Identification of susceptibility loci for colorectal cancer in a genome-wide meta-analysis

Whiffin, Nicola; Hosking, Fay J.; Farrington, Susan M.; Palles, Claire; Dobbins, Sara E.; Zgaga, Lina; Lloyd, Amy; Kinnersley, Ben; Gorman, Maggie; Tenesa, Albert; Broderick, Peter; Wang, Yufei; Barclay, Ella; Hayward, Caroline; Martin, Lynn; Buchanan, Daniel D.; Win, Aung Ko; Hopper, John L.; Jenkins, Mark A.; Lindor, Noralane M.; Newcomb, Polly A.; Gallinger, Steve; Conti, David V.; Schumacher, Fredrick; Casey, Graham; Liu, Tao; Campbell, Harry; Lindblom, Annika; Houlston, Richard S.; Tomlinson, Ian; Dunlop, Malcolm G.

doi:10.1093/hmg/ddu177

Cited by 130 publications

(106 citation statements)

References 40 publications

(44 reference statements)

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“…There was considerable overlap for the number of risk alleles for the simulated people with and without colorectal cancer (for those with colorectal cancer: median 42 [22] 1q25.3 LAMC1 rs10911251 1.05 0.54 1.0006 0.07% [23,24] 1q41 DUSP10; CICP13 rs6687758 1.09 0.2 1.0012 0.15% [25] 2q32.3 NABP1; MYO1B; SDPR rs11903757 1.06 0.36 1.003 0.37% [25,26] 3p14.1 LRIG1 rs812481 1.09 0.58 1.0018 0.22% [27] 3p22.1 RP11; CTNNB1 rs35360328 1.14 0.16 1.0023 0.29% [27] 3q26.2 MYNN; TERC rs10936599 1.08 0.75 1.0011 0.14% [25] 4q26 NDST3 rs3987 1.36 0.44 1.0235 2.87% [28] 4q32.2 FSTL5 rs35509282 1.53 0.09 1.0149 1.83% [29] 5q31.1 PITX1; H2AFY rs647161 1.11 0.67 1.0024 0.30% [30] 6p21.31 CDKN1A rs1321311 1.1 0.23 1.0016 0.20% [18] 8q23. [32,34] 10p13 CUBN rs10904849 1.14 0.68 1.0037 0.46% [22] 10p14 GATA3 rs10795668 1.12 0.67 1.0028 0.35% [31] 10q22.3 ZMIZ1; AS1 rs704017 1.06 0.57 1.0008 0.10% [17] 10q24.2 SLC25A28; ENTPD7; COX15; CUTC; ABCC2 rs11190164 1.09 0.29 1.0015 0.19% [27] 10q25 VTI1A rs12241008 1.13 0.09 1.0012 0.15% [17] 11q12.2 FADS1; FEN1 11qhap ‡ 1.4 0.57 1.0281 3.41% [17] 11q13.4 POLD3 rs3824999 1.08 0.5 1.0015 0.18% [18] 11q23.1 COLCA2 rs3802842 1.11 0.29 1.0022 0.28% [35] 12p13.32 CCND2 rs3217810 1.2 0.16 1.0045 0.55% [23,24] 12p13.32 CCND2 rs3217901 1.1 0.41 1.0022 0.27% [23,24] 12p13.32 CCND2 rs10774214 1.09 0.38 1.0018 0.22% [30] 12q13.13 DIP2B; ATF1 rs11169552 1.09 0.72 1.0015 0.18% [25] 12q13.13 LARP4; DIP2B rs7136702 1.06 0.35 1.0008 0.10% [25] 12q24.12 SH2B3 rs3184504 1.09 0.53 1.0019 0.23% [27] 12q24.21 TBX3 rs59336 1.09 0.48 1.0019 0.23% [26] 12q24.22 NOS1 rs73208120 1.16 0.11 1.0021 0.26% …”

