Summary
These data support the associations between IL genes and breast cancer risk and mortality in a large admixed population.
Abstract
Interleukins (ILs) are key regulators of immune response. Genetic variation in IL genes may influence breast cancer risk and mortality given their role in cell growth, angiogenesis and regulation of inflammatory process. We examined 16 IL genes with breast cancer risk and mortality in an admixed population of Hispanic/Native American (NA) (2111 cases and 2597 controls) and non-Hispanic white (NHW) (1481 cases and 1585 controls) women. Adaptive Rank Truncated Product (ARTP) analysis was conducted to determine gene significance and lasso (least absolute shrinkage and selection operator) was used to identify potential gene by gene and gene by lifestyle interactions. The pathway was statistically significant for breast cancer risk overall (P ARTP = 0.0006), for women with low NA ancestry (P ARTP = 0.01), for premenopausal women (P ARTP = 0.02), for estrogen receptor (ER)+/progesterone receptor (PR)+ tumors (P ARTP = 0.03) and ER−/PR− tumors (P ARTP = 0.02). Eight of the 16 genes evaluated were associated with breast cancer risk (IL1A, IL1B, IL1RN, IL2, IL2RA, IL4, IL6 and IL10); four genes were associated with breast cancer risk among women with low NA ancestry (IL1B, IL6, IL6R and IL10), two were associated with breast cancer risk among women with high NA ancestry (IL2 and IL2RA) and four genes were associated with premenopausal breast cancer risk (IL1A, IL1B, IL2 and IL3). IL4, IL6R, IL8 and IL17A were associated with breast cancer-specific mortality. We confirmed associations with several functional polymorphisms previously associated with breast cancer risk and provide support that their combined effect influences the carcinogenic process.
Introduction
Interleukins (ILs) are a group of cytokines that control cell growth and differentiation, cell migration and inflammatory and anti-inflammatory responses of the immune system. Cytokines are potentially central to the carcinogenic process since they are key regulators of immune response. The balance between inflammatory and anti-inflammatory actions is essential for proper control of the immune response and protection against underlying tissue damage. However, factors such as bacterial or viral infections, diet and lifestyle exposure, as well as individual genetic makeup may disrupt this balance and lead to a heightened state of inflammation resulting in tissue damage (1). Proinflammatory ILs generally include IL-1, IL-2, IL-6, IL-8, IL-15, and IL-17, whereas anti-inflammatory ILs include IL-4 and IL-10. IL-3 supports differentiation and proliferation of various immune cells thereby playing a significant role in immune responses. Chronic inflammation and host defense research indicates that IL-17 works in conjunction with IL-23 to contribute to the pathogenesis observed in inflammatory diseases (2).
ILs have been linked to tumor progression. In particular, serum IL-8 has been shown to promote malignant progression of breast tumors and has been associated with inflammatory pathways and angiogenesis (3,4). IL-8 can bind to two different forms of the IL-8 receptor: IL-8 receptor alpha also called CXCR1 and IL-8 receptor beta also called CXCR2. The combined effects of IL-8 and CXCR2 have been associated with tumor aggressiveness (5). Other cytokines, including IL10 and IL2RA have been associated with disease-free survival (6,7).
Genetic variation in ILs, including IL1B, IL6, IL8 and IL10, has been inconsistently associated with breast cancer risk (8–10). Differences in observed risk estimates in the literature could result from differences in the genetic ancestry of the population. For instance, we evaluated IL6 in the 4-Corners Breast Cancer Study (4-CBCS) (11) and observed stronger associations for postmenopausal women and women who were Hispanic or Native American (NA). Polymorphisms in the IL6 gene promoter have been reported to be related to levels of circulating C-reactive protein (12) and to modify the association with host factors such as high body mass index (BMI) and type 2 diabetes (13).
This analysis builds on our previous work (11) and more comprehensively evaluates associations with IL cytokines in a large population of United States non-Hispanic whites (NHW), United States Hispanics/NAs and women living in Mexico. Breast cancer incidence rates in this population vary, with NHW women having rates considerably higher than the other groups and NA living in the Southwest having extremely low incidence of the disease (14). Using a tagSNP approach we evaluated IL genes and their receptors and assessed associations with breast cancer risk and survival. We also evaluated interactions between these single-nucleotide polymorphisms (SNPs) and lifestyle factors that may influence inflammation, such as diet and body size, and breast cancer risk.
Materials and methods
The Breast Cancer Health Disparities Study includes participants from three population-based case–control studies (14), the 4-CBCS (15), the Mexico Breast Cancer Study (MBCS) (16) and the San Francisco Bay Area Breast Cancer Study (SFBCS) (17,18), who completed an in-person interview and who had a blood or mouthwash sample available for DNA extraction. In the 4-CBCS, participants were between 25 and 79 years; participants from the MBCS were between 28 and 74 years; the SFBCS included women aged 35–79 years. All participants signed informed written consent prior to participation and each study was approved by the Institutional Review Board for Human Subjects at each institution.
Data harmonization
Data were harmonized across all study centers and questionnaires as described previously (14). Women were classified as either premenopausal or postmenopausal based on responses to questions on menstrual history. Women who reported still having periods during the referent year (defined as the year before diagnosis for cases or before selection into the study for controls) were classified as premenopausal. Women were classified as postmenopausal if they reported either a natural menopause or if they reported taking hormone therapy and were still having periods or were at or above the 95th percentile of age for those who reported having a natural menopause (i.e. ≥12 months since their last period). Women in the 4-CBCS and the SFBCS were asked to self-identify their ethnic and racial identify and were classified as NHW, Hispanic, NA or a combination of these groups. Women in the MBCS were not asked their race or ethnicity. Thus, when relaying self-reported race/ethnicity, Hispanic and NA women are grouped together.
Genetic data
DNA was extracted from either whole blood (n = 7287) or mouthwash (n = 634) samples. Whole genome amplification was applied to the mouthwash-derived DNA samples prior to genotyping using the REPLI-g mini kit from Qiagen. A tagSNP approach was used to characterize variation across candidate genes. TagSNPs were selected from the Illumina database using the following parameters: linkage disequilibrium (LD) blocks were defined using a Caucasian LD map and an r 2 = 0.8; minor allele frequency > 0.1; range = −1500 bps from the initiation codon to +1500 bps from the termination codon; and 1 SNP/LD bin. Additionally, 104 ancestry-informative markers were used to distinguish European and NA ancestry (14). All markers were genotyped using a multiplexed bead array assay format based on GoldenGate chemistry (Illumina, San Diego, CA). A genotyping call rate of 99.93% was attained (99.65% for whole genome amplification samples). We included 132 blinded internal replicates representing 1.6% of the sample set. The duplicate concordance rate was 99.996% as determined by 193 297 matching genotypes among sample pairs. In the current analysis we evaluated tagSNPs for IL1A (3 SNPs), IL1B (4 SNPs), IL2 (3 SNPs), IL3 (1 SNP), IL4 (3 SNPs), IL6 (5 SNPs), IL8 (3 SNPs), IL10 (6 SNPs), IL15 (6 SNPs), IL17A (7 SNPs), their receptors CXCR1 (2 SNPs), CXCR2 (2 SNPs), IL23R (24 SNPs), IL6R (6 SNPs), IL2RA (24 SNPs) and the IL-1 receptor antagonist encoded by IL1RN (8 SNPs). Supplementary Table S1, available at Carcinogenesis Online provides a description of these genes and SNPs.
Tumor characteristics and survival
Information on survival and estrogen receptor (ER) and progesterone receptor (PR) tumor status was not available for cases from Mexico and therefore assessment of these variables is limited to data obtained from the 4-CBCS and the SFBCS. Cancer registries in Utah, Colorado, Arizona, New Mexico and California provided information on stage at diagnosis, months of survival after diagnosis, cause of death and ER and PR status. Survival information was monitored by local tumor registries, using vital statistics, National Death Index and physician follow-up. Surveillance, Epidemiology and End Results disease stage was categorized as local, regional or distant.
Statistical methods
Genetic ancestry estimation.
The program STRUCTURE was used to estimate individual ancestry for each study participant assuming two founding populations (19,20). A three-founding population model was assessed but did not fit the population structure. Participants were classified by level of percent NA ancestry. Assessment across categories of ancestry was done using cut-points, ≤28%, >28–70% and >70%, based on the distribution of genetic ancestry in the control population (14).
SNP associations.
Genes and SNPs were assessed for their association with breast cancer risk by strata of genetic ancestry and menopausal status in the whole population and by ER/PR status for the 4-CBCS and SFBCS. All statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC). Logistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals (CI) for breast cancer risk associated with SNPs, adjusting for study, BMI (kg/m2) in the reference year and parity as categorical variables and age and genetic ancestry as continuous variables. A P value of 0.05 was considered statistically significant in all analyses. Associations with SNPs were assessed assuming a codominant model. Based on the initial assessment, SNPs which appeared to have a dominant or recessive mode of inheritance were evaluated with those inheritance models in subsequent analyses. SNPs with a homozygous variant genotype <0.05% were assigned a dominant mode of inheritance to assure adequate power in stratified analyses. For stratified analyses, tests for interactions were calculated using a Wald one degree of freedom (1-df) test; adjustments for multiple comparisons within the gene used the step-down Bonferroni correction, taking into account the degree of correlation of the SNPs within genes using the SNP spectral decomposition method proposed by Nyholt (21) and modified by Li et al. (22).
ARTP analysis.
