Strategies for Integrated Analysis of Genetic, Epigenetic, and Gene Expression Variation in Cancer: Addressing the Challenges

Abstract

The development and progression of cancer, a collection of diseases with complex genetic architectures, is facilitated by the interplay of multiple etiological factors. This complexity challenges the traditional single-platform study design and calls for an integrated approach to data analysis. However, integration of heterogeneous measurements of biological variation is a non-trivial exercise due to the diversity of the human genome and the variety of output data formats and genome coverage obtained from the commonly used molecular platforms. This review article will provide an introduction to integration strategies used for analyzing genetic risk factors for cancer. We critically examine the ability of these strategies to handle the complexity of the human genome and also accommodate information about the biological and functional interactions between the elements that have been measured—making the assessment of disease risk against a composite genomic factor possible. The focus of this review is to provide an overview and introduction to the main strategies and to discuss where there is a need for further development.

Gene-Specific Methylation Profiles for Integrative Methylation-Expression Analysis in Cancer Research

“…In fact, the negative correlation between enhancer methylation and target gene expression was exploited to infer the putative gene targets of active enhancers and build tumor-specific enhancer-gene networks (Yao et al 2015;Rhie et al 2016). In summary, these recent studies indicate that regulatory methylation sites do not occur exclusively in CpG islands or gene promoters, and the effect of DNA methylation on gene expression can depend strongly on the genomic location of the CpG site with respect to the gene region (Thingholm et al 2016).…”

“…Under the assumption that functional importance of a particular CpG is often related to its relative location within or around the gene (Thingholm et al 2016), several studies (Brenet et al 2011;Lou et al 2014;Rhee et al 2013;VanderKraats et al 2013;Schlosberg et al 2017) have investigated the combinatorial effect of methylation of different components of a transcription unit on gene expression, and constructed quantitative models to predict gene expression based on methylation of different genomic regions. Among these studies, Brenet et al (2011) concluded Figure 1: Distribution of the number of associated CpG probes per gene.…”

Cross-Generational Effects of Alcohol Dependence in Humans on HRAS and TP53 Methylation in Offspring

“…Integrated approaches to understanding cancer risk appear to require analysis of genetic and epigenetic factors along with gene expression profiles to address the complexity of cancer etiology [6]. In addition to the direct effect of alcohol on tissues exposed to alcohol, the potential exists for alcohol to change the methylation of oncogenes and tumor suppressor genes involved in tumor development.…”

“…Consumption of alcohol appears to have a direct effect on gastrointestinal tissue promoting the development of malignancy. The incidence of the head and neck cancers is in direct proportion to the dilution of alcohol in the gut so that mouth cancers are most common followed by esophageal cancers with the least incidence occurring in stomach and intestinal cancers [5].Integrated approaches to understanding cancer risk appear to require analysis of genetic and epigenetic factors along with gene expression profiles to address the complexity of cancer etiology [6]. In addition to the direct effect of alcohol on tissues exposed to alcohol, the potential exists for alcohol to change the methylation of oncogenes and tumor suppressor genes involved in tumor development.…”

iGC—an integrated analysis package of gene expression and copy number alteration

“…Previous studies have demonstrated that it is essential to identify prognostic biomarkers in independent datasets [11, 12]. The most popular method for integrating GE and CNA data from independent sources is to use a Venn diagram [12–15]. In this method, gene sets showing significant changes in GE are overlapped with gene sets showing significant changes in CNA.…”