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  • 1
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    The Association of Vitamin D Deficiency and Incident Frailty in Older Women: The Role of Cardiometabolic Diseases
    Wiley ; 2017
    In:  Journal of the American Geriatrics Society Vol. 65, No. 3 ( 2017-03), p. 619-624
    Details
    In: Journal of the American Geriatrics Society, Wiley, Vol. 65, No. 3 ( 2017-03), p. 619-624
    Kurzfassung: Evidence suggests vitamin D deficiency is associated with developing frailty. However, cardiometabolic factors are related to both conditions and may confound and/or mediate the vitamin D–frailty association. We aimed to determine the association of vitamin D concentration with incidence of frailty, and the role of cardiometabolic diseases (cardiovascular disease, diabetes, hyperlipidemia, hypertension) in this relationship. Design Prospective longitudinal cohort study (7 visits from 1994–2008). Setting Baltimore, Maryland. Participants Three hundred sixty‐nine women from the Women's Health and Aging Study II aged 70–79 years, free of frailty at baseline. Measurements Serum circulating 25‐hydroxyvitamin D (25[ OH ]D) concentration was assessed at baseline and categorized as: 〈 10; 10–19.9; 20‐29.9; and ≥30 ng/ mL . Frailty incidence was determined based on presence of three or more criteria: weight loss, low physical activity, exhaustion, weakness, and slowness. Cardiometabolic diseases were ascertained at baseline. Analyses included Cox regression models adjusted for key covariates. Results Incidence rate of frailty was 32.2 per 1,000 person‐years in participants with 25( OH )D  〈  10 ng/ mL , compared to 12.9 per 1,000 person‐years in those with 25( OH )D ≥ 30 ng/ mL (mean follow‐up = 8.5 ± 3.7 years). In cumulative incidence analyses, those with lower 25( OH )D exhibited higher frailty incidence, though differences were non‐significant ( P  = .057). In regression models adjusted for demographics, smoking, and season, 25( OH )D  〈  10 ng/ mL (vs ≥30 ng/ mL ) was associated with nearly three‐times greater frailty incidence (hazard ratio ( HR ) = 2.77, 95% CI  = 1.14, 6.71, P  = .02). After adjusting for BMI , the relationship of 25( OH )D  〈  10 ng/ mL (vs ≥30 ng/ mL ) with incident frailty persisted, but was attenuated after further accounting for cardiometabolic diseases ( HR  = 2.29, 95% CI  = 0.92, 5.69, P  = .07). Conclusion Low serum vitamin D concentration is associated with incident frailty in older women; interestingly, the relationship is no longer significant after accounting for the presence of cardiometabolic diseases. Future studies should explore mechanisms to explain this relationship.
    Materialart: Online-Ressource
    ISSN: 0002-8614 , 1532-5415
    URL: Issue
    URL: Article
    DOI: 10.1111/jgs.2017.65.issue-3
    DOI: 10.1111/jgs.14677
    Sprache: Englisch
    Verlag: Wiley
    Publikationsdatum: 2017
    ZDB Id: 2040494-3
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 2
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    Abstract PR02: Validation of breast cancer risk prediction model using Nurses Health and Nurse Health II Studies
    American Association for Cancer Research (AACR) ; 2017
    In:  Cancer Epidemiology, Biomarkers & Prevention Vol. 26, No. 5_Supplement ( 2017-05-01), p. PR02-PR02
    Details
    In: Cancer Epidemiology, Biomarkers & Prevention, American Association for Cancer Research (AACR), Vol. 26, No. 5_Supplement ( 2017-05-01), p. PR02-PR02