We used the adaptive rank truncated product (ARTP) method that utilizes a highly efficient permutation algorithm to determine the significance of association of each gene and of all genes combined with breast cancer risk overall, by menopausal status, by genetic ancestry and by ER/PR strata. The gene P values were generated using the ARTP package in R, permuting outcome status 10 000 times while adjusting for age, study, reference year BMI, parity and genetic ancestry (23,24). We controlled for age, study, genetic ancestry and Surveillance, Epidemiology and End Results stage when estimating the ARTP for survival. We report both pathway and gene P values (P ARTP). The original R program was modified to incorporate Cox proportional hazard modeling that permuted both vital status and survival months to estimate gene and pathway associations; P values for survival analysis were based on likelihood ratio tests.
Interactions.
We assessed both gene by gene (G × G) and gene by environment (G × E) interactions using a lasso (least absolute shrinkage and selection operator) approach (25) implemented in Mendel v.13 software (26). Since lasso required non-missing values, missing SNP data were inferred by LD patterns using Mendel’s SNP imputation option (Analysis Option 23, model = 1). Missing values of continuous variables were replaced by the mean and missing values of ordinal categorical variables were replaced by the median. The lasso interaction analysis was run using the products of marginal predictors and forcing study center, age, BMI during the referent year (coded as a categorical variable) and percent NA ancestry into all models (Analysis Option 24, model = 2). Mendel was instructed to consider two-way interactions which were evaluated among all available predictors and to return the top 200 predictors (including both marginal predictors and interaction terms). The lasso P values provided in Table IV correspond to the F-test of a regression model that includes the interaction term versus the null model without the interaction term. Interactions that were statistically significant based on a false discovery rate of <0.05 were then assessed in SAS adjusting for parity and the same covariates described above. All tagSNPs were assessed for G × G interactions. For G × E interactions, we targeted environmental and lifestyle factors that could influence IL genes including BMI, parity, hormone therapy, total caloric intake and dietary intake of fiber, folate, vitamin E, beta carotene and vitamin C.
Table IV.
Interactions between IL genes and breast cancer risk
| Gene 1 (G1) | Gene 2 (G2) | G1 (GT2): G2 (GT1) | G1 (GT1): G2 (GT2) | G1 (GT2): G2 (GT2) | Adjusteda | |||
|---|---|---|---|---|---|---|---|---|
| Gene 1 | GT1/GT2 | Gene 2 | GT1/GT2 | OR (95% CI)b,c | OR (95% CI)b,c | OR (95% CI)b,c | P | Lasso P |
| Homozygote rare variants modify risk | ||||||||
| L6R rs11265618 | CC:CT/TT | IL2RA rs11256457 | GG:GC/CC | 1.27 (1.02, 1.57) | 1.22 (1.08, 1.37) | 1.04 (0.90, 1.21) | 0.002 | 0.0002 |
| IL1A rs1878321 | TT:CC | IL2RA rs11598648 | GG:GA/AA | 1.34 (1.02, 1.76) | 1.17 (1.03, 1.33) | 0.98 (0.78, 1.24) | 0.009 | 0.0003 |
| IL2RA rs3118470 | CC/TC:CC | IL23R rs10889675 | CC:AA | 0.74 (0.60, 0.91) | 1.00 (0.83, 1.19) | 1.55 (0.86, 2.78) | 0.003 | 0.0003 |
| CXCR1 rs1008563 | CC:TT | IL1RN rs4251961 | TT:TC/CC | 0.81 (0.66, 1.00) | 0.77 (0.67, 0.89) | 0.94 (0.77, 1.14) | 0.008 | 0.0006 |
| IL2RA rs6602392 | CC:CA/AA | IL6R rs1386821 | AA:AC/CC | 1.08 (0.95, 1.21) | 1.19 (1.06, 1.35) | 0.96 (0.79, 1.17) | 0.02 | 0.001 |
| IL6R rs11265618 | CC:CT/TT | IL2RA rs2076846 | AA:AG/GG | 1.10 (0.96, 1.27) | 1.07 (0.96, 1.20) | 0.84 (0.72, 0.98) | 0.001 | 0.001 |
| IL8 rs2227307 | TT:GG | IL10 rs1800890 | TT:TA/AA | 1.21 (1.00, 1.47) | 1.30 (1.11, 1.51) | 1.03 (0.85, 1.24) | 0.001 | 0.001 |
| IL17A rs10484879 | CC:CA/AA | IL2RA rs11256457 | GG:GC/CC | 1.17 (0.96, 1.43) | 1.21 (1.07, 1.36) | 1.05 (0.91, 1.22) | 0.01 | 0.001 |
| IL6 rs1800797 | GG:GA/AA | IL2RA rs11256457 | GG:GC/CC | 1.08 (0.90, 1.30) | 1.24 (1.08, 1.42) | 1.01 (0.87, 1.18) | 0.009 | 0.002 |
| IL17A rs9395769 | GG:GA/AA | IL2RA rs11598648 | GG:GA/AA | 1.21 (1.00, 1.46) | 1.12 (1.01, 1.24) | 1.04 (0.89, 1.22) | 0.04 | 0.002 |
| CXCR2 rs1126579 | CC:TT | IL2RA rs9663421 | CC:TT | 1.25 (1.02, 1.52) | 0.90 (0.66, 1.22) | 0.79 (0.57, 1.10) | 0.03 | 0.002 |
| IL6 rs1800795 | GG:GC/CC | IL2RA rs11256457 | GG:GC/CC | 1.09 (0.91, 1.31) | 1.24 (1.09, 1.42) | 1.02 (0.88, 1.18) | 0.008 | 0.002 |
| IL1RN rs4251961 | TT:TC/CC | IL2RA rs12722596 | AA:AG/GG | 0.83 (0.75, 0.92) | 0.96 (0.79, 1.17) | 1.05 (0.87, 1.26) | 0.05 | 0.003 |
| IL23R rs10489629 | GG:AA | IL2 rs2069778 | CC:CT/TT | 1.13 (0.98, 1.30) | 1.40 (1.10, 1.78) | 0.99 (0.78, 1.24) | 0.005 | 0.005 |
| IL17A rs10484879 | CC:CA/AA | IL6R rs11265618 | CC:CT/TT | 1.02 (0.91, 1.15) | 1.04 (0.92, 1.18) | 0.78 (0.66, 0.93) | 0.007 | 0.01 |
| Having one homozygote rare variant modifies risk | ||||||||
| IL6R rs4075015 | TT/TA:AA | IL23R rs10889675 | CC:AA | 0.79 (0.67, 0.92) | 1.01 (0.84, 1.22) | 1.04 (0.69, 1.56) | 0.02 | 0.0001 |
| IL4 rs2243263 | GG:GC/CC | IL2RA rs10905669 | CC/CT:TT | 0.87 (0.78, 0.97) | 0.69 (0.57, 0.82) | 0.92 (0.69, 1.23) | 0.02 | <0.0001 |
| IL2RA rs6602392 | CC:CA/AA | IL2RA rs706778 | GG:AA | 0.88 (0.71, 1.09) | 0.85 (0.73, 0.99) | 1.11 (0.91, 1.35) | 0.01 | 0.0001 |
| IL1RN rs3213448 | GG:GA/AA | IL10 rs1554286 | CC/CT:TT | 0.92 (0.82, 1.02) | 0.68 (0.57, 0.83) | 0.89 (0.67, 1.18) | 0.04 | 0.0007 |
| IL6R rs7549250 | TT:TC/CC | IL10 rs1554286 | CC/CT:TT | 0.90 (0.82, 0.99) | 0.59 (0.45, 0.76) | 0.80 (0.65, 0.98) | 0.01 | 0.001 |
| IL6R rs7549250 | TT:TC/CC | IL10 rs1518111 | GA/AA:AA | 0.91 (0.82, 1.00) | 0.63 (0.50, 0.80) | 0.78 (0.64, 0.95) | 0.04 | 0.001 |
| IL6R rs4075015 | TT/TA:AA | IL1RN rs2232354 | TT:TG/GG | 0.83 (0.71, 0.97) | 0.85 (0.77, 0.95) | 0.92 (0.76, 1.12) | 0.05 | 0.002 |
| IL23R rs10889677 | CC:AA | IL1A rs2856838 | CC:TT | 1.09 (0.87, 1.37) | 1.39 (1.12, 1.71) | 1.33 (0.82, 2.15) | 0.02 | 0.02 |
| IL15 rs1519551 | AA:GG | IL23R rs10489628 | CC:CT/TT | 0.87 (0.69, 1.10) | 0.79 (0.66, 0.95) | 0.93 (0.77, 1.13) | 0.03 | 0.02 |
| IL2RA rs2076846 | AA:AG/GG | IL1A rs3783546 | GG/GC:CC | 0.92 (0.83, 1.02) | 0.75 (0.65, 0.87) | 0.98 (0.81, 1.18) | 0.005 | 0.03 |
aAdjusted for age, study center, parity, BMI during referent year and genetic ancestry.
bORs and 95% CI.
cReferent group is gene 1 (G1) genotype 1 (GT1):gene 2 (G2) GT1.
Survival analysis.
Survival months were calculated based on month and year of diagnosis and month and year of death or date of last contact. Survival updates were received in the spring of 2013 which included complete survival surveillance through December of 2011. Associations between SNPs and mortality among the first primary invasive cases were evaluated using Cox proportional hazards models to obtain multivariate hazard ratios (HR) and 95% CI by admixture strata. Since survival data were not available for the MBCS, the upper two admixture strata were combined to evaluate survival by ancestry groups. Individuals were censored when they died of causes other than breast cancer when evaluating breast cancer-specific mortality or were lost to follow-up for all mortality analysis. Models were adjusted for age, study, genetic ancestry and Surveillance, Epidemiology and End Results stage. Interactions between genetic variants and genetic ancestry with survival were assessed using P values from 1-df Wald chi-square tests.
