Genetic variants control gene expression directly or indirectly through interactions with other elements or molecules. They contribute to the expression differences between individuals of the same species Citation[1]. Theoretically, understanding more about the genetic variants associated with cancer may have the potential to improve prevention and early detection, as well as open new avenues for personalized cancer diagnostic and therapeutic approaches.
Single nucleotide polymorphisms (SNPs) are the most common genetic variants in human genomes. There are over ten million SNPs deposited into public databases. They have proven to be useful genetic markers to detect cancer variants via linkage disequilibrium Citation[2].
Structural variation also represents a significant source of genetic variations widespread in human genomes Citation[3]. Structural variations include copy number variant (CNV) and copy neutral variation, in which CNVs include insertion and deletion whereas copy neutral variations include inversion and translocation Citation[4]. Locus-specific de novo mutation rates for CNV can be 100–10,000-times more frequent than for SNPs Citation[5]. Indeed CNVs have the potential to indirectly influence an individual’s susceptibility to cancer, such as varying the gene dosage of tumor suppressors or oncogenes Citation[6]. Thus, change in copy number of the genome is known to be a feature of many cancers and CNV analysis is expected to reveal genes involved in carcinogenesis Citation[7].
Genome-wide association studies in cancer
Genome-wide association (GWA) studies have led to a paradigm shift in the discovery of gene–cancer associations. The ultimate goal of GWA studies is to develop a comprehensive risk prediction model that integrates genetic, environmental and person risk factors to benefit disease diagnosis, prevention and treatment Citation[8]. Over the past few years, numerous well-supported novel genetic variants for common cancers have been identified Citation[9]. Some genetic variants that may enable the development of novel diagnostic and interventional strategies are highlighted in this article.
Prostate cancer
It has been indicated that the presence of rs1447295 and rs6983267 on 8q24 may contribute to prostate cancer in Europeans Citation[10]. A relatively uncommon (2–4%) susceptibility variant in Europeans but very common (∼42%) in African–Americans on 8q24 showed an association with patients who have an earlier age at diagnosis Citation[11]. Multiple susceptibility genes that may predict high risk in select individuals were identified, including HNF1B, MSMB, CTBP2, JAZF1, CPNE3, IL16 and CDH13Citation[12]. A study group showed that two genetic variants (rs5945572 on Xp11.22 and rs721048 on 2p15) have a significantly strong association with aggressiveness of prostate cancer Citation[13]. They also reported that Europeans in the top 1.3% risk distribution of five genetic variants (rs10934853[A] on 3q21.3, rs16902094[G] and rs445114[T] on 8q24.21, rs11228565[A] on 11q13 and rs8102476[C] on 19q13.2) are at a 2.5-times greater risk of developing the disease Citation[14].
Breast cancer
SNP rs1219648 in intron 2 of FGFR2 has been identified to be associated with risk of sporadic postmenopausal breast cancer Citation[15]. Risk from four genetic variants (rs13387042[A] on 2q35, rs4415084 and rs10941679 on 5p12 near MRPS30, and rs3803662[T] on 16q12 near TNRC9) was confined to estrogen receptor-positive tumors Citation[16,17]. Other breast cancer susceptibility genetic variants include rs11249433 on 1p11.2 neighboring NOTCH2 and FCGR1B, rs4973768 on 3p24, including SLC4A7 and NEK10, rs3757318 and rs2046210 on 6q25.1 located upstream of ESR1, rs1562430 on 8q24, rs999737 on 14q24.1 localizing to RAD51L1, and rs6504950 on 17q23.2 including COX11Citation[18–21].
Colorectal & gastric cancer
Genome-wide association studies have identified a number of susceptibility variants (rs16892766 on 8q23.3 tagging EIF3H, rs6983267 and rs7014346 on 8q24, rs10795668 on 10p14, rs3802842 on 11q23, rs4939827 on 18q21) for colorectal cancer Citation[22–24]. These findings extend our understanding of the role of common genetic variants in colorectal cancer etiology. However, a GWA study identified a genetic variant (rs2976392) in PSCA that is possibly involved in regulating gastric epithelial-cell proliferation and influences susceptibility to diffuse-type gastric cancer Citation[25].
