Cross-ancestry polygenic risk score for breast cancer risk assessment.

Tshiaba, Placede; Sun, Jiayi; Ratman, Dariusz K; Tunstall, Tate; Levy, Brynn; Shah, Premal S; Kumar, Akash; Im, Kate

doi:10.1200/jco.2022.40.16_suppl.10540

Cited by 3 publications

(3 citation statements)

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“…The addition of the PRS increased the AUC in both models, from 0.56 to 0.59 in the BCRAT and from 0.51 to 0.55 in the IBIS model. Most recently, Tshiaba et al have evaluated the addition of a cross-ancestry PRS [ 40 ] to the IBIS model in data from the Women’s Health Initiative and the UK Biobank [ 41 ]. Across all ancestry groups, the addition of the PRS to the IBIS model increased the AUC for prediction of risk in the next five years from 0.56 to 0.65 in the WHI and from 0.57 to 0.63 in the UK Biobank.…”

Section: Discussionmentioning

confidence: 99%

Addition of polygenic risk score to a risk calculator for prediction of breast cancer in US Black women

Zirpoli,

Pfeiffer,

Bertrand

et al. 2024

Breast Cancer Res

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Background Previous work in European ancestry populations has shown that adding a polygenic risk score (PRS) to breast cancer risk prediction models based on epidemiologic factors results in better discriminatory performance as measured by the AUC (area under the curve). Following publication of the first PRS to perform well in women of African ancestry (AA-PRS), we conducted an external validation of the AA-PRS and then evaluated the addition of the AA-PRS to a risk calculator for incident breast cancer in Black women based on epidemiologic factors (BWHS model). Methods Data from the Black Women’s Health Study, an ongoing prospective cohort study of 59,000 US Black women followed by biennial questionnaire since 1995, were used to calculate AUCs and 95% confidence intervals (CIs) for discriminatory accuracy of the BWHS model, the AA-PRS alone, and a new model that combined them. Analyses were based on data from 922 women with invasive breast cancer and 1844 age-matched controls. Results AUCs were 0.577 (95% CI 0.556–0.598) for the BWHS model and 0.584 (95% CI 0.563–0.605) for the AA-PRS. For a model that combined estimates from the questionnaire-based BWHS model with the PRS, the AUC increased to 0.623 (95% CI 0.603–0.644). Conclusions This combined model represents a step forward for personalized breast cancer preventive care for US Black women, as its performance metrics are similar to those from models in other populations. Use of this new model may mitigate exacerbation of breast cancer disparities if and when it becomes feasible to include a PRS in routine health care decision-making.

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Section: Discussionmentioning

confidence: 99%

Addition of polygenic risk score to a risk calculator for prediction of breast cancer in US Black women

Zirpoli,

Pfeiffer,

Bertrand

et al. 2024

Breast Cancer Res

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“…So, if geneticists do not control for these differences, such as by excluding relatively genetically dissimilar participants from their samples, they are at risk of identifying associations between genetic variants (whose allele frequencies randomly drifted apart) and complex traits that are actually due to environmental or cultural differences. In more technical terms, they are at risk of being misled by the fact of “population stratification.” That is, if they do not control for population stratification, they are at risk of detecting SNPs that are systematically associated with a given population, but not with the trait within a population 60 …”

Section: Part 3: Polygenic Indexes: General Principles and Applicatio...mentioning

confidence: 99%

Wrestling with Social and Behavioral Genomics: Risks, Potential Benefits, and Ethical Responsibility

