The Cancer Genome Atlas (TCGA) Consortium Integrated DNA Methylation Analysis

Aberrant hypermethylation of CpG dinucleotides located in CpG islands within the promoters of key cancer genes is an epigenetic abnormality associated with heritable transcriptional gene silencing and inactivation in cancer. The genes involved include important tumor suppressors affecting key pathways for tumor initiation and progression. These methylated sequences can serve as potentially valuable markers for cancer risk assessment, diagnosis, prognosis, and prediction of therapeutic responses. In addition, many key cancer genes may be targeted by both epigenetic and genetic alterations and, thus epigenetic analysis can help focus the search for mutations, and vice versa. Studies of major cancer types suggest that any individual patient’s tumor may harbor at least 300 or more DNA hypermethylated genes. In TCGA, a pilot project is underway to begin defining these genes for GBM via genomic approaches. The approach in the epigenetic pilot is a two-tiered one which, first, involves pharmacological treatment of both well established human GBM cell lines, and a cell line grown as a neurosphere to enrich for tumor propagating cells, with a DNA methylation inhibitor (5-aza-2’-deoxycytidine, DAC) or a histone deacetylation inhibitor (trichostatin A) followed by an expression transcriptome analysis as previously described (Schuebel et. al.). This has resulted in identification of more than 3,700 total candidate genes. In the second tier, the top candidates are then analyzed on a custom Illumina GoldenGate array with the capacity to monitor methylation at a single CpG dinucleotide in the CpG islands of 1,498 gene promoters for the high throughput analysis of TCGA GBM samples. Keywords: Microarray, Hypermethylome, DNA-hypermethylation, DAC, TSA, Epigenetic, TCGA, The Cancer Genome Atlas, GBM, Glioblastoma, Glioblastoma multiforme, Brain Cell culture and treatment. For drug treatments, log phase U87MG, T98G, D54MG and the human glioblastoma derived neurosphere cell line HSR-GBM1 cells were cultured in McCoys 5A media (Invitrogen) containing 10% BCS and 1x penicillin/streptomycin with 5M 5aza-deoxycytidine (DAC) (Sigma; stock solution: 1mM in PBS) for 96 hours, replacing media and DAC every 24 hours. Cell treatment with 300nM Trichostatin A (Sigma; stock solution: 1.5mM dissolved in ethanol) was performed for 18 hours. Control cells underwent mock treatment in parallel with addition of equal volume of PBS or ethanol without drugs. Microarray analysis. Total RNA was harvested from log phase cells using the Qiagen kit according to the manufacturers instructions, including a DNAase step. RNA was quantified using the NanoDrop ND-100 followed by quality assessment with 2100 Bioanalyzer (Agilent Technologies). RNA concentrations for individual samples were greater than 200ng/ul, with 28s/18s ratios greater than 2.2 and RNA integrity numbers of 10 (highest). Sample amplification and labeling procedures were carried out using the Low RNA Input Fluorescent Linear Amplification Kit (Agilent Technologies) according to the manufacturers instructions. The labeled cRNA was purified using the RNeasy mini kit (Qiagen) and quantified. RNA spike-in controls (Agilent Technologies) were added to RNA samples before amplification. 0.75 microgram of samples labeled with Cy3 or Cy5 were mixed with control targets (Agilent Technologies), assembled on Oligo Microarray, hybridized, and processed according to the Agilent microarray protocol. Scanning was performed with the Agilent G2565BA microarray scanner under default settings recommended by Agilent Technologies. Data analysis. All arrays were subject to quality checks recommended by the manufacturer. Images were visually inspected for artifacts and distributions of signal and background intensity of both red and green channels were examined to identify anomalous arrays. No irregularities were observed, and all arrays were retained and used. All calculations were performed using the R statistical computing platform and packages from Bioconductor bioinformatics software project. The log ratio of red signal to green signal was calculated and Loess normalization as implemented in the limma package from Bioconductor.