A polygenic risk score for breast cancer risk in a Taiwanese population

Hsieh, Yi Chen; Tu, Shih Hsin; Su, Chien Tien; Cho, Er Chieh; Wu, Chih Hsiung; Hsieh, Mao Chih; Lin, Shiyng Yu; Liu, Yun Ru; Hung, Chin Sheng; Chiou, Hung Yi

doi:10.1007/s10549-017-4144-5

Cited by 36 publications

(45 citation statements)

References 36 publications

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“…The largest number of studies (n ¼ 14) came from the United States (54)(55)(56)(57)(58)(59)(60)(61)(62)(63)(64)(65)(66)(67). 13 studies evaluated risk prediction models among Caucasian or European populations (23, 24, 54-57, 59, 61-64, 68, 69), eight in Asian populations (53,67,(70)(71)(72)(73)(74)(75), three within mixed populations (58,60,65), one among Australians (76), another among African Americans and Hispanics (66). Sample sizes ranged from 324 to 37,033 women.…”

Section: Statistical Analysesmentioning

confidence: 99%

“…Most studies evaluated the risk prediction models in the general population (24, 53, 55-58, 60, 61, 63, 64, 66-68, 70, 71, 73-76) while others specifically studied non-BRCA mutation carriers (54,62,69), post-menopausal (23,59,65,72) or pre-menopausal women (72). Most studies included only patients with invasive breast cancer as case participants (23, 53, 56-61, 63, 64, 66-70, 73, 74, 76), while six included invasive or in situ breast cancer patients (24,54,55,62,71,75) and two focused on estrogen receptor (ER)-positive patients with breast cancer (65,72).…”

Section: Statistical Analysesmentioning

confidence: 99%

“…Seven models used BCRAT as baseline model (55,59,66,69,75,76) and another four used partial BCRAT (57,63,68,74). 11 models used partial BCRAT with additional risk factors (23,55,62,64,67,(70)(71)(72)(73), four used BCSC (60,61,65), four used IBIS (24,66,69) and eight used other types of baseline models (54,56,58,69). Risk factors included in the various traditional baseline models are summarized in Supplementary Table S6 (Online Supplementary Material).…”

Section: Risk Prediction Model Characteristicsmentioning

confidence: 99%

“…Independence between non-genetic risk factors and SNP risk for breast cancer was assumed. In five models (61,64,67,71,75), the PRS was calculated as the sum of the product of the number of risk allele copies of the selected SNPs and corresponding log odds ratio. Another three models used a Bayesian approach to calculate the PRS (60,65).…”

Section: Risk Prediction Model Characteristicsmentioning

confidence: 99%

“…Still, the main results were not significantly affected, as these analyses were not considered in the primary outcome. Some studies did not state the numbers or reasons for non-participation (23,(53)(54)(55)(56)62,(64)(65)(66)(67)(69)(70)(71)(72)(73)(74)76;Item 14). Most studies did not report the measures of association between risk factors and outcome except that for SNPs (23,55,56,58,61,(65)(66)(67)(73)(74)(75)(76)Item 16).…”

Section: Standards Of Reporting and Quality Assessmentmentioning

confidence: 99%

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Performance of Single-Nucleotide Polymorphisms in Breast Cancer Risk Prediction Models: A Systematic Review and Meta-analysis

Fung

Wong

Lee

et al. 2019

Cancer Epidemiology, Biomarkers &Amp; Prevention

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Background: SNP risk information can potentially improve the accuracy of breast cancer risk prediction. We aim to review and assess the performance of SNP-enhanced risk prediction models.Methods: Studies that reported area under the ROC curve (AUC) and/or net reclassification improvement (NRI) for both traditional and SNP-enhanced risk models were identified. Meta-analyses were conducted to compare across all models and within similar baseline risk models.Results: Twenty-six of 406 studies were included. Pooled estimate of AUC improvement is 0.044 [95% confidence interval (CI), 0.038-0.049] for all 38 models, while estimates by baseline models ranged from 0.033 (95% CI, 0.025-0.041) for BCRAT to 0.053 (95% CI, 0.018-0.087) for partial BCRAT. There was no observable trend between AUC improvement and number of SNPs. One study found that the NRI was significantly larger when only intermediate-risk women were included. Two other studies showed that majority of the risk reclassification occurred in intermediate-risk women.Conclusions: Addition of SNP risk information may be more beneficial for women with intermediate risk.Impact: Screening could be a two-step process where a questionnaire is first used to identify intermediate-risk individuals, followed by SNP testing for these women only.