Meyer¹,

Appelbaum²,

Benjamin³

et al. 2023

Hastings Center Report

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Social and behavioral scientists are increasingly frequently collaborating with geneticists or adapting the methods of genetics research to investigate how genomic differences are associated with differences in a wide variety of behavioral and social phenotypes. The huge and varied range of phenotypes investigated in social and behavioral genomics (SBG) research, broadly construed, includes smoking and eating behavior, schizophrenia, attention deficit-hyperactivity disorder (ADHD), a sense of well-being, introversion, risk-taking preferences, income, intelligence, and educational attainment. Researchers study these phenotypes because they believe that doing so can, among other benefits, contribute to more rigorous social (and health) science, which can in turn contribute to more just social policies.Because, as we detail in part 1, there is such a long history of attempts to use claims about genetic differences in such phenotypes to advance unjust social policies-and because of the potential of such claims to undercut efforts at creating more just social policies-SBG research can be deeply controversial. In this report, we seek to convey what our working group, composed both of scientists who conduct SBG research and of scholars who think critically about it (see boxes 1 and 2), learned from three years of wrestling with the historical, social, and scientific facts relevant to the ethics of SBG research. More specifically, we seek to articulate where a majority of our working group did-and did not-achieve consensus about the issues with which we wrestled.To understand the risks and potential benefits of SBG research in more depth, our group first had to wrestle with the scientific question, what can genetics tell us about social outcomes as complex as, for example, educational attainment? Addressing that question required us to put SBG research in the context of genetics research more S3 SPECIAL REPORT: The Ethical Implications of Social and Behavioral Genomics

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“…Recently, we described a method to generate a cross-ancestry PRS (caPRS) that improved risk stratification among women of five ancestries. 37 Here, we improved on our previous caPRS and integrated the caPRS with the Tyrer-Cuzick (T-C) clinical risk model to generate a cross-ancestry integrated risk score (caIRS). We hypothesized that the caIRS would be a better predictor of BC than the T-C score alone.…”

Section: Introductionmentioning

confidence: 99%

Integration of a Cross-Ancestry Polygenic Model With Clinical Risk Factors Improves Breast Cancer Risk Stratification

Tshiaba

Ratman

Sun

et al. 2023

JCO Precision Oncology

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PURPOSE To develop and validate a cross-ancestry integrated risk score (caIRS) that combines a cross-ancestry polygenic risk score (caPRS) with a clinical estimator for breast cancer (BC) risk. We hypothesized that the caIRS is a better predictor of BC risk than clinical risk factors across diverse ancestry groups. METHODS We used diverse retrospective cohort data with longitudinal follow-up to develop a caPRS and integrate it with the Tyrer-Cuzick (T-C) clinical model. We tested the association between the caIRS and BC risk in two validation cohorts including > 130,000 women. We compared model discrimination for 5-year and remaining lifetime BC risk between the caIRS and T-C and assessed how the caIRS would affect screening in the clinic. RESULTS The caIRS outperformed T-C alone for all populations tested in both validation cohorts and contributed significantly to risk prediction beyond T-C. The area under the receiver operating characteristic curve improved from 0.57 to 0.65, and the odds ratio per standard deviation increased from 1.35 (95% CI, 1.27 to 1.43) to 1.79 (95% CI, 1.70 to 1.88) in validation cohort 1 with similar improvements observed in validation cohort 2. We observed the largest gain in positive predictive value using the caIRS in Black/African American women across both validation cohorts, with an approximately two-fold increase and an equivalent negative predictive value as the T-C. In a multivariate, age-adjusted logistic regression model including both caIRS and T-C, caIRS remained significant, indicating that caIRS provides information over T-C alone. CONCLUSION Adding a caPRS to the T-C model improves BC risk stratification for women of multiple ancestries, which could have implications for screening recommendations and prevention.

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Cross-ancestry polygenic risk score for breast cancer risk assessment.

Cited by 3 publications

References 0 publications

Addition of polygenic risk score to a risk calculator for prediction of breast cancer in US Black women

Addition of polygenic risk score to a risk calculator for prediction of breast cancer in US Black women

Wrestling with Social and Behavioral Genomics: Risks, Potential Benefits, and Ethical Responsibility

Integration of a Cross-Ancestry Polygenic Model With Clinical Risk Factors Improves Breast Cancer Risk Stratification

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