Abstract 962: Validation of breast cancer risk model incorporating classical risk factors and polygenic risk scores in 14 prospective cohort studies in 6 countries

2019 ◽  
Author(s):  
Parichoy Pal Choudhury ◽  
Amber Wilcox ◽  
Chi Gao ◽  
Brian Carter ◽  
Anika Husing ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Cohort Studies ◽  
Prospective Cohort ◽  
Risk Model ◽  
Risk Scores ◽  
Polygenic Risk ◽  
Prospective Cohort Studies

scholarly journals Prospective Evaluation of a Breast Cancer Risk Model Integrating Classical Risk Factors and Polygenic Risk in 15 Cohorts from Six Countries

2019 ◽  
Author(s):  
Amber N Wilcox ◽  
Parichoy Pal Choudhury ◽  
Chi Gao ◽  
Anika Hüsing ◽  
Mikael Eriksson ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Cancer Prevention ◽  
Risk Model ◽  
European Ancestry ◽  
Polygenic Risk ◽  
The Us ◽  
Prospective Cohorts

ABSTRACTPURPOSERisk-stratified breast cancer prevention requires accurate identification of women at sufficiently different levels of risk. We conducted a comprehensive evaluation of a model integrating classical risk factors and a recently developed 313-variant polygenic risk score (PRS) to predict breast cancer risk.METHODSFifteen prospective cohorts from six countries with 237,632 women (7,529 incident breast cancer patients) of European ancestry aged 19-75 years at baseline were included. Calibration of five-year risk was assessed by comparing predicted and observed proportions of cases overall and within risk categories. Risk stratification for women of European ancestry aged 50-70 years in those countries was evaluated by the proportion of women and future breast cancer cases crossing clinically-relevant risk thresholds.RESULTSThe model integrating classical risk factors and PRS accurately predicted five-year risk. For women younger than 50 years, median (range) expected-to-observed ratio across the cohorts was 0.94 (0.72 to 1.01) overall and 0.9 (0.7 to 1.4) at the highest risk decile. For women 50 years or older, these ratios were 1.04 (0.73 to 1.31) and 1.2 (0.7 to 1.6), respectively. The proportion of women in the general population identified above the 3% five-year risk threshold (used for recommending risk-reducing medications in the US) ranged from 7.0% in Germany (∼841,000 of 12 million) to 17.7% in the US (∼5.3 of 30 million). At this threshold, 14.7% of US women were re-classified by the addition of PRS to classical risk factors, identifying 12.2% additional future breast cancer cases.CONCLUSIONEvaluation across multiple prospective cohorts demonstrates that integrating a 313-SNP PRS into a risk model substantially improves its ability to stratify women of European ancestry for applying current breast cancer prevention guidelines.

scholarly journals Blood DNA methylation and breast cancer risk: a meta-analysis of four prospective cohort studies

2019 ◽  
Vol 21 (1) ◽  
Author(s):  
Clara Bodelon ◽  
Srikant Ambatipudi ◽  
Pierre-Antoine Dugué ◽  
Annelie Johansson ◽  
Joshua N. Sampson ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Cohort Studies ◽  
Prospective Cohort ◽  
Meta Analysis ◽  
Blood Collection ◽  
Prospective Cohort Studies ◽  
Average Methylation

Abstract Background Environmental and genetic factors play an important role in the etiology of breast cancer. Several small blood-based DNA methylation studies have reported risk associations with methylation at individual CpGs and average methylation levels; however, these findings require validation in larger prospective cohort studies. To investigate the role of blood DNA methylation on breast cancer risk, we conducted a meta-analysis of four prospective cohort studies, including a total of 1663 incident cases and 1885 controls, the largest study of blood DNA methylation and breast cancer risk to date. Methods We assessed associations with methylation at 365,145 CpGs present in the HumanMethylation450 (HM450K) Beadchip, after excluding CpGs that did not pass quality controls in all studies. Each of the four cohorts estimated odds ratios (ORs) and 95% confidence intervals (CI) for the association between each individual CpG and breast cancer risk. In addition, each study assessed the association between average methylation measures and breast cancer risk, adjusted and unadjusted for cell-type composition. Study-specific ORs were combined using fixed-effect meta-analysis with inverse variance weights. Stratified analyses were conducted by age at diagnosis (< 50, ≥ 50), estrogen receptor (ER) status (+/−), and time since blood collection (< 5, 5–10, > 10 years). The false discovery rate (q value) was used to account for multiple testing. Results The average age at blood draw ranged from 52.2 to 62.2 years across the four cohorts. Median follow-up time ranged from 6.6 to 8.4 years. The methylation measured at individual CpGs was not associated with breast cancer risk (q value > 0.59). In addition, higher average methylation level was not associated with risk of breast cancer (OR = 0.94, 95% CI = 0.85, 1.05; P = 0.26; P for study heterogeneity = 0.86). We found no evidence of modification of this association by age at diagnosis (P = 0.17), ER status (P = 0.88), time since blood collection (P = 0.98), or CpG location (P = 0.98). Conclusions Our data indicate that DNA methylation measured in the blood prior to breast cancer diagnosis in predominantly postmenopausal women is unlikely to be associated with substantial breast cancer risk on the HM450K array. Larger studies or with greater methylation coverage are needed to determine if associations exist between blood DNA methylation and breast cancer risk.

