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  • American Association for Cancer Research (AACR)  (7)
  • 1
    Online Resource
    Online Resource
    Cross-Cancer Genome-Wide Analysis of Lung, Ovary, Breast, Prostate, and Colorectal Cancer Reveals Novel Pleiotropic Associations
    American Association for Cancer Research (AACR) ; 2016
    In:  Cancer Research Vol. 76, No. 17 ( 2016-09-01), p. 5103-5114
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 76, No. 17 ( 2016-09-01), p. 5103-5114
    Abstract: Identifying genetic variants with pleiotropic associations can uncover common pathways influencing multiple cancers. We took a two-stage approach to conduct genome-wide association studies for lung, ovary, breast, prostate, and colorectal cancer from the GAME-ON/GECCO Network (61,851 cases, 61,820 controls) to identify pleiotropic loci. Findings were replicated in independent association studies (55,789 cases, 330,490 controls). We identified a novel pleiotropic association at 1q22 involving breast and lung squamous cell carcinoma, with eQTL analysis showing an association with ADAM15/THBS3 gene expression in lung. We also identified a known breast cancer locus CASP8/ALS2CR12 associated with prostate cancer, a known cancer locus at CDKN2B-AS1 with different variants associated with lung adenocarcinoma and prostate cancer, and confirmed the associations of a breast BRCA2 locus with lung and serous ovarian cancer. This is the largest study to date examining pleiotropy across multiple cancer-associated loci, identifying common mechanisms of cancer development and progression. Cancer Res; 76(17); 5103–14. ©2016 AACR.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    URL: Article
    DOI: 10.1158/0008-5472.CAN-15-2980
    RVK:
    XA 36000
    RVK:
    XA 10000
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2016
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    detail.hit.zdb_id: 410466-3
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  • 2
    Online Resource
    Online Resource
    Genetic Susceptibility to Nonalcoholic Fatty Liver Disease and Risk for Pancreatic Cancer: Mendelian Randomization
    American Association for Cancer Research (AACR) ; 2023
    In:  Cancer Epidemiology, Biomarkers & Prevention Vol. 32, No. 9 ( 2023-09-01), p. 1265-1269
    Details
    In: Cancer Epidemiology, Biomarkers & Prevention, American Association for Cancer Research (AACR), Vol. 32, No. 9 ( 2023-09-01), p. 1265-1269
    Abstract: There are conflicting data on whether nonalcoholic fatty liver disease (NAFLD) is associated with susceptibility to pancreatic cancer. Using Mendelian randomization (MR), we investigated the relationship between genetic predisposition to NAFLD and risk for pancreatic cancer. Methods: Data from genome-wide association studies (GWAS) within the Pancreatic Cancer Cohort Consortium (PanScan; cases n = 5,090, controls n = 8,733) and the Pancreatic Cancer Case Control Consortium (PanC4; cases n = 4,163, controls n = 3,792) were analyzed. We used data on 68 genetic variants with four different MR methods [inverse variance weighting (IVW), MR-Egger, simple median, and penalized weighted median] separately to predict genetic heritability of NAFLD. We then assessed the relationship between each of the four MR methods and pancreatic cancer risk, using logistic regression to calculate ORs and 95% confidence intervals (CI), adjusting for PC risk factors, including obesity and diabetes. Results: No association was found between genetically predicted NAFLD and pancreatic cancer risk in the PanScan or PanC4 samples [e.g., PanScan, IVW OR, 1.04; 95% confidence interval (CI), 0.88–1.22; MR-Egger OR, 0.89; 95% CI, 0.65–1.21; PanC4, IVW OR, 1.07; 95% CI, 0.90–1.27; MR-Egger OR, 0.93; 95% CI, 0.67–1.28]. None of the four MR methods indicated an association between genetically predicted NAFLD and pancreatic cancer risk in either sample. Conclusions: Genetic predisposition to NAFLD is not associated with pancreatic cancer risk. Impact: Given the close relationship between NAFLD and metabolic conditions, it is plausible that any association between NAFLD and pancreatic cancer might reflect host metabolic perturbations (e.g., obesity, diabetes, or metabolic syndrome) and does not necessarily reflect a causal relationship between NAFLD and pancreatic cancer.
