A polygenic risk score for breast cancer in women receiving tamoxifen or raloxifene on NSABP P-1 and P-2

Vachon, Celine M.; Schaid, Daniel J.; Ingle, James N.; Wickerham, D. Lawrence; Kubo, Michiaki; Mushiroda, Taisei; Goetz, Matthew P.; Carlson, Erin E.; Paik, Soonmyung; Wolmark, Norman; Nakamura, Yusuke; Wang, Liewei; Weinshilboum, Richard M.; Couch, Fergus J.

doi:10.1007/s10549-014-3175-4

Cited by 24 publications

(23 citation statements)

References 31 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One promising area of study involves using the PRS to identify women who would benefit from chemoprevention, since it is unknown whether a high PRS is predictive of benefit from tamoxifen. Although the PRS has been assessed in high-risk women enrolled in two large tamoxifen prevention trials [47], there are no comparative data on the PRS in women who received placebo, and the relative benefit of tamoxifen in women with high polygenic risk remains undetermined.…”

Section: Discussionmentioning

confidence: 99%

Breast cancer risk prediction using a clinical risk model and polygenic risk score

Shieh

et al. 2016

Breast Cancer Res Treat

Self Cite

140

134

View full text Add to dashboard Cite

Purpose Breast cancer risk assessment can inform the use of screening and prevention modalities. We investigated the performance of the Breast Cancer Surveillance Consortium (BCSC) risk model in combination with a polygenic risk score (PRS) comprised of 83 single nucleotide polymorphisms identified from genome wide association studies. Methods We conducted a nested case-control study of 486 cases and 495 matched controls within a screening cohort. The PRS was calculated using a Bayesian approach. The contributions of the PRS and variables in the BCSC model to breast cancer risk were tested using conditional logistic regression. Discriminatory accuracy of the models was compared using the area under the receiver operating characteristic curve (AUROC). Results Increasing quartiles of the PRS were positively associated with breast cancer risk, with OR 2.54 (95% CI 1.69-3.82) for breast cancer in the highest versus lowest quartile. In a multivariable model, the PRS, family history, and breast density remained strong risk factors. The AUROC of the PRS was 0.60 (95% CI 0.57-0.64), and an Asian-specific PRS had AUROC 0.64 (95% CI 0.53-0.74). A combined model including the BCSC risk factors and PRS had better discrimination than the BCSC model (AUROC 0.65 versus 0.62, p = 0.01). The BCSC-PRS model classified 18% of cases as high-risk (5-year risk ≥ 3%), compared with 7% using the BCSC model. Conclusion The PRS improved discrimination of the BCSC risk model and classified more cases as high-risk. Impact Further consideration of the PRS's role in decision-making around screening and prevention strategies is merited.

show abstract

Section: Discussionmentioning

confidence: 99%

Breast cancer risk prediction using a clinical risk model and polygenic risk score

Shieh

et al. 2016

Breast Cancer Res Treat

Self Cite

140

134

View full text Add to dashboard Cite

show abstract

“…The results indicated that the SNP score provided useful additional information not contained in the TC model, but the overall prediction was somewhat optimistic and calibration was poor. This was also seen for another polygenic risk score in a case-control study of women from two other prevention trials [ 7 ]. However, other studies have not seen a loss in calibration (8, van Veen et al submitted).…”

mentioning

confidence: 53%

SNPs for breast cancer risk assessment

2017

View full text Add to dashboard Cite

“…In addition, breast cancer risk models and SNP based risk stratification could also be focused on ER-positive breast cancer. However, since most breast tumors are ER-positive, SNP-based models designed for overall breast cancer perform well for ER-positive breast cancer[41]. Another assumption behind our approach is that the women who are identified as high risk by SNPs will be protected from breast cancer by chemoprevention to the same degree that women who were included in the breast cancer prevention trials.…”

Section: Discussionmentioning

confidence: 99%

“…Another assumption behind our approach is that the women who are identified as high risk by SNPs will be protected from breast cancer by chemoprevention to the same degree that women who were included in the breast cancer prevention trials. Recent data from breast cancer prevention trials re-analyzed to include SNP-based risk, suggests that SNPs can be used to find women who would benefit from chemoprevention with SERMs[41]. If women identified as high risk based on SNPs have an intrinsically different response to preventive treatments, the risk/benefits would need to be reconsidered.…”

Section: Discussionmentioning

confidence: 99%

Using Breast Cancer Risk Associated Polymorphisms to Identify Women for Breast Cancer Chemoprevention

Ziv¹,

Tice²,

Sprague³

et al. 2017

PLoS ONE

Self Cite

View full text Add to dashboard Cite

BackgroundBreast cancer can be prevented with selective estrogen receptor modifiers (SERMs) and aromatase inhibitors (AIs). The US Preventive Services Task Force recommends that women with a 5-year breast cancer risk ≥3% consider chemoprevention for breast cancer. More than 70 single nucleotide polymorphisms (SNPs) have been associated with breast cancer. We sought to determine how to best integrate risk information from SNPs with other risk factors to risk stratify women for chemoprevention.MethodsWe used the risk distribution among women ages 35–69 estimated by the Breast Cancer Surveillance Consortium (BCSC) risk model. We modeled the effect of adding 70 SNPs to the BCSC model and examined how this would affect how many women are reclassified above and below the threshold for chemoprevention.ResultsWe found that most of the benefit of SNP testing a population is achieved by testing a modest fraction of the population. For example, if women with a 5-year BCSC risk of >2.0% are tested (~21% of all women), ~75% of the benefit of testing all women (shifting women above or below 3% 5-year risk) would be derived. If women with a 5-year risk of >1.5% are tested (~36% of all women), ~90% of the benefit of testing all women would be derived.ConclusionSNP testing is effective for reclassification of women for chemoprevention, but is unlikely to reclassify women with <1.5% 5-year risk. These results can be used to implement an efficient two-step testing approach to identify high risk women who may benefit from chemoprevention.

show abstract

A polygenic risk score for breast cancer in women receiving tamoxifen or raloxifene on NSABP P-1 and P-2

Cited by 24 publications

References 31 publications

Breast cancer risk prediction using a clinical risk model and polygenic risk score

Breast cancer risk prediction using a clinical risk model and polygenic risk score

SNPs for breast cancer risk assessment

Using Breast Cancer Risk Associated Polymorphisms to Identify Women for Breast Cancer Chemoprevention

Contact Info

Product

Resources

About