Integration of high-resolution methylome and transcriptome analyses to dissect epigenomic changes in childhood acute lymphoblastic leukemia
Menée sur 46 patients pédiatriques atteints d'une leucémie aiguë lymphoblastique à précurseurs B, cette étude analyse, à l'échelle du génome, les modifications de méthylation de l'ADN et, en intégrant des données du transcriptome, identifie 17 gènes susceptibles d'être impliqués dans la genèse de la maladie
B-cell precursor acute lymphoblastic leukemia (pre-B ALL) is the most common pediatric cancer. Although the genetic determinants underlying disease onset remain unclear, epigenetic modifications including DNA methylation are suggested to contribute significantly to leukemogenesis. Using the Illumina 450k array we assessed DNA methylation in matched tumor-normal samples of 46 childhood pre-B ALL patients, extending single CpG-site resolution analysis of the pre-B ALL methylome beyond CpG-islands (CGIs). Unsupervised hierarchical clustering of CpG-site neighborhood, gene, or miRNA gene associated methylation levels separated the tumor cohort according to major pre-B ALL subtypes, and methylation in CGIs, CGI shores, and in regions around the transcription start site was found to significantly correlate with transcript expression. Focusing on samples carrying the t(12;21) ETV6-RUNX1 fusion we identified 119 subtype-specific high-confidence marker CpG-loci. Pathway analyses linked the CpG-loci associated genes with hematopoiesis and cancer. Further integration with whole transcriptome data showed the effects of methylation on expression of 17 potential drivers of leukemogenesis. Independent validation of array methylation and sequencing-derived transcript expression with Sequenom Epityper technology and quantitative real-time RT-PCR, respectively, indicates >80% empirical accuracy of our genome-wide findings. In summary, genome-wide DNA methylation profiling enabled us to separate pre-B ALL according to major subtypes, to map epigenetic biomarkers specific for the t(12;21) subtype, and through a combined methylome and transcriptome approach to identify downstream effects on candidate drivers of leukemogenesis.