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Published in: Breast Cancer Research 1/2020

01-12-2020 | Breast Cancer | Review

Clinical applications of polygenic breast cancer risk: a critical review and perspectives of an emerging field

Authors: Tatiane Yanes, Mary-Anne Young, Bettina Meiser, Paul A. James

Published in: Breast Cancer Research | Issue 1/2020

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Abstract

Polygenic factors are estimated to account for an additional 18% of the familial relative risk of breast cancer, with those at the highest level of polygenic risk distribution having a least a twofold increased risk of the disease. Polygenic testing promises to revolutionize health services by providing personalized risk assessments to women at high-risk of breast cancer and within population breast screening programs. However, implementation of polygenic testing needs to be considered in light of its current limitations, such as limited risk prediction for women of non-European ancestry. This article aims to provide a comprehensive review of the evidence for polygenic breast cancer risk, including the discovery of variants associated with breast cancer at the genome-wide level of significance and the use of polygenic risk scores to estimate breast cancer risk. We also review the different applications of this technology including testing of women from high-risk breast cancer families with uninformative genetic testing results, as a moderator of monogenic risk, and for population screening programs. Finally, a potential framework for introducing testing for polygenic risk in familial cancer clinics and the potential challenges with implementing this technology in clinical practice are discussed.
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Literature
1.
go back to reference Collaborative Group on Hormonal Factors in Breast Cancer. Familial breast cancer: collaborative reanalysis of individual data from 52 epidemiological studies including 58,209 women with breast cancer and 101,986 women without the disease. Lancet. 2001;358(9291):1389–99.CrossRef Collaborative Group on Hormonal Factors in Breast Cancer. Familial breast cancer: collaborative reanalysis of individual data from 52 epidemiological studies including 58,209 women with breast cancer and 101,986 women without the disease. Lancet. 2001;358(9291):1389–99.CrossRef
2.
go back to reference Lichtenstein P, et al. Environmental and heritable factors in the causation of cancer--analyses of cohorts of twins from Sweden, Denmark, and Finland. N Engl J Med. 2000;343(2):78–85.CrossRefPubMed Lichtenstein P, et al. Environmental and heritable factors in the causation of cancer--analyses of cohorts of twins from Sweden, Denmark, and Finland. N Engl J Med. 2000;343(2):78–85.CrossRefPubMed
4.
go back to reference Michailidou K, et al. Association analysis identifies 65 new breast cancer risk loci. Nature. 2017;551(7678):92–4. Michailidou K, et al. Association analysis identifies 65 new breast cancer risk loci. Nature. 2017;551(7678):92–4.
5.
go back to reference Thomas DM, James PA, Ballinger ML. Clinical implications of genomics for cancer risk genetics. Lancet Oncol. 2015;16:e303–8.CrossRefPubMed Thomas DM, James PA, Ballinger ML. Clinical implications of genomics for cancer risk genetics. Lancet Oncol. 2015;16:e303–8.CrossRefPubMed
7.
go back to reference Sawyer S, et al. A role for common genomic variants in the assessment of familial breast cancer. J Clin Oncol. 2012;30(35):4330–6.CrossRefPubMed Sawyer S, et al. A role for common genomic variants in the assessment of familial breast cancer. J Clin Oncol. 2012;30(35):4330–6.CrossRefPubMed
9.
go back to reference Khera AV, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50(9):1219–24.PubMedPubMedCentralCrossRef Khera AV, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50(9):1219–24.PubMedPubMedCentralCrossRef
10.
go back to reference Narod SA. Personalised medicine and population health: breast and ovarian cancer. Hum Genet. 2018;137(10):769–78.CrossRefPubMed Narod SA. Personalised medicine and population health: breast and ovarian cancer. Hum Genet. 2018;137(10):769–78.CrossRefPubMed
11.
go back to reference Janssens A, Joyner MJ. Polygenic risk scores that predict common diseases using millions of single nucleotide polymorphisms: is more, better? Clin Chem. 2019;65(5):609–11. Janssens A, Joyner MJ. Polygenic risk scores that predict common diseases using millions of single nucleotide polymorphisms: is more, better? Clin Chem. 2019;65(5):609–11.
12.
