Limitations in next-generation sequencing-based genotyping of breast cancer polygenic risk score loci.
Journal
European journal of human genetics : EJHG
ISSN: 1476-5438
Titre abrégé: Eur J Hum Genet
Pays: England
ID NLM: 9302235
Informations de publication
Date de publication:
21 Jun 2024
21 Jun 2024
Historique:
received:
21
12
2023
accepted:
10
06
2024
revised:
17
05
2024
medline:
22
6
2024
pubmed:
22
6
2024
entrez:
21
6
2024
Statut:
aheadofprint
Résumé
Considering polygenic risk scores (PRSs) in individual risk prediction is increasingly implemented in genetic testing for hereditary breast cancer (BC) based on next-generation sequencing (NGS). To calculate individual BC risks, the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) with the inclusion of the BCAC 313 or the BRIDGES 306 BC PRS is commonly used. The PRS calculation depends on accurately reproducing the variant allele frequencies (AFs) and, consequently, the distribution of PRS values anticipated by the algorithm. Here, the 324 loci of the BCAC 313 and the BRIDGES 306 BC PRS were examined in population-specific database gnomAD and in real-world data sets of five centers of the German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC), to determine whether these expected AFs can be reproduced by NGS-based genotyping. Four PRS loci were non-existent in gnomAD v3.1.2 non-Finnish Europeans, further 24 loci showed noticeably deviating AFs. In real-world data, between 11 and 23 loci were reported with noticeably deviating AFs, and were shown to have effects on final risk prediction. Deviations depended on the sequencing approach, variant caller and calling mode (forced versus unforced) employed. Therefore, this study demonstrates the necessity to apply quality assurance not only in terms of sequencing coverage but also observed AFs in a sufficiently large cohort, when implementing PRSs in a routine diagnostic setting. Furthermore, future PRS design should be guided by the technical reproducibility of expected AFs across commonly used genotyping methods, especially NGS, in addition to the observed effect sizes.
Identifiants
pubmed: 38907004
doi: 10.1038/s41431-024-01647-2
pii: 10.1038/s41431-024-01647-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Bundesministerium für Gesundheit (Federal Ministry of Health, Germany)
ID : genomDE
Organisme : Bundesministerium für Gesundheit (Federal Ministry of Health, Germany)
ID : genomDE
Organisme : Deutsche Krebshilfe (German Cancer Aid)
ID : HerediVar
Investigateurs
Stephan Drukewitz
(S)
Christoph Engel
(C)
Peter Frommolt
(P)
Eva Groß
(E)
Johannes Helmuth
(J)
Zarah Kowalzyk
(Z)
Maximilian Radtke
(M)
Juliane Ramser
(J)
Steffen Uebe
(S)
Shan Wang-Gohrke
(S)
Informations de copyright
© 2024. The Author(s).
Références
Lakeman IM, Hilbers FS, Rodrìguez-Girondo M, Lee A, Vreeswijk MP, Hollestelle A, 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;56:581–9.
doi: 10.1136/jmedgenet-2019-106072
pubmed: 31186341
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.
doi: 10.1016/j.ajhg.2018.11.002
pubmed: 30554720
Shieh Y, Hu D, Ma L, Huntsman S, Gard CC, Leung JW, et al. Breast cancer risk prediction using a clinical risk model and polygenic risk score. Breast Cancer Res Treat. 2016;159:513–25.
doi: 10.1007/s10549-016-3953-2
pubmed: 27565998
pmcid: 5033764
Borde J, Ernst C, Wappenschmidt B, Niederacher D, Weber-Lassalle K, Schmidt G, et al. Performance of breast cancer polygenic risk scores in 760 female CHEK2 germline mutation carriers. J Natl Cancer Inst. 2021;113:893–9.
doi: 10.1093/jnci/djaa203
pubmed: 33372680
Borde J, Laitman Y, Blümcke B, Niederacher D, Weber-Lassalle K, Sutter C, et al. Polygenic risk scores indicate extreme ages at onset of breast cancer in female BRCA1/2 pathogenic variant carriers. BMC Cancer. 2022;22:1–9.
doi: 10.1186/s12885-022-09780-1
Gallagher S, Hughes E, Wagner S, Tshiaba P, Rosenthal E, Roa BB, et al. Association of a polygenic risk score with breast cancer among women carriers of high-and moderate-risk breast cancer genes. JAMA Netw Open. 2020;3:e208501–e208501.
doi: 10.1001/jamanetworkopen.2020.8501
pubmed: 32609350
pmcid: 7330720
Kuchenbaecker KB, McGuffog L, Barrowdale L, Lee A, Soucy P, Healey S, 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:djw302.
doi: 10.1093/jnci/djw302
pubmed: 28376175
pmcid: 5408990
Stiller S, Drukewitz S, Lehmann K, Hentschel J, Strehlow V. Clinical impact of polygenic risk score for breast cancer risk prediction in 382 individuals with hereditary breast and ovarian cancer syndrome. Cancers. 2023;15:3938.