    Kurzfassung: Background: Adding genetic and other biomarkers to breast cancer risk prediction models could markedly improve model discrimination; however, these expanded models have not been validated in a range of populations. In particular, the calibration of these new models how well the predicted absolute risks match observed risks has not been established. Good calibration is essential to confirm the utility of these risk models in precision prevention and treatment programs. Large cohort studies provide an ideal setting to validate risk models, as they can be used to validate both relative and absolute risks. However, in practice, genetic and biomarker data are often not available in the full cohort, but only on a sub sample of cases and controls. When the rules for sampling cases and controls into the sub sample are known, inverse-probability-of-sampling (IPW) weights can be used to estimate empirical absolute risks. When the sampling rules are unknown or complicated, the IPW weights can be estimated by regressing selection into the sub sample on matching and other inclusion criteria. Methods: We evaluated the performance of recently published breast cancer risk prediction models [Maas et al. JAMA Oncol 2016] in the Nurses Health Study (NHS) and Nurses Health Study II (NHSII). We first assess a prediction model that only includes questionnaire data (BMI, hormone replacement therapy (HRT), alcohol consumption, smoking status, height, parity, age at menarche and menopause, age at first birth, and family history of breast cancer). These data are available on all subjects in the NHS and NHSII blood subcohorts: 32,826 women in NHS (with disease follow-up from 1990-2012) and 29,611 women in NHS II (1999-2013). We will then validate a model that includes both questionnaire data and a polygenic risk score based on 92 established risk SNPs. Genetic data are available on case-control samples nested within the blood subcohorts: 2308 breast cancer cases and 3344 controls from NHS and 612 breast cancer cases and 933 controls from NHSII. We estimated IPW weights among controls using logistic regression in the blood subcohorts, with sampling as control being the outcome and the following predictors: age at baseline, menopausal status, HRT, length of HRT use for premenopausal women at baseline, and length of follow up time. We used the iCARE software package (Maas P, Chatterjee N, Wheeler W et al. 2015) to calculate predicted 5 and 10-year absolute risks of breast cancer based on the published models, empirical 5 and 10-year incidence across deciles of predicted risk, and Hosmer-Lemeshow goodness of fit and AUC statistics. Results: For the risk model without genetic information, predicted risks in the blood subcohorts ranged from 6.5/1,000 (1st decile) to 20.1/1,000 (10th decile) for NHS. Although empirical risks increased across deciles at approximately the same rate as predicted rates, empirical risks were higher than predicted (Hosmer-Lemeshow p & lt;10-10). Validation analyses in the NHS II blood subcohort are ongoing. Due to matching and selection on control status, the baseline distribution of questionnaire risk factors differed between the blood subcohorts and the controls from the nested case-control samples. The IPW-weighted distribution in controls closely matched the distribution in the full subcohorts, suggesting a well-calculated weight. We will present IPW-based validation of the risk model in the nested case-control samples (work in progress). Conclusions: These results confirm that breast cancer risk prediction models can discriminate between high-risk and low-risk women, but they also highlight that the accuracy of absolute risk estimates can vary across populations. Findings from this study can add insights into model improvement and model application. Moreover, the method of using IPW weights to approximate a full cohort analysis provides a potential solution for utilizing nested case-control studies in future validation analyses. This abstract is also being presented as Poster A05. Citation Format: Chi Gao, Parichoy Pal Choudhury, Paige Maas, Rulla Tamimi, Heather Eliassen, Nilanjan Chatterjee, Montserrat Garcia-Closas, Peter Kraft. Validation of breast cancer risk prediction model using Nurses Health and Nurse Health II Studies. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr PR02.