Results
The majority of cases were United States Hispanic/NA or women from Mexico, postmenopausal, and had ER+/PR+ tumors (Table I). Approximately 82% of cases were alive at last contact and the majority of deaths were due to breast cancer. Other cancers contributed to 15% of deaths among NHW women and 10 of deaths among Hispanic/NA women; cardiovascular diseases were responsible for 11% of NHW and Hispanic/NA deaths; 13% of deaths were unknown for NHW women and 8% of deaths among Hispanic/NA women; 5% of deaths were attributed to pneumonia or respiratory-related cases for both groups of women. Over 99% of women who self-reported being NHW had low levels of NA ancestry. Slightly over 10% of women who self-reported being Hispanic/NA or living in Mexico had low levels of NA ancestry.
Table I.
Description of study population by self-reported race/ethnicity
| United States non-Hispanic white | United States Hispanic/NA or Mexican | |||||||
|---|---|---|---|---|---|---|---|---|
| Controls | Cases | Controls | Cases | |||||
| N | % | N | % | N | % | N | % | |
| Total | 1585 | 37.9 | 1481 | 41.2 | 2597 | 62.1 | 2111 | 58.8 |
| Study site | ||||||||
| 4-CBCS | 1321 | 83.3 | 1227 | 82.8 | 723 | 27.8 | 597 | 28.3 |
| MBCS | 0 | 0.0 | 0 | 0.0 | 994 | 38.3 | 816 | 38.7 |
| SFBCS | 264 | 16.7 | 254 | 17.2 | 880 | 33.9 | 698 | 33.1 |
| Age (years) | ||||||||
| <40 | 116 | 7.3 | 89 | 6.0 | 311 | 12.0 | 200 | 9.5 |
| 40–49 | 408 | 25.7 | 409 | 27.6 | 831 | 32.0 | 713 | 33.8 |
| 50–59 | 409 | 25.8 | 413 | 27.9 | 756 | 29.1 | 617 | 29.2 |
| 60–69 | 349 | 22.0 | 361 | 24.4 | 526 | 20.3 | 430 | 20.4 |
| >70 | 303 | 19.1 | 209 | 14.1 | 173 | 6.7 | 151 | 7.2 |
| Mean | 56.6 | 56.0 | 52.3 | 52.7 | ||||
| Menopausal status | ||||||||
| Premenopausal | 494 | 31.5 | 489 | 33.5 | 1027 | 40.7 | 836 | 40.9 |
| Postmenopausal | 1075 | 68.5 | 970 | 66.5 | 1499 | 59.3 | 1210 | 59.1 |
| Estimated percent NA | ||||||||
| Ancestry | ||||||||
| 0–28 | 1577 | 99.5 | 1472 | 99.4 | 278 | 10.7 | 275 | 13.0 |
| 29–70 | 7 | 0.4 | 7 | 0.5 | 1686 | 64.9 | 1393 | 66.0 |
| 71–100 | 1 | 0.1 | 2 | 0.1 | 633 | 24.4 | 443 | 21.0 |
| ER/PR statusa | ||||||||
| ER+/PR+ | 650 | 68.8 | 581 | 61.7 | ||||
| ER+/PR− | 109 | 11.5 | 111 | 11.8 | ||||
| ER−/PR+ | 15 | 1.6 | 28 | 3.0 | ||||
| ER−/PR− | 171 | 18.1 | 221 | 23.5 | ||||
| Vital statusa | ||||||||
| Deceased | NAb | 233 | 19.6 | NAb | 219 | 18.9 | ||
| Alive | NAb | 957 | 80.4 | NAb | 939 | 81.1 | ||
| Cause of deatha | ||||||||
| Breast cancer | NAb | 114 | 48.9 | NAb | 121 | 55.3 | ||
| Other | NAb | 119 | 51.1 | NAb | 98 | 44.7 | ||
| SEER summary stagea | ||||||||
| Local | 831 | 71.0 | 650 | 59.6 | ||||
| Regional | 325 | 27.8 | 432 | 39.6 | ||||
| Distant | 15 | 1.3 | 9 | 0.8 | ||||
SEER, Surveillance, Epidemiology and End Results.
aData not available from MBCS; includes first primary invasive breast cancer cases from the 4-CBCS and SFBCS only.
bData not available (NA).
A summary of the P ARTP values for associations of breast cancer risk and survival with overall pathway and genes within the pathway by NA ancestry, menopausal status and ER/PR tumor status is shown in Table II. The IL pathway was statistically significantly associated with breast cancer risk among all women combined (P ARTP = 0.0002), women with low NA ancestry (P ARTP = 0.01), premenopausal women (P ARTP = 0.02), women with ER+/PR+ tumors (P ARTP = 0.03) and women with ER−/PR− tumors (P ARTP = 0.05). Within this pathway and among all women combined, eight of the 16 genes evaluated were associated with breast cancer risk (IL1A, IL1B, IL1RN, IL2, IL2RA, IL4, IL6, IL10). Four genes were associated with breast cancer risk among women with low NA ancestry (IL1B, IL6, IL6R, IL10); IL2 and IL2RA were associated with breast cancer risk among those with high NA ancestry; four genes were associated with premenopausal breast cancer risk (IL1A, IL1B, IL2 and IL3). IL1RN and IL10 were associated with ER+/PR+ tumors, whereas IL2 and IL4 were associated with ER−/PR− tumors. IL4, IL6R, IL8 and IL17A were associated with mortality.
Table II.
Associations between IL genes and breast cancer risk and survival as determined by ARTPa
| Pathway | CXCR1 | CXCR2 | IIL1A | IL1B | IL1RN | IL2 | IL2RA | IL3 | IL4 | IL6 | IL6R | IL8 | IL10 | IL15 | IL17A | IL23R | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| P ARTP | P ARTP | P ARTP | P ARTP | P ARTP | P ARTP | P ARTP | P ARTP | P ARTP | P ARTP | P ARTP | P ARTP | P ARTP | P ARTP | P ARTP | P ARTP | P ARTP | |
| Breast cancer risk | |||||||||||||||||
| All women | 0.0002 | 0.82 | 0.65 | 0.03 | 0.03 | 0.01 | 0.05 | 0.008 | 0.20 | 0.05 | 0.02 | 0.21 | 0.62 | 0.001 | 0.34 | 0.27 | 0.73 |
| Percent NA Ancestry | |||||||||||||||||
| 0–≤28 | 0.01 | 0.86 | 0.73 | 0.11 | 0.02 | 0.22 | 0.09 | 0.11 | 0.35 | 0.13 | 0.04 | 0.03 | 0.31 | 0.03 | 0.36 | 0.30 | 0.77 |
| >28–≤70 | 0.26 | 0.95 | 0.70 | 0.37 | 0.59 | 0.03 | 0.50 | 0.24 | 0.67 | 0.04 | 0.28 | 0.65 | 0.44 | 0.12 | 1.00 | 0.05 | 0.38 |
| >70 | 0.17 | 0.65 | 0.63 | 0.39 | 0.47 | 0.52 | 0.03 | 0.008 | 0.58 | 0.30 | 0.32 | 0.57 | 0.51 | 0.25 | 0.20 | 0.46 | 0.10 |
| Menopausal status | |||||||||||||||||
| Premenopausal | 0.02 | 0.42 | 0.33 | 0.05 | 0.04 | 0.18 | 0.05 | 0.32 | 0.01 | 0.25 | 0.13 | 0.47 | 0.61 | 0.07 | 0.89 | 0.09 | 0.54 |
| Postmenopausal | 0.11 | 0.91 | 0.90 | 0.33 | 0.45 | 0.07 | 0.23 | 0.02 | 0.79 | 0.13 | 0.04 | 0.41 | 0.72 | 0.03 | 0.15 | 0.89 | 0.58 |
| ER/PR status | |||||||||||||||||
| ER+/PR+ | 0.03 | 0.92 | 0.84 | 0.61 | 0.26 | 0.02 | 0.06 | 0.07 | 0.78 | 0.21 | 0.08 | 0.10 | 0.61 | 0.01 | 0.73 | 0.09 | 0.63 |
| ER+/PR− | 0.13 | 0.84 | 0.74 | 0.31 | 0.21 | 0.46 | 0.32 | 0.87 | 0.63 | 0.31 | 0.28 | 0.42 | 0.68 | 0.61 | 0.01 | 0.15 | 0.02 |
| ER−/PR+ | 0.71 | 0.34 | 0.26 | 0.71 | 0.62 | 0.31 | 0.92 | 0.84 | 0.31 | 0.63 | 0.13 | 0.07 | 0.81 | 0.37 | 0.46 | 0.18 | 0.90 |
| ER−/PR− | 0.05 | 0.46 | 0.53 | 0.25 | 0.25 | 0.39 | 0.01 | 0.38 | 0.90 | 0.01 | 0.17 | 0.28 | 0.22 | 0.10 | 0.92 | 0.10 | 0.39 |
| Survival | |||||||||||||||||
| All-cause mortality | |||||||||||||||||
| All women | 0.15 | 0.39 | 0.41 | 0.55 | 0.75 | 0.23 | 0.93 | 0.02 | 0.33 | 0.02 | 0.75 | 0.05 | 0.56 | 0.83 | 0.12 | 0.57 | 0.77 |
| Percent NA ancestry | |||||||||||||||||
| ≤28 | 0.30 | 0.57 | 0.32 | 0.38 | 0.94 | 0.12 | 0.90 | 0.59 | 0.48 | 0.02 | 0.71 | 0.09 | 0.45 | 0.86 | 0.15 | 0.60 | 0.82 |
| >28 | 0.38 | 0.65 | 1.00 | 0.53 | 0.12 | 0.86 | 0.59 | 0.02 | 0.50 | 0.45 | 0.52 | 0.19 | 1.00 | 0.76 | 0.56 | 0.27 | 0.19 |
| Breast cancer-specific mortality | |||||||||||||||||
| All women | 0.33 | 0.49 | 0.67 | 0.85 | 0.87 | 0.19 | 0.90 | 0.69 | 0.80 | 0.03 | 0.22 | 0.23 | 0.04 | 0.76 | 0.24 | 0.67 | 0.97 |
| Percent NA ancestry | |||||||||||||||||
| ≤28 | 0.07 | 0.47 | 0.26 | 0.80 | 0.23 | 0.38 | 0.59 | 0.66 | 0.76 | 0.009 | 0.70 | 0.62 | 0.008 | 0.61 | 0.22 | 0.85 | 0.48 |
| >28 | 0.27 | 0.81 | 0.72 | 0.75 | 0.11 | 0.11 | 0.99 | 0.24 | 0.96 | 0.65 | 0.24 | 0.02 | 0.86 | 0.73 | 0.57 | 0.05 | 0.37 |
aARTP utilizes a highly efficient permutation algorithm to determine the significance of association of each gene and of all genes. P values reported here are derived using that statistical technique. Bold text shows statistically significant associations.
