Int J Cancer Clin Res ISSN: 2378-3419

• Abstract
• Keywords
• Introduction
• Conclusions and Prospects
• References

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International Journal of Cancer and Clinical Research

Hudson Alpha Institute for Biotechnology, Huntsville, USA

Abstract

Investigations of the genetic basis of cancers have identified hundreds of robust risk loci associated with cancers using large-scale, case-control, candidate gene studies as well as genome-wide association studies (GWASs) during the past ten years. Most leading single nucleotide polymorphisms (SNPs) associated with cancer sensitivity lie in non-protein-coding regions, suggesting the potentially regulatory functions as targets for susceptible variants. That is a critical question to understand the molecular mechanisms and causality within cancer susceptible loci. In this short review, the latest findings about functional implications of genetic variants in cancer etiology were summarized on the basis of genetics, epigenetics and environmental factors. Several helpful directions in post-GWAS functional determinants of cancer-associated polymorphisms were also previewed.

Keywords

Genetic polymorphism, Cancer risk locus, Chromatin interaction, QTL, Methylation, Non-Coding RNA

Introduction

Recent advances in genome-wide association studies (GWASs) and fine-mapping analyses have yielded a plethora of common and rare loci associated with diverse cancers and other complex diseases [1-3]. These different penetrant risk loci together substantially unveil the heritable fraction of diseases [4]. Due to the majority of cancer risk variants reside in intronic or intergenic regions of unknown function, how to effectively understand their molecular mechanisms has become a challenging but critical question in post-GWAS research [5]. Several pioneering studies have revealed some promising molecular evidences at both transcriptional and regulatory levels for cancer genetic variants [6,7], and the contributions are continuously underway, especially in the area of gene expression and regulation.

Thanks to the development of diverse of high-throughput technologies, particularly next-generation sequencing (NGS) technologies, genome-scale large data sets - including genomic, epigenomic, transcriptomic and proteomic information, are now freely accessible from large collaborative projects, including Encyclopedia of DNA Elements (ENCODE) [8,9], NIH Epigenomics Roadmap [10], The Cancer Genome Atlas (TCGA) [11], Genotype-Tissue Expression (GTEx) [12]. Concomitantly, an integrative genomic approach [13] has increasingly adopted to interrogate whether genetic variants of interest are the causal potential. More recently, Ward et al. [14] and Edwards et al. [15] depicted a systematic flowchart for functional prediction of genetic variants by integrating publicly accessible functional genomic data, including expression quantitative trait loci (eQTLs), chromatin modification and states across diverse cell types and disease relevant tissues/cells, transcription factors binding motif. This short article will review the latest findings about functional implications of cancer genetic loci focusing on QTLs and epigenetic regulation, as well as the interactions of cancer-associated variants with environmental and nutritional factors. The summary might help to comprehensively understand the cancer pathogenesis, and provide an additional direction for future cancer genetic studies.

Quantitative Trait Loci

DNA methylation QTL: Numerous studies have revealed a common profile of a global DNA hypomethylation and local DNA hypermethylation associated with tumorigenesis [16-19], which provides a clue to determine the functional feature of identified common and rare genetic variants associated with cancer risk. A growing attention has focused on the correlation between cancer-risk variants and DNA methylation levels, a definition referring to as the methylation quantitative trait loci (meQTL) [20,21]. Heyn et al. first conducted [22] a comprehensive meQTL analyses via integrating genome-wide DNA methylation profiles with 109 GWAS-SNPs in 13 solid cancer types. They found 23 cis-0meQTLs, accounting for approximately one-quarter of interrogated cancer risk polymorphisms. Several other studies also found several cancer-associated meQTLs in lung cancer [17], prostate cancer [23], myeloma [24], etc. Thus, screening the genomic variants and epigenomic modifications at high resolution could elucidate a direct functional implication of the underlying genetic genotypes associated with DNA methylation at specific sites.