    Materialart: Online-Ressource
    ISSN: 1055-9965 , 1538-7755
    URL: Article
    DOI: 10.1158/1538-7755.CARISK16-PR02
    Sprache: Englisch
    Verlag: American Association for Cancer Research (AACR)
    Publikationsdatum: 2017
    ZDB Id: 2036781-8
    ZDB Id: 1153420-5
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 3
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    Abstract 2254: Risk models for lung cancer in never-smoking women in Shanghai with implications for screening
    American Association for Cancer Research (AACR) ; 2022
    In:  Cancer Research Vol. 82, No. 12_Supplement ( 2022-06-15), p. 2254-2254
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 12_Supplement ( 2022-06-15), p. 2254-2254
    Kurzfassung: Background: The majority of female lung cancer cases in Asia are never-smokers with distinct risk factor profiles. Given the high burden of disease in this population, there is an increasing need to improve the understanding of lung cancer. Current risk models for lung cancer focus on active smokers and individuals of European ancestry. Therefore, we developed statistical models by integrating genetic and environmental risk factors to estimate absolute and population attribute risk of lung cancer among never-smoking women in Asia. Methods: We built absolute risk models for lung cancer among never-smoking women using data from 71,300 women (760 incident cases) in the Shanghai Women’s Health Study (SWHS), a population-based prospective cohort study. Relative risks were estimated using a multivariable Cox regression model with questionnaire-based risk factors. To account for missing genetic data for some subjects, we simulated genotypes for 10 common single nucleotide polymorphisms (SNP) using information on minor allele frequencies (MAF) and odds ratio estimates from previous genome-wide association studies (GWAS), conditional on family history of lung cancer. We used the iCARE tool to build two models for predicting lifetime (40 years) and 6-year absolute risk of lung cancer using age-specific lung cancer incidence rates, age-specific competing mortality rates, and risk factor distribution with: 1) questionnaire-based risk factors only and 2) questionnaire and genetic data. We then used the full absolute risk model to estimate the population attributable risk (PAR) due to modifiable risk factors, namely coal use and exposure to environmental tobacco smoke (ETS). Results: The questionnaire-based only model included family history of lung cancer, coal use, exposure to ETS, and body mass index (BMI). The full model also included data on 10 lung cancer related SNPs from our previous GWAS and had a wider spread in distribution of absolute lifetime risk (median=2.41%; range=0.43-12.36) compared to the questionnaire-based only model (median=2.72%; range=1.93-4.87). We used the full model to estimate the PAR and found that 1.74% and 6.33% of lung cancer cases could be prevented if never-smoking women in Shanghai did not use coal and were not exposed to ETS, respectively. Furthermore, we found that the full model estimated that 2.5% of the study population had a 6-year absolute risk of lung cancer higher than 1.51%, which is the suggested risk threshold for screening by existing risk models. Conclusion: We built risk models for never-smoking Asian women and estimated the contribution of coal use and ETS to the burden of lung cancer in Shanghai. This initial work shows promise for expanding and validating risk models in this population with potential translational implications, such as providing insight to identifying high risk individuals that may be eligible for lung cancer screening and primary prevention efforts. Citation Format: Batel Blechter, Parichoy Pal Choudhury, Xiao-ou Shu, Wei Zheng, Qiuyin Cai, Gong Yang, Jason Y.Y. Wong, Bu-Tian Ji, Wei Hu, Anne Rositch, Nilanjan Chatterjee, Nathaniel Rothman, Qing Lan. Risk models for lung cancer in never-smoking women in Shanghai with implications for screening [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2254.
