Robust prediction of gene regulation in colorectal cancer tissues from DNA methylation profiles

DNA methylation is recognized as one of several epigenetic regulators of gene expression and as potential driver of carcinogenesis through gene-silencing of tumor suppressors and activation of oncogenes. However, abnormal methylation, even of promoter regions, does not necessarily alter gene expression levels, especially if the gene is already silenced, leaving the exact mechanisms of methylation unanswered. Using a large cohort of matching DNA methylation and gene expression samples of colorectal cancer (CRC; n = 77) and normal adjacent mucosa tissues (n = 108), we investigated the regulatory role of methylation on gene expression. We show that on a subset of genes enriched in common cancer pathways, methylation is significantly associated with gene regulation through gene-specific mechanisms. We built two classification models to infer gene regulation in CRC from methylation differences of tumor and normal tissues, taking into account both gene-silencing and gene-activation effects through hyper- and hypo-methylation of CpGs. The classification models result in high prediction performances in both training and independent CRC testing cohorts (0.92<AUC<0.97) as well as in individual patient data (average AUC = 0.82), suggesting a robust interplay between methylation and gene regulation. Validation analysis in other cancerous tissues resulted in lower prediction performances (0.69<AUC<0.90); however, it identified genes that share robust dependencies across cancerous tissues. In conclusion, we present a robust classification approach that predicts the gene-specific regulation through DNA methylation in CRC tissues with possible transition to different cancer entities. Furthermore, we present HMGA1 as consistently associated with methylation across cancers, suggesting a potential candidate for DNA methylation targeting cancer therapy.     

Racial differences in genome-wide methylation profiling and gene expression in breast tissues from healthy women

Breast cancer is more common in European Americans (EAs) than in African Americans (AAs) but mortality from breast cancer is higher among AAs. While there are racial differences in DNA methylation and gene expression in breast tumors, little is known whether such racial differences exist in breast tissues of healthy women. Genome-wide DNA methylation and gene expression profiling was performed in histologically normal breast tissues of healthy women. Linear regression models were used to identify differentially-methylated CpG sites (CpGs) between EAs (n = 61) and AAs (n = 22). Correlations for methylation and expression were assessed. Biological functions of the differentially-methylated genes were assigned using the Ingenuity Pathway Analysis. Among 485 differentially-methylated CpGs by race, 203 were hypermethylated in EAs, and 282 were hypermethylated in AAs. Promoter-related differentially-methylated CpGs were more frequently hypermethylated in EAs (52%) than AAs (27%) while gene body and intergenic CpGs were more frequently hypermethylated in AAs. The differentially-methylated CpGs were enriched for cancer-associated genes with roles in cell death and survival, cellular development, and cell-to-cell signaling. In a separate analysis for correlation in EAs and AAs, different patterns of correlation were found between EAs and AAs. The correlated genes showed different biological networks between EAs and AAs; networks were connected by Ubiquitin C. To our knowledge, this is the first comprehensive genome-wide study to identify differences in methylation and gene expression between EAs and AAs in breast tissues from healthy women. These findings may provide further insights regarding the contribution of epigenetic differences to racial disparities in breast cancer.     

Expression levels of 63 p53-related genes add up to similar values in 24 different tissues and are unified in cancer

The expression patterns of 62 genes interacting with p53 have been investigated in 24 normal and cancerous tissues using NIH's dbEST library. The expression levels of individual genes, such as the TTP53 gene itself, but also other genes, vary up to 33-fold among the 24 different tissues and no consistent pattern can be recognized. However, when expression levels for all 63 genes are summed, these "cumulated levels" are surprisingly constant over the 24 investigated normal tissues. In cancers, the variation is further reduced. Essentially, the cumulated expression levels in cancer are independent of those in normal tissue. We furthermore constructed a linear statistical classifier, i.e., a weighted sum of gene expression levels, which robustly distinguishes normal from cancer tissue independent of the particular kind of tissue. Thus, despite very large differences for individual genes and considerable changes during carcinogenesis, the cumulated expressions have narrowly defined levels.     

