Pro-inflammatory cytokine polymorphisms and interactions with dietary alcohol and estrogen, risk factors for invasive breast cancer using a post genome-wide analysis for gene-gene and gene-lifestyle interaction

Abstract

Molecular and genetic immune-related pathways connected to breast cancer and lifestyles in postmenopausal women are not fully characterized. In this study, we explored the role of pro-inflammatory cytokines such as C-reactive protein (CRP) and interleukin-6 (IL-6) in those pathways at the genome-wide level. With single-nucleotide polymorphisms (SNPs) in the biomarkers and lifestyles together, we further constructed risk profiles to improve predictability for breast cancer. Our earlier genome-wide association gene-environment interaction study used large cohort data from the Women's Health Initiative Database for Genotypes and Phenotypes Study and identified 88 SNPs associated with CRP and IL-6. For this study, we added an additional 68 SNPs from previous GWA studies, and together with 48 selected lifestyles, evaluated for the association with breast cancer risk via a 2-stage multimodal random survival forest and generalized multifactor dimensionality reduction methods. Overall and in obesity strata (by body mass index, waist, waist-to-hip ratio, exercise, and dietary fat intake), we identified the most predictive genetic and lifestyle variables. Two SNPs (SALL1 rs10521222 and HLA-DQA1 rs9271608) and lifestyles, including alcohol intake, lifetime cumulative exposure to estrogen, and overall and visceral obesity, are the most common and strongest predictive markers for breast cancer across the analyses. The risk profile that combined those variables presented their synergistic effect on the increased breast cancer risk in a gene-lifestyle dose-dependent manner. Our study may contribute to improved predictability for breast cancer and suggest potential interventions for the women with the risk genotypes and lifestyles to reduce their breast cancer risk.

Figure 1

Overall analysis: the second stage of random survival forest (RSF) with 13 single-nucleotide polymorphisms and 12 behavioral factors selected from the first stage of RSF analysis. (A) Comparing minimal depth and VIMP rankings. (BMI, body mass index; E + P, exogenous estrogen + progestin; VIMP, variable of importance. 8 variables within the gold ellipse were identified as the most influential predictors). (B) Out-of-bag concordance index (c-index) (improvement in the out-of-bag c-index was observed when the top 8 variables [filled black circle] were added to the model, whereas other variables [open circle] did not further improve the accuracy of prediction)

Figure 2

Cumulative breast cancer incidence rate for the 9 most influential variables (5 SNPs and 4 behavioral factors) based on random survival forest analyses. (E + P, exogenous estrogen + progestin; SNPs, single-nucleotide polymorphisms. Dashed red lines indicate 95% confidence intervals).

Figure 3

Forest plot of the joint effect of E + P use with risk behavioral factors and genotypes on breast cancer risk overall and in subgroups (A BMI < 30 and WHR ≤ 0.85; B MET ≥ 10 and SFA ≥ 9). The plot shows the independent and combined effect of risk behaviors and genotypes on breast cancer risk, jointly testing with E + P use, presented as the 95% CIs (indicated with red lines) and the estimates (proportional to the size of the blue squares). BMI, body mass index; CI, confidence interval; E + P, E + P, exogenous estrogen + progestin; HR, hazard ratio; MET, metabolic equivalent; SFA, saturated fatty acids; WHR, waist-to-hip ratio. * The combined number of risk genotypes and behavioral factors was based on risk genotypes defined as 0 (low risk: none or < total number of risk alleles) and 1 (high risk: combined all risk alleles) and based on behavioral factors defined as 0 (low risk: null risk behavior) and 1 (high risk: 1 or more risk behaviors). The ultimate number of risk genotypes combined with behavioral factors was defined as 0 (low risk for genotypes and behaviors), 1 (either high risk for genotypes or behaviors), and 2 (both high risk for genotypes and behaviors). ** The number of behavioral factors was defined as 0 (null risk behavior) vs. 1 (1 risk behavior) vs. 2 (2 or more risk behaviors).