    Materialart: Online-Ressource
    ISSN: 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2022-2254
    Sprache: Englisch
    Verlag: American Association for Cancer Research (AACR)
    Publikationsdatum: 2022
    ZDB Id: 2036785-5
    ZDB Id: 1432-1
    ZDB Id: 410466-3
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 4
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    Abstract 962: Validation of breast cancer risk model incorporating classical risk factors and polygenic risk scores in 14 prospective cohort studies in 6 countries
    American Association for Cancer Research (AACR) ; 2019
    In:  Cancer Research Vol. 79, No. 13_Supplement ( 2019-07-01), p. 962-962
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 79, No. 13_Supplement ( 2019-07-01), p. 962-962
    Kurzfassung: Background: Prospective validation of breast cancer risk models integrating classical risk factors and genetic variants is required for risk-stratified prevention and screening strategies. The objective of this study was to validate a breast cancer risk model integrating classical risk factors and a 313-variant polygenic risk score (PRS) in multiple prospective cohort studies, and to project five-year risk of breast cancer in six different countries. Methods: The study population included 7,529 cases and 230,103 controls from 14 prospective cohort studies in Australia, Germany, the Netherlands, Sweden, UK, and USA. We used the Individualized Coherent Absolute Risk Estimator (iCARE) tool for risk model building, validation, and risk projection. Expected five-year risk of invasive or in situ breast cancer was compared to observed risk, overall and within deciles of expected risk using goodness of fit statistics. We evaluated calibration of the relative risk through meta-analysis across cohorts, and of the absolute risk within each cohort. Model discrimination was evaluated using the area under the curve (AUC), and percentages of women crossing risk thresholds. Projections of five-year risk distributions were estimated for women of European ancestry aged 50-70 years in the general populations of these six countries. Results: Analysis showed overall good calibration of the integrated iCARE-based model relative risk for both women younger than 50 years (χ2=14.9, P=0.09) and aged 50 years or older (χ2=14.6, P=0.10), with a small overestimation of risk for women in the highest decile of expected risk (RR = 3.5 expected vs 2.3 (95% CI 1.6 to 3.2) observed for women & lt;50 years; and 2.8 expected vs 2.3 (95% CI 2.0 to 2.7) observed for women 50+ years). The age-adjusted AUCs for the integrated model were 63.1 (95% CI 60.9 to 65.3) and 62.9 (95% CI 61.8 to 64.0), for the two age groups respectively. The calibration of absolute risk showed substantial variation across cohorts, particularly for the older group, but had no systematic bias. Model based projections in the general populations showed that compared to the population average, women in the 1st and 99th percentiles of the integrated risk score had relative risks 0.19 and 3.56 respectively. The proportion of women of European ancestry aged 50-70 years with a five-year risk greater than 3% (threshold for consideration of risk-lowering drugs by U.S. Preventive Services Task Force) ranged from 7.1% in Germany to 18.2% in the US, which corresponds to ~5.5 million women in the US. Conclusions: Five-year risk predictions from a model with classical risk factors and PRS are well calibrated and provide substantial risk stratification across multiple cohorts in six different countries. Further studies are needed to evaluate the clinical utility of the validated model for risk stratified screening and prevention of breast cancer. Citation Format: Parichoy Pal Choudhury, Amber Wilcox, Chi Gao, Brian Carter, Anika Husing, Mark Brook, Mikael Eriksson, Kara Martin, Chris Scott, Min Shi, Thomas Ahearn, Michael Jones, Nick Orr, Minouk Schoemaker, Kamila Czene, Jenny Chang-Claude, Jacques Simard, Doug Easton, Marjanka K. Schmidt, Dale Sandler, Clarice R. Weinberg, Celine Vachon, Roger Milne, Per Hall, Anthony Swerdlow, Rudolph Kaaks, Myrto Barrdahl, Mia Gaudet, Antonis Antoniou, Peter Kraft, Montserrat Garcia-Closas, Nilanjan Chatterjee. Validation of breast cancer risk model incorporating classical risk factors and polygenic risk scores in 14 prospective cohort studies in 6 countries [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 962.
    Materialart: Online-Ressource
    ISSN: 0008-5472 , 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2019-962
    RVK:
    XA 36000
    RVK:
    XA 10000
    Sprache: Englisch
    Verlag: American Association for Cancer Research (AACR)
    Publikationsdatum: 2019
    ZDB Id: 2036785-5
    ZDB Id: 1432-1
    ZDB Id: 410466-3
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 5
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    Abstract A05: Validation of breast cancer risk prediction model using Nurses Health and Nurse Health II Studies
    American Association for Cancer Research (AACR) ; 2017
    In:  Cancer Epidemiology, Biomarkers & Prevention Vol. 26, No. 5_Supplement ( 2017-05-01), p. A05-A05
    Details
    In: Cancer Epidemiology, Biomarkers & Prevention, American Association for Cancer Research (AACR), Vol. 26, No. 5_Supplement ( 2017-05-01), p. A05-A05
    Kurzfassung: This abstract is being presented as a short talk in the scientific program. A full abstract is printed in the Proffered Abstracts section (PR02) of the Conference Proceedings. Citation Format: Chi Gao, Parichoy Pal Choudhury, Paige Maas, Rulla Tamimi, Heather Eliassen, Nilanjan Chatterjee, Montserrat Garcia-Closas, Peter Kraft. Validation of breast cancer risk prediction model using Nurses Health and Nurse Health II Studies. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr A05.
