BACKGROUND: The polygenic risk score (PRS) allows the quantification of the polygenic effect of many low-penetrance alleles on the risk of breast cancer (BC). This study aimed to evaluate the performance of two sets comprising 77 or 313 low-penetrance loci (PRS77 and PRS313) in patients with BC in the Czech population. METHODS: In a retrospective case-control study, variants were genotyped from both the PRS77 and PRS313 sets in 1329 patients with BC and 1324 noncancer controls, all women without germline pathogenic variants in BC predisposition genes. Odds ratios (ORs) were calculated according to the categorical PRS in individual deciles. Weighted Cox regression analysis was used to estimate the hazard ratio (HR) per standard deviation (SD) increase in PRS. RESULTS: The distributions of standardized PRSs in patients and controls were significantly different (p < 2.2 × 10-16) with both sets. PRS313 outperformed PRS77 in categorical and continuous PRS analyses. For patients in the highest 2.5% of PRS313, the risk reached an OR of 3.05 (95% CI, 1.66-5.89; p = 1.76 × 10-4). The continuous risk was estimated as an HRper SD of 1.64 (95% CI, 1.49-1.81; p < 2.0 × 10-16), which resulted in an absolute risk of 21.03% at age 80 years for individuals in the 95th percentile of PRS313. Discordant categorization into PRS deciles was observed in 248 individuals (9.3%). CONCLUSIONS: Both PRS77 and PRS313 are able to stratify individuals according to their BC risk in the Czech population. PRS313 shows better discriminatory ability. The results support the potential clinical utility of using PRS313 in individualized BC risk prediction.

Centre for Familial Breast and Ovarian Cancer Center for Integrated Oncology Medical Faculty University of Cologne and University Hospital Cologne Cologne Germany

Centre for Medical Genetics and Reproductive Medicine GENNET Prague Czech Republic

Department of Biochemistry Faculty of Science Charles University Prague Czech Republic

Department of Cancer Epidemiology and Genetics Masaryk Memorial Cancer Institute Brno Czech Republic

Department of Genetics and Microbiology Faculty of Science Charles University Prague Prague Czech Republic

Department of Medical Genetics AGEL Research and Training Institute AGEL Laboratories Novy Jicin Czech Republic

Department of Oncology 1st Faculty of Medicine Charles University and General University Hospital Prague Prague Czech Republic

Department of Paediatrics and Inherited Metabolic Disorders 1st Faculty of Medicine Charles University and General University Hospital Prague Prague Czech Republic

Institute of Biology and Medical Genetics 1st Faculty of Medicine Charles University and General University Hospital Prague Prague Czech Republic

Institute of Medical Biochemistry and Laboratory Diagnostics 1st Faculty of Medicine Charles University and General University Hospital Prague Prague Czech Republic

Institute of Pathological Physiology 1st Faculty of Medicine Charles University Prague Czech Republic

See more in PubMed

Ferlay JEM, Lam F, Colombet M, et al. Global Cancer Observatory: Cancer Today. International Agency for Research on Cancer; 2022.

Mukama T, Kharazmi E, Sundquist K, Sundquist J, Brenner H, Fallah M. Familial risk of breast cancer by dynamic, accumulative, and static definitions of family history. Cancer. 2020;126(12):2837‐2848. doi:10.1002/cncr.32815

Stratton MR, Rahman N. The emerging landscape of breast cancer susceptibility. Nat Genet. 2008;40(1):17‐22. doi:10.1038/ng.2007.53

Kleibl Z, Kristensen VN. Women at high risk of breast cancer: molecular characteristics, clinical presentation and management. Breast. 2016;28:136‐144. doi:10.1016/j.breast.2016.05.006

Ghoussaini M, Fletcher O, Michailidou K, et al. Genome‐wide association analysis identifies three new breast cancer susceptibility loci. Nat Genet. 2012;44(3):312‐318. doi:10.1038/ng.1049

Gracia‐Aznarez FJ, Fernandez V, Pita G, et al. Whole exome sequencing suggests much of non‐BRCA1/BRCA2 familial breast cancer is due to moderate and low penetrance susceptibility alleles. PLoS One. 2013;8(2):e55681. doi:10.1371/journal.pone.0055681

