Ht 12 v4 arrays

Eight-day-old neurons were treated with DMSO or 10 μM 109, 966, or 233 for 24 h. RNA purification, labeling, and hybridization to Illumina HT-12 v4 arrays (see: http://www.illumina.com/products/humanht_12_expression_beadchip_kits_v4.html) were performed as previously described (32 (link)). The HT-12 array targets more than 31,000 annotated genes and splice variants, with more than 47,000 probes [derived from the National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq) Release 38, November 7, 2009]. After normalization, genes differentially expressed between the DMSO groups (three replicates) and the 109-treated groups (three replicates) were identified with a Student’s t-test at a significance level of p < 0.01 (uncorrected p value). Genes with expression level changes greater than 2 in the 109 groups and smaller that 1.5 in the 233 or 966 groups (one replicate per compound) were then subjected to functional annotation analysis via the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Illumina probes were converted to gene symbols using DAVID gene ID conversion tool. Microarray data are deposited in the Gene Expression Omnibus as accession number GSE65399.

Gene expression data from 59 primary CNS-PNETs arising in different locations were generated using the Illumina HT-12v4 arrays and normalized as previously described [17 (link)]. Methylation data generated using the Illumina human 450 k arrays for 45 primary CNS-PNETs were background-normalized in Genome Studio (v. 2011.1) to obtain beta values for downstream analyses. All X and Y chromosome probes (n = 11,649), single-nucleotide polymorphisms (dbSNP, n = 88,679), and unannotated probes (relative to hg19, n = 65) were excluded leaving a total of 385,184 probes for methylation analyses. Genes and probes were ranked by largest coefficient of variation and standard deviation, respectively. Unsupervised hierarchical clustering (HCL; Partek Genomics Suite, v6.6) of gene expression and methylation data were, respectively, established using Pearson’s Dissimilarity performed iteratively on 200–2,000 genes and 200–10,000 probes to identify a minimal gene set associated with the most stable gene expression and methylation tumor cluster patterns (Supplemental Fig. 2).

Raw IDAT files were obtained from the Illumina human HT-12v4 arrays. These raw IDAT files were entered into Genome Studio to convert them into readable text files. Processing of raw text files was carried out in R using the lumi package. (1) LumiR function was used to read raw .txt data. (2) Data were background corrected, normalized, and stabilized using the lumiExpresso function. Normalization was performed using quantile normalization, whereby variance was stabilized using the variance stabilization transformation (VST) method. (3) Processed data were further filtered for detection of p-values. Samples that were not expressed in comparison to background noise were filtered out of the processed data. Final processed data were entered into Qlucore Omics Explorer for differential expression analysis. A t-test was used for a two-sample comparison, while ANOVA was used for multiple-sample comparisons. A threshold of p < 0.05 or 5% of a false discovery rate (FDR) was considered statistically significant for both comparisons.