The Infinium Human Methylation 450 and Human Methylation 27 Bead Chips [22-25] are two popular platforms to measure DNA methylation profiles [26]. At present, the measurement of DNA methylation levels for meQTL analyses also largely depend on these platforms. Nonetheless, a large proportion of human genomic CpG sites are still uncovered in the design of DNA methylation chips in current microarray-based meQTL studies. It causes that the meQTLs accounting for cancer risk variants are far less discovered. So it is possible to conduct fine-scale mapping of meQTLs associated with cancer risk by employing genome-wide bisulfite sequencing or targeted bisulfite-sequencing technologies [27,28] in single CpG site resolution.

Apart from methylation on cytosine (5-mC) at CpG sites, recent studies have discovered several newtypes of DNA modifications [29-32], including 5-Hydroxymethylcytosine (5-hmC), 5-formylcytosine (5-fC), 5-carboxylcytosine (5-caC), and N⁶-adenine methylation (N⁶-methyladenine, 6 mA). QTL analysis with these DNA modifications may help to reveal novel molecular phenotypes for genetic effects on cancer risk.

Expression QTL: Expression QTL analysis has been widely used to detect the regulatory feature of genetic variants that influence the expression level of genes in cis and trans [15]. Recent works did more deep exploration in the study of expression QTLs for cancer-associated genetic variants. One of the most powerful approaches to perform eQTL analyses in cancer genetic studies is the Li et al. developed method [33], which directly works through the TCGA data sets. The method did a residual linear regression test with tumor gene expression adjusted by somatic copy-number alternation and CpG methylation. This method shows considerably robust in functional annotation of cancer-associated variants. For example, Cai et al. applied this model to examine eQTLs for novel discovered breast cancer risk variants [34]. Based on this model, Wong et al. also identified thousands of expression-associated somatic single nucleotide variants (eSNVs) in endometrial cancer [35].

It should be also noted that the non-coding RNAs, including long non-coding RNAs (lncRNAs), piRNAs, endogenous siRNAs, snoRNAs, have been reported to show aberrant expression in tumors [36-38]. However, there are still less findings about whether cancer-associated genetic variants could functionally control the expression of the non-coding RNAs. Studies in other diseases or human traits have implicated the relationship between the expression of lncRNAs and genetic variants. In the study of Kumar et al. [39], they found that the majority of lncRNAs-eQTLs were specific to lncRNA alone and did not affect the expression of neighboring protein-coding genes in blood. Brown et al. proposed that the most promising molecular phenotype for genetic risk variants is cancer-relevant tissue or cell type specific eQTLs [40]. Due to high tissue-restricted expression pattern for most lncRNAs, exploring the lncRNA-eQTL association for cancer risk loci could be conducted to understand the specificity of cancer-associated genetic variants. In Addition, relative to genetically steady eQTLs, some eQTLs are probably inducible [41], another missing layer involved in disease predisposition.

Transcriptional regulation by genetic variants is involved in allelic imbalance. The allelic specificity on gene expression is a phenomenon where two alleles for heterozygous SNPs show significantly biased expression [42]. Recent studies have showed the allelic biased expression for cancer risk loci. For example, two breast cancer risk loci (rs2046210 at 6q25 and rs418269 at 8q24) are significantly associated with allelic specific expression of ESR1 and MYC, respectively [33]. Due to the transcriptomic complexity and fine-tune regulation at both transcriptional and post-transcriptional levels, genetic variants associated with gene expression could be comprehensively analyzed, so as to maximize the understanding of cancer risk loci as regulators.

