A Breast Cancer Polygenic Risk Score Is Feasible for Risk Stratification in the Norwegian Population

Abstract

Background: Statistical associations of numerous single nucleotide polymorphisms with breast cancer (BC) have been identified in genome-wide association studies (GWAS). Recent evidence suggests that a Polygenic Risk Score (PRS) can be a useful risk stratification instrument for a BC screening strategy, and a PRS test has been developed for clinical use. The performance of the PRS is yet unknown in the Norwegian population. Aim: To evaluate the performance of PRS models for BC in a Norwegian dataset. Methods: We investigated a sample of 1053 BC cases and 7094 controls from different regions of Norway. PRS values were calculated using four PRS models, and their performance was evaluated by the area under the curve (AUC) and the odds ratio (OR). The effect of the PRS on the age of onset of BC was determined by a Cox regression model, and the lifetime absolute risk of developing BC was calculated using the iCare tool. Results: The best performing PRS model included 3820 SNPs, which yielded an AUC = 0.625 and an OR = 1.567 per one standard deviation increase. The PRS values of the samples correlate with an increased risk of BC, with a hazard ratio of 1.494 per one standard deviation increase (95% confidence interval of 1.406–1.588). The individuals in the highest decile of the PRS have at least twice the risk of developing BC compared to the individuals with a median PRS. The results in this study with Norwegian samples are coherent with the findings in the study conducted using Estonian and UK Biobank samples. Conclusion: The previously validated PRS models have a similar observed accuracy in the Norwegian data as in the UK and Estonian populations. A PRS provides a meaningful association with the age of onset of BC and lifetime risk. Therefore, as suggested in Estonia, a PRS may also be integrated into the screening strategy for BC in Norway.

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

“…The application of ML with the integration of multi-omics data has resulted in several scores for risk prediction and diagnostic/therapeutic potential, such as the polygenic risk score. The polygenic risk score considers all genetic inheritance variants known to be associated with a particular disease and measures the risk associated with the development of the disease under investigation, thereby improving risk stratification and screening ( Akdeniz et al, 2023 ). Other examples include the BRECADA application, which uses genetic and nongenetic risk factors for early detection of breast cancer, and the OncoNPC signature, which classifies cancer of unknown primary and accordingly tailors initial palliative treatment intent, a strategy that often leads to better patient outcomes compared with cancer treated without querying the OncoNPC signature ( Moon et al, 2023 ; Tao et al, 2023 ).…”

“…The application of ML with the integration of multi-omics data has resulted in several scores for risk prediction and diagnostic/ Frontiers in Pharmacology frontiersin.org therapeutic potential, such as the polygenic risk score. The polygenic risk score considers all genetic inheritance variants known to be associated with a particular disease and measures the risk associated with the development of the disease under investigation, thereby improving risk stratification and screening (Akdeniz et al, 2023).…”