13.
go back to reference Black M, et al. Validation of a polygenic r isk score for breast cancer in unaffected Caucasian women referred for genetic testing. J Clin Oncol. 2018;36(15_suppl):1508.CrossRef Black M, et al. Validation of a polygenic r isk score for breast cancer in unaffected Caucasian women referred for genetic testing. J Clin Oncol. 2018;36(15_suppl):1508.CrossRef
14.
go back to reference Hughes E, et al. Development and validation of a residual risk score to predict breast cancer risk in unaffected women negative for mutations on a multi-gene hereditary cancer panel. J Clin Oncol. 2017;35(15_suppl):1579.CrossRef Hughes E, et al. Development and validation of a residual risk score to predict breast cancer risk in unaffected women negative for mutations on a multi-gene hereditary cancer panel. J Clin Oncol. 2017;35(15_suppl):1579.CrossRef
16.
go back to reference Amos CI, et al. The OncoArray consortium: a network for understanding the genetic architecture of common cancers. Cancer Epidemiol Biomark Prev. 2017;26(1):126–35.CrossRef Amos CI, et al. The OncoArray consortium: a network for understanding the genetic architecture of common cancers. Cancer Epidemiol Biomark Prev. 2017;26(1):126–35.CrossRef
19.
20.
go back to reference Michailidou K, et al. Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer. Nat Genet. 2015;47:373–80. Michailidou K, et al. Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer. Nat Genet. 2015;47:373–80.
22.
go back to reference Huo D, et al. Genome-wide association studies in women of African ancestry identified 3q26.21 as a novel susceptibility locus for oestrogen receptor negative breast cancer. Hum Mol Genet. 2016;25(21):4835–46.PubMedPubMedCentral Huo D, et al. Genome-wide association studies in women of African ancestry identified 3q26.21 as a novel susceptibility locus for oestrogen receptor negative breast cancer. Hum Mol Genet. 2016;25(21):4835–46.PubMedPubMedCentral
23.
go back to reference Fejerman L, et al. Genome-wide association study of breast cancer in Latinas identifies novel protective variants on 6q25. Nat Commun. 2014;5:5260.PubMedCrossRef Fejerman L, et al. Genome-wide association study of breast cancer in Latinas identifies novel protective variants on 6q25. Nat Commun. 2014;5:5260.PubMedCrossRef
24.
go back to reference Lee JY, et al. BRCA1/2-negative, high-risk breast cancers (BRCAX) for Asian women: genetic susceptibility loci and their potential impacts. Sci Rep. 2018;8(1):15263.PubMedPubMedCentralCrossRef Lee JY, et al. BRCA1/2-negative, high-risk breast cancers (BRCAX) for Asian women: genetic susceptibility loci and their potential impacts. Sci Rep. 2018;8(1):15263.PubMedPubMedCentralCrossRef
25.
26.
go back to reference Wang S, et al. Genetic variants demonstrating flip-flop phenomenon and breast cancer risk prediction among women of African ancestry. Breast Cancer Res Treat. 2018;168(3):703–12.CrossRefPubMedPubMedCentral Wang S, et al. Genetic variants demonstrating flip-flop phenomenon and breast cancer risk prediction among women of African ancestry. Breast Cancer Res Treat. 2018;168(3):703–12.CrossRefPubMedPubMedCentral
27.
go back to reference Milne RL, et al. Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer. Nat Genet. 2017;49(12):1767–78. Milne RL, et al. Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer. Nat Genet. 2017;49(12):1767–78.
28.
go back to reference Husing A, et al. Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status. J Med Genet. 2012;49(9):601–8.PubMedCrossRef Husing A, et al. Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status. J Med Genet. 2012;49(9):601–8.PubMedCrossRef
30.
31.
go back to reference Evans DG, et al. The impact of a panel of 18 SNPs on breast cancer risk in women attending a UK familial screening clinic: a case-control study. J Med Genet. 2016;54(2):111–13.CrossRefPubMed Evans DG, et al. The impact of a panel of 18 SNPs on breast cancer risk in women attending a UK familial screening clinic: a case-control study. J Med Genet. 2016;54(2):111–13.CrossRefPubMed
32.
go back to reference Muranen TA, et al. Polygenic risk score is associated with increased disease risk in 52 Finnish breast cancer families. Breast Cancer Res Treat. 2016; 158(3):463–69.CrossRefPubMedPubMedCentral Muranen TA, et al. Polygenic risk score is associated with increased disease risk in 52 Finnish breast cancer families. Breast Cancer Res Treat. 2016; 158(3):463–69.CrossRefPubMedPubMedCentral
33.
