We tested the ability of MLDA to identify loci differentially methylated as analysed by DMH between cisplatin sensitive and resistant ovarian cancer cell lines. MLDA identified 115 differentially methylated loci and 23 out of 26 of these loci have been independently validated by Methylation Specific PCR and/or bisulphite pyrosequencing. &lt;b&gt;Conclusion:&lt;/b&gt; MLDA has advantages for analyzing methylation data from CpG island microarrays, since there is a clear rational for the definition of methylation status, it uses DMH data without between-group normalisation and is less influenced by cross-hybridisation of loci. The MLDA algorithm successfully identified differentially methylated loci between two classes of samples analysed by DMH using CpG island microarrays

METHODS AND COMPOSITIONS FOR OBTAINING USEFUL PLANT TRAITS

The present invention provides methods for obtaining plants that exhibit useful traits by perturbation of plastid function in plant rootstocks and grafting the rootstocks to scions. Methods for identifying genetic loci that provide for useful traits in plants and plants produced with those loci are also provided. In addition, plants that exhibit the useful traits, parts of the plants including seeds, and products of the plants are provided as well as methods of using the plants. Recombinant DNA vectors and transgenic plants comprising those vectors that provide for plastid perturbation are also provided

Methylation Linear Discriminant Analysis (MLDA) for identifying differentially methylated CpG islands-5

Transformed Cy5 (digested) intensities. b: The unmethylated model constructed using 94 mitochondrial sequences as a unmethylation reference. c: The intermediate model constructed through the 97.5 quantile residual. The point X is the 97.5 quantile residual. The microarray probes colored in blue (standardised residual to the intermediate model is less than 2) are selected to construct the methylated model. d: Methylated (in blue) and unmethylated (in red) models in A2780 cell line.<p><b>Copyright information:</b></p><p>Taken from "Methylation Linear Discriminant Analysis (MLDA) for identifying differentially methylated CpG islands"</p><p>http://www.biomedcentral.com/1471-2105/9/337</p><p>BMC Bioinformatics 2008;9():337-337.</p><p>Published online 8 Aug 2008</p><p>PMCID:PMC2529322.</p><p></p

Methylation Linear Discriminant Analysis (MLDA) for identifying differentially methylated CpG islands-4

Sed on the fitted model (dashed smooth line in red). The red and blue solid line are the positive and negative cut-offs, respectively. b: Scatter plot of sensitive scores against resistant scores in A2780 series cell lines. The hypermethylated loci are colored in red and hypomethylated loci are in blue. The robust regression model is Y = 0.9956X + 0.0019.<p><b>Copyright information:</b></p><p>Taken from "Methylation Linear Discriminant Analysis (MLDA) for identifying differentially methylated CpG islands"</p><p>http://www.biomedcentral.com/1471-2105/9/337</p><p>BMC Bioinformatics 2008;9():337-337.</p><p>Published online 8 Aug 2008</p><p>PMCID:PMC2529322.</p><p></p

Methylation Linear Discriminant Analysis (MLDA) for identifying differentially methylated CpG islands-3

He increase of CR slowly, but starts to increase dramatically when the CR goes above 140%, at which point the inconsistency rate is generally about 1%. Not all cell lines could reach this point e.g. MCP3.<p><b>Copyright information:</b></p><p>Taken from "Methylation Linear Discriminant Analysis (MLDA) for identifying differentially methylated CpG islands"</p><p>http://www.biomedcentral.com/1471-2105/9/337</p><p>BMC Bioinformatics 2008;9():337-337.</p><p>Published online 8 Aug 2008</p><p>PMCID:PMC2529322.</p><p></p

Biostatistical Analysis of DNA Methylation Profiling in Ovarian Cancer

Ovarian cancer is the most lethal gynaecological cancer. Although having good response to chemotherapy, the majority of the patients with advanced disease will eventually relapse. Aberrant DNA methylation in tumours has been proposed as biomarkers to predict patients’ clinical outcome and response to chemotherapy. An algorithm, Methylation Linear Discriminant Analysis (MLDA), was developed for large-scale methylation analysis using differential methylation hybridsation (DMH). MLDA identified loci differentially methylated between cisplatin sensitive and resistant derivatives of an ovarian tumour cell line with 89% accuracy and showed hypermethylation, rather than hypomethylation, predominantly occurred during the acquisition of cisplatin resistance. Customised microarrays targeting promoter CpG islands in 10 key signaling pathways were designed for DMH analysis. Based on the power analysis epithelial ovarian tumours (screening study n=120, validation study n=61) prospectively collected through a cohort study, were firstly analysed by DMH at 302 loci spanning 189 promoter CGIs at 137 genes in the Wnt pathways for the association with progression free survival (PFS). Increased methylation of 6 loci, at FZD4, FZD9, DVL1, NFATC3, ROCK1 and NKD1 genes, were associated with shorter PFS independent from clinical parameters. A multivariate Cox model incorporates only NKD1 and DVL1, identifying two groups differing in PFS (HR=2.72; permutation test p = 4x10-3). Consistent with epigenetic regulation, reduced expression of FZD4 and DVL1 is associated with poor relapse free survival in an independent cohort (p<0.05,n=321). Analysis in further 9 pathways/families found 6 more independent biomarkers relevant to PFS at PIK3R5, AKT1 and VEGFB from AKT/mTOR pathway, PRDX2 and TR2IT2 from Redox pathway and MLH3 from MMR system. The study shows DNA methylation changes are involved in acquired drug resistance, and demonstrates the importance of methylation at multiple promoter CGIs in key signaling pathways, especially in the Wnt pathway, for predicting clinical outcome in ovarian cancer and their potential as stratification biomarkers in future clinical studies for personalised treatment