Impact of Personalized Genetic Breast Cancer Risk Estimation With Polygenic Risk Scores on Preventive Endocrine Therapy Intention and Uptake

Population-based genomic screening is at the forefront of a new approach to disease prevention. Yet the lack of diversity in genome wide association studies and ongoing debates about the appropriate use of racial and ethnic categories in genomics raise key questions about the translation of genomic knowledge into clinical practice. This article reports on an ethnographic study of a large pragmatic clinical trial of breast cancer screening called WISDOM (Women Informed to Screen Depending On Measures of Risk). Our ethnography illuminates the challenges of using race or ethnicity as a risk factor in the implementation of precision breast cancer risk assessment. Our analysis provides critical insights into how categories of race, ethnicity and ancestry are being deployed in the production of genomic knowledge and medical practice, and key challenges in the development and implementation of novel Polygenic Risk Scores in the research and clinical applications of this emerging science. Specifically, we show how the conflation of social and biological categories of difference can influence risk prediction for individuals who exist at the boundaries of these categories, affecting the perceptions and practices of scientists, clinicians, and research participants themselves. Our research highlights the potential harms of practicing genomic medicine using under-theorized and ambiguous categories of race, ethnicity, and ancestry, particularly in an adaptive, pragmatic trial where research findings are applied in the clinic as they emerge. We contribute to the expanding literature on categories of difference in post-genomic science by closely examining the implementation of a large breast cancer screening study that aims to personalize breast cancer risk using both common and rare genomic markers.

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Polygenic Risk Scores for Breast Cancer Risk Prediction: Lessons Learned and Future Opportunities

JNCI Journal of the National Cancer Institute ◽

10.1093/jnci/djz176 ◽

2019 ◽

Vol 112 (6) ◽

pp. 555-556

Author(s):

Julie R Palmer

Keyword(s):

Breast Cancer ◽

Breast Cancer Risk ◽

Cancer Risk ◽

Risk Prediction ◽

Lessons Learned ◽

Risk Scores ◽

Polygenic Risk

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Polygenic Risk Scores for Prediction of Breast Cancer Risk in Women of African Ancestry: a Cross-Ancestry Approach

10.1101/2021.12.16.21266424 ◽

2021 ◽

Author(s):

Guimin Gao ◽

Fangyuan Zhao ◽

Thomas Ahearn ◽

Kathryn L. Lunetta ◽

Melissa A. Troester ◽

...

Keyword(s):

Breast Cancer ◽

Breast Cancer Risk ◽

Cancer Risk ◽

African Ancestry ◽

Penalized Regression ◽

European Ancestry ◽

Risk Scores ◽

Training Set ◽

Polygenic Risk ◽

Validation Set

Polygenic risk scores (PRSs) are useful to predict breast cancer risk, but the prediction accuracy of existing PRSs in women of African ancestry (AA) remain relatively low. We aim to develop optimal PRSs for prediction of overall and estrogen receptor (ER) subtype-specific breast cancer risk in women of African ancestry. The AA dataset comprised 9,235 cases and 10,184 controls from four genome-wide association study (GWAS) consortia and a GWAS study in Ghana. We randomly divided samples into training and validation sets. Genetic variants were selected by forward stepwise logistic regression or lasso penalized regression in the training set and the corresponding PRSs were evaluated in the validation set. To improve accuracy, we also developed joint PRSs that combined 1) the best PRSs built in the AA training dataset, 2) a previously-developed 313-variant PRS in women of European ancestry, and 3) PRSs using variants that were discovered in previous GWASs in women of European and African ancestry and were nominally significant the training set. For overall breast cancer, the odd ratio (OR) per standard deviation of the joint PRS in the validation set was 1.39 (95%CI: 1.31-1.46) with area under receiver operating characteristic curve (AUC) of 0.590. Compared to women with average risk (40th-60th PRS percentile), women in the top decile of the PRS had a 2.03-fold increased risk (95%CI: 1.68-2.44). For PRSs of ER-positive and ER-negative breast cancer, the AUCs were 0.609 and 0.597, respectively. The proposed PRS can improve prediction of breast cancer risk in women of African ancestry.