    Materialart: Online-Ressource
    ISSN: 1055-9965 , 1538-7755
    URL: Article
    DOI: 10.1158/1538-7755.CARISK16-A05
    Sprache: Englisch
    Verlag: American Association for Cancer Research (AACR)
    Publikationsdatum: 2017
    ZDB Id: 2036781-8
    ZDB Id: 1153420-5
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 6
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    Moving Toward Findable, Accessible, Interoperable, Reusable Practices in Epidemiologic Research
    Oxford University Press (OUP) ; 2023
    In:  American Journal of Epidemiology Vol. 192, No. 6 ( 2023-06-02), p. 995-1005
    Details
    In: American Journal of Epidemiology, Oxford University Press (OUP), Vol. 192, No. 6 ( 2023-06-02), p. 995-1005
    Kurzfassung: Data sharing is essential for reproducibility of epidemiologic research, replication of findings, pooled analyses in consortia efforts, and maximizing study value to address multiple research questions. However, barriers related to confidentiality, costs, and incentives often limit the extent and speed of data sharing. Epidemiological practices that follow Findable, Accessible, Interoperable, Reusable (FAIR) principles can address these barriers by making data resources findable with the necessary metadata, accessible to authorized users, and interoperable with other data, to optimize the reuse of resources with appropriate credit to its creators. We provide an overview of these principles and describe approaches for implementation in epidemiology. Increasing degrees of FAIRness can be achieved by moving data and code from on-site locations to remote, accessible (“Cloud”) data servers, using machine-readable and nonproprietary files, and developing open-source code. Adoption of these practices will improve daily work and collaborative analyses and facilitate compliance with data sharing policies from funders and scientific journals. Achieving a high degree of FAIRness will require funding, training, organizational support, recognition, and incentives for sharing research resources, both data and code. However, these costs are outweighed by the benefits of making research more reproducible, impactful, and equitable by facilitating the reuse of precious research resources by the scientific community.
    Materialart: Online-Ressource
    ISSN: 0002-9262 , 1476-6256
    URL: Article
    DOI: 10.1093/aje/kwad040
    Sprache: Englisch
    Verlag: Oxford University Press (OUP)
    Publikationsdatum: 2023
    ZDB Id: 2030043-8
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 7
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    Breast Cancer Risk in Women from Ghana Carrying Rare Germline Pathogenic Mutations: A Resource for Genetic Counseling of West African Women
    Elsevier BV ; 2021
    In:  SSRN Electronic Journal
    Details
    In: SSRN Electronic Journal, Elsevier BV
    Materialart: Online-Ressource
    ISSN: 1556-5068
    URL: Article
    DOI: 10.2139/ssrn.3891444
    Sprache: Englisch
    Verlag: Elsevier BV
    Publikationsdatum: 2021
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 8
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    Toward Risk-Stratified Breast Cancer Screening: Considerations for Changes in Screening Guidelines
    American Medical Association (AMA) ; 2020
    In:  JAMA Oncology Vol. 6, No. 1 ( 2020-01-01), p. 31-
    Details
    In: JAMA Oncology, American Medical Association (AMA), Vol. 6, No. 1 ( 2020-01-01), p. 31-
    Materialart: Online-Ressource
    ISSN: 2374-2437
    URL: Article
    DOI: 10.1001/jamaoncol.2019.3820
    Sprache: Englisch
    Verlag: American Medical Association (AMA)
    Publikationsdatum: 2020
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 9