    Type of Medium: Online Resource
    ISSN: 1055-9965 , 1538-7755
    URL: Article
    DOI: 10.1158/1055-9965.EPI-23-0453
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
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    detail.hit.zdb_id: 1153420-5
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  • 3
    Online Resource
    Online Resource
    Smoking Modifies Pancreatic Cancer Risk Loci on 2q21.3
    American Association for Cancer Research (AACR) ; 2021
    In:  Cancer Research Vol. 81, No. 11 ( 2021-06-01), p. 3134-3143
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 81, No. 11 ( 2021-06-01), p. 3134-3143
    Abstract: Germline variation and smoking are independently associated with pancreatic ductal adenocarcinoma (PDAC). We conducted genome-wide smoking interaction analysis of PDAC using genotype data from four previous genome-wide association studies in individuals of European ancestry (7,937 cases and 11,774 controls). Examination of expression quantitative trait loci data from the Genotype-Tissue Expression Project followed by colocalization analysis was conducted to determine whether there was support for common SNP(s) underlying the observed associations. Statistical tests were two sided and P & lt; 5 × 10–8 was considered statistically significant. Genome-wide significant evidence of qualitative interaction was identified on chr2q21.3 in intron 5 of the transmembrane protein 163 (TMEM163) and upstream of the cyclin T2 (CCNT2). The most significant SNP using the Empirical Bayes method, in this region that included 45 significantly associated SNPs, was rs1818613 [per allele OR in never smokers 0.87, 95% confidence interval (CI), 0.82–0.93; former smokers 1.00, 95% CI, 0.91–1.07; current smokers 1.25, 95% CI 1.12–1.40, Pinteraction = 3.08 × 10–9). Examination of the Genotype-Tissue Expression Project data demonstrated an expression quantitative trait locus in this region for TMEM163 and CCNT2 in several tissue types. Colocalization analysis supported a shared SNP, rs842357, in high linkage disequilibrium with rs1818613 (r2 = 0. 94) driving both the observed interaction and the expression quantitative trait loci signals. Future studies are needed to confirm and understand the differential biologic mechanisms by smoking status that contribute to our PDAC findings. Significance: This large genome-wide interaction study identifies a susceptibility locus on 2q21.3 that significantly modified PDAC risk by smoking status, providing insight into smoking-associated PDAC, with implications for prevention.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    URL: Article
    DOI: 10.1158/0008-5472.CAN-20-3267
    RVK:
    XA 36000
    RVK:
    XA 10000
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2021
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    detail.hit.zdb_id: 410466-3
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  • 4
    Online Resource
    Online Resource
    Mendelian Randomization Study of Body Mass Index and Colorectal Cancer Risk
    American Association for Cancer Research (AACR) ; 2015
    In:  Cancer Epidemiology, Biomarkers & Prevention Vol. 24, No. 7 ( 2015-07-01), p. 1024-1031
    Details
    In: Cancer Epidemiology, Biomarkers & Prevention, American Association for Cancer Research (AACR), Vol. 24, No. 7 ( 2015-07-01), p. 1024-1031
    Abstract: Background: High body mass index (BMI) is consistently linked to increased risk of colorectal cancer for men, whereas the association is less clear for women. As risk estimates from observational studies may be biased and/or confounded, we conducted a Mendelian randomization study to estimate the causal association between BMI and colorectal cancer. Methods: We used data from 10,226 colorectal cancer cases and 10,286 controls of European ancestry. The Mendelian randomization analysis used a weighted genetic risk score, derived from 77 genome-wide association study–identified variants associated with higher BMI, as an instrumental variable (IV). We compared the IV odds ratio (IV-OR) with the OR obtained using a conventional covariate-adjusted analysis. Results: Individuals carrying greater numbers of BMI-increasing alleles had higher colorectal cancer risk [per weighted allele OR, 1.31; 95% confidence interval (CI), 1.10–1.57]. Our IV estimation results support the hypothesis that genetically influenced BMI is directly associated with risk for colorectal cancer (IV-OR per 5 kg/m2, 1.50; 95% CI, 1.13–2.01). In the sex-specific IV analyses higher BMI was associated with higher risk of colorectal cancer among women (IV-OR per 5 kg/m2, 1.82; 95% CI, 1.26–2.61). For men, genetically influenced BMI was not associated with colorectal cancer (IV-OR per 5 kg/m2, 1.18; 95% CI, 0.73–1.92). Conclusions: High BMI was associated with increased colorectal cancer risk for women. Whether abdominal obesity, rather than overall obesity, is a more important risk factor for men requires further investigation. Impact: Overall, conventional epidemiologic and Mendelian randomization studies suggest a strong association between obesity and the risk of colorectal cancer. Cancer Epidemiol Biomarkers Prev; 24(7); 1024–31. ©2015 AACR.