Easton DF, Pooley KA, Dunning AM, et al. Genome‐wide association study identifies novel breast cancer susceptibility loci. Nature. 2007;447(7148):1087‐1093. doi:10.1038/nature05887

Michailidou K, Hall P, Gonzalez‐Neira A, et al. Large‐scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet. 2013;45(4):353‐361. doi:10.1038/ng.2563

Sawyer S, Mitchell G, McKinley J, et al. A role for common genomic variants in the assessment of familial breast cancer. J Clin Oncol. 2012;30(35):4330‐4336. doi:10.1200/JCO.2012.41.7469

Mavaddat N, Pharoah PDP, Michailidou K, et al. Prediction of breast cancer risk based on profiling with common genetic variants. J Natl Cancer Inst. 2015;107(5):djv036. doi:10.1093/jnci/djv036

Reeves GK, Travis RC, Green J, et al. Incidence of breast cancer and its subtypes in relation to individual and multiple low‐penetrance genetic susceptibility loci. J Am Med Assoc. 2010;304(4):426‐434. doi:10.1001/jama.2010.1042

Pharoah PD, Antoniou A, Bobrow M, Zimmern RL, Easton DF, Ponder BA. Polygenic susceptibility to breast cancer and implications for prevention. Nat Genet. 2002;31(1):33‐36. doi:10.1038/ng853

Pain O, Gillett AC, Austin JC, Folkersen L, Lewis CM. A tool for translating polygenic scores onto the absolute scale using summary statistics. Eur J Hum Genet. 2022;30(3):339‐348. doi:10.1038/s41431‐021‐01028‐z

Mealiffe ME, Stokowski RP, Rhees BK, Prentice RL, Pettinger M, Hinds DA. Assessment of clinical validity of a breast cancer risk model combining genetic and clinical information. J Natl Cancer Inst. 2010;102(21):1618‐1627. doi:10.1093/jnci/djq388

Evans DG, Brentnall A, Byers H, 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. 2017;54(2):111‐113. doi:10.1136/jmedgenet‐2016‐104125

Mavaddat N, Michailidou K, Dennis J, et al. Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. Am J Hum Genet. 2019;104(1):21‐34. doi:10.1016/j.ajhg.2018.11.002

Brentnall AR, van Veen EM, Harkness EF, et al. A case‐control evaluation of 143 single nucleotide polymorphisms for breast cancer risk stratification with classical factors and mammographic density. Int J Cancer. 2020;146(8):2122‐2129. doi:10.1002/ijc.32541

Foretova L, Navratilova M, Svoboda M, et al. Recommendations for preventive care for women with rare genetic cause of breast and ovarian cancer. Klin Onkol. 2019;32(suppl 2):6‐13. doi:10.14735/amko2019S6

Soukupova J, Zemankova P, Lhotova K, et al. Validation of CZECANCA (CZEch CAncer paNel for Clinical Application) for targeted NGS‐based analysis of hereditary cancer syndromes. PLoS One. 2018;13(4):e0195761. doi:10.1371/journal.pone.0195761

Soukupova J, Zemankova P, Kleiblova P, Janatova M, Kleibl Z. CZECANCA: CZEch CAncer paNel for Clinical Application—design and optimization of the targeted sequencing panel for the identification of cancer susceptibility in high‐risk individuals from the Czech Republic. Klin Onkol. 2016;29(suppl 1):S46‐S54. doi:10.14735/amko2016s46

Kral J, Jelinkova S, Zemankova P, et al. Germline multigene panel testing of patients with endometrial cancer. Oncol Lett. 2023;25(6):216. doi:10.3892/ol.2023.13802

Michailidou K, Beesley J, Lindstrom S, et al. Genome‐wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer. Nat Genet. 2015;47(4):373‐380. doi:10.1038/ng.3242

Michailidou K, Lindstrom S, Dennis J, et al. Association analysis identifies 65 new breast cancer risk loci. Nature. 2017;551(7678):92‐94. doi:10.1038/nature24284

Vachon CM, Pankratz VS, Scott CG, et al. The contributions of breast density and common genetic variation to breast cancer risk. J Natl Cancer Inst. 2015;107(5):dju397. doi:10.1093/jnci/dju397

