Breast Cancer Susceptibility—Towards Individualised Risk Prediction

Abstract

Purpose of Review Breast cancer is the most common cancer among females in developed countries. Strategies such as early detection by breast cancer screening can reduce the burden of disease but have disadvantages including overdiagnosis and increased cost. Stratification of women according to the risk of developing breast cancer, based on genetic and lifestyle risk factors, could improve risk-reduction and screening strategies by targeting those most likely to benefit. Recent Findings Breast cancer risk is partly determined by genetic factors including rare pathogenic variants in susceptibility genes and common low-risk variants. Other risk factors include alcohol use, smoking, reproductive factors, hormonal factors, family history, mammographic density, BMI, and body height. Ideally, all risk factors are combined into an individual breast cancer lifetime risk score, but this requires knowledge about their interactions as well as accurate effect sizes. A few risk models seem to be sufficiently developed to inform clinical risk management to minimise cancer risk of those at increased risk and avoid overtreatment of those at decreased risk. Summary In this review, we briefly summarise the breast cancer susceptibility factors and discuss avenues towards combining all these factors to create individual risk scores.

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

“…Many risk factors for breast cancer, both genetic and nongenetic, have been identified in the past decades. 18 , 19 Increasingly, these are being integrated into computational models that allow personalized breast cancer risk assessment, which has potential application beyond current practice of genetic testing in family cancer clinics. 8 , 9 , 20 The BOADICEA algorithm is among the most comprehensive risk models presently available for breast cancer risk assessment.…”

“…Many risk factors for breast cancer, both genetic and nongenetic, have been identified in the past decades. 18,19 Increasingly, these are being integrated into computational models that allow personalized breast cancer risk assessment, which has potential application beyond current practice of Table 1. The solid line represents the continuous distribution based on the per SD effect size of the PRS 313 .…”

Analysis of the autoimmune regulator (AIRE) gene variant rs2075876 (G/A) association with breast cancer susceptibility

“…However, this small impact could be included in more advanced multiplicative polygenic risk scores 52 . Individually, these risk alleles confer a very small increase in BC risk but their joint effect with other lifestyle risk factors could “improve risk‐reduction and screening strategies by targeting those most likely to benefit” as concluded by Lakeman et al 53 Fourth, the underlying mechanisms of the implication of this SNP in BC were not investigated. Further functional analyses are required to explore the molecular mechanisms by which this variant could participate in BC susceptibility.…”

Characterizing the Relationship between Expression Quantitative Trait Loci (eQTLs), DNA Methylation Quantitative Trait Loci (mQTLs), and Breast Cancer Risk Variants

“…Collaborative entities like the Breast Cancer Association Consortium (BCAC) have pinpointed over 200 SNPs exhibiting significant associations with breast cancer [ 4 ]. While the known SNPs account for 18% of the familial relative risk associated with breast cancer, the inclusion of variants reliably imputed from the OncoArray data can substantially increase this proportion to approximately 40% [ 3 , 5 , 6 ]. The effects of the top breast cancer-associated variants have been aggregated into a genetic metric (i.e., polygenic risk score (PRS)) to evaluate an individual’s predisposition to breast cancer [ 5 ].…”