The post GWAS era: strategies to identify gene-gene and gene-environment interactions in urinary bladder cancer

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Bladder cancer is a smoking- and occupational exposure-related disease with a substantial genetic component (Boffetta, 2008[4]; Golka et al., 2012[17]; Roth et al., 2012[39]; Rushton et al., 2012[41]; Schwender et al., 2012[43]; Burger et al., 2013[7]). Approximately 30 % of all urinary bladder cancer cases can be attributed to genetic risk factors (Lichtenstein et al., 2000[30]; Selinski, 2012[44]; Hammad, 2013[21]). Both family studies and large genome-wide association analyses support a polygenetic basis for urinary bladder carcinomas, mainly because there is no evidence for a major gene (Aben et al., 2006[1]; Kiemeney, 2008[25]; Kiemeney et al., 2010[26]; Rafnar et al., 2011[38]; Stewart and Marchan, 2012[53]; Bolt, 2013[5][6]), and all known susceptibility variants show moderate risks (Grotenhuis et al., 2010[20]; Lehmann et al., 2010[29]; Golka et al., 2011[19]; Selinski et al., 2012[52][51]; Dudek et al., 2013[10]; Selinski, 2014[46]). Several of these moderate-risk variants, especially those categorized as phase II metabolism genes, have been shown to modulate bladder cancer risk depending on exposure to bladder carcinogens, in particular, aromatic amines and polycyclic aromatic hydrocarbons (Garcia-Closas et al., 2005[13], 2013[14]; Golka et al., 2009[15]; Rothman et al., 2010[40]; Moore et al., 2011[35]; Selinski et al., 2011[50], 2012[51]). These gene-environment interactions are well-investigated for several phase II genes, including the deletion variant of glutathione-S-transferase M1 (GSTM1) and the N-acetyltransferase 2 (NAT2) polymorphisms, both of which are particularly relevant in the presence of their carcinogenic substrates due to occupational or tobacco smoke exposure (Engel et al., 2002[11]; Golka et al., 2002[17], 2008[18], 2009[15]; Garcia-Closas et al., 2005[13]; Kopps et al., 2008[27]; Hengstler, 2010[22]; Moore et al., 2011[35]; Ovsiannikov et al., 2012[36]; Selinski, 2013[45], 2014[46]; Selinski et al., 2013[49][47], 2014[48]). Current studies focus on a broader range of polymorphisms identified by genome-wide association studies (GWAS) and the interaction of these polymorphisms with tobacco smoke exposure. Garcia-Closas et al. (2013[14]) investigated the interaction between smoking habits and the well-known panel of eleven single nucleotide polymorphisms (SNPs) from GWAS, in addition to GSTM1, in studies, which were all part of the NCI bladder cancer GWAS. The NCI bladder cancer GWAS led to the discovery of several of these bladder cancer susceptibility SNPs. The authors found additive interactions between exposure and six of the variants, in particular, rs1495741 (NAT2), rs17863783 (UDP glucuronosyltransferase 1 family, polypeptide A6 UGT1A6), GSTM1, rs2294008 (prostate stem cell antigen PSCA), rs9642880 (v-myc avian myelocytomatosis viral oncogene homolog MYC) and rs1014971 (chromobox homolog 6 CBX6, apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3A APOBEC3A) (Garcia-Closas et al., 2013[14]). Figueroa et al. (2014[12]) searched genome-wide for SNP × smoking interactions in the same multicentric case-control series. Two novel SNPs could be validated in independent study groups: the non-smoker SNP rs1711973 near forkhead box F2 (FOXF2) and the ever smoker SNP rs12216499 in an intergenic region between the radial spoke 3 homolog (Chlamydomonas) (RSPH3), T-cell activation RhoGTPase activating protein (TAGAP) and ezrin (EZR) genes (Figueroa et al., 2014[12]). Meanwhile, further studies focused on the common effects of several genetic variants on urinary bladder cancer risk instead of analysing single variants or their gene-environment interactions. The approaches used encompassed SNP-SNP and gene-gene interaction analysis (Andrew et al., 2012[2]; Binder et al., 2012[3]; Schwender et al., 2012[43]; Hu et al., 2013[23]), pathway analysis (Menashe et al., 2012[33]; Pan et al., 2014[37]) and polygenetic scores (Garcia-Closas et al., 2013[14]; Wang et al., 2014[54][55]). Results from recent genetic interaction studies are summarised in Table 1(Tab. 1). Generally, SNP-SNP or gene-gene interaction analyses aim to identify single genetic variants that interact in an additive or multiplicative way to modify the outcome of interest, e. g. bladder cancer risk. Pathway analyses consider sets of variants associated with genes that belong to the same biological or artificial pathway. The association with a phenotype of interest is often tested via enrichment analysis, i. e., a significant overrepresentation of variants of a particular pathway. Polygenic risk scores are calculated as weighted or unweighted sums of risks alleles of a set of risk variants. The unweighted variant usually sums up all risk alleles of the SNP set whereas, the weighted variant uses the individual variant odds ratio (OR) to account for higher or lower impact of each polymorphism. Usually, higher versus the lowest quartiles are compared but thresholds are also common.

Table 1. Genetic interactions and pathways that confer urinary bladder cancer in recent studies.

Genetic interaction studies are currently an important issue in cancer research. A number of approaches aim to elucidate the complex processes and interactions that lead to tumor development and progression, which has also recently been intensively studied in breast cancer (Chuang et al., 2013[9]; Sapkota et al., 2013[42]; Milne et al., 2014[34]; Yang et al., 2014[56]), prostate cancer (Lin et al., 2008[32], 2013[31]; Lavender et al., 2012[28]), lung cancer (Chu et al., 2014[8]) and colorectal cancer (Jiao et al., 2012[24]). Therefore, a new era has begun after successful identification of the most influential genetic variants. One of the goals of the post GWAS era is to understand and quantify SNP × SNP and SNP × environment interactions. The discussion on the most adequate techniques is still ongoing. A relatively easy and straight forward method is to sum up all risk alleles of relevant SNPs and study the association of the sum ('risk score') with cancer risk. A more challenging strategy is to calculate odds ratios for all combinations of variants and identify the most powerful interactions of high risk alleles. Although this approach is theoretically superior to simple 'risk score' approaches, it requires high computing capacity and very high case numbers. Currently, only few studies are available and the most critical interactions have most probably not yet been identified. However, the post GWAS era has only just begun.