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    Abstract 2256: Validation of an integrated breast cancer risk model with mammographic density in three prospective cohort studies
    American Association for Cancer Research (AACR) ; 2022
    In:  Cancer Research Vol. 82, No. 12_Supplement ( 2022-06-15), p. 2256-2256
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 12_Supplement ( 2022-06-15), p. 2256-2256
    Kurzfassung: Introduction: Incorporation of mammographic density to breast cancer risk models could improve risk stratification to tailor screening and prevention strategies according to risk. However, robust validation of such models in prospective cohort studies is needed to determine their accuracy in identifying women at different risk levels. Methods: We incorporated Breast Imaging and Reporting Data System (BI-RADS) breast density to a literature-based model with questionnaire-based risk factors and a 313-variant polygenic risk score (PRS). The Individualized Coherent Absolute Risk Estimator (iCARE) tool was used to build and validate a 5-year absolute risk model for breast cancer. The model was evaluated for calibration and discrimination in three prospective cohorts of women of European ancestry (1,468 cases, 19,104 controls): US-based Nurses’ Health Study (NHS I and II) and Mayo Mammography Health Study (MMHS); and Sweden-based Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) study. Analyses were done separately for women younger (NHS II, KARMA) and older than 50 years (NHS I, MMHS, KARMA). Improvements in risk stratification were assessed among US non-Hispanic White women aged 50-70 years. Results: For women younger than 50 years, the model with questionnaire-based risk factors, PRS and BI-RADS was well calibrated across risk deciles in NHS II, but overestimated risk at the highest risk decile in KARMA. For women 50 years or older, the model showed good calibration in all studies, with evidence of slight overestimation at the highest risk decile and underestimation at the lowest risk decile. The model with PRS and BI-RADS was well calibrated for women at high-risk in both age groups. Incorporation of BI-RADS to questionnaire-based risk factors and PRS improved risk discrimination: area under the curves (AUC) 67.0% (95% CI: 63.5-70.6%) vs. 65.6% (95% CI: 61.9-69.3%) for models with and without BI-RADS for younger women and 66.1% (95% CI 64.4-67.8%) vs. 65.5% (95% CI: 63.8-67.2%) for older women. The model with BI-RADS identified 18.4% population of non-Hispanic US women 50-70 years old above 3% 5-year risk (used for recommending risk-reducing medication in the US), with 42.4% of future cases expected to occur in this group. Addition of BI-RADS led to the reclassification of ~8.0% of US non-Hispanic White women aged 50-70 years at the high-risk threshold (3% 5-year risk), resulting in identification of 4.2% of additional future cases. Conclusion: Integrating BI-RADS with questionnaire-based risk factors and PRS resulted in improved risk stratification among women of European ancestry, with evidence of slight overestimation of risk for women at elevated risk. Additional validation of the integrated model in diverse populations is needed prior to considering clinical applications. Citation Format: Charlotta V. Mulder, Yon Ho Jee, Xin Yang, Christopher G. Scott, Chi Gao, Amber N. Hurson, Mikael Eriksson, Per Hall, Peter Kraft, Celine M. Vachon, Antonis C. Antoniou, Gretchen Gierach, Montserrat Garcia-Closas, Parichoy Pal Choudhury. Validation of an integrated breast cancer risk model with mammographic density in three prospective cohort studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2256.