Age-dependent methylation of ESR1 gene in prostate cancer

The incidence of prostate cancer increases dramatically with age and the mechanism underlying this association is unclear. Age-dependent methylation of estrogen receptor alpha (ESR1) gene has been previously implicated in other cancerous and benign diseases. We evaluated the age-dependent methylation of ESR1 in prostate cancer. The methylation status of ESR1 in 83 prostate cancer samples from patients aged 49 to 77 years (mean age at 67.4 years) was examined using the bisulfite genomic sequencing technique. The samples were divided into three age groups: men aged 60 years and under (n = 14), men aged 61-70 years (n = 40), and men aged over 70 years (n = 29). Overall, ESR1 promoter methylation was detected in 54 out of 83 (65.1%) prostate samples. The methylation rate of ESR1 increased dramatically with age from 50.0% in patients aged 60 years and under to 89.7% for patients aged 70 years and over. Logistic regression analyses revealed that age and Gleason score were the only variables that affect incidence of ESR1 methylation; other clinical factors such as prostate-specific antigen level and clinical stage did not. We also calculated ESR1 methylation density (the percentage of methylated CpGs among all CpGs within the analyzed region) and severity (the percentage of methylated CpG alleles) for each sample analyzed. Multiple regression analyses showed a positive correlation between age and methylation density (beta, 0.35; P, 0.012; 95% CI, 0.26-2.01); while Gleason score was positively associated with methylation severity (beta, 0.45; P, 0.018; 95% CI, 1.04-4.26). These findings suggest that methylation of ESR1 is both age-dependent and tumor differentiation-dependent and age-dependent methylation of ESR1 may represent a mechanism linking aging and prostate cancer.     

The dynamics and prognostic potential of DNA methylation changes at stem cell gene loci in women's cancer

Aberrant DNA methylation is an important cancer hallmark, yet the dynamics of DNA methylation changes in human carcinogenesis remain largely unexplored. Moreover, the role of DNA methylation for prediction of clinical outcome is still uncertain and confined to specific cancers. Here we perform the most comprehensive study of DNA methylation changes throughout human carcinogenesis, analysing 27,578 CpGs in each of 1,475 samples, ranging from normal cells in advance of non-invasive neoplastic transformation to non-invasive and invasive cancers and metastatic tissue. We demonstrate that hypermethylation at stem cell PolyComb Group Target genes (PCGTs) occurs in cytologically normal cells three years in advance of the first morphological neoplastic changes, while hypomethylation occurs preferentially at CpGs which are heavily Methylated in Embryonic Stem Cells (MESCs) and increases significantly with cancer invasion in both the epithelial and stromal tumour compartments. In contrast to PCGT hypermethylation, MESC hypomethylation progresses significantly from primary to metastatic cancer and defines a poor prognostic signature in four different gynaecological cancers. Finally, we associate expression of TET enzymes, which are involved in active DNA demethylation, to MESC hypomethylation in cancer. These findings have major implications for cancer and embryonic stem cell biology and establish the importance of systemic DNA hypomethylation for predicting prognosis in a wide range of different cancers.     

Factors affecting the persistence of drug-induced reprogramming of the cancer methylome

Aberrant DNA methylation is a critical feature of cancer. Epigenetic therapy seeks to reverse these changes to restore normal gene expression. DNA demethylating agents, including 5-aza-2′-deoxycytidine (DAC), are currently used to treat certain leukemias, and can sensitize solid tumors to chemotherapy and immunotherapy. However, it has been difficult to pin the clinical efficacy of these agents to specific demethylation events, and the factors that contribute to the durability of response remain largely unknown. Here we examined the genome-wide kinetics of DAC-induced DNA demethylation and subsequent remethylation after drug withdrawal in breast cancer cells. We find that CpGs differ in both their susceptibility to demethylation and propensity for remethylation after drug removal. DAC-induced demethylation was most apparent at CpGs with higher initial methylation levels and further from CpG islands. Once demethylated, such sites exhibited varied remethylation potentials. The most rapidly remethylating CpGs regained >75% of their starting methylation within a month of drug withdrawal. These sites had higher pretreatment methylation levels, were enriched in gene bodies, marked by H3K36me3, and tended to be methylated in normal breast cells. In contrast, a more resistant class of CpG sites failed to regain even 20% of their initial methylation after 3 months. These sites had lower pretreatment methylation levels, were within or near CpG islands, marked by H3K79me2 or H3K4me2/3, and were overrepresented in sites that become aberrantly hypermethylated in breast cancers. Thus, whereas DAC-induced demethylation affects both endogenous and aberrantly methylated sites, tumor-specific hypermethylation is more slowly regained, even as normal methylation promptly recovers. Taken together, these data suggest that the durability of DAC response is linked to its selective ability to stably reset at least a portion of the cancer methylome.     

Aging effects on DNA methylation modules in human brain and blood tissue

Background

Several recent studies reported aging effects on DNA methylation levels of individual CpG dinucleotides. But it is not yet known whether aging-related consensus modules, in the form of clusters of correlated CpG markers, can be found that are present in multiple human tissues. Such a module could facilitate the understanding of aging effects on multiple tissues.

Results

We therefore employed weighted correlation network analysis of 2,442 Illumina DNA methylation arrays from brain and blood tissues, which enabled the identification of an age-related co-methylation module. Module preservation analysis confirmed that this module can also be found in diverse independent data sets. Biological evaluation showed that module membership is associated with Polycomb group target occupancy counts, CpG island status and autosomal chromosome location. Functional enrichment analysis revealed that the aging-related consensus module comprises genes that are involved in nervous system development, neuron differentiation and neurogenesis, and that it contains promoter CpGs of genes known to be down-regulated in early Alzheimer's disease. A comparison with a standard, non-module based meta-analysis revealed that selecting CpGs based on module membership leads to significantly increased gene ontology enrichment, thus demonstrating that studying aging effects via consensus network analysis enhances the biological insights gained.

