Abstract Background The shared inherited genetic contribution to risk of different cancers is not fully known. In this study, we leverage results from 12 cancer genome-wide association studies (GWAS) to quantify pairwise genome-wide genetic correlations across cancers and identify novel cancer susceptibility loci. Methods We collected GWAS summary statistics for 12 solid cancers based on 376 759 participants with cancer and 532 864 participants without cancer of European ancestry. The included cancer types were breast, colorectal, endometrial, esophageal, glioma, head and neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancers. We conducted cross-cancer GWAS and transcriptome-wide association studies to discover novel cancer susceptibility loci. Finally, we assessed the extent of variant-specific pleiotropy among cancers at known and newly identified cancer susceptibility loci. Results We observed widespread but modest genome-wide genetic correlations across cancers. In cross-cancer GWAS and transcriptome-wide association studies, we identified 15 novel cancer susceptibility loci. Additionally, we identified multiple variants at 77 distinct loci with strong evidence of being associated with at least 2 cancer types by testing for pleiotropy at known cancer susceptibility loci. Conclusions Overall, these results suggest that some genetic risk variants are shared among cancers, though much of cancer heritability is cancer-specific and thus tissue-specific. The increase in statistical power associated with larger sample sizes in cross-disease analysis allows for the identification of novel susceptibility regions. Future studies incorporating data on multiple cancer types are likely to identify additional regions associated with the risk of multiple cancer types.
Design and quality control of large-scale two-sample Mendelian randomization studies
Abstract Background Mendelian randomization (MR) studies are susceptible to metadata errors (e.g. incorrect specification of the effect allele column) and other analytical issues that can introduce substantial bias into analyses. We developed a quality control (QC) pipeline for the Fatty Acids in Cancer Mendelian Randomization Collaboration (FAMRC) that can be used to identify and correct for such errors. Methods We collated summary association statistics from fatty acid and cancer genome-wide association studies (GWAS) and subjected the collated data to a comprehensive QC pipeline. We identified metadata errors through comparison of study-specific statistics to external reference data sets (the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalogue and 1000 genome super populations) and other analytical issues through comparison of reported to expected genetic effect sizes. Comparisons were based on three sets of genetic variants: (i) GWAS hits for fatty acids, (ii) GWAS hits for cancer and (iii) a 1000 genomes reference set. Results We collated summary data from 6 fatty acid and 54 cancer GWAS. Metadata errors and analytical issues with the potential to introduce substantial bias were identified in seven studies (11.6%). After resolving metadata errors and analytical issues, we created a data set of 219 842 genetic associations with 90 cancer types, generated in analyses of 566 665 cancer cases and 1 622 374 controls. Conclusions In this large MR collaboration, 11.6% of included studies were affected by a substantial metadata error or analytical issue. By increasing the integrity of collated summary data prior to their analysis, our protocol can be used to increase the reliability of downstream MR analyses. Our pipeline is available to other researchers via the CheckSumStats package (https://github.com/MRCIEU/CheckSumStats).
Detecting deficient DNA mismatch repair in stage II and III colon cancers.
419 Background: Deficient DNA mismatch repair (MMR) results in microsatellite instability (MSI) that is detected in ∼15% of sporadic colon cancers. MMR status has been shown to provide prognostic and predictive information. We developed a model to predict MMR deficiency using clinically available data, and thereby facilitate the selection of patient tumors for MMR testing. Methods: Data were utilized from stage II and III colon carcinoma patients (n = 2016) who participated in 5-fluorouracil-based adjuvant studies (NCCTG, FFCD, NCIC, GIVIO, NSABP) and an Italian cohort. MMR status in tumors had been determined by MSI testing or by immunohistochemistry for hMLH1 and hMSH2 proteins. Logistic regression and a recursive partitioning and amalgamation analysis was used to identify factors (histologic grade, gender, tumor site, stage, age, lymph node status, T-stage) predictive of MMR status. Results: Of the cancers, 357 (17.7%) showed deficient MMR. Tumor site was the most important predictor of MMR status followed by histologic grade, then stage (II vs. III) and then gender. Distal tumors had a low likelihood of deficient MMR (5% rate overall), whereas proximal tumors had a greater likelihood of deficient MMR (30%). For patients with proximal tumors, the addition of histologic grade and stage increased the prediction of deficient MMR (Table). Using tumor site, histologic grade, and stage, the logistic regression model showed excellent discrimination (c-statistic = 0.80). Conclusions: Routine clinicopathological data can facilitate the identification of MMR deficient cases. Tumor site and histologic grade were the strongest predictors of MMR deficiency. Within proximal, poorly differentiated tumors, stage was highly predictive. These findings suggest that our model can assist in selecting sporadic colon cancers for MMR testing for use in clinical decision-making, especially for stage II patients. [Table: see text] [Table: see text]
Genetic Variation in the Inflammation and Innate Immunity Pathways and Colorectal Cancer Risk