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

10.1158/1538-7445.sabcs18-962 ◽

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

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Development and validation of polygenic risk scores for prediction of breast cancer and breast cancer subtypes in Chinese women

10.21203/rs.3.rs-1109771/v1 ◽

2021 ◽

Author(s):

Can Hou ◽

Daowen Yang ◽

Yu Hao ◽

Bin Xu ◽

Huan Song ◽

...

Keyword(s):

Breast Cancer ◽

Breast Cancer Risk ◽

Cancer Risk ◽

Chinese Women ◽

Risk Scores ◽

Polygenic Risk Score ◽

Nucleotide Polymorphisms ◽

Cancer Risk Factors ◽

Polygenic Risk ◽

Cancer Subtypes

Abstract Background Studies investigating breast cancer polygenic risk score (PRS) in Chinese women are scarce. The objectives of this study were to develop and validate PRSs that could be used to stratify risk for overall and subtype-specific breast cancer in Chinese women, and to evaluate the performance of a newly proposed Artificial Neural Network (ANN) based approach for PRS construction. Methods The PRSs were constructed using the a GWAS dataset and validated in an independent case-control study. Three approaches, including repeated logistic regression (RLR), logistic ridge regression (LRR) and ANN based approach, were used to build the PRSs for overall and subtype-specific breast cancer based on 24 selected single nucleotide polymorphisms (SNPs). Predictive performance and calibration of the PRSs were evaluated unadjusted and adjusted for Gail-2 model 5-year risk or classical breast cancer risk factors. Results The primary PRSANN and PRSLRR both showed good predictive ability for overall breast cancer (IQ-OR 1.76 vs 1.58; AUC 0.601 vs 0.598) and remained to be predictive after adjustment. Although estrogen receptor negative (ER-) breast cancer was poorly predicted by the primary PRSs, the ER- PRSs trained solely on ER- breast cancer cases saw a substantial improvement in predictions of ER- breast cancer. Conclusions The SNP-24 based PRSs can provide additional risk information to help breast cancer risk stratification in the general population of China. The newly proposed ANN approach for PRS construction has potential to replace the traditional approaches, but more studies are needed to validate and investigate its performance.

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Generalizability of Polygenic Risk Scores for Breast Cancer in the Multiethnic eMERGE Study

10.1101/2020.08.17.20176685 ◽

2020 ◽

Author(s):

Cong Liu ◽

Nur Zeinomar ◽

Wendy K Chung ◽

Krzysztof Kiryluk ◽

Ali G Ghravi ◽

...

Keyword(s):

Breast Cancer ◽

Breast Cancer Risk ◽

Cancer Risk ◽

African Ancestry ◽

European Ancestry ◽

Risk Scores ◽

Polygenic Risk ◽

Emerge Network ◽

Increase Odds Ratio ◽

Breast Cancer Family History

Background: The majority of polygenic risk scores (PRS) for breast cancer have been developed and validated using cohorts of European ancestry (EA). Less is known about the generalizability of these PRS in other ancestral groups. Methods: The Electronic Medical Records and Genomics (eMERGE) network cohort dataset was used to evaluate the performance of seven previously developed PRS (three EA-based PRSs, and four non-EA based PRSs) in three major ancestral groups. Each PRS was separately evaluated in EA (cases: 3939; controls: 28840), African ancestry (AA) (cases: 121; controls: 1173) and self-reported LatinX ancestry (LA) (cases: 92; controls: 1363) women. We assessed the association between breast cancer risk and each PRS, adjusting forage, study site, breast cancer family history, and first three ancestry informative principal components. Results: EA-based PRSs were significantly associated with breast cancer risk in EA women per one SD increase (odds ratio [OR]=1.45, 95% confidence interval [CI]=1.40-1.51), and LA women (OR=1.41, 95% CI=1.13-1.77), but not AA women (OR=1.13, 95% CI=0.92-1.40). There was no statistically significant association for the non-EA PRSs in all ancestry groups, including an LA-based PRS and an AA-based PRS. Conclusion: We evaluated EA-derived PRS for estimating breast cancer risk using the eMERGE dataset and found they generalized well in LA women but not in AA women. For non-EA based PRSs, we did not replicate previously reported associations for the respective ancestries in the eMERGE cohort. Our results highlight the need to improve representation of diverse population groups, particularly AA women, in research cohorts.

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