scholarly journals Potential of polygenic risk scores for improving population estimates of women’s breast cancer genetic risks

2021 ◽  
Author(s):  
Michael Wolfson ◽  
Steve Gribble ◽  
Nora Pashayan ◽  
Douglas F. Easton ◽  
Antonis C. Antoniou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Family History ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Breast Cancer Incidence ◽  
Population Level ◽  
Risk Scores ◽  
Polygenic Risk ◽  
Risk Algorithm

Abstract Purpose Breast cancer risk has conventionally been assessed using family history (FH) and rare high/moderate penetrance pathogenic variants (PVs), notably in BRCA1/2, and more recently PALB2, CHEK2, and ATM. In addition to these PVs, it is now possible to use increasingly predictive polygenic risk scores (PRS) as well. The comparative population-level predictive capability of these three different indicators of genetic risk for risk stratification is, however, unknown. Methods The Canadian heritable breast cancer risk distribution was estimated using a novel genetic mixing model (GMM). A realistically representative sample of women was synthesized based on empirically observed demographic patterns for appropriately correlated family history, inheritance of rare PVs, PRS, and residual risk from an unknown polygenotype. Risk assessment was simulated using the BOADICEA risk algorithm for 10-year absolute breast cancer incidence, and compared to heritable risks as if the overall polygene, including its measured PRS component, and PV risks were fully known. Results Generally, the PRS was most predictive for identifying women at high risk, while family history was the weakest. Only the PRS identified any women at low risk of breast cancer. Conclusion PRS information would be the most important advance in enabling effective risk stratification for population-wide breast cancer screening.

scholarly journals Integrating Clinical and Polygenic Factors to Predict Breast Cancer Risk in Women Undergoing Genetic Testing

2021 ◽  
pp. 307-316
Author(s):  
Elisha Hughes ◽  
Placede Tshiaba ◽  
Susanne Wagner ◽  
Thaddeus Judkins ◽  
Eric Rosenthal ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Family History ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Risk Score ◽  
Risk Model ◽  
European Ancestry ◽  
Polygenic Risk ◽  
Screening And Prevention

PURPOSE Screening and prevention decisions for women at increased risk of developing breast cancer depend on genetic and clinical factors to estimate risk and select appropriate interventions. Integration of polygenic risk into clinical breast cancer risk estimators can improve discrimination. However, correlated genetic effects must be incorporated carefully to avoid overestimation of risk. MATERIALS AND METHODS A novel Fixed-Stratified method was developed that accounts for confounding when adding a new factor to an established risk model. A combined risk score (CRS) of an 86–single-nucleotide polymorphism polygenic risk score and the Tyrer-Cuzick v7.02 clinical risk estimator was generated with attenuation for confounding by family history. Calibration and discriminatory accuracy of the CRS were evaluated in two independent validation cohorts of women of European ancestry (N = 1,615 and N = 518). Discrimination for remaining lifetime risk was examined by age-adjusted logistic regression. Risk stratification with a 20% risk threshold was compared between CRS and Tyrer-Cuzick in an independent clinical cohort (N = 32,576). RESULTS Simulation studies confirmed that the Fixed-Stratified method produced accurate risk estimation across patients with different family history. In both validation studies, CRS and Tyrer-Cuzick were significantly associated with breast cancer. In an analysis with both CRS and Tyrer-Cuzick as predictors of breast cancer, CRS added significant discrimination independent of that captured by Tyrer-Cuzick ( P < 10−11 in validation 1; P < 10−7 in validation 2). In an independent cohort, 18% of women shifted breast cancer risk categories from their Tyrer-Cuzick–based risk compared with risk estimates by CRS. CONCLUSION Integrating clinical and polygenic factors into a risk model offers more effective risk stratification and supports a personalized genomic approach to breast cancer screening and prevention.

scholarly journals Polygenic risk, susceptibility genes, and breast cancer over the life course

2020 ◽  
Author(s):  
Nina Mars ◽  
Elisabeth Widén ◽  
Sini Kerminen ◽  
Tuomo Meretoja ◽  
Matti Pirinen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Family History ◽  
Clinical Decision Making ◽  
Breast Cancer Mortality ◽  
Breast Cancer Incidence ◽  
Positive Family History ◽  
Risk Scores ◽  
Limited Information ◽  
Polygenic Risk ◽  
Fold Enrichment

ABSTRACTPolygenic risk scores (PRS) for breast cancer have potential to improve risk prediction, but there is limited information on their clinical applicability. We set out to study how PRS could help in clinical decision making. Among 99,969 women in the FinnGen study with 6,879 breast cancer cases, the PRS was associated not only with breast cancer incidence but also with a range of breast cancer-related endpoints. Women with a breast cancer PRS above the 90th percentile had both higher breast cancer mortality (HR 2.40, 95%CI 1.82-3.17) and higher risk for non-localized disease at diagnosis (HR 2.94, 95%CI 2.63-3.28), compared to those with PRS <80th percentile. The PRS modified the breast cancer risk of two high-impact frameshift risk variants. Women with the c.1592delT variant in PALB2 (242-fold enrichment in Finland, 263 carriers) and an average PRS (20-80th percentile) had a lifetime risk of breast cancer at 58% (95%CI 50-66%), which increased to 85% (70-100%) with a high PRS (>90th percentile), and decreased to 27% (15-39%) with a low PRS (<20th percentile). Similarly, for c.1100delC in CHEK2 (3.7-fold enrichment; 1,543 carriers), the respective lifetime risks were 27% (95%CI 25-30%), 59% (52-67%), and 18% (13-22%). Among breast cancer cases, a PRS >90th percentile was associated with risk of contralateral breast cancer with HR 1.66 (95%CI 1.24-2.22). Finally, the PRS significantly refined the risk assessment of women with first-degree relatives diagnosed with breast cancer, i.e. the combination of high PRS (>90th percentile) and a positive family-history was associated with a 2.33-fold elevated risk (95%CI 1.57-3.46) compared to a positive family history alone. These findings demonstrate opportunities for a comprehensive way of assessing genetic risk in the general population, in breast cancer patients, and in unaffected family members.