    Type of Medium: Online Resource
    ISSN: 1055-9965 , 1538-7755
    URL: Article
    DOI: 10.1158/1055-9965.EPI-14-1309
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2015
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    detail.hit.zdb_id: 1153420-5
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  • 5
    Online Resource
    Online Resource
    Abstract 881: Benchmarking genome-wide polygenic risk score development techniques in colorectal cancer risk prediction
    American Association for Cancer Research (AACR) ; 2021
    In:  Cancer Research Vol. 81, No. 13_Supplement ( 2021-07-01), p. 881-881
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 81, No. 13_Supplement ( 2021-07-01), p. 881-881
    Abstract: Colorectal cancer (CRC) is a leading cause of cancer death, yet many CRC deaths are preventable via CRC screening. Currently only age and family history are used to define screening eligibility. However, CRC risk varies substantially in the population. In recent years polygenic risk scores (PRS) have gained attention as powerful risk prediction tool to personalize interventions. PRS provides a quantitative measure of an individual's inherited risk based on the cumulative effect of many genetic risk variants. Here, we benchmark several genome wide PRS techniques to select the best performing models in CRC risk prediction. We built CRC risk prediction models that incorporate genome-wide genotype data from large-scale research studies (55,105 cases and 65,079 controls, European ancestries) with the imputed genetic data on over 40 million variants. The risk prediction models were externally evaluated in the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort, including 101,987 genotyped individuals within the Kaiser Permanente Northern California (KPNC) integrated healthcare delivery system. We built genome-wide PRS using various methods including known CRC risk variants, thresholding and pruning followed by machine learning approaches (ML), LDpred, improved LDpred2, SBayesR, PRS-CS, Lassosum and empirical Bayes. Among 55,033 individuals of European ancestry in the GERA cohort, we evaluated the performance of models in terms of the age and sex-adjusted AUC. We showed that LDpred, LDpred2, LDpred2-sparse, SBayesR and PRS-CS perform equally well in terms of discriminatory accuracy (AUC=0.65). In addition, the PRS developed using the above-mentioned techniques identified the top 30% of the GERA European population has a hazard ratio estimate of ~2.2 on CRC risk, which is comparable to that for having an affected first-degree relative. The developed CRC PRSs will provide way for risk-stratified CRC screening and other targeted interventions. PRS derivation methodsNo. of variantsAUC(1,311 cases and 53,722 controls)Hazard ratio estimates (CI)Top 30% of population vs. remainingKnown variants1400.631.92 (1.75-2.23)PT Clumping + ML (Ridge)10,0000.631.94 (1.72-2.19)LDpred1.2M0.652.20 (1.94-2.47)LDPred21.2M0.652.20 (1.93-2.45)LDpred2 Sparse530K0.652.20 (1.90-2.41)SBayesR1.2M0.652.20 (1.88-2.38)PRS-CS1.2M0.652.20 (1.91-2.43)Lassosum1.2M0.621.76 (1.56-2.58)EBPRS1.2M0.621.81 (1.66-2.11)AUC based on family history in GERA cohort is 0.54 Citation Format: Minta Thomas, Lori C Sakoda, Jeffrey K Lee, Mark A Jenkins, Andrea Burnett-Hartman, Heather Hampel, Elisabeth A Rosenthal, Hermann Brenner, Jenny Chang-Claude, Marc J Gunter, Polly A Newcomb, Steven Gallinger, Tabitha A Harrison, Graham Casey, Victor Moreno, Gail P Jarvik, Stephen B Gruber, Robert E Schoen, Andrew T Chan, Richard B Hayes, Douglas A Corley, Ulrike Peters, Li Hsu. Benchmarking genome-wide polygenic risk score development techniques in colorectal cancer risk prediction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 881.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2021-881
    RVK:
    XA 36000
    RVK:
    XA 10000
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2021
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    detail.hit.zdb_id: 410466-3
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  • 6
    Online Resource
    Online Resource
    Personal History of Diabetes, Genetic Susceptibility to Diabetes, and Risk of Brain Glioma: A Pooled Analysis of Observational Studies
    American Association for Cancer Research (AACR) ; 2014
    In:  Cancer Epidemiology, Biomarkers & Prevention Vol. 23, No. 1 ( 2014-01-01), p. 47-54
    Details
    In: Cancer Epidemiology, Biomarkers & Prevention, American Association for Cancer Research (AACR), Vol. 23, No. 1 ( 2014-01-01), p. 47-54
