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A Personal Breast Cancer Risk Stratification Model Using Common Variants and Environmental Risk Factors in Japanese Females
SIMPLE SUMMARY: Breast 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 show...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8345053/ https://www.ncbi.nlm.nih.gov/pubmed/34359697 http://dx.doi.org/10.3390/cancers13153796 |
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author | Oze, Isao Ito, Hidemi Kasugai, Yumiko Yamaji, Taiki Kijima, Yuko Ugai, Tomotaka Kasuga, Yoshio Ouellette, Tomoyo K. Taniyama, Yukari Koyanagi, Yuriko N. Imoto, Issei Tsugane, Shoichiro Koriyama, Chihaya Iwasaki, Motoki Matsuo, Keitaro |
author_facet | Oze, Isao Ito, Hidemi Kasugai, Yumiko Yamaji, Taiki Kijima, Yuko Ugai, Tomotaka Kasuga, Yoshio Ouellette, Tomoyo K. Taniyama, Yukari Koyanagi, Yuriko N. Imoto, Issei Tsugane, Shoichiro Koriyama, Chihaya Iwasaki, Motoki Matsuo, Keitaro |
author_sort | Oze, Isao |
collection | PubMed |
description | SIMPLE SUMMARY: Breast 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. ABSTRACT: 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. |
format | Online Article Text |
id | pubmed-8345053 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83450532021-08-07 A Personal Breast Cancer Risk Stratification Model Using Common Variants and Environmental Risk Factors in Japanese Females Oze, Isao Ito, Hidemi Kasugai, Yumiko Yamaji, Taiki Kijima, Yuko Ugai, Tomotaka Kasuga, Yoshio Ouellette, Tomoyo K. Taniyama, Yukari Koyanagi, Yuriko N. Imoto, Issei Tsugane, Shoichiro Koriyama, Chihaya Iwasaki, Motoki Matsuo, Keitaro Cancers (Basel) Article SIMPLE SUMMARY: Breast 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. ABSTRACT: 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. MDPI 2021-07-28 /pmc/articles/PMC8345053/ /pubmed/34359697 http://dx.doi.org/10.3390/cancers13153796 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Oze, Isao Ito, Hidemi Kasugai, Yumiko Yamaji, Taiki Kijima, Yuko Ugai, Tomotaka Kasuga, Yoshio Ouellette, Tomoyo K. Taniyama, Yukari Koyanagi, Yuriko N. Imoto, Issei Tsugane, Shoichiro Koriyama, Chihaya Iwasaki, Motoki Matsuo, Keitaro A Personal Breast Cancer Risk Stratification Model Using Common Variants and Environmental Risk Factors in Japanese Females |
title | A Personal Breast Cancer Risk Stratification Model Using Common Variants and Environmental Risk Factors in Japanese Females |
title_full | A Personal Breast Cancer Risk Stratification Model Using Common Variants and Environmental Risk Factors in Japanese Females |
title_fullStr | A Personal Breast Cancer Risk Stratification Model Using Common Variants and Environmental Risk Factors in Japanese Females |
title_full_unstemmed | A Personal Breast Cancer Risk Stratification Model Using Common Variants and Environmental Risk Factors in Japanese Females |
title_short | A Personal Breast Cancer Risk Stratification Model Using Common Variants and Environmental Risk Factors in Japanese Females |
title_sort | personal breast cancer risk stratification model using common variants and environmental risk factors in japanese females |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8345053/ https://www.ncbi.nlm.nih.gov/pubmed/34359697 http://dx.doi.org/10.3390/cancers13153796 |
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