34.
go back to reference Li H, et al. Breast cancer risk prediction using a polygenic risk score in the familial setting: a prospective study from the Breast Cancer Family Registry and kConFab. Genet Med. 2017;19(1):30–5.CrossRefPubMed Li H, et al. Breast cancer risk prediction using a polygenic risk score in the familial setting: a prospective study from the Breast Cancer Family Registry and kConFab. Genet Med. 2017;19(1):30–5.CrossRefPubMed
35.
go back to reference Hsieh YC, et al. A polygenic risk score for breast cancer risk in a Taiwanese population. Breast Cancer Res Treat. 2017;163(1):131–8.CrossRefPubMed Hsieh YC, et al. A polygenic risk score for breast cancer risk in a Taiwanese population. Breast Cancer Res Treat. 2017;163(1):131–8.CrossRefPubMed
36.
go back to reference Chan CHT, et al. Evaluation of three polygenic risk score models for the prediction of breast cancer risk in Singapore Chinese. Oncotarget. 2018;9(16):12796–804.PubMedPubMedCentral Chan CHT, et al. Evaluation of three polygenic risk score models for the prediction of breast cancer risk in Singapore Chinese. Oncotarget. 2018;9(16):12796–804.PubMedPubMedCentral
37.
go back to reference Wang Z, et al. Polygenic determinants for subsequent breast cancer risk in survivors of childhood cancer: The St Jude Lifetime Cohort Study (SJLIFE). Clin Cancer Res. 2018;24(24):6230–35.CrossRefPubMedPubMedCentral Wang Z, et al. Polygenic determinants for subsequent breast cancer risk in survivors of childhood cancer: The St Jude Lifetime Cohort Study (SJLIFE). Clin Cancer Res. 2018;24(24):6230–35.CrossRefPubMedPubMedCentral
38.
go back to reference Mavaddat N, et al. Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. Am J Hum Genet. 2019;104(1):21–34.CrossRefPubMed Mavaddat N, et al. Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. Am J Hum Genet. 2019;104(1):21–34.CrossRefPubMed
39.
41.
go back to reference van Veen EM, et al. Use of single-nucleotide polymorphisms and mammographic density plus classic risk factors for breast cancer risk prediction. JAMA Oncol. 2018;4(4):476–82.PubMedPubMedCentralCrossRef van Veen EM, et al. Use of single-nucleotide polymorphisms and mammographic density plus classic risk factors for breast cancer risk prediction. JAMA Oncol. 2018;4(4):476–82.PubMedPubMedCentralCrossRef
42.
go back to reference Lall K, et al. Polygenic prediction of breast cancer: comparison of genetic predictors and implications for risk stratification. BMC Cancer. 2019;19(1):557.PubMedPubMedCentralCrossRef Lall K, et al. Polygenic prediction of breast cancer: comparison of genetic predictors and implications for risk stratification. BMC Cancer. 2019;19(1):557.PubMedPubMedCentralCrossRef
43.
go back to reference Lee CP, et al. Breast cancer risk assessment using genetic variants and risk factors in a Singapore Chinese population. Breast Cancer Res. 2014;16(3):R64.PubMedPubMedCentralCrossRef Lee CP, et al. Breast cancer risk assessment using genetic variants and risk factors in a Singapore Chinese population. Breast Cancer Res. 2014;16(3):R64.PubMedPubMedCentralCrossRef
45.
go back to reference Holm J, et al. Associations of breast cancer risk prediction tools with tumor characteristics and metastasis. J Clin Oncol. 2016;34(3):251–8.CrossRefPubMed Holm J, et al. Associations of breast cancer risk prediction tools with tumor characteristics and metastasis. J Clin Oncol. 2016;34(3):251–8.CrossRefPubMed
46.
go back to reference Dite GS, et al. Breast cancer risk prediction using clinical models and 77 independent risk-associated SNPs for women aged under 50 years: Australian breast cancer family registry. Cancer Epidemiol Biomark Prev. 2016;25(2):359–65.CrossRef Dite GS, et al. Breast cancer risk prediction using clinical models and 77 independent risk-associated SNPs for women aged under 50 years: Australian breast cancer family registry. Cancer Epidemiol Biomark Prev. 2016;25(2):359–65.CrossRef
47.