Leukemia
Three risk variants (rs4132601 on 7p12.2 near IKZF1, rs7089424 on 10q21.2 near ARID5B and rs2239633 on 14q11.2 near CEBPE) for acute lymphoblastic leukemia appeared to be involved in transcriptional regulation and differentiation of B-cell progenitors Citation[26]. Furthermore, 12 chronic lymphocytic leukemia risk variants (rs17483466 on 2q13, rs13397985 on 2q37.1 near SP140, rs757978 on 2q37.3 near FARP2, rs872071 on 6p25.3 near IRF4, rs2456449 on 8q24.21, rs735665 on 11q24.1, rs7169431 on 15q21.3, rs7176508 on 15q23, rs783540 on 15q25.2 near CPEB1, rs305061 on 16q24.1, rs1036935 on 18q21.1 and rs11083846 on 19q13.32 near PRKD2) have been identified, which may provide new insights into disease causation in this hematological malignancy Citation[27,28].
Melanoma & basal cell carcinoma
Two genetic variants (rs910873 and rs1885120 on chromosome 20) have been identified as a melanoma risk locus with strong association in early-onset cases Citation[29] while three genetic variants (rs7023329 on 9p21 near MTAP and CDKN2A, rs1393350 on 11q14-q21 encompassing TYR, and rs258322 on 16q24 encompassing MC1R) showed independent association with melanoma risk across European Citation[30]. Several genetic variants (rs801114 on 1q42 near RHOU, rs7538876 on 1p36 containing PADI4, PADI6, RCC2, and ARHGFF10L, rs2151280[C] on 9p21 near CDKN2A and CDKN2B, rs157935[T] on 7q32 near imprinted gene KLF14, and rs11170164 encoding G138E substitution in KRT5) conferred susceptibility to basal cell carcinoma, but were not found to be associated with melanoma or pigmentation traits. Conclusive evidence reported that rs401681[C] in the TERT-CLPTM1L locus confers susceptibility to basal cell carcinoma but protects against melanoma Citation[31,32].
Lung cancer
Genome-wide association studies have also identified several disease markers (rs401681, rs402710 and rs2736100 on 5p15.33 containing CLPTM1L and TERT, rs3117582 on 6p21.33 mapping to BAT3-MSH5, rs1051730 and rs8034191 on 15q25.1 containing PSMA4, CHRNA3, and CHRNA5) significantly associated with lung cancer risk, suggesting that these genetic variants may have a role in lung cancer etiology Citation[33–35]. Several clinical trials have shown that a higher number of EGFR gene copies is associated with a better response to treatment with gefitinib Citation[36] and some studies hypothesized that increased EGFR copy number is associated with more aggressive and poorly differentiated lung cancer Citation[37–39].
Glioma & neuroblastoma
A GWA study of neuroblastoma found two genetic variants (rs3768716 and rs6435862 on 2q35 within the BARD1 locus) that contribute to the etiology of the aggressive and most clinically relevant subset of human neuroblastoma Citation[40]. For the study of glioma, two genetic variants (rs1412829 on 9p21 near CDKN2B and rs6010620 on 20q13.3 intronic to RTEL1) were discovered to associate with high-grade glioma susceptibility Citation[41].
Pancreatic cancer
Two GWA studies have identified several genetic variants (rs3790844 on 1q32.1 mapping to NR5A2, rs401681 on 5p15.33 mapping to CLPTM1L-TERT, rs505922 on 9q34, rs9543325 and rs9564966 on 13q22.1) associated with pancreatic cancer Citation[42,43]. SNP rs505922 maps to the first intron of the ABO blood group gene, suggesting that people with blood group O may have a lower risk of pancreatic cancer than those with groups A or B Citation[42].
Nasopharyngeal carcinoma
A GWA study of nasopharyngeal carcinoma has identified three susceptibility variants (rs6774494 on 3q26 near MDS1–EVI1, rs1412829 on 9p21 near CDKN2A–CDKN2B, rs9510787 on 13q12 near TNFRSF19). These findings provide new insights into the pathogenesis of nasopharyngeal carcinoma by highlighting the involvement of pathways related to TNFRSF19 and MDS1–EVI1 in addition to HLA molecules Citation[44].