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

2020 ◽  
pp. canprevres.0154.2020
Author(s):  
Julian O. Kim ◽  
Daniel J. Schaid ◽  
Celine M. Vachon ◽  
Andrew Cooke ◽  
Fergus J. Couch ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Endocrine Therapy ◽  
Risk Estimation ◽  
Risk Scores ◽  
Polygenic Risk

scholarly journals Prospective Evaluation over 15 Years of Six Breast Cancer Risk Models

Cancers ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 5194
Author(s):  
Sherly X. Li ◽  
Roger L. Milne ◽  
Tú Nguyen-Dumont ◽  
Dallas R. English ◽  
Graham G. Giles ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Family History ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Age Groups ◽  
Risk Scores ◽  
P Value ◽  
Risk Models ◽  
Discriminatory Accuracy ◽  
Incident Cases

Prospective validation of risk models is needed to assess their clinical utility, particularly over the longer term. We evaluated the performance of six commonly used breast cancer risk models (IBIS, BOADICEA, BRCAPRO, BRCAPRO-BCRAT, BCRAT, and iCARE-lit). 15-year risk scores were estimated using lifestyle factors and family history measures from 7608 women in the Melbourne Collaborative Cohort Study who were aged 50–65 years and unaffected at commencement of follow-up two (conducted in 2003–2007), of whom 351 subsequently developed breast cancer. Risk discrimination was assessed using the C-statistic and calibration using the expected/observed number of incident cases across the spectrum of risk by age group (50–54, 55–59, 60–65 years) and family history of breast cancer. C-statistics were higher for BOADICEA (0.59, 95% confidence interval (CI) 0.56–0.62) and IBIS (0.57, 95% CI 0.54–0.61) than the other models (p-difference ≤ 0.04). No model except BOADICEA calibrated well across the spectrum of 15-year risk (p-value < 0.03). The performance of BOADICEA and IBIS was similar across age groups and for women with or without a family history. For middle-aged Australian women, BOADICEA and IBIS had the highest discriminatory accuracy of the six risk models, but apart from BOADICEA, no model was well-calibrated across the risk spectrum.

scholarly journals Polygenic risk scores for prediction of breast cancer risk in Asian populations

2021 ◽  
Author(s):  
Weang-Kee Ho ◽  
Mei-Chee Tai ◽  
Joe Dennis ◽  
Xiang Shu ◽  
Jingmei Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Scores ◽  
Polygenic Risk ◽  
Asian Populations

scholarly journals The limits of personalization in precision medicine: Polygenic risk scores and racial categorization in a precision breast cancer screening trial

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258571
Author(s):  
Jennifer Elyse James ◽  
Leslie Riddle ◽  
Barbara Ann Koenig ◽  
Galen Joseph
Keyword(s):  
Breast Cancer ◽  
Cancer Screening ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Breast Cancer Screening ◽  
Risk Scores ◽  
Polygenic Risk ◽  
Genomic Knowledge ◽  
Breast Cancer Risk Assessment ◽  
Race Ethnicity

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.

Identifying women at increased risk of breast cancer: Can we use genotyping at low penetrance loci?

2011 ◽  
Vol 29 (27_suppl) ◽  
pp. 162-162
Author(s):  
C. Merrick ◽  
J. Dunlop ◽  
L. Baker ◽  
E. Gellatly ◽  
A. Martin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Family History ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Control Group ◽  
Risk Category ◽  
Genome Wide Association Studies ◽  
Polygenic Risk ◽  
Increased Risk ◽  
Combining Data

162 Background: Most inherited predisposition to breast cancer is attributable to low penetrance susceptibility loci, a number of which have been identified through genome-wide association studies. Although individually each locus has a small effect, combining data from multiple loci would be expected to provide more risk information. We investigated the size of risk determination that can be achieved using genotyping at 18 loci. We then calculated its effect when combined with risk estimated from family history alone in terms of management under UK guidelines, where a woman who has a 10 year risk of 3% or greater requires additional breast screening from a younger age. Methods: Genotyping for 18 loci was carried out in 253 women at increased risk of breast cancer due to a positive family history and 118 matched controls. The relative risks conferred by genotype at the 18 loci were combined under a log-additive model and transformed into a log-polygenic risk. The BOADICEA risk estimation tool was used to calculate breast cancer risk due to family history. Results: Both the increased risk and control groups demonstrated a normal distribution of log-polygenic risk with similar variance. There was a significantly higher mean in the increased risk compared to the control group (mean = 0.1313 and 0.0874 respectively, p = 0.007). No significant correlation was found between polygenic risk calculated from genotype data and the family history risk estimated using BOADICEA. When polygenic risk was combined with family history risk there was significant reclassification of risk for those with a family history. 36.76% moved into a higher risk category while 3.68% moved into a lower risk category. Conclusions: Our data suggests that genotyping will be clinically relevant for estimating breast cancer risk. Individuals with a family history overall have a higher genotype risk than the population. The lack of correlation of genotype risk with BOADICEA risk suggests that the two risk estimates can be considered independently. By combining genotype with family history data, we demonstrated a significant reclassification of risk for individuals with a family history, with better identification of women in this group requiring intervention.

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