Risk estimates for SNPs within genes that were statistically significant showed generally modest associations (ORs between >0.7 and <1.5) (Table III). ORs for overall breast cancer risk were also generally within the modest range. However, evaluation within population strata, showed a few risk estimates that were slightly stronger. IL10 rs1554286 (ORTT = 0.66, 95% CI: 0.46, 0.95) and rs1518111 (ORAA = 0.68, 95% CI: 0.50, 0.91) were associated with breast cancer risk among women with ≤28% NA ancestry and IL2 rs2069778 (ORCT/TT = 2.21, 95% CI: 1.24, 3.92) and IL2RA rs12722605 (ORAT/TT = 0.51, 95% CI: 0.31, 0.85), rs9663421 (ORTT = 0.57, 95% CI: 0.39,0.84) and rs10905669 (ORTT = 0.56, 95% CI: 0.40, 0.78) showed stronger associations for breast cancer risk among women with higher NA ancestry. IL10 rs1554286, rs1518111 and rs1800871 showed stronger associations for ER+/PR+ tumors. Several SNPs in IL23R were associated with ER+/PR− tumors including rs10889677 (ORAA = 0.49, 95% CI: 0.27, 0.88), rs10489629 (ORAA = 0.63, 95% CI: 0.42, 0.93), rs10489628 (ORCT/TT = 1.54, 95% CI: 1.13, 2.11) and rs10889675 (ORAA = 2.02, 95% CI: 1.20, 3.40). Two SNP in IL4 were associated with ER−/PR− tumors, rs2243250 (ORTT = 1.56, 95% CI: 1.09, 2.22) and rs2243263 (ORGC/CC = 0.66, 95% CI: 0.50, 0.86).
Table III.
Associations between IL genes and risk of breast cancer by genetic ancestry, menopausal status and ER/PR tumor status
| Overall | SNP | Genotype | Controls, N | Cases, N | ORa | 95% CI |
|---|---|---|---|---|---|---|
| Risk versus referent | Risk versus referent | |||||
| IL1A | rs3783546 | CC versus GG/GC | 887/3268 | 635/2931 | 0.85 | (0.76, 0.96) |
| IL1B | rs1143633 | AA versus GG | 425/1938 | 428/1567 | 1.21 | (1.04, 1.41) |
| rs1143627 | TC/CC versus TT | 2960/1188 | 2404/1155 | 0.90 | (0.81, 0.99) | |
| IL1RN | rs4251961 | TC/CC versus TT | 2243/1912 | 1830/1739 | 0.86 | (0.79, 0.94) |
| rs2232354 | TG/GG versus TT | 1509/2643 | 1205/2358 | 0.89 | (0.81, 0.98) | |
| rs452204 | AA versus GG/GA | 843/3312 | 777/2783 | 1.13 | (1.01, 1.26) | |
| rs397211 | CC versus TT | 722/1519 | 638/1281 | 1.16 | (1.01, 1.33) | |
| IL2 | rs2069776 | TC/CC versus TT | 1375/2781 | 1314/2253 | 1.12 | (1.02, 1.24) |
| rs2069778 | CT/TT versus CC | 689/3468 | 694/2875 | 1.13 | (1.00, 1.28) | |
| IL2RA | rs9663421 | TT versus CC | 496/1848 | 366/1674 | 0.85 | (0.73, 0.99) |
| rs2386841 | AA versus CC/CA | 447/3709 | 275/3293 | 0.75 | (0.64, 0.89) | |
| rs11256457 | GC/CC versus GG | 2959/1197 | 2661/908 | 1.11 | (1.00, 1.23) | |
| rs10905669 | TT versus CC/CT | 456/3701 | 294/3275 | 0.77 | (0.66, 0.90) | |
| IL4 | rs2227282 | GG versus CC/CG | 1061/3093 | 764/2804 | 0.87 | (0.77, 0.98) |
| IL6 | rs1800797 | GT/TT versus GG | 1794/2363 | 1525/2042 | 0.88 | (0.79, 0.97) |
| rs1800795 | GC/CC versus GG | 1827/2330 | 1556/2011 | 0.88 | (0.80, 0.97) | |
| rs2069832 | GA/AA versus GG | 1828/2329 | 1553/2013 | 0.88 | (0.79, 0.97) | |
| rs2069840 | GG versus CC | 417/1894 | 416/1573 | 1.18 | (1.01, 1.37) | |
| IL10 | rs1554286 | TT versus CC/CT | 454/3703 | 284/3283 | 0.76 | (0.65, 0.89) |
| rs1518111 | AA versus GG/GA | 533/3624 | 342/3226 | 0.77 | (0.66, 0.89) | |
| rs1800871 | TT versus CC/CT | 557/3598 | 368/3201 | 0.79 | (0.68, 0.91) | |
| Genetic ancestry: <29% | ||||||
| IL1B | rs1143633 | AA versus GG | 220/800 | 256/688 | 1.36 | (1.10, 1.67) |
| IL6 | rs1800797 | GA/AA versus GG | 1164/683 | 1030/711 | 0.85 | (0.74, 0.98) |
| rs1800795 | GC/CC versus GG | 1187/660 | 1047/694 | 0.84 | (0.73, 0.97) | |
| rs2069832 | GA/AA versus GG | 1186/661 | 1053/687 | 0.86 | (0.75, 0.99) | |
| IL6R | rs1386821 | AC/CC versus AA | 621/1226 | 637/1102 | 1.15 | (1.00, 1.32) |
| rs7549250 | TC/CC versus TT | 1281/566 | 1123/619 | 0.81 | (0.70, 0.93) | |
| IL10 | rs1554286 | TT versus CC/CT | 78/1769 | 49/1693 | 0.66 | (0.46, 0.95) |
| rs1518111 | AA versus GG/GA | 116/1731 | 76/1665 | 0.68 | (0.50, 0.91) | |
| rs1800871 | TT versus CC/CT | 129/1718 | 92/1650 | 0.74 | (0.56, 0.98) | |
| Genetic ancestry: >70% | ||||||
| IL2 | rs2069778 | CT/TT versus CC | 21/608 | 33/404 | 2.21 | (1.24, 3.92) |
| IL2RA | rs12722605 | AT/TT versus AA | 60/569 | 23/414 | 0.51 | (0.31, 0.85) |
| rs9663421 | TT versus CC | 113/206 | 57/181 | 0.57 | (0.39, 0.84) | |
| rs11256457 | GC/CC versus GG | 312/317 | 254/183 | 1.34 | (1.04, 1.73) | |
| rs791587 | GA/AA versus GG | 372/257 | 284/153 | 1.30 | (1.00, 1.68) | |
| rs10905669 | TT versus CC/CT | 140/489 | 63/374 | 0.56 | (0.40, 0.78) | |
| Menopausal status | ||||||
| Premenopausal | ||||||
| IL1A | rs3783546 | CC versus GG/GC | 343/1172 | 239/1077 | 0.81 | (0.67, 0.99) |
| IL1B | rs1143633 | AA versus GG | 149/740 | 150/574 | 1.29 | (1.00, 1.67) |
| IL2 | rs2069778 | CT/TT versus CC | 225/1291 | 263/1055 | 1.31 | (1.07, 1.61) |
| IL3 | rs40401 | CT/TT versus CC | 693/821 | 543/775 | 0.82 | (0.71, 0.95) |
| Postmenopausal | ||||||
| IL2RA | rs9663421 | TT versus CC | 296/1131 | 197/1031 | 0.76 | (0.62, 0.93) |
| rs12722596 | AG/GG versus AA | 327/2229 | 340/1824 | 1.24 | (1.05, 1.46) | |
| rs7072398 | GG versus AA | 439/920 | 450/682 | 1.27 | (1.07, 1.51) | |
| rs11256457 | GC/CC versus GG | 1841/714 | 1651/513 | 1.18 | (1.03, 1.35) | |
| rs6602398 | TT versus GG/GT | 242/2314 | 162/2002 | 0.76 | (0.61, 0.93) | |
| rs10905669 | TT versus CC/CT | 281/2275 | 181/1983 | 0.77 | (0.63, 0.95) | |
| IL6 | rs2069827 | GT/TT versus GG | 296/2260 | 226/1937 | 0.82 | (0.68, 0.99) |
| rs1800797 | GA/AA versus GG | 1141/1415 | 952/1211 | 0.87 | (0.77, 0.99) | |
| rs1800795 | GC/CC versus GG | 1166/1390 | 976/1186 | 0.88 | (0.78, 1.00) | |
| rs2069832 | GA/AA versus GG | 1167/1389 | 977/1186 | 0.88 | (0.78, 1.00) | |
| IL10 | rs1554286 | TT versus CC/CT | 255/2301 | 166/1997 | 0.81 | (0.66, 1.00) |
| rs1518111 | AA versus GG/GA | 310/2246 | 196/1967 | 0.77 | (0.63, 0.93) | |
| Tumor phenotype | ||||||
| ER+/PR+ | ||||||
| IL1RN | rs4251961 | TC/CC versus TT | 1819/1352 | 698/600 | 0.84 | (0.74, 0.96) |
