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

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. Howe...

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Autores principales: Hughes, Elisha, Tshiaba, Placede, Wagner, Susanne, Judkins, Thaddeus, Rosenthal, Eric, Roa, Benjamin, Gallagher, Shannon, Meek, Stephanie, Dalton, Kathryn, Hedegard, Wade, Adami, Carol A., Grear, Danna F., Domchek, Susan M., Garber, Judy, Lancaster, Johnathan M., Weitzel, Jeffrey N., Kurian, Allison W., Lanchbury, Jerry S., Gutin, Alexander, Robson, Mark E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society of Clinical Oncology 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8140787/
https://www.ncbi.nlm.nih.gov/pubmed/34036224
http://dx.doi.org/10.1200/PO.20.00246
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author Hughes, Elisha
Tshiaba, Placede
Wagner, Susanne
Judkins, Thaddeus
Rosenthal, Eric
Roa, Benjamin
Gallagher, Shannon
Meek, Stephanie
Dalton, Kathryn
Hedegard, Wade
Adami, Carol A.
Grear, Danna F.
Domchek, Susan M.
Garber, Judy
Lancaster, Johnathan M.
Weitzel, Jeffrey N.
Kurian, Allison W.
Lanchbury, Jerry S.
Gutin, Alexander
Robson, Mark E.
author_facet Hughes, Elisha
Tshiaba, Placede
Wagner, Susanne
Judkins, Thaddeus
Rosenthal, Eric
Roa, Benjamin
Gallagher, Shannon
Meek, Stephanie
Dalton, Kathryn
Hedegard, Wade
Adami, Carol A.
Grear, Danna F.
Domchek, Susan M.
Garber, Judy
Lancaster, Johnathan M.
Weitzel, Jeffrey N.
Kurian, Allison W.
Lanchbury, Jerry S.
Gutin, Alexander
Robson, Mark E.
author_sort Hughes, Elisha
collection PubMed
description 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.
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spelling pubmed-81407872021-05-24 Integrating Clinical and Polygenic Factors to Predict Breast Cancer Risk in Women Undergoing Genetic Testing Hughes, Elisha Tshiaba, Placede Wagner, Susanne Judkins, Thaddeus Rosenthal, Eric Roa, Benjamin Gallagher, Shannon Meek, Stephanie Dalton, Kathryn Hedegard, Wade Adami, Carol A. Grear, Danna F. Domchek, Susan M. Garber, Judy Lancaster, Johnathan M. Weitzel, Jeffrey N. Kurian, Allison W. Lanchbury, Jerry S. Gutin, Alexander Robson, Mark E. JCO Precis Oncol ORIGINAL REPORTS 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. American Society of Clinical Oncology 2021-01-28 /pmc/articles/PMC8140787/ /pubmed/34036224 http://dx.doi.org/10.1200/PO.20.00246 Text en © 2021 by American Society of Clinical Oncology https://creativecommons.org/licenses/by-nc-nd/4.0/Creative Commons Attribution Non-Commercial No Derivatives 4.0 License: https://creativecommons.org/licenses/by-nc-nd/4.0/
spellingShingle ORIGINAL REPORTS
Hughes, Elisha
Tshiaba, Placede
Wagner, Susanne
Judkins, Thaddeus
Rosenthal, Eric
Roa, Benjamin
Gallagher, Shannon
Meek, Stephanie
Dalton, Kathryn
Hedegard, Wade
Adami, Carol A.
Grear, Danna F.
Domchek, Susan M.
Garber, Judy
Lancaster, Johnathan M.
Weitzel, Jeffrey N.
Kurian, Allison W.
Lanchbury, Jerry S.
Gutin, Alexander
Robson, Mark E.
Integrating Clinical and Polygenic Factors to Predict Breast Cancer Risk in Women Undergoing Genetic Testing
title Integrating Clinical and Polygenic Factors to Predict Breast Cancer Risk in Women Undergoing Genetic Testing
title_full Integrating Clinical and Polygenic Factors to Predict Breast Cancer Risk in Women Undergoing Genetic Testing
title_fullStr Integrating Clinical and Polygenic Factors to Predict Breast Cancer Risk in Women Undergoing Genetic Testing
title_full_unstemmed Integrating Clinical and Polygenic Factors to Predict Breast Cancer Risk in Women Undergoing Genetic Testing
title_short Integrating Clinical and Polygenic Factors to Predict Breast Cancer Risk in Women Undergoing Genetic Testing
title_sort integrating clinical and polygenic factors to predict breast cancer risk in women undergoing genetic testing
topic ORIGINAL REPORTS
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8140787/
https://www.ncbi.nlm.nih.gov/pubmed/34036224
http://dx.doi.org/10.1200/PO.20.00246
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