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Section: Statistical Analysesmentioning

confidence: 99%

Section: Statistical Analysesmentioning

confidence: 99%

Section: Risk Prediction Model Characteristicsmentioning

confidence: 99%

Section: Risk Prediction Model Characteristicsmentioning

confidence: 99%

Section: Standards Of Reporting and Quality Assessmentmentioning

confidence: 99%

See 3 more Smart Citations

Performance of Single-Nucleotide Polymorphisms in Breast Cancer Risk Prediction Models: A Systematic Review and Meta-analysis

Fung

Wong

Lee

et al. 2019

Cancer Epidemiology, Biomarkers &Amp; Prevention

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Incorporating Polygenic Risk Scores and Nongenetic Risk Factors for Breast Cancer Risk Prediction Among Asian Women

et al. 2022

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Key Points Question How well do breast cancer risk prediction models that incorporate polygenic risk scores (PRSs) and nongenetic risk factors perform for Asian women? Findings In this diagnostic study of 126 894 women, a PRS including 111 genetic variants was developed and tested using data from a prospective cohort study. The PRS was significantly associated with breast cancer risk, and adding 7 nongenetic risk factors improved the model’s accuracy. Meaning These findings support the utility of prediction models in identifying Asian women with high risk of breast cancer.

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Genetic and lifestyle factors for breast cancer risk assessment in Southeast China

Zou

Lin

et al. 2023

Cancer Medicine

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BackgroundDespite the rising incidence and mortality of breast cancer among women in China, there are currently few predictive models for breast cancer in the Chinese population and with low accuracy. This study aimed to identify major genetic and life‐style risk factors in a Chinese population for potential application in risk assessment models.MethodsA case–control study in southeast China was conducted including 1321 breast cancer patients and 2045 controls during 2013–2016, in which the data were randomly divided into a training set and a test set on a 7:3 scale. The association between genetic and life‐style factors and breast cancer was examined using logistic regression models. Using AUC curves, we also compared the performance of the logistic model to machine learning models, namely LASSO regression model and support vector machine (SVM), and the scores calculated from CKB, Gail and Tyrer–Cuzick models in the test set.ResultsAmong all factors considered, the best model was achieved when polygenetic risk score, lifestyle, and reproductive factors were considered jointly in the logistic regression model (AUC = 0.73; 95% CI: 0.70–0.77). The models created in this study performed better than those using scores calculated from the CKB, Gail, and Tyrer–Cuzick models. However, the logistic model and machine learning models did not significantly differ from one another.ConclusionIn summary, we have found genetic and lifestyle risk predictors for breast cancer with moderate discrimination, which might provide reference for breast cancer screening in southeast China. Further population‐based studies are needed to validate the model for future applications in personalized breast cancer screening programs.

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A polygenic risk score for breast cancer risk in a Taiwanese population

Abstract: Our data identified a genetic risk predictor of breast cancer in Taiwanese population and suggest that risk models including PRS and clinical risk factors are useful in discriminating women at high risk of breast cancer from those at low risk.

Cited by 36 publications

References 36 publications

Performance of Single-Nucleotide Polymorphisms in Breast Cancer Risk Prediction Models: A Systematic Review and Meta-analysis

Performance of Single-Nucleotide Polymorphisms in Breast Cancer Risk Prediction Models: A Systematic Review and Meta-analysis

Incorporating Polygenic Risk Scores and Nongenetic Risk Factors for Breast Cancer Risk Prediction Among Asian Women

Genetic and lifestyle factors for breast cancer risk assessment in Southeast China

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