Shieh Y, Hu D, Ma L, et al. Breast cancer risk prediction using a clinical risk model and polygenic risk score. Breast Cancer Res Treat. 2016;159(3):513‐525. doi:10.1007/s10549‐016‐3953‐2

Muranen TA, Greco D, Blomqvist C, et al. Genetic modifiers of CHEK2*1100delC‐associated breast cancer risk. Genet Med. 2017;19(5):599‐603. doi:10.1038/gim.2016.147

Kuchenbaecker KB, McGuffog L, Barrowdale D, et al. Evaluation of polygenic risk scores for breast and ovarian cancer risk prediction in BRCA1 and BRCA2 mutation carriers. J Natl Cancer Inst. 2017;109(7):djw302. doi:10.1093/jnci/djw302

Phelan CM, Kuchenbaecker KB, Tyrer JP, et al. Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer. Nat Genet. 2017;49(5):680‐691. doi:10.1038/ng.3826

Yang X, Leslie G, Gentry‐Maharaj A, et al. Evaluation of polygenic risk scores for ovarian cancer risk prediction in a prospective cohort study. J Med Genet. 2018;55(8):546‐554. doi:10.1136/jmedgenet‐2018‐105313

Lakeman IMM, Hilbers FS, Rodriguez‐Girondo M, 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):581‐589. doi:10.1136/jmedgenet‐2019‐106072

Jia G, Lu Y, Wen W, et al. Evaluating the utility of polygenic risk scores in identifying high‐risk individuals for eight common cancers. JNCI Cancer Spectr. 2020;4(3):pkaa021. doi:10.1093/jncics/pkaa021

Hilbers FS, van Hof PJ, Meijers CM, et al. Clustering of known low and moderate risk alleles rather than a novel recessive high‐risk gene in non‐BRCA1/2 sib trios affected with breast cancer. Int J Cancer. 2020;147(10):2708‐2716. doi:10.1002/ijc.33039

Zhang H, Ahearn TU, Lecarpentier J, et al. Genome‐wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype‐specific analyses. Nat Genet. 2020;52(6):572‐581. doi:10.1038/s41588‐020‐0609‐2

Jervis S, Song H, Lee A, et al. Ovarian cancer familial relative risks by tumour subtypes and by known ovarian cancer genetic susceptibility variants. J Med Genet. 2014;51(2):108‐113. doi:10.1136/jmedgenet‐2013‐102015

Lilyquist J, Ruddy KJ, Vachon CM, Couch FJ. Common genetic variation and breast cancer risk—past, present, and future. Cancer Epidemiol Biomarkers Prev. 2018;27(4):380‐394. doi:10.1158/1055‐9965.EPI‐17‐1144

Horackova K, Frankova S, Zemankova P, et al. Low frequency of cancer‐predisposition gene mutations in liver transplant candidates with hepatocellular carcinoma. Cancers. 2022;15(1):201. doi:10.3390/cancers15010201

Borde J, Ernst C, Wappenschmidt B, et al. Performance of breast cancer polygenic risk scores in 760 female CHEK2 germline mutation carriers. J Natl Cancer Inst. 2021;113(7):893‐899. doi:10.1093/jnci/djaa203

Lambert SA, Gil L, Jupp S, et al. The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. Nat Genet. 2021;53(4):420‐425. doi:10.1038/s41588‐021‐00783‐5

Dusek L, Muzik J, Kubasek M, Koptikova J, Zaloudik J, Vyzula R. Epidemiology of Malignant Tumours in the Czech Republic. Masaryk University; 2005. Accessed March 3, 2022. http://www.svod.cz

Antoniou AC, Goldgar DE, Andrieu N, et al. A weighted cohort approach for analysing factors modifying disease risks in carriers of high‐risk susceptibility genes. Genet Epidemiol. 2005;29(1):1‐11. doi:10.1002/gepi.20074

Zhang Z, Reinikainen J, Adeleke KA, Pieterse ME, Groothuis‐Oudshoorn CGM. Time‐varying covariates and coefficients in Cox regression models. Ann Transl Med. 2018;6(7):121. doi:10.21037/atm.2018.02.12

