A DNA methylation fingerprint of 1628 human samples

Abstract

Most of the studies characterizing DNA methylation patterns have been restricted to particular genomic loci in a limited number of human samples and pathological conditions. Herein, we present a compromise between an extremely comprehensive study of a human sample population with an intermediate level of resolution of CpGs at the genomic level. We obtained a DNA methylation fingerprint of 1628 human samples in which we interrogated 1505 CpG sites. The DNA methylation patterns revealed show this epigenetic mark to be critical in tissue-type definition and stemness, particularly around transcription start sites that are not within a CpG island. For disease, the generated DNA methylation fingerprints show that, during tumorigenesis, human cancer cells underwent a progressive gain of promoter CpG-island hypermethylation and a loss of CpG methylation in non-CpG-island promoters. Although transformed cells are those in which DNA methylation disruption is more obvious, we observed that other common human diseases, such as neurological and autoimmune disorders, had their own distinct DNA methylation profiles. Most importantly, we provide proof of principle that the DNA methylation fingerprints obtained might be useful for translational purposes by showing that we are able to identify the tumor type origin of cancers of unknown primary origin (CUPs). Thus, the DNA methylation patterns identified across the largest spectrum of samples, tissues, and diseases reported to date constitute a baseline for developing higher-resolution DNA methylation maps and provide important clues concerning the contribution of CpG methylation to tissue identity and its changes in the most prevalent human diseases.

Figure 1.

DNA methylation fingerprints for human normal tissues. (A) Unsupervised hierarchical clustering and heatmap including CpG dinucleotides with differential DNA methylation encountered between different normal primary samples. Tissue type and development layers are displayed in the different colors indicated in the figure legends. Average methylation values are displayed from 0 (green) to 1 (red). (B) Deviation plot for the 1322 CpG sites studied in leukocyte samples showing that little CpG methylation heterogeneity (yellow area) occurs overall at CpG sites within CpG islands (red lines in the track below), while more differences in CpG methylation are observed outside CpG islands (blue lines in the track below). (C) Unsupervised hierarchical clustering and heatmap including sets of genes with high correlation values between hypomethylation (up) and hypermethylation (down) with aging. (D) Unsupervised hierarchical clustering and heatmap showing the DNA methylation patterns of embryonic and adult stem cells, comparing them with corresponding normal and differentiated tissues (muscle, bone, and neuron; and muscle and brain, respectively).

Figure 2.

DNA methylation fingerprint of human cancer. (A) Unsupervised hierarchical clustering and heatmap showing distinction of primary tumor DNA methylation fingerprints according to the tissue of origin. (B) Unsupervised hierarchical clustering and heatmap of primary tumors excluding CpG sites with tissue-specific methylation. (C, above) Pie charts displaying the percentage of hypermethylated CpG sites (red) and hypomethylated CpG sites (green) in human malignancies, and their distribution in CpG islands (CGI in red) and outside CpG islands (non-CGI in blue). (Below) Deviation plot for the 1322 CpG sites showing the great methylation heterogeneity (yellow area) of primary tumors in comparison with normal primary tissues.

Figure 3.

Scenarios of DNA methylation changes in human tumorigenesis. (A) Bart plot showing the CpG hypermethylation or hypomethylation changes observed when comparing paired normal–tumor tissues from the same colorectal cancer patient. They can be distinguished if the methylation change occurs in CpG island (CGI) or non-CpG island (non-CGI)–associated CpG. (B) Unsupervised hierarchical clustering and heatmap including a set of specific CpG sites that undergo differential DNA methylation only in cancer cell lines. (C) Deviation plot for the 1322 CpG sites shows greater CpG methylation heterogeneity (yellow area) in established tumors (colon, breast, and endometrial cancers) than in their corresponding premalignant lesions. (D) DNA methylation unsupervised clustering analyses and heatmap of primary tumors, local liver metastases, and distant brain metastases from the same colorectal cancer patient. A CpG methylation-specific pattern for brain metastases (green lanes) is observed. (E) CpG methylation prediction heatmap showing the CUP classification to a specific tumor type.

Figure 4.

DNA methylation fingerprint in non-tumoral human diseases. (A) Unsupervised hierarchical clustering and heatmap of several non-tumoral diseases showing distinct DNA methylation profiles. (B) Unsupervised hierarchical clustering and heatmap showing significant differences between the DNA methylation patterns of dementia with Lewy bodies and normal controls. The CpG methylation platform used was unable to detect significant differences in the case of Alzheimer's versus healthy brain tissues. (C) Unsupervised hierarchical clustering and heatmap showing differences between dementia with Lewy bodies and neuroectodermal tumors (glioma and neuroblastoma).

Figure 5.

A DNA methylation fingerprint of 1628 human samples. Unsupervised hierarchical clustering and heatmap of all the CpG methylation maps obtained in the study, by tissue and disease type.