Abstract
Purpose of Review
Breast cancer is a complex disease that is fueled by genetic as well as non-genetic factors. As data risk estimates become better, stratifying a woman’s risk for breast cancer can lead to better prevention strategies. The purpose of this review is to introduce the polygenic risk score (PRS) and shed light on its clinical applications as well as shortcomings in the field of breast cancer prevention.
Recent Findings
A PRS combines relevant single-nucleotide polypeptides (SNPs) and generates an estimated risk of a specific cancer. It has the ability of questioning the whole genome and incorporating the added benefit of an individualized assessment. The PRS has become a part of the risk assessment evaluation without being officially approved.
Summary
The benefit of the PRS can be substantial and holds the promise of improved breast cancer prevention. However, more studies are needed to justify its routine use in our clinics.
Trial Registration
NCT03688204
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References
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Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65:87–108 Available from: http://www.ncbi.nlm.nih.gov/pubmed/25651787.
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. 2019;69:7–34 Available from: http://www.ncbi.nlm.nih.gov/pubmed/30620402.
Miki Y, Swensen J, Shattuck-Eidens D, Futreal PA, Harshman K, Tavtigian S, et al. A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science (80- ). 1994;266:66–71.
Berchuck A, Carney M, Lancaster JM, Marks J, Futreal AP. Familial breast-ovarian cancer syndromes: BRCA1 and BRCA2. Clin Obstet Gynecol. 1998:157–66.
Hall JM, Lee MK, Newman B, Morrow JE, Anderson LA, Huey B, et al. Linkage of early-onset familial breast cancer to chromosome 17q21. Science (80- ). 1990;250:1684–9.
Wooster R, Neuhausen SL, Mangion J, Quirk Y, Ford D, Collins N, et al. Localization of a breast cancer susceptibility gene, BRCA2, to chromosome 13q12–13. Science (80- ). 1994;265:2088–90.
Couch FJ, Shimelis H, Hu C, Hart SN, Polley EC, Na J, et al. Associations between cancer predisposition testing panel genes and breast cancer. JAMA Oncol. 2017;3:1190–6.
Rahman N, Seal S, Thompson D, Kelly P, Renwick A, Elliott A, et al. PALB2, which encodes a BRCA2-interacting protein, is a breast cancer susceptibility gene. Nat Genet. 2007;39:165–7.
Renwick A, Thompson D, Seal S, Kelly P, Chagtai T, Ahmed M, et al. ATM mutations that cause ataxia-telangiectasia are breast cancer susceptibility alleles. Nat Genet. 2006;38:873–5.
Meijers-Heijboer H, Van den Ouweland A, Klijn J, Wasielewski M, De Shoo A, Oldenburg R, et al. Low-penetrance susceptibility to breast cancer due to CHEK2*1100delC in noncarriers of BRCA1 or BRCA2 mutations: The CHEK2-breast cancer consortium. Nat Genet. 2002;31:55–9.
Chatterjee N, Shi J, García-Closas M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nat. Rev. Genet. 2016:392–406.
Canela-Xandri O, Rawlik K, Tenesa A. An atlas of genetic associations in UK Biobank. Nat Genet. 2018; 13. g. https://www.ncbi.nlm.nih.gov/pubmed/?term=McClean%20PE%5BAuthor%5D&cauthor=true&cauthor_uid=25225893.
Spindel JE, McCouch SR. When more is better: How data sharing would accelerate genomic selection of crop plants. New Phytol. 2016;212:814–26.
Mistry S, Harrison JR, Smith DJ, Escott-Price V, Zammit S. The use of polygenic risk scores to identify phenotypes associated with genetic risk of schizophrenia: systematic review. Schizophr Res. 2018:2–8.
Mistry S, Harrison JR, Smith DJ, Escott-Price V, Zammit S. The use of polygenic risk scores to identify phenotypes associated with genetic risk of bipolar disorder and depression: A systematic review. J Affect Disord. 2018:148–55.
Fabbri C, Serretti A. Role of 108 schizophrenia-associated loci in modulating psychopathological dimensions in schizophrenia and bipolar disorder. Am J Med Genet B Neuropsychiatr Genet. 2017;174:757–64.
Cooney MT, Dudina AL, Graham IM. Value and limitations of existing scores for the assessment of cardiovascular risk. A review for clinicians. J Am Coll Cardiol. 2009:1209–27.
Szulkin R, Whitington T, Eklund M, Aly M, Eeles RA, Easton D, et al. Prediction of individual genetic risk to prostate cancer using a polygenic score. Prostate. 2015;75:1467–74.
Aly M, Wiklund F, Xu J, Isaacs WB, Eklund M, D’Amato M, et al. Polygenic risk score improves prostate cancer risk prediction: Results from the Stockholm-1 cohort study. Eur Urol. 2011;60:21–8.
Torkamani A, Wineinger NE, Topol EJ. The personal and clinical utility of polygenic risk scores. Nat Rev Genet. 2018:581–90.
Maas P, Barrdahl M, Joshi AD, Auer PL, Gaudet MM, Milne RL, et al. Breast cancer risk From modifiable and nonmodifiable risk factors among white women in the United States. JAMA Oncol. 2016;2:1295–302.