MicroRNA target SNPs
As master gene regulators, microRNAs (miRNAs) are emerging as important modulators in cellular pathways, and they appear to play a key role in tumorigenesis Citation[45]. A number of miRNAs have been identified as oncogenes, tumor suppressors or even modulators of cancer stem cells and metastasis Citation[46]. Many loci map to noncoding regions of the genome and SNPs in miRNA loci or target sites may act as low-penetrance modifiers of cancer risk Citation[47]. Thus, investigating the functional roles of SNPs in miRNA target sites poses tremendous potential for diagnostics development Citation[48]. In the past few years, miRNA studies based on SNPs have yielded a number of genetic variants related to various cancers Citation[49] and some of them are highlighted here.
A SNP in a let-7 complementary site LCS6 in the KRAS 3´ untranslated region was found to be significantly associated with an increased risk for non-small-cell lung cancer among moderate smokers Citation[50]. Another variant (rs2747648) inside a miR-453 target in ESR1 was found to be clinically associated with a stronger risk of familial breast cancer in premenopausal women Citation[51]. A variant (rs11614913, C/T) in the miR-196A2 locus has demonstrated association with lung, breast, esophageal, gastric, and head and neck cancers Citation[52].
Rare variants
Despite many important successes in GWA studies published recently, much of the genetic risk for cancer remains unexplained. Most identified genetic variants confer relatively small increments in cancer risk and explain only a small proportion of familial clustering Citation[53]. This represents the dark matter of genetic risk. Much of the missing genetic control is due to variants that are too rare to be picked up by current GWA studies and these rare variants have relatively large effects on disease risk Citation[54]. Efforts to identify rare variants have been directed towards the systematic screening of candidate genes. The advent of next-generation sequencing technologies provides monumental increases in the speed and volume of generated data, free of the cloning biases and arduous sample preparation characteristic of capillary sequencing Citation[55]. The 1000 Genomes project also identifies many genetic variants at lower allele frequencies Citation[101].
Implications of cancer-associated genetic variants in diagnostics development
During the past few years, GWA studies have proved effective in identifying genetic variants in various cancers. Regardless of the type (SNP or CNV) and the frequency (common or rare variants), it is likely that hundreds or even thousands of genetic variants are implicated in cancer risk. These variants may provide useful biomarkers for diagnosis, patient stratification, and therapeutic or prognostic categorization. In addition, cataloguing genetic variants may help connect biological pathways controlling cellular activities in disease. The identification of common pathways underlying genetic heterogeneity may lead to the design of new diagnosis tests based on biochemical targets of the causal loci and the design of new therapeutic strategies to control cancer outcome Citation[56].
Several companies have already begun to offer SNP genotyping for personalized risk prediction. However, the predictive power of all known SNP risk alleles remains limited and more variants will need to be found to improve their implications in cancer screening. Genetic variants may have greater clinical utility if combined with family history information, such as determining who should undergo colonscopy or MRI screening for breast cancer Citation[57].
Future challenges
In the GWA studies era, the trends observed in recent GWA studies are anticipated to continue. Although many novel genetic variants have been identified for cancer, the task of identifying the actual functional genetic variants remains ahead. Since statistical power drops drastically for rare SNPs, cohort sizes will need to be increased to detect rare variants. Proper study design also plays a key role in determining the success of GWA studies. Chips with more SNPs accounting for rare variants and application of stringent control will need to be designed to improve genotyping accuracies. Approaches will need to be developed to integrate analysis of CNVs and GWA studies, including innovation in the design of GWA arrays and the use of the linkage disequilibrium relationships between SNPs and common copy number polymorphisms. More uniform experimental designs and statistical methods are recommended to enable more meaningful meta-analysis. We are still at the early stage in our understanding for the roles of genetic variants in cancer management. The coming research trend will be an omics approach integrating all kinds of useful DNA and RNA information, both coding and noncoding transcription, and databases for the identifications of genetic variants for cancer diagnostic or therapeutic developments Citation[58,59].
Financial & competing interests disclosure
The author has no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patients received or pending, or royalties.
No writing assistance was utilized in the production of this manuscript.
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