Conclusions

Overall, our analysis revealed a robustly defined age-related co-methylation module that is present in multiple human tissues, including blood and brain. We conclude that blood is a promising surrogate for brain tissue when studying the effects of age on DNA methylation profiles.     

LINE-1 methylation patterns of different loci in normal and cancerous cells

This study evaluated methylation patterns of long interspersed nuclear element-1 (LINE-1) sequences from 17 loci in several cell types, including squamous cell cancer cell lines, normal oral epithelium (NOE), white blood cells and head and neck squamous cell cancers (HNSCC). Although sequences of each LINE-1 are homologous, LINE-1 methylation levels at each locus are different. Moreover, some loci demonstrate the different methylation levels between normal tissue types. Interestingly, in some chromosomal regions, wider ranges of LINE-1 methylation levels were observed. In cancerous cells, the methylation levels of most LINE-1 loci demonstrated a positive correlation with each other and with the genome-wide levels. Therefore, the loss of genome-wide methylation in cancerous cells occurs as a generalized process. However, different LINE-1 loci showed different incidences of HNSCC hypomethylation, which is a lower methylation level than NOE. Additionally, we report a closer direct association between two LINE-1s in different EPHA3 introns. Finally, hypermethylation of some LINE-1s can be found sporadically in cancer. In conclusion, even though the global hypomethylation process that occurs in cancerous cells can generally deplete LINE-1 methylation levels, LINE-1 methylation can be influenced differentially depending on where the particular sequences are located in the genome.     

A DNA Methylation Network Interaction Measure,and Detection of Network Oncomarkers

Epigenetic processes–including DNA methylation–are increasingly seen as having a fundamental role in chronic diseases like cancer. DNA methylation patterns offer a route to develop prognostic measures based directly on DNA measurements, rather than less-stable RNA measurements. A novel DNA methylation-based measure of the co-ordinated interactive behaviour of genes is developed, in a network context. It is shown that this measure reflects well the co-regulatory behaviour linked to gene expression (at the mRNA level) over the same network interactions. This measure, defined for pairs of genes in a single patient/sample, associates with overall survival outcome independent of known prognostic clinical features, in several independent data sets relating to different cancer types. In total, more than half a billion CpGs in over 1600 samples, taken from nine different cancer entities, are analysed. It is found that groups of gene-pair interactions which associate significantly with survival identify statistically significant subnetwork modules. Many of these subnetwork modules are shown to be biologically relevant by strong correlation with pre-defined gene sets, such as immune function, wound healing, mitochondrial function and MAP-kinase signalling. In particular, the wound healing module corresponds to an increase in co-ordinated interactive behaviour between genes for worse prognosis, and the immune module corresponds to a decrease in co-ordinated interactive behaviour between genes for worse prognosis. This measure has great potential for defining DNA-based cancer biomarkers. Such biomarkers could naturally be developed further, by drawing on the rapidly expanding knowledge base of network science.     

Deep Bisulfite Sequencing of Aberrantly Methylated Loci in a Patient with Multiple Methylation Defects

NLRP7 is a maternal effect gene as maternal mutations in this gene cause recurrent hydatidiform moles, spontaneous abortions and stillbirths, whereas live births are very rare. We have studied a patient with multiple anomalies born to a mother with a heterozygous NLRP7 mutation. By array-based CpG methylation analysis of blood DNA from the patient, his parents and 18 normal controls on Illumina Infinium HumanMethylation27 BeadChips we found that the patient had methylation changes (delta ß ≥ 0.3) at many imprinted loci as well as at 87 CpGs associated with 85 genes of unknown imprinting status. Using a pseudoproband (permutation) approach, we found methylation changes at only 7-24 CpGs (mean 15; standard deviation 4.84) in the controls. Thus, the number of abberantly methylated CpGs in the patient is more than 14 standard deviations higher. In order to identify novel imprinted genes among the 85 conspicuous genes in the patient, we selected 19 (mainly hypomethylated) genes for deep bisulfite amplicon sequencing on the ROCHE/454 Genome Sequencer in the patient and at least two additional controls. These controls had not been included in the array analysis and were heterozygous for a single nucleotide polymorphism at the test locus, so that allele-specific DNA methylation patterns could be determined. Apart from FAM50B, which we proved to be imprinted in blood, we did not observe allele-specific DNA methylation at the other 18 loci. We conclude that the patient does not only have methylation defects at imprinted loci but (at least in blood) also an excess of methylation changes at apparently non-imprinted loci.