doi: 10.3390/cancers15153938
pubmed: 37568754
pmcid: 10417109
Carver T, Hartley S, Lee A, Cunningham AP, Archer S, Babb de Villiers C, et al. CanRisk tool – a web interface for the prediction of breast and ovarian cancer risk and the likelihood of carrying genetic pathogenic variants. Cancer Epidemiol Biomark Prev. 2021;30:469–73.
doi: 10.1158/1055-9965.EPI-20-1319
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;21:1708–18.
doi: 10.1038/s41436-018-0406-9
pubmed: 30643217
pmcid: 6687499
Tüchler A, De Pauw A, Ernst C, Anota A, Lakeman IMM, Dick J, et al. Clinical implications of incorporating genetic and non-genetic risk factors in CanRisk-based breast cancer risk prediction. Breast 2024;73:103615.
doi: 10.1016/j.breast.2023.103615
pubmed: 38061307
Carver T. CanRisk knowledgebase. 2022. https://canrisk.atlassian.net/wiki/spaces/FAQS/pages/35979266/What+variants+are+used+in+the+PRS . Accessed 30 Nov 2022.
Mavaddat N, Ficorella L, Carver T, Lee A, Cunningham AP, Lush M, et al. Incorporating alternative polygenic risk scores into the BOADICEA breast cancer risk prediction model. Cancer Epidemiol Biomark Prev. 2023;32:422–7.
doi: 10.1158/1055-9965.EPI-22-0756
Kiialainen A, Karlberg O, Ahlford A, Sigurdsson S, Lindblad-Toh K, Syvänen AC. Performance of microarray and liquid based capture methods for target enrichment for massively parallel sequencing and SNP discovery. PLoS ONE. 2011;6:e16486.
doi: 10.1371/journal.pone.0016486
pubmed: 21347407
pmcid: 3036585
Sulonen AM, Ellonen P, Almusa H, Lepistö M, Eldfors S, Hannula S, et al. Comparison of solution-based exome capture methods for next generation sequencing. Genome Biol. 2011;12:1–18.
doi: 10.1186/gb-2011-12-9-r94
Teer JK, Bonnycastle LL, Chines PS, Hansen NF, Aoyama N, Swift AJ, et al. Systematic comparison of three genomic enrichment methods for massively parallel DNA sequencing. Genome Res. 2010;20:1420–31.
doi: 10.1101/gr.106716.110
pubmed: 20810667
pmcid: 2945191
Yi M, Zhao Y, Jia L, He M, Kebebew E, Stephens RM. Performance comparison of SNP detection tools with Illumina exome sequencing data – an assessment using both family pedigree information and sample-matched SNP array data. Nucleic Acids Res. 2014;42:e101–e101.
doi: 10.1093/nar/gku392
pubmed: 24831545
pmcid: 4081058
Li H. Toward better understanding of artifacts in variant calling from high-coverage samples. Bioinformatics. 2014;30:2843–51.
doi: 10.1093/bioinformatics/btu356
pubmed: 24974202
pmcid: 4271055
Reis AL, Deveson IW, Madala BS, Wong T, Barker C, Xu J, et al. Using synthetic chromosome controls to evaluate the sequencing of difficult regions within the human genome. Genome Biol. 2022;23:1–24.
doi: 10.1186/s13059-021-02579-6
Stoler N, Nekrutenko A. Sequencing error profiles of Illumina sequencing instruments. NAR Genom Bioinform. 2021;3:lqab019.
doi: 10.1093/nargab/lqab019
pubmed: 33817639
pmcid: 8002175
Gudmundsson S, Singer-Berk M, Watts NA, Phu W, Goodrich JK, Solomonson M, et al. Variant interpretation using population databases: lessons from gnomAD. Hum Mutat. 2022;43:1012–30.
doi: 10.1002/humu.24309
pubmed: 34859531
Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581:434–43.
doi: 10.1038/s41586-020-2308-7
pubmed: 32461654
pmcid: 7334197
Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29:308–11.
doi: 10.1093/nar/29.1.308
pubmed: 11125122
pmcid: 29783
Collins RL, Brand H, Karczewski KJ, Zhao X, Alföldi J, Francioli LC, et al. A structural variation reference for medical and population genetics. Nature. 2020;581:444–51.
doi: 10.1038/s41586-020-2287-8
pubmed: 32461652
pmcid: 7334194
Adeyemo A, Balaconis MK, Darnes DR, Fatumo S, Moreno PG, Hodonsky CJ, et al. Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps. Nat Med. 2021;27:1876–84.
doi: 10.1038/s41591-021-01549-6
Sugrue LP, Desikan RS. What are polygenic scores and why are they important? JAMA. 2019;321:1820–1.
doi: 10.1001/jama.2019.3893
pubmed: 30958510