Section: Resultsmentioning

confidence: 99%

“…[32,34] 10p13 CUBN rs10904849 1.14 0.68 1.0037 0.46% [22] 10p14 GATA3 rs10795668 1.12 0.67 1.0028 0.35% [31] 10q22.3 ZMIZ1; AS1 rs704017 1.06 0.57 1.0008 0.10% [17] 10q24.2 SLC25A28; ENTPD7; COX15; CUTC; ABCC2 rs11190164 1.09 0.29 1.0015 0.19% [27] 10q25 VTI1A rs12241008 1.13 0.09 1.0012 0.15% [17] 11q12.2 FADS1; FEN1 11qhap ‡ 1.4 0.57 1.0281 3.41% [17] 11q13.4 POLD3 rs3824999 1.08 0.5 1.0015 0.18% [18] 11q23.1 COLCA2 rs3802842 1.11 0.29 1.0022 0.28% [35] 12p13.32 CCND2 rs3217810 1.2 0.16 1.0045 0.55% [23,24] 12p13.32 CCND2 rs3217901 1.1 0.41 1.0022 0.27% [23,24] 12p13.32 CCND2 rs10774214 1.09 0.38 1.0018 0.22% [30] 12q13.13 DIP2B; ATF1 rs11169552 1.09 0.72 1.0015 0.18% [25] 12q13.13 LARP4; DIP2B rs7136702 1.06 0.35 1.0008 0.10% [25] 12q24.12 SH2B3 rs3184504 1.09 0.53 1.0019 0.23% [27] 12q24.21 TBX3 rs59336 1.09 0.48 1.0019 0.23% [26] 12q24.22 NOS1 rs73208120 1.16 0.11 1.0021 0.26% [27] 14q22.2 BMP4 rs1957636 1.08 0.4 1.0014 0.18% [36] 14q22.2 BMP4 rs4444235 1.11 0.46 1.0027 0.33% [36,37] 15q13.3 SCG5; GREM1 rs11632715 1.12 0.47 1.0032 0.39% [36] 15q13.3 SCG5; GREM1 rs16969681 1.18 0.09 1.0022 0.28% [36] 16q22.1 CDH1 rs9929218 1.1 0.71 1.0019 0.23% [37] 16q24.1 FOXL1 rs16941835 1.15 0.21 1.0032 0.40% [22] 17q21 STAT3 rs744166 1.27 0.55 1.0142 1.74% [38] 18q21.1 SMAD7 rs4939827 1.18 0.52 1.0069 0.84% [35,39] 19q13.11 RHPN2 rs10411210 1.15 0.9 1.0018 0.22% [37] 19q13.2 TMEM91; TGFB1 19qhap ‡ 1.16 0.49 1.0055 0.68% [17] 20p12.3 FERMT1; BMP2 rs2423279, 1.14 0.3 1.0036 0.44% [24,30] 20p12.3 FERMT1; BMP2 rs4813802 1.09 0.36 1.0017 0.21% …”

Section: Resultsmentioning

confidence: 99%

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Quantifying the Utility of Single Nucleotide Polymorphisms to Guide Colorectal Cancer Screening

et al. 2016

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Aim:To determine whether single nucleotide polymorphisms (SNPs) can be used to identify people who should be screened for colorectal cancer. Methods: We simulated one million people with and without colorectal cancer based on published SNP allele frequencies and strengths of colorectal cancer association. We estimated 5-year risks of colorectal cancer by number of risk alleles. Results: We identified 45 SNPs with an average 1.14-fold increase colorectal cancer risk per allele (range: 1.05-1.53). The colorectal cancer risk for people in the highest quintile of risk alleles was 1.81-times that for the average person. Conclusion: We have quantified the extent to which known susceptibility SNPs can stratify the population into clinically useful colorectal cancer risk categories.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Quantifying the Utility of Single Nucleotide Polymorphisms to Guide Colorectal Cancer Screening

et al. 2016

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“…Recently, high-throughput single nucleotide polymorphism (SNP) arrays have been used to search for CRC-susceptibility alleles by genome-wide association studies (GWAS) and, to-date, identified 27 genome-wide significant low penetrance loci mapping to 8q24 (13,14), 18q21 (15,16), 15q13 (17,18), 11q23 (16), 10p14 (19), 8q23 (19), 14q22 (20), 16q22 (20), 19q13 (20), 20p12 (20,21), 1q41 (22), 3q26 (22), 12q13 (22), 20q13 (22), 6p21 (23), 11q13 (23), Xp22 (23), 2q32 (24), 12p13 (21,25,26), 5q31 (21), 1q25.3 (24,25), 10q24 (25), 10q22 (26), 10q25 (26), 11q12 (26), 17p13 (26) and 19q13 (26). Studies have suggested that some of these risk alleles may also affect patient survival (27-32); however, none of these survival findings, nor any prognostic biomarkers identified through the candidate gene analyses, have been validated in independent studies (33-35).…”

Section: Introductionmentioning

confidence: 99%

Analyses of 7,635 Patients with Colorectal Cancer Using Independent Training and Validation Cohorts Show That rs9929218 in CDH1 Is a Prognostic Marker of Survival