| rs2232354 | TG/GG versus TT | 1186/1982 | 430/866 | 0.83 | (0.72, 0.95) | |
| rs452204 | AA versus GA/AA | 554/2617 | 263/1034 | 1.22 | (1.03, 1.44) | |
| rs315949 | CT/TT versus CC | 1892/1281 | 737/561 | 0.85 | (0.74, 0.97) | |
| IL10 | rs1554286 | TT versus CC/CT | 264/2909 | 70/1227 | 0.68 | (0.51, 0.89) |
| rs1518111 | AA versus GG/GA | 323/2850 | 92/1206 | 0.72 | (0.56, 0.91) | |
| rs1800871 | TT versus CC/CT | 348/2823 | 104/1194 | 0.74 | (0.59, 0.94) | |
| ER+/PR− | ||||||
| IL15 | rs12498901 | GC/CC versus GG | 682/2491 | 38/197 | 0.70 | (0.49, 1.00) |
| rs6850492 | AA versus GG | 398/1326 | 23/124 | 0.58 | (0.36, 0.92) | |
| IL23R | rs10889677 | AA versus CC | 315/1528 | 13/130 | 0.49 | (0.27, 0.88) |
| rs12030948 | TT versus GG | 427/1286 | 18/111 | 0.49 | (0.29, 0.82) | |
| rs10489629 | AA versus GG | 839/767 | 44/64 | 0.63 | (0.42, 0.93) | |
| rs10489628 | CT/TT versus CC | 2140/1033 | 179/56 | 1.54 | (1.13, 2.11) | |
| rs6693831 | CT/TT versus CC | 1722/1451 | 142/93 | 1.35 | (1.02, 1.78) | |
| rs11465817 | CA/AA versus CC | 1468/1531 | 92/131 | 0.73 | (0.55, 0.97) | |
| rs10889675 | AA versus CC | 177/1987 | 20/129 | 2.02 | (1.20, 3.40) | |
| ER−/PR− | ||||||
| IL4 | rs2243250 | TT versus CC | 279/1735 | 54/215 | 1.56 | (1.09, 2.22) |
| rs2243263 | GC/CC versus GG | 765/2408 | 72/343 | 0.66 | (0.50, 0.86) | |
aOR and 95% CI adjusted for age, study center, parity, BMI during the referent year and genetic admixture.
We observed several G × G interactions identified in lasso that remained statistically significant after adjusting for confounding variables (Table IV). Two major association patterns were observed. Among those who had both homozygous rare genotypes the association with breast cancer risk was strongest, or the strongest association was observed among women who had one gene with a homozygote rare genotype and the other with a homozygote common genotype. In some instances having two homozygote rare genotypes modified the association previously seen for having one homozygote rare genotype, such as in the case of 1L6R rs11265618 and IL2RA rs11256457, IL1A rs1878321 and IL2RA rs11598648, CXCR1 rs1008563 and IL1RN rs4251961, IL8 rs2227307 and IL10 rs1800890 or IL23R rs10489629 and IL2 rs2069778. IL2RA was involved in 12 of the 17 interactions with this pattern, whereas IL6R was involved in five interactions. Interactions between IL15 rs1519551 and IL23R rs10489628, between IL2RA rs2076846 and IL1A rs3783546 and between IL4 rs2243263 and IL2RA rs10905669 illustrate the second type of interaction. IL2RA, IL6R and IL10 were involved in over three of the 11 interactions in this category.
After adjustment for confounding factors, only four G × E interactions remained statistically significant (Table V). Total energy intake interacted with IL2RA rs2104286 and IL10 rs3024493. Having the homozygote rare genotypes was associated with reduced risk of breast cancer in the presence of high energy intake. Dietary folate interacted with IL17A rs3819024; women with an A allele were at reduced risk of breast cancer if they consumed a diet high in folate. BMI interacted with IL6, where the GG genotype of rs2069832 was associated with reduced breast cancer risk among those women who were obese.
Table V.
Associations between lifestyle factors in IL genes
| Low | Intermediate | High | P interaction | |||||
|---|---|---|---|---|---|---|---|---|
| ORa | 95% CI | OR | 95% CI | OR | 95% CI | Raw | Lasso | |
| Energy intake | ||||||||
| IL2RA (rs2104286) | ||||||||
| AA | 1 | 1.36 | (1.18, 1.57) | 1.77 | (1.51, 2.07) | 0.005 | <0.0001 | |
| AG/GG | 1.34 | (1.09, 1.64) | 1.37 | (1.15, 1.62) | 1.58 | (1.28, 1.94) | ||
| IL10 (rs3024493) | ||||||||
| GG | 1 | 1.29 | (1.13, 1.47) | 1.69 | (1.46, 1.96) | 0.01 | <0.0001 | |
| GT/TT | 1.37 | (1.08, 1.74) | 1.43 | (1.19, 1.71) | 1.49 | (1.16, 1.90) | ||
| Dietary folate | ||||||||
| IL17A (rs3819024) | ||||||||
| AA | 1 | 0.84 | (0.73, 0.98) | 0.68 | (0.57, 0.82) | 0.04 | <0.0001 | |
| AG | 0.87 | (0.73, 1.05) | 0.83 | (0.70, 0.97) | 0.70 | (0.58, 0.86) | ||
| GG | 0.80 | (0.59, 1.09) | 0.84 | (0.65, 1.10) | 0.89 | (0.62, 1.29) | ||
| BMI | ||||||||
| <25kg/m2 | 25–30kg/m2 | >30kg/m2 | ||||||
| IL6 (rs2069832) | ||||||||
| GG | 1 | 0.86 | (0.73, 1.01) | 0.7 | (0.60, 0.82) | 0.002 | <0.0001 | |
| GA/AA | 0.76 | (0.65, 0.90) | 0.69 | (0.58, 0.81) | 0.76 | (0.64, 0.90) | ||
aOR and 95% CI adjusted for age, study, BMI during referent year, parity and genetic admixture.
IL4 and IL6R were associated with both all-cause mortality and breast cancer-specific mortality (Table VI shows HR for SNPs in genes that were significantly associated with survival by ARTP shown in Table II). Although IL2RA was strongly associated with all-cause mortality, it was not associated with breast cancer-specific mortality. Likewise, IL8 was associated only with breast cancer-specific mortality. IL17A rs8193036 was significantly associated with breast cancer mortality and all-cause mortality among women with greater NA ancestry [HRCC = 1.93, 95% CI: 1.09, 3.41 for all-cause mortality (data not shown in table) and HRcc = 2.90, 95% CI: 1.53, 5.49 for breast cancer-specific mortality]; the gene P value was statistically significant (P ARTP = 0.05) for breast cancer-specific mortality only (Table II). One SNP in IL23R, rs4655692, was significantly associated with all-cause mortality (data not shown in table) among women with greater NA ancestry (HRGA/AA = 0.64, 95% CI: 0.45, 0.92) which was significantly different than the risk estimated for women with lower NA ancestry (P = 0.04). Two SNPs in IL23R, rs11465817 and rs10889675, were significantly associated with breast cancer-specific mortality among women with lower NA ancestry (HRCA/AA = 0.69, 95% CI: 0.49, 0.98 and HRAA = 2.76, 95% CI: 1.38, 5.51) although the gene was not significantly associated in this group of women.
Table VI.