Antoniou AC, Beesley J, McGuffog L, et al. Common breast cancer susceptibility alleles and the risk of breast cancer for BRCA1 and BRCA2 mutation carriers: implications for risk prediction. Cancer Res. 2010;70(23):9742‐9754. doi:10.1158/0008‐5472.CAN‐10‐1907

Duffy SW, Tabar L, Yen AM, et al. Mammography screening reduces rates of advanced and fatal breast cancers: results in 549,091 women. Cancer. 2020;126(13):2971‐2979. doi:10.1002/cncr.32859

Giaquinto AN, Sung H, Miller KD, et al. Breast cancer statistics, 2022. CA Cancer J Clin. 2022;72(6):524‐541. doi:10.3322/caac.21754

Kim G, Bahl M. Assessing risk of breast cancer: a review of risk prediction models. J Breast Imaging. 2021;3(2):144‐155. doi:10.1093/jbi/wbab001

Dite GS, MacInnis RJ, Bickerstaffe A, 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 Biomarkers Prev. 2016;25(2):359‐365. doi:10.1158/1055‐9965.EPI‐15‐0838

Rudolph A, Song M, Brook MN, 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):526‐536. doi:10.1093/ije/dyx242

Tasa T, Puustusmaa M, Tõnisson N, Kolk B, Padrik P. Precision breast cancer screening with a polygenic risk score. medRxiv. Preprint posted online August 19, 2020. doi:10.1101/2020.08.17.20176263

Jiao Y, Truong T, Eon‐Marchais S, et al. Association and performance of polygenic risk scores for breast cancer among French women presenting or not a familial predisposition to the disease. Eur J Cancer. 2023;179:76‐86. doi:10.1016/j.ejca.2022.11.007

Barnes DR, Rookus MA, McGuffog L, et al. Polygenic risk scores and breast and epithelial ovarian cancer risks for carriers of BRCA1 and BRCA2 pathogenic variants. Genet Med. 2020;22(10):1653‐1666. doi:10.1038/s41436‐020‐0862‐x

Lakeman IMM, van den Broek AJ, Vos JAM, et al. The predictive ability of the 313 variant‐based polygenic risk score for contralateral breast cancer risk prediction in women of European ancestry with a heterozygous BRCA1 or BRCA2 pathogenic variant. Genet Med. 2021;23(9):1726‐1737. doi:10.1038/s41436‐021‐01198‐7

Evans DGR, van Veen EM, Harkness EF, et al. Breast cancer risk stratification in women of screening age: incremental effects of adding mammographic density, polygenic risk, and a gene panel. Genet Med. 2022;24(7):1485‐1494. doi:10.1016/j.gim.2022.03.009

Yiangou K, Mavaddat N, Dennis J, et al. Differences in polygenic score distributions in European ancestry populations: implications for breast cancer risk prediction. medRxiv. Preprint posted online February 13, 2024. doi:10.1101/2024.02.12.24302043

Lakeman IMM, Rodriguez‐Girondo MDM, Lee A, et al. Clinical applicability of the polygenic risk score for breast cancer risk prediction in familial cases. J Med Genet. 2023;60(4):327‐336. doi:10.1136/jmg‐2022‐108502

Mavaddat N, Ficorella L, Carver T, et al. Incorporating alternative polygenic risk scores into the BOADICEA breast cancer risk prediction model. Cancer Epidemiol Biomarkers Prev. 2023;32(3):422‐427. doi:10.1158/1055‐9965.EPI‐22‐0756

Hughes E, Tshiaba P, Gallagher S, et al. Development and validation of a clinical polygenic risk score to predict breast cancer risk. JCO Precis Oncol. 2020;4:585‐592. doi:10.1200/PO.19.00360

Bolze A, Cirulli ET, Hajek C, Schnell Blitstein JM, Grzymski JJ. The potential of genetics in identifying women at lower risk of breast cancer. JAMA Oncol. 2024;10(2):236‐239. doi:10.1001/jamaoncol.2023.5468

Mars N, Kerminen S, Tamlander M, et al. Comprehensive inherited risk estimation for risk‐based breast cancer screening in women. J Clin Oncol. Published online February 29, 2024. doi:10.1200/jco.23.00295

A comprehensive analysis of germline predisposition to early-onset ovarian cancer