A genome-wide screen identifies frequently methylated genes in haematological and epithelial cancers

Abstract

Background: Genetic as well as epigenetic alterations are a hallmark of both epithelial and haematological malignancies. High throughput screens are required to identify epigenetic markers that can be useful for diagnostic and prognostic purposes across malignancies.

Results: Here we report for the first time the use of the MIRA assay (methylated CpG island recovery assay) in combination with genome-wide CpG island arrays to identify epigenetic molecular markers in childhood acute lymphoblastic leukemia (ALL) on a genome-wide scale. We identified 30 genes demonstrating methylation frequencies of > or =25% in childhood ALL, nine genes showed significantly different methylation frequencies in B vs T-ALL. For majority of the genes expression could be restored in methylated leukemia lines after treatment with 5-azaDC. Forty-four percent of the genes represent targets of the polycomb complex. In chronic myeloid leukemia (CML) two of the genes, (TFAP2A and EBF2), demonstrated increased methylation in blast crisis compared to chronic phase (P < 0.05). Furthermore hypermethylation of an autophagy related gene ATG16L2 was associated with poorer prognosis in terms of molecular response to Imatinib treatment. Lastly we demonstrated that ten of these genes were also frequently methylated in common epithelial cancers.

Conclusion: In summary we have identified a large number of genes showing frequent methylation in childhood ALL, methylation status of two of these genes is associated with advanced disease in CML and methylation status of another gene is associated with prognosis. In addition a subset of these genes may act as epigenetic markers across hematological malignancies as well as common epithelial cancers.

Figure 1

Candidate gene selection. This schematic details the criteria used in the microarray data analysis for the selection of the short list of 398 genes. The short list, only contains genes which had two or more probes labelled as 'promoter' which were methyled in at least three of the five primary samples analysed, this list does not contain any microRNAs or unidentified genes/chromosomal regions.

Figure 2

Methylation analysis in B and T-ALL. Representative combined bisulfite restriction analysis (COBRA) results for primary T-ALL and B-ALL samples and control bone marrow. No methylation was detected in any of the control bone marrow samples. U = undigested PCR product, B = BstUI digested PCR product. The samples labelled with * correspond to those ALL samples for which methylation was assessed by cloning and sequencing.

Figure 3

Methylation assessed by sequencing. Representative results of cloning and sequencing of ten genes. COBRA PCR products were cloned into PGem T-easy vector and colonies were picked and inserts sequenced. Each line is representative of an individual allele. Each circle represents a single CpG dinucleotide, filled circles represent a methylated CpG dinucleotide whereas clear circles represent an unmethylated CpG dinucleotide. The MI was calculated as a percentage using the equation; number of CpG dinucleotides methylated/total number of CpG dinucleotides sequenced x 100. Sequencing of the DNA from control bone marrow shows no or very infrequent methylation, with MIs all below 3.5%. For each gene two samples for which COBRA analysis detected hypermethylation and two for which it did not were sequenced. The figure shows control bone marrow and two methylated samples for each gene. The sequencing results correlate with the COBRA detected methylation.

Figure 4

Re-expression of Methylated genes. Representative examples of leukemia cell line expression data. Expression from cell line RNA samples before demethylation treatment are labelled '-' and after demethylation treatment '+'. Methylated cell lines are labelled with a '*', expression of these genes in methylated cell lines before treatment could not be detected, expression in the same cell lines was detected after demethylation treatment. In unmethylated cell lines expression of these genes was detected before and after demethylation treatment to similar levels.

Figure 5

DAVID functional annotation. The table shows the results of using the online DAVID functional clustering and annotation program to cluster the short list of 398 genes in to functional groups and pathways. The analysis revealed that the majority of genes cluster as being involved in transcription, with smaller number of genes involved in functions such as apoptosis, DNA repair, cell cycle progression and cell signalling.