Implementation of Risk-Stratified Breast Cancer Prevention With a Polygenic Risk Score Test in Clinical Practice
Background: Breast cancer (BC) screening with mammography reduces mortality but considers currently only age as a risk factor. Personalized risk-based screening has been proposed as a more efficient alternative. For that, risk prediction tools are necessary. Genome-wide association studies have iden...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2023-10-01
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| Series: | Breast Cancer: Basic and Clinical Research |
| Online Access: | https://doi.org/10.1177/11782234231205700 |
| _version_ | 1797660788428636160 |
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| author | Peeter Padrik Mikk Puustusmaa Neeme Tõnisson Berit Kolk Regina Saar Anna Padrik Tõnis Tasa |
| author_facet | Peeter Padrik Mikk Puustusmaa Neeme Tõnisson Berit Kolk Regina Saar Anna Padrik Tõnis Tasa |
| author_sort | Peeter Padrik |
| collection | DOAJ |
| description | Background: Breast cancer (BC) screening with mammography reduces mortality but considers currently only age as a risk factor. Personalized risk-based screening has been proposed as a more efficient alternative. For that, risk prediction tools are necessary. Genome-wide association studies have identified numerous genetic variants (single-nucleotide polymorphisms [SNPs]) associated with BC. The effects of SNPs are combined into a polygenic risk score (PRS) as a risk prediction tool. Objectives: We aimed to develop a clinical-grade PRS test suitable for BC risk-stratified screening with clinical recommendations and implementation in clinical practice. Design and methods: In the first phase of our study, we gathered previously published PRS models for predicting BC risk from the literature and validated them using the Estonian Biobank and UK Biobank data sets. We selected the best performing model based on prevalent data and independently validated it in both incident data sets. We then conducted absolute risk simulations, developed risk-based recommendations, and implemented the PRS test in clinical practice. In the second phase, we carried out a retrospective analysis of the PRS test’s performance results in clinical practice. Results: The best performing PRS included 2803 SNPs. The C-index of the Cox regression model associating BC status with PRS was 0.656 (SE = 0.05) with a hazard ratio of 1.66. The PRS can stratify individuals with more than a 3-fold risk increase. A total of 2637 BC PRS tests have been performed for women between the ages 30 and 83. Results in clinical use overlap well with expected PRS performance with 5.7% of women with more than 2-fold and 1.4% with more than 3-fold higher risk than the population average. Conclusion: The PRS test separates different BC risk levels and is feasible to implement in clinical practice. |
| first_indexed | 2024-03-11T18:35:54Z |
| format | Article |
| id | doaj.art-79c39c0d87e342e7b23c97e751bf7264 |
| institution | Directory Open Access Journal |
| issn | 1178-2234 |
| language | English |
| last_indexed | 2024-03-11T18:35:54Z |
| publishDate | 2023-10-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Breast Cancer: Basic and Clinical Research |
| spelling | doaj.art-79c39c0d87e342e7b23c97e751bf72642023-10-13T01:33:20ZengSAGE PublishingBreast Cancer: Basic and Clinical Research1178-22342023-10-011710.1177/11782234231205700Implementation of Risk-Stratified Breast Cancer Prevention With a Polygenic Risk Score Test in Clinical PracticePeeter Padrik0Mikk Puustusmaa1Neeme Tõnisson2Berit Kolk3Regina Saar4Anna Padrik5Tõnis Tasa6Clinic of Hematology and Oncology, Tartu University Hospital, Tartu, EstoniaOÜ Antegenes, Tartu, EstoniaGenetics and Personalized Medicine Clinic, Tartu University Hospital, Tartu, EstoniaOÜ Antegenes, Tartu, EstoniaOÜ Antegenes, Tartu, EstoniaOÜ Antegenes, Tartu, EstoniaOÜ Antegenes, Tartu, EstoniaBackground: Breast cancer (BC) screening with mammography reduces mortality but considers currently only age as a risk factor. Personalized risk-based screening has been proposed as a more efficient alternative. For that, risk prediction tools are necessary. Genome-wide association studies have identified numerous genetic variants (single-nucleotide polymorphisms [SNPs]) associated with BC. The effects of SNPs are combined into a polygenic risk score (PRS) as a risk prediction tool. Objectives: We aimed to develop a clinical-grade PRS test suitable for BC risk-stratified screening with clinical recommendations and implementation in clinical practice. Design and methods: In the first phase of our study, we gathered previously published PRS models for predicting BC risk from the literature and validated them using the Estonian Biobank and UK Biobank data sets. We selected the best performing model based on prevalent data and independently validated it in both incident data sets. We then conducted absolute risk simulations, developed risk-based recommendations, and implemented the PRS test in clinical practice. In the second phase, we carried out a retrospective analysis of the PRS test’s performance results in clinical practice. Results: The best performing PRS included 2803 SNPs. The C-index of the Cox regression model associating BC status with PRS was 0.656 (SE = 0.05) with a hazard ratio of 1.66. The PRS can stratify individuals with more than a 3-fold risk increase. A total of 2637 BC PRS tests have been performed for women between the ages 30 and 83. Results in clinical use overlap well with expected PRS performance with 5.7% of women with more than 2-fold and 1.4% with more than 3-fold higher risk than the population average. Conclusion: The PRS test separates different BC risk levels and is feasible to implement in clinical practice.https://doi.org/10.1177/11782234231205700 |
| spellingShingle | Peeter Padrik Mikk Puustusmaa Neeme Tõnisson Berit Kolk Regina Saar Anna Padrik Tõnis Tasa Implementation of Risk-Stratified Breast Cancer Prevention With a Polygenic Risk Score Test in Clinical Practice Breast Cancer: Basic and Clinical Research |
| title | Implementation of Risk-Stratified Breast Cancer Prevention With a Polygenic Risk Score Test in Clinical Practice |
| title_full | Implementation of Risk-Stratified Breast Cancer Prevention With a Polygenic Risk Score Test in Clinical Practice |
| title_fullStr | Implementation of Risk-Stratified Breast Cancer Prevention With a Polygenic Risk Score Test in Clinical Practice |
| title_full_unstemmed | Implementation of Risk-Stratified Breast Cancer Prevention With a Polygenic Risk Score Test in Clinical Practice |
| title_short | Implementation of Risk-Stratified Breast Cancer Prevention With a Polygenic Risk Score Test in Clinical Practice |
| title_sort | implementation of risk stratified breast cancer prevention with a polygenic risk score test in clinical practice |
| url | https://doi.org/10.1177/11782234231205700 |
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