Validation of the BOADICEA model and a 313-variant polygenic risk score for breast cancer risk prediction in a Dutch prospective cohort

Abstract

Purpose We evaluated the performance of the recently extended Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA version 5) in a Dutch prospective cohort, using a polygenic risk score (PRS) based on 313 breast cancer (BC)–associated variants (PRS313) and other, nongenetic risk factors. Methods Since 1989, 6522 women without BC aged 45 or older of European descent have been included in the Rotterdam Study. The PRS313 was calculated per 1 SD in controls from the Breast Cancer Association Consortium (BCAC). Cox regression analysis was performed to estimate the association between the PRS313 and incident BC risk. Cumulative 10-year risks were calculated with BOADICEA including different sets of variables (age, risk factors and PRS313). C-statistics were used to evaluate discriminative ability. Results In total, 320 women developed BC. The PRS313 was significantly associated with BC (hazard ratio [HR] per SD of 1.56, 95% confidence interval [CI] [1.40–1.73]). Using 10-year risk estimates including age and the PRS313, other risk factors improved the discriminatory ability of the BOADICEA model marginally, from a C-statistic of 0.636 to 0.653. Conclusions The effect size of the PRS313 is highly reproducible in the Dutch population. Our results validate the BOADICEA v5 model for BC risk assessment in the Dutch general population.

Genetic variants of the human host influencing the coronavirus-associated phenotypes (SARS, MERS and COVID-19): rapid systematic review and field synopsis

“…The use of polygenic models might overcome the intrinsic constraint due to low effect size attributable to single-gene variants and allows to explain part of the missing heritability in complex disorders. This is the case, for instance, of Alzheimer's disease, for which complex trait analysis has been proved to successfully interpret a large proportion of the genetic variance [88], as well as breast cancer, in which multifactorial models incorporating multiple genetic variants are under validation [89,90]. By the way, family studies should be warranted to estimate the heritability of the COVID-19 phenotypes through a formal assessment and provide the grounded basis for subsequent hypotheses.…”

Comparative validation of the BOADICEA and Tyrer-Cuzick breast cancer risk models incorporating classical risk factors and polygenic risk in a population-based prospective cohort of women of European ancestry

“…The Breast and Ovarian Analysis of Disease and Carrier Estimation Algorithm (BOADICEA) breast cancer model was originally developed to predict breast cancer risk for women using pedigree-level family history information and genetic testing results on rare pathogenic variants in high and moderate risk genes [1,2]. This model has been updated (version 5.0) to include reproductive and lifestyle factors and the recently developed polygenic risk score (PRS) based on 313 common germline variants [3] for applications in both general and high-risk populations [4,5]. The Tyrer-Cuzick or International Breast Intervention Study (IBIS) model [6], commonly used in clinical and research settings, also includes extensive family history and comprehensive risk factor information and has been updated to include information on PRS.…”

Overall Survival Benefit in Rectal Cancer After Neoadjuvant Radiotherapy and Adjuvant Chemotherapy: A Propensity-Matched Population-Based Study

“…Recently, there have been growing advances in development of survival prediction models and prognostication tools ( 27 ). A number of risk and outcome prediction models has been explored today for many important cancer types, such as gastric cancer ( 28 , 29 ), gallbladder cancer ( 30 ), breast cancer ( 31 , 32 ), and other sites. Although prediction model can never substitute for evidence from large-scale prospective randomized clinical trials, these tools are particular helpful to provide information in clinical decision-making in case of tumors for which rare data from clinical trials are available.…”