Associations between IL genes and all-cause mortality and breast cancer-specific mortality
| All women | 0–28% NA ancestry | 29–100% NA ancestry | Interaction | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Deaths/person year | HRa | (95% CI) | Deaths/person year | HRa | (95% CI) | Deaths/person year | HRa | (95% CI) | P valueb | |
| All-cause mortality | ||||||||||
| IL2RA (rs12244380) | ||||||||||
| AA | 167/7078 | 1.00 | 86/3933 | 1.00 | 81/3146 | 1.00 | 0.10 | |||
| AG | 221/11058 | 0.84 | (0.69, 1.03) | 146/6551 | 0.98 | (0.75, 1.28) | 75/4507 | 0.66 | (0.48, 0.90) | |
| GG | 64/4325 | 0.61 | (0.46, 0.81) | 44/2772 | 0.70 | (0.49, 1.01) | 20/1553 | 0.46 | (0.28, 0.75) | |
| IL2RA (rs6602392) | ||||||||||
| CC | 335/17005 | 1.00 | 222/10427 | 1.00 | 113/6578 | 1.00 | 0.03 | |||
| CA/AA | 117/5447 | 1.07 | (0.86, 1.32) | 54/2820 | 0.88 | (0.65, 1.19) | 63/2628 | 1.39 | (1.02, 1.90) | |
| IL2RA (rs791587) | ||||||||||
| GG | 110/6368 | 1.00 | 72/3603 | 1.00 | 38/2765 | 1.00 | 0.15 | |||
| GA/AA | 342/16067 | 1.24 | (1.00, 1.54) | 204/9641 | 1.11 | (0.85, 1.45) | 138/6426 | 1.58 | (1.10, 2.27) | |
| IL2RA (rs706779) | ||||||||||
| GG | 134/6456 | 1.00 | 70/3115 | 1.00 | 64/3341 | 1.00 | 0.06 | |||
| GA | 210/10780 | 0.96 | (0.77, 1.19) | 138/6232 | 1.05 | (0.79, 1.41) | 72/4548 | 0.81 | (0.58, 1.14) | |
| AA | 107/5207 | 0.99 | (0.77, 1.29) | 68/3894 | 0.81 | (0.58, 1.13) | 39/1312 | 1.57 | (1.05, 2.35) | |
| IL2RA (rs3118470) | ||||||||||
| TT/TC | 404/20427 | 1.00 | 250/11938 | 1.00 | 154/8489 | 1.00 | 0.04 | |||
| CC | 48/2034 | 1.26 | (0.94, 1.71) | 26/1317 | 0.96 | (0.64, 1.44) | 22/717 | 1.88 | (1.20, 2.95) | |
| IL4 (rs2243250) | ||||||||||
| CC | 247/12156 | 1.00 | 186/9048 | 1.00 | 61/3109 | 1.00 | 0.88 | |||
| CT | 145/8036 | 0.91 | (0.73, 1.12) | 71/3577 | 0.92 | (0.70, 1.22) | 74/4459 | 0.87 | (0.62, 1.23) | |
| TT | 50/1769 | 1.41 | (1.01, 1.98) | 12/293 | 2.23 | (1.23, 4.03) | 38/1476 | 1.23 | (0.81, 1.87) | |
| IL4 (rs2227282) | ||||||||||
| CC/CG | 367/18981 | 1.00 | 253/12516 | 1.00 | 114/6465 | 1.00 | 0.26 | |||
| GG | 85/3472 | 1.30 | (1.01, 1.68) | 23/739 | 1.66 | (1.08, 2.56) | 62/2733 | 1.21 | (0.88, 1.66) | |
| IL4 (rs2243263) | ||||||||||
| GG | 333/17545 | 1.00 | 202/10624 | 1.00 | 131/6921 | 1.00 | 0.22 | |||
| GC/CC | 119/4902 | 1.31 | (1.06, 1.61) | 74/2617 | 1.46 | (1.12, 1.91) | 45/2285 | 1.12 | (0.79, 1.57) | |
| IL6R (rs4075015) | ||||||||||
| TT/TA | 394/18804 | 1.00 | 239/10798 | 1.00 | 155/8007 | 1.00 | 0.31 | |||
| AA | 58/3622 | 0.73 | (0.55, 0.96) | 37/2446 | 0.66 | (0.47, 0.94) | 21/1176 | 0.91 | (0.57, 1.43) | |
| IL6R (rs2229238) | ||||||||||
| CC | 248/13151 | 1.00 | 170/8146 | 1.00 | 78/5004 | 1.00 | 0.16 | |||
| CT/TT | 141/6258 | 1.24 | (1.01, 1.53) | 94/4213 | 1.10 | (0.86, 1.42) | 47/2045 | 1.51 | (1.05, 2.18) | |
| Breast cancer-specific mortality | ||||||||||
| IL4 (rs2243263) | ||||||||||
| GG | 167/17545 | 1.00 | 98/10624 | 1.00 | 69/6921 | 1.00 | 0.32 | |||
| GC/CC | 68/4902 | 1.49 | (1.12, 1.98) | 40/2617 | 1.69 | (1.17, 2.45) | 28/2285 | 1.27 | (0.82, 1.98) | |
| IL8 (rs4073) | ||||||||||
| TT | 73/7316 | 1.00 | 36/4121 | 1.00 | 37/3195 | 1.00 | 0.06 | |||
| TA | 105/10944 | 1.00 | (0.74, 1.35) | 63/6343 | 1.20 | (0.80, 1.82) | 42/4601 | 0.82 | (0.52, 1.28) | |
| AA | 56/4188 | 1.37 | (0.96, 1.94) | 39/2791 | 1.80 | (1.15, 2.84) | 17/1397 | 0.92 | (0.51, 1.65) | |
| IL8 (rs2227307) | ||||||||||
| TT | 79/7789 | 1.00 | 37/4301 | 1.00 | 42/3488 | 1.00 | 0.04 | |||
| TG | 102/10796 | 0.97 | (0.72, 1.31) | 63/6268 | 1.26 | (0.83, 1.89) | 39/4528 | 0.73 | (0.47, 1.14) | |
| GG | 54/3876 | 1.40 | (0.99, 1.98) | 38/2687 | 1.86 | (1.18, 2.93) | 16/1189 | 0.95 | (0.52, 1.73) | |
| IL8 (rs2227543) | ||||||||||
| CC | 88/9108 | 1.00 | 41/4808 | 1.00 | 47/4300 | 1.00 | 0.11 | |||
| CT | 100/10161 | 1.04 | (0.78, 1.38) | 62/6058 | 1.29 | (0.87, 1.93) | 38/4103 | 0.81 | (0.52, 1.25) | |
| TT | 47/3164 | 1.65 | (1.15, 2.37) | 35/2362 | 1.98 | (1.26, 3.12) | 12/803 | 1.32 | (0.69, 2.52) | |
| IL17A (rs8193036) | ||||||||||
| TT/TC | 212/20884 | 1.00 | 126/12127 | 1.00 | 86/8757 | 1.00 | 0.01 | |||
| CC | 22/1555 | 1.41 | (0.90, 2.19) | 11/1106 | 0.90 | (0.49, 1.67) | 11/448 | 2.90 | (1.53, 5.49) | |
| IL6R (rs4509570) | ||||||||||
| CC | 134/13678 | 1.00 | 88/7952 | 1.00 | 46/5726 | 1.00 | 0.02 | |||
| CG | 101/8784 | 1.17 | (0.90, 1.51) | 50/5303 | 0.88 | (0.62, 1.25) | 51/3480 | 1.69 | (1.13, 2.53) | |
| IL6R (rs2229238) | ||||||||||
| CC | 129/13151 | 1.00 | 88/8146 | 1.00 | 41/5004 | 1.00 | 0.10 | |||
| CT/TT | 71/6258 | 1.22 | (0.91, 1.63) | 42/4213 | 1.01 | (0.70, 1.46) | 29/2045 | 1.69 | (1.05, 2.72) | |
aHR and 95% CI adjusted for age, study center, SEER stage and genetic ancestry.
bInteraction P value for G × genetic ancestry interaction.
Discussion
Although most estimates of risk were relatively weak, eight of the 16 IL genes evaluated were associated with breast cancer risk. We observed more significant gene associations for women with low NA ancestry than for women with high NA ancestry and for premenopausal rather than postmenopausal breast cancer. It is not clear why differences in ancestry exist; however, it could be speculated that these differences could relate to other underlying characteristics of the population that could modify disease risk. In addition to independent associations with breast cancer risk, we observed several genes that interacted with each other to alter breast cancer risk. Several IL genes also were associated with all-cause and breast cancer-specific mortality.
Pro- and anti-inflammatory ILs have different functions that could explain some of the differences observed in associations. IL-1 is a proinflammatory cytokine and includes three genes IL-1A and IL-1B and the IL-1 receptor antagonist (IL1RN). IL-1A promotes tumor growth, whereas IL-1B increases transcriptional activity of ERα (27,28). IL-1RN is an anti-inflammatory cytokine that can bind equally to either IL-1A or IL-1B and impacts the regulation of the IL-1 system. IL1RN has been associated with acute lymphoblastic leukemia (29). A meta-analysis of eight case–control studies examined three polymorphisms in IL1B that were associated with breast cancer and observed that rs1143627 was significantly associated (ORcc = 1.37, 95% CI: 1.10,1.70) with breast cancer, whereas rs16944 and rs114364 were not (30). A study in China showed a significant association between the functional rs16944 variant and breast cancer risk (31). This variant is in high LD with rs1143627 (R 2 = 0.96) and rs1143633 (R 2 = 0.79), both of which were associated with breast cancer risk in this study. We observed significant P ARTP values for all three genes with breast cancer risk for all women overall; IL1B was significantly associated with risk among women with low NA ancestry and both IL1A and IL1B were uniquely associated with breast cancer risk among premenopausal women. We confirmed the association with rs1143627 for women overall and also observed that rs1143633 was associated with breast cancer risk overall, among women with low NA ancestry, and among premenopausal women. Estrogen has been shown to influence the production of proinflammatory cytokines; studies of IL-1B and IL1RN and estrogen have shown reduced production of IL-1B in response to estrogen exposure (32). Others have suggested that IL-1 family, especially IL1RN, is an important mediator of the breast cancer microenvironment because of their relationship with ER and PR status (33); in our study, IL1RN was associated with ER+/PR+ tumors.
IL-2 and its alpha receptor IL-2RA, also known as CD25, play an important role in immune response. We showed that IL2 and IL2RA were associated with breast cancer risk overall, but primarily influenced risk among women with greater NA ancestry. IL2 was more strongly associated with premenopausal breast cancer, whereas IL2RA was associated more with postmenopausal breast cancer risk. IL2RA also had a greater impact on all-cause mortality among women with greater NA ancestry. Also of interest was the observation that IL2RA was one of the primary genes interacting with other genes to alter breast cancer risk. There has been limited information on genetic variation in IL2 or IL2RA and breast cancer risk or survival. IL2RA expression has been associated with more advanced disease stage at breast cancer diagnosis, possibly by promoting proliferation and/or by inhibiting apoptosis (7,34). Of interest is our observation that IL2 and IL2RA were more strongly associated with breast cancer risk and IL2RA with all-cause mortality among women with greater NA ancestry. The reason for this is unknown; however, this population has been shown to have higher rates of diabetes and it has been suggested that diabetic patients may have an imbalanced cellular immune response and lower levels of IL-2 and IL-2R (35–37). Increased levels of IL-2 have been associated with insulin resistance and high levels of C peptide and diabetes have been associated with the risk of breast cancer in Mexican women (38).