    Materialart: Online-Ressource
    ISSN: 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2022-2256
    Sprache: Englisch
    Verlag: American Association for Cancer Research (AACR)
    Publikationsdatum: 2022
    ZDB Id: 2036785-5
    ZDB Id: 1432-1
    ZDB Id: 410466-3
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 10
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    Abstract 826: Large-scale genome-wide association study identifies multiple novel germline susceptibility variants associated with bladder cancer risk
    American Association for Cancer Research (AACR) ; 2021
    In:  Cancer Research Vol. 81, No. 13_Supplement ( 2021-07-01), p. 826-826
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 81, No. 13_Supplement ( 2021-07-01), p. 826-826
    Kurzfassung: Background: Genomic regions that confer susceptibility for bladder cancer have provided important insights into the mechanisms of this disease. Sixteen genomic regions harboring bladder cancer susceptibility loci have been reported to date. To identify additional loci associated with bladder cancer risk, we conducted a meta-analysis including data from previously published genome-wide association studies as well as unpublished data. Methods: Data from 32 studies including 13,790 bladder cancer cases and 343,502 controls of European ancestry were included. Stratified analyses by sex and smoking status, as well as heterogeneity in risk by muscle-invasiveness, were also conducted. We tested for multiplicative and additive interactions between cigarette smoking and susceptibility loci that achieved genome-wide significance. After genotyping, quality control, and imputation log-additive effects were calculated by study/array group using regression models adjusting for age and significant eigenvectors. Results were combined by meta-analysis using a fixed-effects model. Results: We report strengthened or independent signals in several previously published regions at 4p16.3 (TACC3, FGFR3), 5p15.33 (CLPTM1L, TERT) and 11p15.5 (TNNT3, LSP1), as well as nine novel loci. In addition, we observed the first evidence of effect modification by sex at the FGFR3 locus, with a stronger risk observed in women (pmultiplicative-interaction=0.002). Further, we confirmed an interaction between smoking and a susceptibility locus at 8p22 (NAT2), indicating an increased risk of bladder cancer among smokers with the NAT2 slow acetylation genotype/phenotype (pmultiplicative-interaction=0.001). We also identified additional multiplicative, as well as additive, interactions between several novel loci and cigarette smoking status. In addition, we are currently building a risk stratification model using the combined effects of smoking and genetic susceptibility to identify subgroups of individuals at higher and lower absolute risk of bladder cancer. Conclusions: This meta-analysis identified 3 novel loci in previously reported regions and 9 novel bladder cancer susceptibility loci in new regions. These results add to our knowledge of the genetic architecture of bladder cancer. In addition, observed gene-smoking interactions suggest that risk stratification models may have translational implications, informing future disease screening efforts. Citation Format: Stella Koutros, Lambertus A. Kiemeney, Roger L. Milne, Yuanqing Ye, Vijai Joseph, Jonine Figueroa, Nilanjan Chatterjee, Graham G. Giles, Michelle A. Hildebrandt, Lars Dyrskjot, Kenneth Offit, Manolis Kogevinas, Elisabete Weiderpass, Marjorie L. McCullough, Neal D. Freedman, Demetrius Albanes, Charles Kooperberg, Victoria Cortessis, Margaret R. Karagas, Dalsu Baris, Alison Johnson, Molly R. Schwenn, Helena Furberg, Dean F. Bajorin, Parichoy Pal Choudhury, Oscar Florez-Vargas, Olivier Cussenot, Geraldine Cancel-Tassin, Simone Benhamou, Peter Kraft, Stefano Porru, Mark P. Purdue, Katherine A. McGlynn, Cari M. Kitahara, Christopher A. Haiman, Mark H. Greene, Thorunn Rafnar, Stephen J. Chanock, Xifeng Wu, Francisco X. Real, Debra T. Silverman, Montserrat Garcia-Closas, Kari Stefansson, Ludmila Prokunina-Olsson, Nuria Malats, Nathaniel Rothman. Large-scale genome-wide association study identifies multiple novel germline susceptibility variants associated with bladder cancer risk [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 826.
    Materialart: Online-Ressource
    ISSN: 0008-5472 , 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2021-826
    RVK:
    XA 36000
    RVK:
    XA 10000
    Sprache: Englisch
    Verlag: American Association for Cancer Research (AACR)
    Publikationsdatum: 2021
    ZDB Id: 2036785-5
    ZDB Id: 1432-1
    ZDB Id: 410466-3
    Standort Signatur Einschränkungen Verfügbarkeit
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