go back to reference Vachon CM, et al. A polygenic risk score for breast cancer in women receiving tamoxifen or raloxifene on NSABP P-1 and P-2. Breast Cancer Res Treat. 2015;149(2):517–23.PubMedPubMedCentralCrossRef Vachon CM, et al. A polygenic risk score for breast cancer in women receiving tamoxifen or raloxifene on NSABP P-1 and P-2. Breast Cancer Res Treat. 2015;149(2):517–23.PubMedPubMedCentralCrossRef
48.
go back to reference Li J, et al. Breast cancer genetic risk profile is differentially associated with interval and screen-detected breast cancers. Ann Oncol. 2015;26(3):517–22.CrossRefPubMed Li J, et al. Breast cancer genetic risk profile is differentially associated with interval and screen-detected breast cancers. Ann Oncol. 2015;26(3):517–22.CrossRefPubMed
49.
go back to reference Cuzick J, et al. Impact of a panel of 88 single nucleotide polymorphisms on the risk of breast cancer in high-risk women: results from two randomized tamoxifen prevention trials. J Clin Oncol. 2017;35(7):743–50.CrossRefPubMed Cuzick J, et al. Impact of a panel of 88 single nucleotide polymorphisms on the risk of breast cancer in high-risk women: results from two randomized tamoxifen prevention trials. J Clin Oncol. 2017;35(7):743–50.CrossRefPubMed
50.
go back to reference Rudolph A, et al. Joint associations of a polygenic risk score and environmental risk factors for breast cancer in the breast cancer association consortium. Int J Epidemiol. 2018;47(2):526–36. Rudolph A, et al. Joint associations of a polygenic risk score and environmental risk factors for breast cancer in the breast cancer association consortium. Int J Epidemiol. 2018;47(2):526–36.
51.
go back to reference Li N, et al. Evaluating the breast cancer predisposition role of rare variants in genes associated with low-penetrance breast cancer risk SNPs. Breast Cancer Res. 2018;20(1):3.PubMedPubMedCentralCrossRef Li N, et al. Evaluating the breast cancer predisposition role of rare variants in genes associated with low-penetrance breast cancer risk SNPs. Breast Cancer Res. 2018;20(1):3.PubMedPubMedCentralCrossRef
52.
go back to reference Evans DGR, et al. Breast cancer pathology and stage are better predicted by risk stratification models that include mammographic density and common genetic variants. Breast Cancer Res Treat. 2019;176(1):141–8.PubMedPubMedCentralCrossRef Evans DGR, et al. Breast cancer pathology and stage are better predicted by risk stratification models that include mammographic density and common genetic variants. Breast Cancer Res Treat. 2019;176(1):141–8.PubMedPubMedCentralCrossRef
53.
go back to reference Li J, et al. Differential burden of rare and common variants on tumor characteristics, survival, and mode of detection in breast cancer. Cancer Res. 2018;78(21):6329–38.CrossRefPubMed Li J, et al. Differential burden of rare and common variants on tumor characteristics, survival, and mode of detection in breast cancer. Cancer Res. 2018;78(21):6329–38.CrossRefPubMed
54.
go back to reference Curtit E, et al. Assessment of the prognostic role of a 94-single nucleotide polymorphisms risk score in early breast cancer in the SIGNAL/PHARE prospective cohort: no correlation with clinico-pathological characteristics and outcomes. Breast Cancer Res. 2017;19(1):98.PubMedPubMedCentralCrossRef Curtit E, et al. Assessment of the prognostic role of a 94-single nucleotide polymorphisms risk score in early breast cancer in the SIGNAL/PHARE prospective cohort: no correlation with clinico-pathological characteristics and outcomes. Breast Cancer Res. 2017;19(1):98.PubMedPubMedCentralCrossRef
55.
go back to reference Amir E, et al. Assessing women at high risk of breast cancer: a review of risk assessment models. J Natl Cancer Inst. 2010;102(10):680–91.CrossRefPubMed Amir E, et al. Assessing women at high risk of breast cancer: a review of risk assessment models. J Natl Cancer Inst. 2010;102(10):680–91.CrossRefPubMed
56.