We observed a significant association for IL6 overall but stronger associations for women with low NA ancestry and for postmenopausal women. We observed that IL6R was associated with breast cancer-specific mortality among women with greater NA ancestry. Studies on the association between IL6 and breast cancer risk and survival have generated mixed findings (11). IL-6 serum levels have been associated with estrogen and in postmenopausal women with ER− tumors (negative tumors) (39,40). However, IL6 rs1800795 rare alleles were associated with an increased risk of breast cancer in a study of 269 breast cancer cases in Germany (41). One meta-analysis of IL6 rs1800795 supported only a weak association with this SNP (42), whereas another meta-analysis did not support the association (43). In another small study in the USA only a modest association was observed for IL6 rs2069861 (10). Our own previous work in the 4-CBCS suggested that women with higher NA ancestry may be more influenced by variation in IL6 and that this variation might be influenced by hormonal status (11). This study allowed us to expand on our previous work in women with a broader range of NA ancestry and a larger sample size. Both IL6 rs1800795 and rs1800797 in the gene promoter region are thought to be functional and the variant allele has been linked to decreased disease-free survival (8), especially among women with ER+ tumors (44).
We observed strong associations between IL10 and breast cancer risk overall as well as for subgroups of women with low NA ancestry, postmenopausal status and ER+/PR+ tumors.IL-10 acts as an anti-inflammatory agent having both antimetastatic and antitumor effects (45). Most studies that have examined functional polymorphisms in IL10, such as rs1800896, did not see an association with breast cancer risk (42), although a significant association was observed in a small study in Italy (46). This polymorphism also was associated with disease-free survival in a cohort of lymph node-positive breast cancer cases (47). We observed significant associations between three IL10 SNPs which were not in LD with rs1800896 while one that was, rs1800890, was not associated with either breast cancer risk or survival in this population. Several IL genes were associated with mortality, including IL4, IL6R (discussed above), IL8 and IL17A. Review of the literature suggests that IL4 and IL8 are linked to tumor aggressiveness, metastasis and survival. In our study, IL4 also was significantly associated with ER−/PR− tumors, which have a worse prognosis than ER+/PR+ tumors. IL4 rs3024543 has been associated with shorter breast cancer survival in a group of African-American and Hispanic women, but not among Caucasian women (48). Interestingly, all of the SNPs we evaluated in IL4 were associated with all-cause mortality and rs2243263 remained significant with breast cancer-specific mortality. IL4 rs2343250 also has been linked to worse prognosis for colorectal cancer patients (49). IL-8 (alias CXCL8) signaling promotes angiogenesis and tumor metastasis (50,51) and is involved in several important pathways, including mitogen-activated protein kinase, Akt and vascular endothelial growth factor. IL8 rs4073 has been previously associated with aggressive breast tumors (5) and poor prognosis (52). We observed increased risk of breast cancer-specific mortality with this SNP among women with lower NA ancestry. IL17 has been associated with poor breast cancer prognosis (53) and was significantly associated with breast cancer-specific mortality among women with higher NA ancestry in our study.
ILs have both pro- and anti-inflammatory properties that must be in balance for a proper immune response. Thus, we were interested in the interaction of these genes as well as their independent effects on breast cancer risk. Our results suggest that interaction between genes is an important predictor of risk. Although we also hypothesized that lifestyle factors, especially those associated with inflammation, such as obesity (54), might interact with genes to influence risk, our data did not support this hypothesis.
In this study we examined breast cancer risk and mortality in a large population of Hispanic/NA and NHW women living in the USA and Mexico. The population examined is one of the largest well-characterized genetically admixed populations available. We were able to evaluate tumor phenotype, survival, genetic ancestry and interaction with diet and lifestyle factors. In conducting the comprehensive examination of genetic variation in IL genes and breast cancer risk and mortality, we evaluated associations with the pathway, the gene and tagSNPs within the gene. These tagSNPs were based on predominately Caucasian population. This could have resulted in our missing associations from other LD blocks that could be relevant for the Native population. However, since we have observed many associations in women with the highest NA using this method of SNP selection, we do not believe that these SNPs are not relevant for evaluation. We incorporated those genes that appeared to be most important in the pathway based on the literature; however, other genes and SNPs could importantly influence risk. The ARTP method used a permutation test approach to evaluate the combined effects of SNPs at the gene and pathway levels. We believe that this is a strength of the study because it evaluates the overall strength of the SNPs within genes and genes within pathways to determine their potential significance to disease. This summary method allows to focus on genes and pathways, especially when using tagSNPs. We used lasso as a method of discovery to identify interactions based on the false discovery rate and then perform follow-up analysis using traditional regression methods. This method allowed us to efficiently check for interactions in the data. Despite these modern statistical approaches, chance findings are possible. Thus, we encourage others to replicate these findings.
In conclusion, the IL pathway appears to be important for both breast cancer risk and survival. Eight of the 16 genes evaluated were associated with breast cancer risk, and four genes were associated with survival. We also observed that genes interacted with each other to alter risk, where the majority of the interactions were among proinflammatory cytokines. Our results suggest that IL genes work together in the carcinogenic process.
Supplementary material
Supplementary Table S1 can be found at http://carcin.oxfordjournals.org/
Funding
The Breast Cancer Health Disparities Study was funded by grant CA14002 from the National Cancer Institute to Dr M.L.S. The San Francisco Bay Area Breast Cancer Study was supported by grants CA63446 and CA77305 from the National Cancer Institute, grant DAMD17-96-1-6071 from the U.S. Department of Defense and grant 7PB-0068 from the California Breast Cancer Research Program. The collection of cancer incidence data used in this study was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885; the National Cancer Institute’s Surveillance, Epidemiology and End Results Program under contract HHSN261201000036C awarded to the Cancer Prevention Institute of California; and the Centers for Disease Control and Prevention’s National Program of Cancer Registries, under agreement #1U58 DP000807-01 awarded to the Public Health Institute. The 4-Corner’s Breast Cancer Study was funded by grants CA078682, CA078762, CA078552 and CA078802 from the National Cancer Institute. The research also was supported by the Utah Cancer Registry, which is funded by contract N01-PC-67000 from the National Cancer Institute, with additional support from the State of Utah Department of Health, the New Mexico Tumor Registry, and the Arizona and Colorado cancer registries, funded by the Centers for Disease Control and Prevention National Program of Cancer Registries and additional state support. The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official view of the National Cancer Institute or endorsement by the State of California Department of Public Health, the National Cancer Institute, and the Centers for Disease Control and Prevention or their Contractors and Subcontractors. The Mexico Breast Cancer Study was funded by Consejo Nacional de Ciencia y Tecnología (CONACyT) (SALUD-2002-C01-7462).
Supplementary Material
Acknowledgements
We would also like to acknowledge the contributions of the following individuals to the study: S.Edwards for data harmonization oversight; E.Wolff and M.Hoffman for laboratory support; C.Ortega for her assistance with data management for the Mexico Breast Cancer Study, J.Koo for data management for the San Francisco Bay Area Breast Cancer Study; Dr T.Byers for his contribution to the 4-Corners Breast Cancer Study and Dr J.Galanter for assistance in selection of AIMs markers.
Conflict of Interest Statement: None declared.
Glossary
Abbreviations:
- 4-CBCS
4-Corners Breast Cancer Study
- ARTP
Adaptive Rank Truncated Product
- BMI
body mass index
- CI
confidence interval
- ER
estrogen receptor
- HR
hazard ratio
- IL
interleukin
- lasso
least absolute shrinkage and selection operator
- LD
linkage disequilibrium
- MBCS
Mexico Breast Cancer Study
- NA
Native American
- NHW
non-Hispanic white
- OR
odds ratio
- PR
progesterone receptor
- SFBCS
San Francisco Bay Area Breast Cancer Study
- SNP
single-nucleotide polymorphism.
