Epigenetic regulation of gene expression in cancer: techniques, resources and analysis

Abstract

Cancer is a complex disease, driven by aberrant activity in numerous signaling pathways in even individual malignant cells. Epigenetic changes are critical mediators of these functional changes that drive and maintain the malignant phenotype. Changes in DNA methylation, histone acetylation and methylation, noncoding RNAs, posttranslational modifications are all epigenetic drivers in cancer, independent of changes in the DNA sequence. These epigenetic alterations were once thought to be crucial only for the malignant phenotype maintenance. Now, epigenetic alterations are also recognized as critical for disrupting essential pathways that protect the cells from uncontrolled growth, longer survival and establishment in distant sites from the original tissue. In this review, we focus on DNA methylation and chromatin structure in cancer. The precise functional role of these alterations is an area of active research using emerging high-throughput approaches and bioinformatics analysis tools. Therefore, this review also describes these high-throughput measurement technologies, public domain databases for high-throughput epigenetic data in tumors and model systems and bioinformatics algorithms for their analysis. Advances in bioinformatics data that combine these epigenetic data with genomics data are essential to infer the function of specific epigenetic alterations in cancer. These integrative algorithms are also a focus of this review. Future studies using these emerging technologies will elucidate how alterations in the cancer epigenome cooperate with genetic aberrations during tumor initiation and progression. This deeper understanding is essential to future studies with epigenetics biomarkers and precision medicine using emerging epigenetic therapies.

Keywords: bioinformatics; cancer; chromatin; data integration; epigenetics; methylation.

Figure 1.

Epigenetic mechanisms of gene expression regulation. Gene transcription occurs in regions, where the chromatin conformation is more open (active chromatin regions). Transcriptionally silenced genes are found in regions with compact chromatin. Active chromatin areas are characterized by unmethylated DNA CpG sites, histone acetylation and histone active markers, such as H3K4 and H3K36 methylation. Transcriptional silencing is characterized by methylated CpG sites, histone deacetylation and repressive histone markers. These epigenetic alterations occur in areas of condensed chromatin, in which nucleosomes are positioned close to each other. In these regions, the DNA structure and protein complexes that regulate compact confirmation block DNA accessibility to transcription factors resulting in epigenetic silencing. (A colour version of this figure is available online at: https://academic.oup.com/bfg)

Figure 2.

Epigenetics measurement techniques. A wide variety of methods characterize epigenetic alterations. Currently, the most common genome-wide approaches identify nucleosome-free regions (DNaseI-Seq; MNase-Seq; FAIRE-Seq; ATAC-Seq), protein-mediated DNA interaction sites (Hi-C; 5-C), histone marks and DNA-binding proteins (ChIP-Seq; ChIA-PET) and DNA methylation (array hybridization, WGBS, MBD-Seq, PacBio, nanopore). (A colour version of this figure is available online at: https://academic.oup.com/bfg)

Figure 3.

Epigenetic data can be obtained in vivo from primary tumor samples, PDXs and mouse models of cancer. While there are no limitations to measuring DNA methylation in patient samples, demands of high tissue quality and quantity limit measurements of chromatin structure and interaction data in patient samples. All epigenetic data can also be obtained in vitro with 2D (cancer cell lines) and 3D (organoids and conditionally reprogrammed cells) culture systems. (A colour version of this figure is available online at: https://academic.oup.com/bfg)

Figure 4.

Complete data integration to determine epigenetic regulation of gene expression can be performed for data sets containing both gene expression data (top center) and epigenetic data (bottom center) on the same samples. Clustering-based techniques such as iClusterPlus (left) seek sets of samples that have epigenetic alterations with coordinated gene expression changes. Matrix factorization-based techniques such as CoGAPS (right) infer quantitative relationships between epigenetic alterations and gene expression. These algorithms simultaneously quantify the extent of the coordinated alterations in gene expression and DNA methylation in each sample. Post hoc analyses of the clusters in iClusterPlus or CoGAPS patterns can determine their functional impact in cancer. (A colour version of this figure is available online at: https://academic.oup.com/bfg)