Genetic and environmental factors and serum hormones, and risk of estrogen receptor-positive breast cancer in pre- and postmenopausal Japanese women

Abstract

Breast cancer incidence in Japanese women has more than tripled over the past two decades. We have previously shown that this marked increase is mostly due to an increase in the estrogen receptor (ER)-positive, HER2-negative subtype. We conducted a case–control study; ER-positive, HER2-negative breast cancer patients who were diagnosed since 2011 and women without disease were recruited. Environmental factors, serum levels of testosterone and 25-hydroxyvitamin D, and common genetic variants reported as predictors of ER-positive breast cancer or found in Asian women were evaluated between patients and controls in pre- and postmenopausal women. To identify important risk predictors, risk prediction models were created by logistic regression models. In premenopausal women, two environmental factors (history of breastfeeding, and history of benign breast disease) and four genetic variants (TOX3-rs3803662, ESR1-rs2046210, 8q24-rs13281615, and SLC4A7-rs4973768) were considered to be risk predictors, whereas three environmental factors (body mass index, history of breastfeeding, and hyperlipidemia), serum levels of testosterone and 25-hydroxyvitamin D, and two genetic variants (TOX3-rs3803662 and ESR1-rs2046210) were identified as risk predictors. Inclusion of common genetic variants and serum hormone measurements as well as environmental factors improved risk assessment models. The decline in the birthrate according to recent changes of lifestyle might be the main cause of the recent notable increase in the incidence of ER-positive breast cancer in Japanese women.

Performance of Single-Nucleotide Polymorphisms in Breast Cancer Risk Prediction Models: A Systematic Review and Meta-analysis

“…The largest number of studies (n ¼ 14) came from the United States (54)(55)(56)(57)(58)(59)(60)(61)(62)(63)(64)(65)(66)(67). 13 studies evaluated risk prediction models among Caucasian or European populations (23, 24, 54-57, 59, 61-64, 68, 69), eight in Asian populations (53,67,(70)(71)(72)(73)(74)(75), three within mixed populations (58,60,65), one among Australians (76), another among African Americans and Hispanics (66). Sample sizes ranged from 324 to 37,033 women.…”

“…Reported measures of central tendency of age ranged from 44.2 (mean) to 64.6 (mean) years. Most studies evaluated the risk prediction models in the general population (24, 53, 55-58, 60, 61, 63, 64, 66-68, 70, 71, 73-76) while others specifically studied non-BRCA mutation carriers (54,62,69), post-menopausal (23,59,65,72) or pre-menopausal women (72). Most studies included only patients with invasive breast cancer as case participants (23, 53, 56-61, 63, 64, 66-70, 73, 74, 76), while six included invasive or in situ breast cancer patients (24,54,55,62,71,75) and two focused on estrogen receptor (ER)-positive patients with breast cancer (65,72).…”

“…From the 25 studies, 38 risk prediction models were selected for our meta-analysis ( Supplementary Table S5). Some studies evaluated more than one risk prediction model by including different risk factors in the baseline model (23,55,69) or different number of SNPs in the SNP-enhanced model (56,66,72). Seven models used BCRAT as baseline model (55,59,66,69,75,76) and another four used partial BCRAT (57,63,68,74).…”

“…Seven models used BCRAT as baseline model (55,59,66,69,75,76) and another four used partial BCRAT (57,63,68,74). 11 models used partial BCRAT with additional risk factors (23,55,62,64,67,(70)(71)(72)(73), four used BCSC (60,61,65), four used IBIS (24,66,69) and eight used other types of baseline models (54,56,58,69). Risk factors included in the various traditional baseline models are summarized in Supplementary Table S6 (Online Supplementary Material).…”

“…Two models (23) used a multiplicative penetrance model and one model (56) used a multiple log-additive model for incorporating the SNPs. Six models (55,57,58,72) used other methods to include genetic information in the model.…”