go back to reference Dite GS, et al. Using SNP genotypes to improve the discrimination of a simple breast cancer risk prediction model. Breast Cancer Res Treat. 2013;139(3):887–96.PubMedPubMedCentralCrossRef Dite GS, et al. Using SNP genotypes to improve the discrimination of a simple breast cancer risk prediction model. Breast Cancer Res Treat. 2013;139(3):887–96.PubMedPubMedCentralCrossRef
57.
go back to reference Wang WYS, et al. Genome-wide association studies: theoretical and practical concerns. Nat Rev Genet. 2005;6(2):109–18.CrossRefPubMed Wang WYS, et al. Genome-wide association studies: theoretical and practical concerns. Nat Rev Genet. 2005;6(2):109–18.CrossRefPubMed
59.
go back to reference Mealiffe ME, et al. Assessment of clinical validity of a breast cancer risk model combining genetic and clinical information. J Natl Cancer Inst. 2010;102(21):1618–27.PubMedPubMedCentralCrossRef Mealiffe ME, et al. Assessment of clinical validity of a breast cancer risk model combining genetic and clinical information. J Natl Cancer Inst. 2010;102(21):1618–27.PubMedPubMedCentralCrossRef
60.
go back to reference Darabi H, et al. Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement. Breast Cancer Res. 2012;14(1):1–11.CrossRef Darabi H, et al. Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement. Breast Cancer Res. 2012;14(1):1–11.CrossRef
61.
go back to reference Zhang X, et al. Addition of a polygenic risk score, mammographic density, and endogenous hormones to existing breast cancer risk prediction models: a nested case-control study. PLoS Med. 2018;15(9):e1002644.PubMedPubMedCentralCrossRef Zhang X, et al. Addition of a polygenic risk score, mammographic density, and endogenous hormones to existing breast cancer risk prediction models: a nested case-control study. PLoS Med. 2018;15(9):e1002644.PubMedPubMedCentralCrossRef
63.
go back to reference Lee A, et al. BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors. Genet Med. 2019;21(8):1708–18.CrossRefPubMedPubMedCentral Lee A, et al. BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors. Genet Med. 2019;21(8):1708–18.CrossRefPubMedPubMedCentral
64.
go back to reference Lakeman IMM, et al. Addition of a 161-SNP polygenic risk score to family history-based risk prediction: impact on clinical management in non-BRCA1/2 breast cancer families. J Med Genet. 2019;56(9): 581–89.CrossRefPubMed Lakeman IMM, et al. Addition of a 161-SNP polygenic risk score to family history-based risk prediction: impact on clinical management in non-BRCA1/2 breast cancer families. J Med Genet. 2019;56(9): 581–89.CrossRefPubMed
65.
go back to reference Shieh Y, et al. Joint relative risks for estrogen receptor-positive breast cancer from a clinical model, polygenic risk score, and sex hormones. Breast Cancer Res Treat. 2017;166(2):603–12.PubMedPubMedCentralCrossRef Shieh Y, et al. Joint relative risks for estrogen receptor-positive breast cancer from a clinical model, polygenic risk score, and sex hormones. Breast Cancer Res Treat. 2017;166(2):603–12.PubMedPubMedCentralCrossRef
66.
go back to reference Lee A, et al. BOADICEA: a comprehensive breast cancer risk prediction modelincorporating genetic and nongenetic risk factors. Genet Med. 2019;21(8):1708–18.PubMedPubMedCentralCrossRef Lee A, et al. BOADICEA: a comprehensive breast cancer risk prediction modelincorporating genetic and nongenetic risk factors. Genet Med. 2019;21(8):1708–18.PubMedPubMedCentralCrossRef
67.
go back to reference Schwartz MD, et al. Long-term outcomes of BRCA1/BRCA2 testing: risk reduction and surveillance. Cancer. 2012;118(2):510–7.CrossRefPubMed Schwartz MD, et al. Long-term outcomes of BRCA1/BRCA2 testing: risk reduction and surveillance. Cancer. 2012;118(2):510–7.CrossRefPubMed
68.
go back to reference Morgan D, et al. Cancer prevention and screening practices among women at risk for hereditary breast and ovarian cancer after genetic counseling in the community setting. Familial Cancer. 2009;8(4):277–87.CrossRefPubMed Morgan D, et al. Cancer prevention and screening practices among women at risk for hereditary breast and ovarian cancer after genetic counseling in the community setting. Familial Cancer. 2009;8(4):277–87.CrossRefPubMed
69.
