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  • The American Association for Cancer Research (AACR)  (3)
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  • 1
    Publication Date: 2018-01-09
    Description: Background: Pancreatic cancer mutation signatures closely resemble breast cancer, suggesting that both cancers may have common predisposition mechanisms that may include commonly inherited SNPs. Methods: We examined 23 genetic variants known to be associated with pancreatic cancer as breast cancer risk factors in the Root genome-wide association study (GWAS; 1,657 cases and 2,029 controls of African diaspora) and GAME-ON/DRIVE GWAS (16,003 cases and 41,335 controls of European ancestry). Results: None of the pancreatic cancer susceptibility variants were individually associated with breast cancer risk after adjustment for multiple testing (at α = 0.002) in the two populations. In Root GWAS, a change by one SD in the polygenic risk score (PRS) was not significantly associated with breast cancer. In addition, we did not observe a trend in the relationship between PRS percentiles and breast cancer risk. Conclusions: The association between reported pancreatic cancer genetic susceptibility variants and breast cancer development in women of African or European ancestry is likely weak, if it does exist. Impact: Known GWAS-derived susceptibility variants of pancreatic cancer do not explain its shared genetic etiology with breast cancer. Cancer Epidemiol Biomarkers Prev; 27(1); 116–8. ©2017 AACR .
    Print ISSN: 1055-9965
    Electronic ISSN: 1538-7755
    Topics: Medicine
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  • 2
    Publication Date: 2018-06-02
    Description: Background: Risk prediction models have been widely used to identify women at higher risk of breast cancer. We aimed to develop a model for absolute breast cancer risk prediction for Nigerian women. Methods: A total of 1,811 breast cancer cases and 2,225 controls from the Nigerian Breast Cancer Study (NBCS, 1998–2015) were included. Subjects were randomly divided into the training and validation sets. Incorporating local incidence rates, multivariable logistic regressions were used to develop the model. Results: The NBCS model included age, age at menarche, parity, duration of breastfeeding, family history of breast cancer, height, body mass index, benign breast diseases, and alcohol consumption. The model developed in the training set performed well in the validation set. The discriminating accuracy of the NBCS model [area under ROC curve (AUC) = 0.703, 95% confidence interval (CI), 0.687–0.719] was better than the Black Women's Health Study (BWHS) model (AUC = 0.605; 95% CI, 0.586–0.624), Gail model for white population (AUC = 0.551; 95% CI, 0.531–0.571), and Gail model for black population (AUC = 0.545; 95% CI, 0.525–0.565). Compared with the BWHS and two Gail models, the net reclassification improvement of the NBCS model were 8.26%, 13.45%, and 14.19%, respectively. Conclusions: We have developed a breast cancer risk prediction model specific to women in Nigeria, which provides a promising and indispensable tool to identify women in need of breast cancer early detection in Sub-Saharan Africa populations. Impact: Our model is the first breast cancer risk prediction model in Africa. It can be used to identify women at high risk for breast cancer screening. Cancer Epidemiol Biomarkers Prev; 27(6); 636–43. ©2018 AACR .
    Print ISSN: 1055-9965
    Electronic ISSN: 1538-7755
    Topics: Medicine
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  • 3
    Publication Date: 2018-09-05
    Description: Background: Although germline genetics influences breast cancer incidence, published research only explains approximately half of the expected association. Moreover, the accuracy of prediction models remains low. For women who develop breast cancer early, the genetic architecture is less established. Methods: To identify loci associated with early-onset breast cancer, gene-based tests were carried out using exome array data from 3,479 women with breast cancer diagnosed before age 50 and 973 age-matched controls. Replication was undertaken in a population that developed breast cancer at all ages of onset. Results: Three gene regions were associated with breast cancer incidence: FGFR2 ( P = 1.23 x 10 –5 ; replication P 〈 1.00 x 10 –6 ), NEK10 ( P = 3.57 x 10 –4 ; replication P 〈 1.00 x 10 –6 ), and SIVA1 ( P = 5.49 x 10 –4 ; replication P 〈 1.00 x 10 –6 ). Of the 151 gene regions reported in previous literature, 19 (12.5%) showed evidence of association ( P 〈 0.05) with the risk of early-onset breast cancer in the early-onset population. To predict incidence, whole-genome prediction was implemented on a subset of 3,076 participants who were additionally genotyped on a genome wide array. The whole-genome prediction outperformed a polygenic risk score [AUC, 0.636; 95% confidence interval (CI), 0.614–0.659 compared with 0.601; 95% CI, 0.578–0.623], and when combined with known epidemiologic risk factors, the AUC rose to 0.662 (95% CI, 0.640–0.684). Conclusions: This research supports a role for variation within FGFR2 and NEK10 in breast cancer incidence, and suggests SIVA1 as a novel risk locus. Impact: This analysis supports a shared genetic etiology between women with early- and late-onset breast cancer, and suggests whole-genome data can improve risk assessment. Cancer Epidemiol Biomarkers Prev; 27(9); 1057–64. ©2018 AACR .
    Print ISSN: 1055-9965
    Electronic ISSN: 1538-7755
    Topics: Medicine
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