References
- 1. DeNardo D.G., et al. (2007). Inflammation and breast cancer. Balancing immune response: crosstalk between adaptive and innate immune cells during breast cancer progression. Breast Cancer Res., 9, 212. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Shen W., et al. (2010). Synergy of IL-23 and Th17 cytokines: new light on inflammatory bowel disease. Neurochem. Res., 35, 940–946 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Korantzis I., et al. (2012). Expression of angiogenic markers in the peripheral blood of patients with advanced breast cancer treated with weekly docetaxel. Anticancer Res., 32, 4569–4580 [PubMed] [Google Scholar]
- 4. Lerebours F., et al. (2008). NF-kappa B genes have a major role in inflammatory breast cancer. BMC Cancer, 8, 41. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Snoussi K., et al. (2010). Combined effects of IL-8 and CXCR2 gene polymorphisms on breast cancer susceptibility and aggressiveness. BMC Cancer, 10, 283. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Gerger A., et al. (2010). Association of interleukin-10 gene variation with breast cancer prognosis. Breast Cancer Res. Treat., 119, 701–705 [DOI] [PubMed] [Google Scholar]
- 7. Kuhn D.J., et al. (2005). The role of interleukin-2 receptor alpha in cancer. Front. Biosci., 10, 1462–1474 [DOI] [PubMed] [Google Scholar]
- 8. Snoussi K., et al. (2005). Genetic variation in pro-inflammatory cytokines (interleukin-1beta, interleukin-1alpha and interleukin-6) associated with the aggressive forms, survival, and relapse prediction of breast carcinoma. Eur. Cytokine Netw., 16, 253–260 [PubMed] [Google Scholar]
- 9. Wang L., et al. (2012). A miRNA binding site single-nucleotide polymorphism in the 3’-UTR region of the IL23R gene is associated with breast cancer. PLoS One, 7, e49823. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Madeleine M.M., et al. (2011). Genetic variation in proinflammatory cytokines IL6, IL6R, TNF-region, and TNFRSF1A and risk of breast cancer. Breast Cancer Res. Treat., 129, 887–899 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Slattery M.L., et al. (2007). IL6, aspirin, nonsteroidal anti-inflammatory drugs, and breast cancer risk in women living in the southwestern United States. Cancer Epidemiol. Biomarkers Prev., 16, 747–755 [DOI] [PubMed] [Google Scholar]
- 12. Ferrari S.L., et al. (2003). Two promoter polymorphisms regulating interleukin-6 gene expression are associated with circulating levels of C-reactive protein and markers of bone resorption in postmenopausal women. J. Clin. Endocrinol. Metab., 88, 255–259 [DOI] [PubMed] [Google Scholar]
- 13. Möhlig M., et al. (2004). Body mass index and C-174G interleukin-6 promoter polymorphism interact in predicting type 2 diabetes. J. Clin. Endocrinol. Metab., 89, 1885–1890 [DOI] [PubMed] [Google Scholar]
- 14. Slattery M.L., et al. (2012). Genetic variation in genes involved in hormones, inflammation and energetic factors and breast cancer risk in an admixed population. Carcinogenesis, 33, 1512–1521 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Slattery M.L., et al. (2007). Body size, weight change, fat distribution and breast cancer risk in Hispanic and non-Hispanic white women. Breast Cancer Res. Treat., 102, 85–101 [DOI] [PubMed] [Google Scholar]
- 16. Angeles-Llerenas A., et al. (2010). Moderate physical activity and breast cancer risk: the effect of menopausal status. Cancer Causes Control, 21, 577–586 [DOI] [PubMed] [Google Scholar]
- 17. John E.M., et al. (2003). Lifetime physical activity and breast cancer risk in a multiethnic population: the San Francisco Bay area breast cancer study. Cancer Epidemiol. Biomarkers Prev., 12(11 Pt 1), 1143–1152 [PubMed] [Google Scholar]
- 18. John E.M., et al. (2005). Migration history, acculturation, and breast cancer risk in Hispanic women. Cancer Epidemiol. Biomarkers Prev., 14, 2905–2913 [DOI] [PubMed] [Google Scholar]
- 19. Falush D., et al. (2003). Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics, 164, 1567–1587 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Pritchard J.K., et al. (2000). Inference of population structure using multilocus genotype data. Genetics, 155, 945–959 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Nyholt D.R. (2004) A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. Am. J. Hum. Genet., 74, 765–769 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Li J., et al. (2005). Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity (Edinb)., 95, 221–227 [DOI] [PubMed] [Google Scholar]
- 23. Yu K., et al. (2009) Pathway analysis by adaptive combination of P-values. Genet. Epidemiol., 33, 700–709 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Kai Yu O.L., et al. (2011). ARTP Gene and Pathway p-values computed using the Adaptive Rank Truncated Product. pp. R package.
- 25. Tibshirani T. (1996). Regression shrinkage and selection via the Lasso. J. R. Stat. Soc., 58, 267–288 [Google Scholar]
- 26. Lange K., et al. (2001). Mendel version 4.0: a complete package for the exact genetic analysis of discrete traits in pedigree and population data sets. Am. J. Hum. Gen., 69 (suppl.), 504 [Google Scholar]
- 27. Kumar S., et al. (2003). Interleukin-1 alpha promotes tumor growth and cachexia in MCF-7 xenograft model of breast cancer. Am. J. Pathol., 163, 2531–2541 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Speirs V., et al. (1999). Evidence for transcriptional activation of ERalpha by IL-1beta in breast cancer cells. Int. J. Oncol., 15, 1251–1254 [DOI] [PubMed] [Google Scholar]
- 29. Zapata-Tarrés M., et al. (2013). Interleukin-1 receptor antagonist gene polymorphism increases susceptibility to septic shock in children with acute lymphoblastic leukemia. Pediatr. Infect. Dis. J., 32, 136–139 [DOI] [PubMed] [Google Scholar]
- 30. Liu X., et al. (2010). Three polymorphisms in interleukin-1β gene and risk for breast cancer: a meta-analysis. Breast Cancer Res. Treat., 124, 821–825 [DOI] [PubMed] [Google Scholar]
- 31. Liu J., et al. (2006). Functional variants in the promoter of interleukin-1beta are associated with an increased risk of breast cancer: a case-control analysis in a Chinese population. Int. J. Cancer, 118, 2554–2558 [DOI] [PubMed] [Google Scholar]
- 32. Rogers A., et al. (2007). Different effects of raloxifene and estrogen on interleukin-1beta and interleukin-1 receptor antagonist production using in vitro and ex vivo studies. Bone, 40, 105–110 [DOI] [PubMed] [Google Scholar]
- 33. Miller L.J., et al. (2000). Interleukin-1 family expression in human breast cancer: interleukin-1 receptor antagonist. Cancer Invest., 18, 293–302 [DOI] [PubMed] [Google Scholar]
- 34. García-Tuñón I., et al. (2004). Interleukin-2 and its receptor complex (alpha, beta and gamma chains) in in situ and infiltrative human breast cancer: an immunohistochemical comparative study. Breast Cancer Res., 6, R1–R7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Roncarolo M.G., et al. (1988). Interleukin-2 production and interleukin-2 receptor expression in children with newly diagnosed diabetes. Clin. Immunol. Immunopathol., 49, 53–62 [DOI] [PubMed] [Google Scholar]
- 36. Zier K.S., et al. (1984). Decreased synthesis of interleukin-2 (IL-2) in insulin-dependent diabetes mellitus. Diabetes, 33, 552–555 [DOI] [PubMed] [Google Scholar]
- 37. Hulme M.A., et al. (2012). Central role for interleukin-2 in type 1 diabetes. Diabetes, 61, 14–22 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Torres-Mejía G., et al. (2012). Moderate-intensity physical activity ameliorates the breast cancer risk in diabetic women. Diabetes Care, 35, 2500–2502 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Mantovani G., et al. (2002). Quantitative evaluation of oxidative stress, chronic inflammatory indices and leptin in cancer patients: correlation with stage and performance status. Int. J. Cancer, 98, 84–91 [DOI] [PubMed] [Google Scholar]
- 40. Salgado R., et al. (2003). Circulating interleukin-6 predicts survival in patients with metastatic breast cancer. Int. J. Cancer, 103, 642–646 [DOI] [PubMed] [Google Scholar]
- 41. Hefler L.A., et al. (2005). Interleukin-1 and interleukin-6 gene polymorphisms and the risk of breast cancer in caucasian women. Clin. Cancer Res., 11, 5718–5721 [DOI] [PubMed] [Google Scholar]
- 42. Balasubramanian S.P., et al. (2006). Interleukin gene polymorphisms and breast cancer: a case control study and systematic literature review. BMC Cancer, 6, 188. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Yu K.D., et al. (2010). Lack of an association between a functional polymorphism in the interleukin-6 gene promoter and breast cancer risk: a meta-analysis involving 25,703 subjects. Breast Cancer Res. Treat., 122, 483–488 [DOI] [PubMed] [Google Scholar]
- 44. DeMichele A., et al. (2009). Host genetic variants in the interleukin-6 promoter predict poor outcome in patients with estrogen receptor-positive, node-positive breast cancer. Cancer Res., 69, 4184–4191 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Kundu N., et al. (1996). Antimetastatic and antitumor activities of interleukin 10 in a murine model of breast cancer. J. Natl Cancer Inst., 88, 536–541 [DOI] [PubMed] [Google Scholar]
- 46. Giordani L., et al. (2003). Association of breast cancer and polymorphisms of interleukin-10 and tumor necrosis factor-alpha genes. Clin. Chem., 49, 1664–1667 [DOI] [PubMed] [Google Scholar]
- 47. Knechtel G., et al. (2010). Analysis of common germline polymorphisms as prognostic factors in patients with lymph node-positive breast cancer. J. Cancer Res. Clin. Oncol., 136, 1813–1819 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48. Murray J.L., et al. (2013). Prognostic value of single nucleotide polymorphisms of candidate genes associated with inflammation in early stage breast cancer. Breast Cancer Res. Treat., 138, 917–924 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49. Wilkening S., et al. (2008). Interleukin promoter polymorphisms and prognosis in colorectal cancer. Carcinogenesis, 29, 1202–1206 [DOI] [PubMed] [Google Scholar]
- 50. Waugh D.J., et al. (2008). The interleukin-8 pathway in cancer. Clin. Cancer Res., 14, 6735–6741 [DOI] [PubMed] [Google Scholar]
- 51. Zuccari D.A., et al. (2012). An immunohistochemical study of interleukin-8 (IL-8) in breast cancer. Acta Histochem., 114, 571–576 [DOI] [PubMed] [Google Scholar]
- 52. Snoussi K., et al. (2006). Genetic variation in IL-8 associated with increased risk and poor prognosis of breast carcinoma. Hum. Immunol., 67, 13–21 [DOI] [PubMed] [Google Scholar]
- 53. Chen W.C., et al. (2013). Interleukin-17-producing cell infiltration in the breast cancer tumour microenvironment is a poor prognostic factor. Histopathology, 63, 225–233 [DOI] [PubMed] [Google Scholar]
- 54. Simpson E.R., et al. (2013) Obesity and breast cancer: role of inflammation and aromatase. J. Mol. Endocrinol., 51, T51–T59 [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.