A Personal Breast Cancer Risk Stratification Model Using Common Variants and Environmental Risk Factors in Japanese Females.

Isao Oze,Yuko Kijima,Shoichiro Tsugane,Tomotaka Ugai,Yuriko N Koyanagi,Taiki Yamaji,Yukari Taniyama,Issei Imoto,Hidemi Ito,Tomoyo K Ouellette,Yoshio Kasuga,Motoki Iwasaki,Keitaro Matsuo,Chihaya Koriyama,Yumiko Kasugai

doi:10.3390/cancers13153796

Abstract

Simple SummaryBreast cancer remains the most common cancer in females, warranting the development of new approaches to prevention. One such approach is personalized prevention using genetic risk models. Here, we developed a risk model using both genetic and environmental risk factors. Results showed that a genetic risk score defined by the number of risk alleles for 14 breast cancer risk SNPs clearly stratified breast cancer risk. Moreover, the combination of this genetic risk score model with an environmental risk model which included established environmental risk factors showed significantly better C-statistics than the environmental risk model alone. This genetic risk score model in combination with the environmental model may be suitable for stratifying individual breast cancer risk, and may form the basis for a new personalized approach to breast cancer prevention.Personalized approaches to prevention based on genetic risk models have been anticipated, and many models for the prediction of individual breast cancer risk have been developed. However, few studies have evaluated personalized risk using both genetic and environmental factors. We developed a risk model using genetic and environmental risk factors using 1319 breast cancer cases and 2094 controls from three case–control studies in Japan. Risk groups were defined based on the number of risk alleles for 14 breast cancer susceptibility loci, namely low (0–10 alleles), moderate (11–16) and high (17+). Environmental risk factors were collected using a self-administered questionnaire and implemented with harmonization. Odds ratio (OR) and C-statistics, calculated using a logistic regression model, were used to evaluate breast cancer susceptibility and model performance. Respective breast cancer ORs in the moderate- and high-risk groups were 1.69 (95% confidence interval, 1.39–2.04) and 3.27 (2.46–4.34) compared with the low-risk group. The C-statistic for the environmental model of 0.616 (0.596–0.636) was significantly improved by combination with the genetic model, to 0.659 (0.640–0.678). This combined genetic and environmental risk model may be suitable for the stratification of individuals by breast cancer risk. New approaches to breast cancer prevention using the model are warranted.

Highlights

Breast cancer is the most common cancer in females, with an estimated global incidence in 2018 of 2,088,849 [1]
We previously identified 23 breast cancer-associated SNPs reported in previous GWAS or candidate-gene association studies [17,18,19,20,21,22,23,24,25,26,27,28,29,40]
Among 114 genotyped breast cancer susceptibility loci identified by GWAS studies (Table S1), 19 SNPs had statistically significant summary p-values of less than 0.05

Summary

Introduction

Breast cancer is the most common cancer in females, with an estimated global incidence in 2018 of 2,088,849 [1]. In Japan breast cancer incidence has increased rapidly for the last 30 years and is the most common cancer in women [3]. Several breast cancer risk prediction models have been developed to evaluate individual risk based on lifestyle factors, reproductive factors, family history and clinical factors [7,8,9,10,11]. These are in clinical use as tools for individual cancer prevention. The American Cancer Society has developed a guideline which recommends MRI as an adjunct to mammography screening for women at high risk, as identified by a risk prediction model [12]

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