Mavaddat N, Pharoah PDP, Michailidou K, Tyrer J, Brook MN, Bolla MK, et al. Prediction of breast cancer risk based on profiling with common genetic variants. J Natl Cancer Inst. 2015;107.
Hunter DJ, Kraft P, Jacobs KB, Cox DG, Yeager M, Hankinson SE, et al. A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet. 2007;39:870–4.
• Easton DF, Pooley KA, Dunning AM, Pharoah PDP, Thompson D, Ballinger DG, et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature. 2007;447:1087–93. One of the earliest studies to identify loci linked to breast cancer risk.
Michailidou K, Beesley J, Lindstrom S, Canisius S, Dennis J, Lush MJ, 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, Lindström S, Dennis J, Beesley J, Hui S, Kar S, et al. Association analysis identifies 65 new breast cancer risk loci. Nature. 2017;551:92–4.
Milne RL, Kuchenbaecker KB, Michailidou K, Beesley J, Kar S, Lindström S, et al. Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer. Nat Genet. 2017.
Stacey SN, Manolescu A, Sulem P, Thorlacius S, Gudjonsson SA, Jonsson GF, et al. Common variants on chromosome 5p12 confer susceptibility to estrogen receptor-positive breast cancer. Nat Genet. 2008;40:703–6.
Garcia-Closas M, Hall P, Nevanlinna H, Pooley K, Morrison J, Richesson DA, et al. Heterogeneity of breast cancer associations with five susceptibility loci by clinical and pathological characteristics. PLoS Genet. 2008;4.
Haiman CA, Chen GK, Vachon CM, Canzian F, Dunning A, Millikan RC, et al. A common variant at the TERT-CLPTM1L locus is associated with estrogen receptor-negative breast cancer. Nat Genet. 2011;43:1210–4.
Garcia-Closas M, Couch FJ, Lindstrom S, Michailidou K, Schmidt MK, Brook MN, et al. Genome-wide association studies identify four ER negative-specific breast cancer risk loci. Nat Genet. 2013;45:392–8.
Stevens KN, Vachon CM, Lee AM, Slager S, Lesnick T, Olswold C, et al. Common breast cancer susceptibility loci are associated with triple-negative breast cancer. Cancer Res. 2011;71:6240–9.
Purrington KS, Slager S, Eccles D, Yannoukakos D, Fasching PA, Miron P, et al. Genome-wide association study identifies 25 known breast cancer susceptibility loci as risk factors for triple-negative breast cancer. Carcinogenesis. 2014;35:1012–9.
Mavaddat N, Michailidou K, Dennis J, Lush M, Fachal L, Lee A, et al. Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. Am J Hum Genet. 2019;104:21–34.
Parker JS, Mullins M, Cheang MCU, Leung S, Voduc D, Vickery T, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009;27:1160–7.
Rudolph A, Song M, Brook MN, Milne RL, Mavaddat N, Michailidou K, 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;47:526–36.
Evans DGR, Harkness EF, Brentnall AR, 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. 2019:1–8.
Mavaddat N, Rebbeck TR, Lakhani SR, Easton DF, Antoniou AC. Incorporating tumour pathology information into breast cancer risk prediction algorithms. Breast Cancer Res. 2010;12(3):R28.
Esserman LJ. The WISDOM Study: Breaking the deadlock in the breast cancer screening debate. NPJ Breast Cancer. 2017;13(3):34.
Dinan MA, Wilson LE, Reed SD. Chemotherapy costs and 21-gene recurrence score genomic testing among Medicare beneficiaries with early-stage breast cancer, 2005 to 2011. J Natl Compr Canc Netw. 2019;17(3):245–54.
Pashayan N, Morris S, Gilbert FJ, Pharoah PDP. 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.
•• Ziv E, Tice JA, Sprague B, Vachon CM, Cummings SR, Kerlikowske K. Using breast cancer risk associated polymorphisms to identify women for breast cancer chemoprevention. PLoS One. 2017;12(1):e0168601. This study investigates the predictive power of the PRS to guide chemoprevention.
•• Vachon CM, Schaid DJ, Ingle JN, Wickerham DL, Kubo M, Mushiroda T, 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;149:517–23. This study investigates the predictive power of the PRS to guide chemoprevention.
•• Lee A, Mavaddat N, Wilcox AN, Cunningham AP, Carver T, Hartley S, et al. BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors. Genet Med. 2019. https://doi.org/10.1038/s41436-018-0406-9. This study is the most recent to incorporate the PRS into a risk model.
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Banu Arun reports contributing research support to and acting as a non-paid steering committee member for BROCADE trial with Abbvie; personal fees from and contributing research support to AstraZeneca; and contributing research support to PharmaMar and Invitae outside the submitted work. Lida Mina declares no conflicts of interest relevant to this manuscript.
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Mina, L.A., Arun, B. Polygenic Risk Scores in Breast Cancer. Curr Breast Cancer Rep 11, 117–122 (2019). https://doi.org/10.1007/s12609-019-00320-8
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DOI: https://doi.org/10.1007/s12609-019-00320-8