Protein-level QTL: At the translational level, many studies and reviews reported the regulatory effects of cancer genetic variants by transcription factors (TFs) focused on the difference of binding affinity on genetic variants in trans regulation [15,43-45]. Several in vitro and in vivo functional assays are developed to determine the binding affinity difference at two alleles of a locus of interest, including Electrophoretic Mobility Shift Assay (EMAS), gene reporter assays, chromatin immune-precipitation followed by quantitative PCR (ChIP-qPCR), and the rest [43,44,46,47]. Here one newly discovery of the functional relationship of genetic variants with protein abundance, termed as protein quantitative trait loci (pQTLs), was first reported in the study of Wu et al. [48]. Interestingly, some pQTLs could not be detected as eQTLs, providing a new layer for genetic variants in molecular phenotypic regulation. Similarly, another study identified that the SNP rs6834 was significantly correlated with DIDO1 protein levels relevant for cancer chemotherapy [49]. Thus, combining genomic and proteomic quantification data could be considered for cancer risk variants.

Epigenetic regulation

Although several studies have manifested that cancers and other diseases associated causal non-coding variants function as enhancer or other cis-regulatory roles [50-52], how to establish the functional interaction between cis-regulatory elements and gene regulation becomes a critical issue. The latest studies have made some efforts on the chromatin architecture scale.

Chromosome conformation capture (3C) and its derived methods are the high-throughput molecular biology techniques used to analyze the topology of chromosomal regions in viable cells [53]. With 3C technology, it is possible to identify the physically local or distal interaction between regulatory elements and genetic variants. For example, 3C followed by real-time PCR (3C-qPCR) has been successfully utilized in determining whether the two pre-defined genomic regions are physically interacted [6,33,43,54-56]. More recently, the development of targeted 3C and relevant technologies [57-60] further allow for a high-resolution survey of the whole genome for potential interactions with multiple regions of interest simultaneously. With these targeted chromosome architecture technologies, several studies have found both intra-chromosomal and inter-chromosomal physical interactions in high precision for cancer genetic variants. For example, Jager et al. found a regulatory network about the looping interactions between CCAT2, CCAT1 and MYC, at 8q24 risk locus associated with colorectal cancer [61]. Another study also discovered the strongest long-range interaction was not at intra-chromosomal locus 8q24 but at inter-chromosomal locus 3q13 associated with prostate cancer [57]. Therefore, for a given cancer risk locus, it may function as a regulatory hub by physical interactions with multiple genes important for carcinogenesis, implicating the multiple-directional regulation probably exists at specific risk loci.

There are still some issues for current epigenomic assays to link the chromosome-level DNA looping with cancer risk loci, because most chromosome-capturing assays are carried out in well-established cell lines. Due to the potential difference of genetic background between cell lines and patient-derived tissues or cells, it will be attractive to develop patient-derived cell/tissue system to characterize the personalized chromatin interaction landscape for cancer genetic studies. In addition, combining with single-cell based genomic and epigenomic sequencing technologies [62-64], experiments on the patient-derived ex vivo cells could provide a deeper knowledge about the genetic and etiologic susceptibility of cancers.

Environmental factors

Besides the genetic and epigenetic implications for cancer risk loci, a few studies reported other potential non-(epi)genetic factors in association with cancer susceptibility, including gene-trait interaction [65,66], gene-nutrition interaction [67,68], genetic-microbiome interaction [69], as well as electronic medical record (EMR)-based genetic integrative analysis [70].Taking one as an example, Ramagopalan et al. [71] reported that the vitamin D receptor (VDR) binding sites were significantly enriched near cancer and autoimmune disease associated genes. Biochemical study demonstrated that the chromatin remodeler JMJD3 was regulated by vitamin D in colon cancer cells [72]. Clinical surveys also demonstrated that a higher vitamin D level was significantly associated with a lower colorectal cancer risk [73,74], suggesting that cancer risk variants could be also implicated in the interaction with vitamins and other nutrients.

Conclusions and Prospects

In this review, the latest progression in the molecular phenotypes for cancer risk variants at multiple levels is summarized. We expect that these discussions will provide additional understanding of cancer molecular genetics in post-GWAS functional characterization. Undoubtedly, future advances in exploring the gene expression and regulation will identify more molecular genetic evidences for cancer susceptibility.

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