Smith

Fisher

Harris

et al. 2015

Clinical Cancer Research

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Purpose Genome wide association studies have identified numerous loci associated with colorectal cancer (CRC) risk. Several of these have also been associated with patient survival, although none have been validated. Here, we used large independent training and validation cohorts to identify robust prognostic biomarkers for CRC. Experimental Design In our training phase, we analysed 20 CRC-risk single nucleotide polymorphisms (SNPs) from 14 genome wide associated loci, for their effects on survival in 2083 patients with advanced CRC. A Cox survival model was used, stratified for treatment, adjusted for known prognostic factors and corrected for multiple testing. Three SNPs were subsequently analysed in an independent validation cohort of 5552 CRC patients. A validated SNP was analysed by disease stage and response to treatment. Results Three variants associated with survival in the training phase; however, only rs9929218 at 16q22 (intron 2 of CDH1, encoding E-cadherin) was significant in the validation phase. Patients homozygous for the minor allele (AA-genotype) had worse survival (training phase HR=1.43, 95%CI 1.20-1.71, P=5.8×10−5; validation phase HR=1.18, 95%CI 1.01-1.37, P=3.2×10−2; combined HR=1.28 95%CI 1.14-1.43, P=2.2×10−5). This effect was independent of known prognostic factors, and was significant amongst patients with stage 4 disease (P=2.7×10−5). rs9929218 was also associated with poor response to chemotherapy (P=3.9×10−4). Conclusions We demonstrate the potential of common inherited genetic variants to inform patient outcome and show that rs9929218 identifies ~8% of CRC patients with poor prognosis. rs9929218 may affect CDH1 expression and E-cadherin plays a role in epithelial-mesenchymal transition providing a mechanism underlying its prognostic potential.

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“…With the introducing of advanced molecular techniques e.g. next-generation sequencing multiple genetic defects were investigated through genomewide studies [40][41][42]. In the present study, we were able to investigate 15 genes generally affected in CRC through TruSight sequencing panel (Illumina).…”

Section: Tp53 Showed the Highest Percentage Of Germline Inherited Mutmentioning

confidence: 91%

Genetic Variations of Selected Genes Using Target Deep Sequencing in Colorectal Cancer Patients

Farghal¹,

Saied²,

Ghaith³

et al. 2017

J Cancer Sci Ther

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Background: Colorectal carcinoma (CRC) is a burden problem in a developing country like Egypt since patients are usually admitted in late stage with bad prognosis and short overall survival. Because of genetic predisposition of CRC and introduction of advanced molecular techniques, efforts are directed to screen for potential pathogenic or disease-causing variants in CRC patients Methods: DNA was isolated from formalin fixed paraffin embedded tissue sections collected from 24 CRC confirmed diagnosed patients. TruSight CRC panel (Illumina) was used for detection of different variants in 15 genes. The generated reads were obtained from Illumina Miseq were clustered into single nucleotide polymorphism (SNPs) and small insertions/deletions (Indels). Further pathogenic variants with somatic and germline mutations were identified according to the recommended criteria. Some CRC patients were subjected to anti-EGFR target therapy.Results: Most of the variants were detected in TP53 gene 140 variants (65%); 105 short deletions none of them was pathogenic, 29 missense mutations and 6 SNPs at splicing sites. Next, ERBB2 has got 17 variants (8.8%) (missense and splicing), 8 of them were damaging disease causing variants. Besides, 16 pathogenic variants were identified in 12 patients (6 in TP53 and 7 in KRAS). Some pathogenic variants were not reported before in CRC e.g. TP53 C>A, rs121912654, Val157Phe. Additionally, patients carried different KRAS wild mutations showed variable response to anti-EGFR target therapy. Conclusion:The most affected pathway in CRC was TP53 pathway followed by ERBB2, NRAS, KRAS and PIK3CA genes. Variable response to target therapy suggested dependence on the type of pathogenic variant identified, also a possible role of ERBB2 which had a significant variant frequency.NRAS. The prevalence rate of TP53 mutation in Arab population is 52.5% compared to 47.5% in matched Western population [7]. TP53 mutations have roles in determining progression, invasiveness and also metastasis of CRC. So, CRC patients with mutant TP53 have more progressive phenotype and poorer survival than those with TP53 wild type [8]. The phosphatidylinositol 3-kinase/Akt/mammalian target of

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Identification of susceptibility loci for colorectal cancer in a genome-wide meta-analysis

Cited by 130 publications

References 40 publications

Quantifying the Utility of Single Nucleotide Polymorphisms to Guide Colorectal Cancer Screening

Quantifying the Utility of Single Nucleotide Polymorphisms to Guide Colorectal Cancer Screening

Analyses of 7,635 Patients with Colorectal Cancer Using Independent Training and Validation Cohorts Show That rs9929218 in CDH1 Is a Prognostic Marker of Survival

Genetic Variations of Selected Genes Using Target Deep Sequencing in Colorectal Cancer Patients

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