    Abstract: Background: Brain glioma is a relatively rare and fatal malignancy in adulthood with few known risk factors. Some observational studies have reported inverse associations between diabetes and subsequent glioma risk, but possible mechanisms are unclear. Methods: We conducted a pooled analysis of original data from five nested case–control studies and two case–control studies from the United States and China that included 962 glioma cases and 2,195 controls. We examined self-reported diabetes history in relation to glioma risk, as well as effect modification by seven glioma risk-associated single-nucleotide polymorphisms (SNP). We also examined the associations between 13 diabetes risk-associated SNPs, identified from genome-wide association studies, and glioma risk. Odds ratios (OR) and 95% confidence intervals (CI) were calculated using multivariable-adjusted logistic regression models. Results: We observed a 42% reduced risk of glioma for individuals with a history of diabetes (OR = 0.58; 95% CI, 0.40–0.84). The association did not differ by sex, study design, or after restricting to glioblastoma, the most common histological subtype. We did not observe any significant per-allele trends among the 13 diabetes-related SNPs examined in relation to glioma risk. Conclusion: These results support an inverse association between diabetes history and glioma risk. The role of genetic susceptibility to diabetes cannot be excluded, and should be pursued in future studies together with other factors that might be responsible for the diabetes–glioma association. Impact: These data suggest the need for studies that can evaluate, separately, the association between type 1 and type 2 diabetes and subsequent risk of adult glioma. Cancer Epidemiol Biomarkers Prev; 23(1); 47–54. ©2013 AACR.
    Type of Medium: Online Resource
    ISSN: 1055-9965 , 1538-7755
    URL: Article
    DOI: 10.1158/1055-9965.EPI-13-0913
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2014
    detail.hit.zdb_id: 2036781-8
    detail.hit.zdb_id: 1153420-5
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  • 7
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    Online Resource
    Genome-Wide Gene–Diabetes and Gene–Obesity Interaction Scan in 8,255 Cases and 11,900 Controls from PanScan and PanC4 Consortia
    American Association for Cancer Research (AACR) ; 2020
    In:  Cancer Epidemiology, Biomarkers & Prevention Vol. 29, No. 9 ( 2020-09-01), p. 1784-1791
    Details
    In: Cancer Epidemiology, Biomarkers & Prevention, American Association for Cancer Research (AACR), Vol. 29, No. 9 ( 2020-09-01), p. 1784-1791
    Abstract: Obesity and diabetes are major modifiable risk factors for pancreatic cancer. Interactions between genetic variants and diabetes/obesity have not previously been comprehensively investigated in pancreatic cancer at the genome-wide level. Methods: We conducted a gene–environment interaction (GxE) analysis including 8,255 cases and 11,900 controls from four pancreatic cancer genome-wide association study (GWAS) datasets (Pancreatic Cancer Cohort Consortium I–III and Pancreatic Cancer Case Control Consortium). Obesity (body mass index ≥30 kg/m2) and diabetes (duration ≥3 years) were the environmental variables of interest. Approximately 870,000 SNPs (minor allele frequency ≥0.005, genotyped in at least one dataset) were analyzed. Case–control (CC), case-only (CO), and joint-effect test methods were used for SNP-level GxE analysis. As a complementary approach, gene-based GxE analysis was also performed. Age, sex, study site, and principal components accounting for population substructure were included as covariates. Meta-analysis was applied to combine individual GWAS summary statistics. Results: No genome-wide significant interactions (departures from a log-additive odds model) with diabetes or obesity were detected at the SNP level by the CC or CO approaches. The joint-effect test detected numerous genome-wide significant GxE signals in the GWAS main effects top hit regions, but the significance diminished after adjusting for the GWAS top hits. In the gene-based analysis, a significant interaction of diabetes with variants in the FAM63A (family with sequence similarity 63 member A) gene (significance threshold P & lt; 1.25 × 10−6) was observed in the meta-analysis (PGxE = 1.2 ×10−6, PJoint = 4.2 ×10−7). Conclusions: This analysis did not find significant GxE interactions at the SNP level but found one significant interaction with diabetes at the gene level. A larger sample size might unveil additional genetic factors via GxE scans. Impact: This study may contribute to discovering the mechanism of diabetes-associated pancreatic cancer.
    Type of Medium: Online Resource
    ISSN: 1055-9965 , 1538-7755
    URL: Article
    DOI: 10.1158/1055-9965.EPI-20-0275
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2020
    detail.hit.zdb_id: 2036781-8
    detail.hit.zdb_id: 1153420-5
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