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Breast cancer risk prediction using Tyrer-Cuzick algorithm with an 18-SNPs polygenic risk score in a European population with below-average breast cancer incidence

GOALS: To determine whether an 18 single nucleotide polymorphisms (SNPs) polygenic risk score (PRS18) improves breast cancer (BC) risk prediction for women at above-average risk of BC, aged 40–49, in a Central European population with BC incidence below EU average. METHODS: 502 women aged 40–49 year...

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Autores principales: Oblak, Tjaša, Škerl, Petra, Narang, Benjamin J., Blagus, Rok, Krajc, Mateja, Novaković, Srdjan, Žgajnar, Janez
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10587756/
https://www.ncbi.nlm.nih.gov/pubmed/37857130
http://dx.doi.org/10.1016/j.breast.2023.103590
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author Oblak, Tjaša
Škerl, Petra
Narang, Benjamin J.
Blagus, Rok
Krajc, Mateja
Novaković, Srdjan
Žgajnar, Janez
author_facet Oblak, Tjaša
Škerl, Petra
Narang, Benjamin J.
Blagus, Rok
Krajc, Mateja
Novaković, Srdjan
Žgajnar, Janez
author_sort Oblak, Tjaša
collection PubMed
description GOALS: To determine whether an 18 single nucleotide polymorphisms (SNPs) polygenic risk score (PRS18) improves breast cancer (BC) risk prediction for women at above-average risk of BC, aged 40–49, in a Central European population with BC incidence below EU average. METHODS: 502 women aged 40–49 years at the time of BC diagnosis completed a questionnaire on BC risk factors (as per Tyrer-Cuzick algorithm) with data known at age 40 and before BC diagnosis. Blood samples were collected for DNA isolation. 250 DNA samples from healthy women aged 50 served as a control cohort. 18 BC-associated SNPs were genotyped in both groups and PRS18 was calculated. The predictive power of PRS18 to detect BC was evaluated using a ROC curve. 10-year BC risk was calculated using the Tyrer-Cuzick algorithm adapted to the Slovenian incidence rate (S-IBIS): first based on questionnaire-based risk factors and, second, including PRS18. RESULTS: The AUC for PRS18 was 0.613 (95 % CI 0.570–0.657). 83.3 % of women were classified at above-average risk for BC with S-IBIS without PRS18 and 80.7 % when PRS18 was included. CONCLUSION: BC risk prediction models and SNPs panels should not be automatically used in clinical practice in different populations without prior population-based validation. In our population the addition of an 18SNPs PRS to questionnaire-based risk factors in the Tyrer-Cuzick algorithm in general did not improve BC risk stratification, however, some improvements were observed at higher BC risk scores and could be valuable in distinguishing women at intermediate and high risk of BC.
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spelling pubmed-105877562023-10-21 Breast cancer risk prediction using Tyrer-Cuzick algorithm with an 18-SNPs polygenic risk score in a European population with below-average breast cancer incidence Oblak, Tjaša Škerl, Petra Narang, Benjamin J. Blagus, Rok Krajc, Mateja Novaković, Srdjan Žgajnar, Janez Breast Original Article GOALS: To determine whether an 18 single nucleotide polymorphisms (SNPs) polygenic risk score (PRS18) improves breast cancer (BC) risk prediction for women at above-average risk of BC, aged 40–49, in a Central European population with BC incidence below EU average. METHODS: 502 women aged 40–49 years at the time of BC diagnosis completed a questionnaire on BC risk factors (as per Tyrer-Cuzick algorithm) with data known at age 40 and before BC diagnosis. Blood samples were collected for DNA isolation. 250 DNA samples from healthy women aged 50 served as a control cohort. 18 BC-associated SNPs were genotyped in both groups and PRS18 was calculated. The predictive power of PRS18 to detect BC was evaluated using a ROC curve. 10-year BC risk was calculated using the Tyrer-Cuzick algorithm adapted to the Slovenian incidence rate (S-IBIS): first based on questionnaire-based risk factors and, second, including PRS18. RESULTS: The AUC for PRS18 was 0.613 (95 % CI 0.570–0.657). 83.3 % of women were classified at above-average risk for BC with S-IBIS without PRS18 and 80.7 % when PRS18 was included. CONCLUSION: BC risk prediction models and SNPs panels should not be automatically used in clinical practice in different populations without prior population-based validation. In our population the addition of an 18SNPs PRS to questionnaire-based risk factors in the Tyrer-Cuzick algorithm in general did not improve BC risk stratification, however, some improvements were observed at higher BC risk scores and could be valuable in distinguishing women at intermediate and high risk of BC. Elsevier 2023-10-12 /pmc/articles/PMC10587756/ /pubmed/37857130 http://dx.doi.org/10.1016/j.breast.2023.103590 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original Article
Oblak, Tjaša
Škerl, Petra
Narang, Benjamin J.
Blagus, Rok
Krajc, Mateja
Novaković, Srdjan
Žgajnar, Janez
Breast cancer risk prediction using Tyrer-Cuzick algorithm with an 18-SNPs polygenic risk score in a European population with below-average breast cancer incidence
title Breast cancer risk prediction using Tyrer-Cuzick algorithm with an 18-SNPs polygenic risk score in a European population with below-average breast cancer incidence
title_full Breast cancer risk prediction using Tyrer-Cuzick algorithm with an 18-SNPs polygenic risk score in a European population with below-average breast cancer incidence
title_fullStr Breast cancer risk prediction using Tyrer-Cuzick algorithm with an 18-SNPs polygenic risk score in a European population with below-average breast cancer incidence
title_full_unstemmed Breast cancer risk prediction using Tyrer-Cuzick algorithm with an 18-SNPs polygenic risk score in a European population with below-average breast cancer incidence
title_short Breast cancer risk prediction using Tyrer-Cuzick algorithm with an 18-SNPs polygenic risk score in a European population with below-average breast cancer incidence
title_sort breast cancer risk prediction using tyrer-cuzick algorithm with an 18-snps polygenic risk score in a european population with below-average breast cancer incidence
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10587756/
https://www.ncbi.nlm.nih.gov/pubmed/37857130
http://dx.doi.org/10.1016/j.breast.2023.103590
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