Abstract Introduction: Genome-wide association studies of colorectal cancer have identified 18 risk variants that explain ∼25% of the hereditable risk of colorectal cancer. These variants are located in intronic, inter-genic or gene-desert regions. To uncover whether some of these variants may have a regulatory function on the expression of near-by genes, we conducted expression quantitative trait loci (eQTL) analyses of these 18 regions. Methods: DNA and RNA were extracted from 40 fresh-frozen paired colon tumor (microsatellite stable and CpG island methylator phenotype-negative) and adjacent normal tissue samples collected by the Colon Cancer Family Registry. Whole genome gene expression assessment was performed in the tumors and adjacent normal tissue with the Affymetrix GeneChip Human Exon 1.0 ST array. Genome-wide SNP data was generated with DNA extracted from the same normal tissue samples using the Affymetrix Genome-Wide Human SNP 6.0 array. Partek software was used to compare the expression levels by genotype for each gene spanning a 2 Mb region up- and down- stream of each of the 18 established risk variants for colorectal cancer. Results: Three of the 18 risk variants (rs10795668, rs4444235, rs9929218) demonstrated significant cis-eQTLs (FDR q-values<0.05). Specifically, differential gene expression was observed in genes neighboring rs10795668 at 10p14 (ATP5C1, q=0.024), rs4444235 at 14q22.2 (DLGAP5, q=0.041), and rs9929218 at 16q22.1 (NOL3, q=0.017; DDX28, q=0.046). In particular, DLGAP5 (discs, large (Drosophila) homolog-associated protein 5) at 14q22.2 and NOL3 (nucleolar protein 3) at 16q22 are interesting candidate genes for colorectal cancer. DLGAP5 encodes for a microtubule-associated protein and functions as a mitotic regulator that has been reported to be overexpressed in colon tumors. NOL3 (nucleolar protein 3) encodes for an anti-apoptotic protein that down-regulates the expression of caspase 2 and 8, and p53. NOL3 also appears to be overexpressed in colorectal tumors. Conclusion: In summary, these findings suggest putative functional activities for three established colorectal risk variants and highlight potentially interesting candidate genes for this malignancy. If confirmed, these results may help explain the total genetic risk of the disease, as well as further our understanding of the underlying biology of colorectal cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4732. doi:10.1158/1538-7445.AM2011-4732
Confirmation of Linkage to and Localization of Familial Colon Cancer Risk Haplotype on Chromosome 9q22
Abstract Purpose: Lynch syndrome family members with inherited germline mutations in DNA mismatch repair (MMR) genes have a high risk of colorectal cancer (CRC), and cases typically have tumors that exhibit a high level of microsatellite instability (MSI). There is some evidence that smoking is a risk factor for CRCs with high MSI; however, the association of smoking with CRC among those with Lynch syndrome is unknown. Experimental Design: A multicentered retrospective cohort of 752 carriers of pathogenic MMR gene mutations was analyzed, using a weighted Cox regression analysis, adjusting for sex, ascertainment source, the specific mutated gene, year of birth, and familial clustering. Results: Compared with never smokers, current smokers had a significantly increased CRC risk [adjusted hazard ratio (HR), 1.62; 95% confidence interval (95% CI), 1.01-2.57] and former smokers who had quit smoking for 2 or more years were at decreased risk (HR, 0.53; 95% CI, 0.35-0.82). CRC risk did not vary according to age at starting. However, light smoking (<10 cigarettes per day) and shorter duration of smoking (<10 years) were associated with decreased CRC risk (HR, 0.51; 95% CI, 0.29-0.91 and HR, 0.52; 95% CI, 0.30-0.89, respectively). For former smokers, CRC risk decreased with years since quitting (P trend <0.01). Conclusions: People with Lynch syndrome may be at increased risk of CRC if they smoke regularly. Although our data suggest that former smokers, short-term smokers, and light smokers are at decreased CRC risk, these findings need further confirmation, preferably using prospective designs. Clin Cancer Res; 16(4); 1331–9
Risks of Lynch Syndrome Cancers for MSH6 Mutation Carriers
Abstract Family history is a strong predictor of colorectal cancer risk; however, a diagnosis of colorectal cancer among first-degree relatives has not been systematically investigated as a function of the colorectal cancer molecular subtypes related to tumor microsatellite instability (MSI) status. We investigated whether the observable familial colorectal cancer risks differed according to tumor MSI subtypes, stratified as MSI-High (>30% instability), MSI-Low (<30% instability), and MSS (no instability). Data from 3,143 population-based colorectal cancer cases from five institutions were assessed for family history according to the Amsterdam criteria and the Bethesda guidelines, age at diagnosis, sex, tumor location, and MSI status. The distribution of patient characteristics by MSI status was compared using polytomous logistic regression. Overall, 2.8% colorectal cancer cases met the Amsterdam criteria and 37% met the Bethesda guidelines. There were 14% MSI-High, 13% MSI-Low, and 73% MSS colorectal cancers. MSI-High (P < 0.0001) and MSI-Low tumors (P = 0.01) were more proximally located than MSS tumors. MSI-High tumors were more common among females (P < 0.001). The highest proportion of MSI-High tumors occurred in cases <40 years of age whereas the age-dependent distribution of MSI-Low tumors was unchanged. MSI-High tumors showed a statistically significant association with increasing numbers of first-degree relatives with colorectal cancer (P = 0.002); this association disappeared, however, when MSI-High cases meeting Amsterdam criteria were removed from the analysis. MSI-Low tumors did not show a similar association with family history of colorectal cancer. Familial risk associated with MSI-High tumors is primarily driven by the Amsterdam-criteria patients. MSI-Low tumors may represent a distinct subtype of colorectal cancer with respect to certain epidemiologic variables studied here. (Cancer Epidemiol Biomarkers Prev 2009;18(3):967–75)