scholarly journals Incorporating Polygenic Risk Scores and Nongenetic Risk Factors for Breast Cancer Risk Prediction among Asian Women, Results from Asia Breast Cancer Consortium

2021 ◽  
Author(s):  
Yaohua Yang ◽  
Ran Tao ◽  
Xiang Shu ◽  
Qiuyin Cai ◽  
Wanqing Wen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Prospective Cohort ◽  
Risk Scores ◽  
Asian Women ◽  
Genome Wide ◽  
Using Data

Importance Polygenic risk scores (PRSs) have shown promises in breast cancer risk prediction; however, limited studies have been conducted among Asian women. Objective To develop breast cancer risk prediction models for Asian women incorporating PRSs and nongenetic risk factors. Design PRSs were developed using data from genome-wide association studies (GWAS) of breast cancer conducted among 123 041 Asian-ancestry women (including 18 650 cases) using three approaches (1) reported PRS for European-ancestry women; (2) breast cancer-associated single-nucleotide polymorphisms (SNPs) identified by fine-mapping of GWAS-identified risk loci; (3) genome-wide risk prediction algorithms. A nongenetic risk score (NgRS) was built including six well-established nongenetic risk factors using data from 1974 Asian women. Integrated risk scores (IRSs) were constructed using PRSs and the NgRS. PRSs were initially validated in an independent dataset including 1426 cases and 1323 controls and further evaluated, along with the NgRS and IRSs, in the second dataset including 368 cases and 736 controls nested withing a prospective cohort study. Setting Case-control and prospective cohort studies. Participants 20 444 breast cancer cases and 106 450 controls from the Asia Breast Cancer Consortium. Main Outcomes and Measures Logistic regression was used to examine associations of risk scores with breast cancer risk to estimate odds ratios (ORs) with 95% confidence intervals (CIs) and area under the receiver operating characteristic curve (AUC). Results In the prospective cohort, PRS111, a PRS with 111 SNPs, developed using the fine-mapping approach showed a prediction performance comparable to a genome-wide PRS including over 855,000 SNPs. The OR per standard deviation increase of PRS111 was 1.67 (95% CI=1.46-1.92) with an AUC of 0.639 (95% CI=0.604-0.674). The NgRS had a limited predictive ability (AUC=0.565; 95% CI=0.529-0.601); while IRS111, the combination of PRS111 and NgRS, achieved the highest prediction accuracy (AUC=0.650; 95% CI=0.616-0.685). Compared with the average risk group (40th-60th percentile), women in the top 5% of PRS111 and IRS111 were at a 3.84-folded (95% CI=2.30-6.46) and 4.25- folded (95% CI=2.57-7.11) elevated risk of breast cancer, respectively. Conclusions and Relevance PRSs derived using breast cancer-associated risk SNPs have similar prediction performance in Asian and European descendants. Including nongenetic risk factors in models further improved prediction accuracy. Our findings support the utility of these models in developing personalized screening and prevention strategies.

Dietary isoflavones or isoflavone-rich food intake and breast cancer risk: A meta-analysis of prospective cohort studies

2019 ◽  
Vol 38 (1) ◽  
pp. 136-145 ◽  
Author(s):  
Ting-Ting Zhao ◽  
Feng Jin ◽  
Ji-Guang Li ◽  
Ying-Ying Xu ◽  
Hui-Ting Dong ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Food Intake ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Cohort Studies ◽  
Prospective Cohort ◽  
Meta Analysis ◽  
Prospective Cohort Studies ◽  
Rich Food
Sign in / Sign up

Export Citation Format

Share Document