go back to reference Cox DG, et al. Transmission of breast cancer polygenic risk based on single nucleotide polymorphisms. Breast. 2018;41:14–8.CrossRefPubMed Cox DG, et al. Transmission of breast cancer polygenic risk based on single nucleotide polymorphisms. Breast. 2018;41:14–8.CrossRefPubMed
70.
go back to reference Waxler JL, et al. Genetic counseling as a tool for type 2 diabetes prevention: a genetic counseling framework for common polygenetic disorders. J Genet Couns. 2012;21(5):684–91.CrossRefPubMed Waxler JL, et al. Genetic counseling as a tool for type 2 diabetes prevention: a genetic counseling framework for common polygenetic disorders. J Genet Couns. 2012;21(5):684–91.CrossRefPubMed
71.
go back to reference Peay H, Austin J. How to talk with families about: genetics and psychiatric illness. In: How to talk with families about: genetics and psychiatric illness, vol. xii. New York: W W Norton & Co; 2011. p. 266. xii, 266. Peay H, Austin J. How to talk with families about: genetics and psychiatric illness. In: How to talk with families about: genetics and psychiatric illness, vol. xii. New York: W W Norton & Co; 2011. p. 266. xii, 266.
72.
go back to reference Young MA, et al. Making sense of SNPs: women's understanding and experiences of receiving a personalized profile of their breast cancer risks. J Genet Couns. 2018;27(3):702–8.CrossRefPubMed Young MA, et al. Making sense of SNPs: women's understanding and experiences of receiving a personalized profile of their breast cancer risks. J Genet Couns. 2018;27(3):702–8.CrossRefPubMed
73.
go back to reference Forrest LE, et al. High-risk women’s risk perception after receiving personalized polygenic breast cancer risk information. J Community Genet. 2019;10(2):197–206.CrossRefPubMed Forrest LE, et al. High-risk women’s risk perception after receiving personalized polygenic breast cancer risk information. J Community Genet. 2019;10(2):197–206.CrossRefPubMed
74.
go back to reference Muranen TA, et al. Genetic modifiers of CHEK2*1100delC-associated breast cancer risk. Genet Med. 2017;19(5):599–603.CrossRefPubMed Muranen TA, et al. Genetic modifiers of CHEK2*1100delC-associated breast cancer risk. Genet Med. 2017;19(5):599–603.CrossRefPubMed
75.
go back to reference Pashayan N, et al. Cost-effectiveness and benefit-to-harm ratio of risk-stratified screening for breast cancer: a life-table model. JAMA Oncol. 2018;4(11):1504–10.PubMedPubMedCentralCrossRef Pashayan N, et al. Cost-effectiveness and benefit-to-harm ratio of risk-stratified screening for breast cancer: a life-table model. JAMA Oncol. 2018;4(11):1504–10.PubMedPubMedCentralCrossRef
76.
go back to reference Henneman L, et al. ‘A low risk is still a risk’: exploring women's attitudes towards genetic testing for breast cancer susceptibility in order to target disease prevention. Public Health Genomics. 2011;14(4–5):238–47.CrossRefPubMed Henneman L, et al. ‘A low risk is still a risk’: exploring women's attitudes towards genetic testing for breast cancer susceptibility in order to target disease prevention. Public Health Genomics. 2011;14(4–5):238–47.CrossRefPubMed
77.
go back to reference Evans DG, et al. Programme Grants for Applied Research. In: Improvement in risk prediction, early detection and prevention of breast cancer in the NHS Breast Screening Programme and family history clinics: a dual cohort study. Southampton: NIHR Journals Library; 2016.CrossRef Evans DG, et al. Programme Grants for Applied Research. In: Improvement in risk prediction, early detection and prevention of breast cancer in the NHS Breast Screening Programme and family history clinics: a dual cohort study. Southampton: NIHR Journals Library; 2016.CrossRef
Metadata
Title
Clinical applications of polygenic breast cancer risk: a critical review and perspectives of an emerging field
Authors
Tatiane Yanes
Mary-Anne Young
Bettina Meiser
Paul A. James
Publication date
01-12-2020
Publisher
BioMed Central
Published in
Breast Cancer Research / Issue 1/2020
Electronic ISSN: 1465-542X
DOI
https://doi.org/10.1186/s13058-020-01260-3

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