81111041254Breast Cancer Res TreatBreast Cancer Res. Treat.Breast cancer research and treatment0167-68061573-721725104439416736610.1007/s10549-014-3071-yNIHMS619904ArticleGenetic Variation in the JAK/STAT/SOCS signaling pathway influences breast cancer-specific mortality through interaction with cigarette smoking and use of aspirin/NSAIDs: The Breast Cancer Health Disparities StudySlatteryMartha L.1LundgreenAbbie1abbie.lundgreen@hsc.utah.eduHinesLisa M.2lhines@uccs.eduTorres-MejiaGabriela3México.gtorres@insp.mxWolffRoger K.1Roger.Wolff@hsc.utah.eduSternMariana C.4marianas@usc.eduJohnEsther M.5Esther.John@cpic.orgUniversity of Utah, Department of Medicine, 383 Colorow, Salt Lake City, UT 84108University of Colorado at Colorado Springs, Department of Biology, Colorado Springs, CO 80918Instituto Nacional de Salud Pública, Centro de Investigación en Salud Poblacional, Av. Universidad No. 655, Col. Sta. Ma. Ahuacatitlán, Cuernavaca Morelos CP 62100Department of Preventive Medicine, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90089Cancer Prevention Institute of California, Fremont, CA 94538, and Division of Epidemiology, Department of Health Research and Policy and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305Address correspondence to: Dr. Slattery at this address. Marty.Slattery@hsc.utah.edu88201408820148201408820151471145158Purpose

The Janus kinase (JAK)/signal transducer and activator of transcription (STAT)-signaling pathway is involved in immune function and cell growth; genetic variation in this pathway could influence breast cancer risk.

Methods

We examined 12 genes in the JAK/STAT/SOCS-signaling pathway with breast cancer risk and mortality in an admixed population of Hispanic (2111 cases, 2597 controls) and non-Hispanic white (1481 cases, 1585 controls) women. Associations were assessed by Indigenous American (IA) ancestry.

Results

After adjustment for multiple comparisons, JAK1 (3 of 10 SNPs) and JAK2 (4 of 11 SNPs) interacted with body mass index (BMI) among pre-menopausal women, while STAT3 (4 of 5 SNPs) interacted significantly with BMI among post-menopausal women to alter breast cancer risk. STAT6 rs3024979 and TYK2 rs280519 altered breast cancer-specific mortality among all women. Associations with breast cancer-specific mortality differed by IA ancestry; SOCS1 rs193779, STAT3 rs1026916, and STAT4 rs11685878 associations were limited to women with low IA ancestry and associations with JAK1 rs2780890, rs2254002, and rs310245 and STAT1 rs11887698 were observed among women with high IA ancestry. JAK2 (5 of 11 SNPs), SOCS2 (1of 3 SNPs), and STAT4 (2 of 20 SNPs) interacted with cigarette smoking status to alter breast-cancer specific mortality. SOCS2 (1 of 3 SNPs) and all STAT3, STAT5A, and STAT5B SNPs significantly interacted with use of aspirin/NSAIDs to alter breast cancer-specific mortality.

Conclusions

Genetic variation in the JAK/STAT/SOCS pathway was associated with breast cancer-specific mortality. The proportion of SNPs within a gene that significantly interacted with lifestyle factors lends support for the observed associations.

Breast CancerBreast cancer-specific mortalityJAK/STAT/SOCSpolymorphismsBMIcigarette smokingaspirin/NSAIDs

The Janus kinase (JAK)/signal transducer and activator of transcription (STAT) signaling pathway is involved in immune function and cell growth and differentiation[1, 2]. The JAK family consists of four non-receptor protein tyrosine kinases, JAK1, JAK2, JAK3, and TYK2. Of these, JAK1, JAK2, and TYK2 are expressed ubiquitously in mammals [3]. Once activated by cytokines, JAKs serve as docking sites for signaling molecules such as STATs. Activated STATs translocate from the cytoplasm to the nucleus where they increase the transcription rate of several genes. STAT1 and STAT2 were first identified as contributing to activation of genes involved in immune response [4]. STAT5 was first described in the mammary gland and has considerable specificity for mammary gland development [5]. Altogether, seven STATS have been identified in mammalian cells, STAT1, STAT2, STAT3, STAT4, STAT5a, STAT5b, and STAT6 [6]. STAT signaling has been shown to be important for mammary cell survival and tumorigenesis [6]. It has also been suggested that expression of STATs are associated with unique breast cancer subtypes defined by estrogen receptor (ER) and progesterone receptor (PR) status [7].

Cytokines up-regulate suppressors of cytokine signaling (SOCS) that inhibit the activity of JAKs and STATs [8]. Thus, research targeting an understanding of the JAK/STAT/SOCS signaling pathway often has involved the interaction between JAK/STAT/SOCS with cytokines. STAT1 and STAT2 were first identified from work involving downstream events of receptor binding of interferon γ (IFNγ) on transcriptional activation of genes involved in immune response[4]. Pro-inflammatory cytokines, such as IL-6 have been shown to up-regulate STAT proteins [4, 9, 10]. Both JAK1 and JAK2 are important for cytokines through use of the shared receptor subunits; IL-6 is an important pro-inflammatory cytokine that uses these receptors since they are essential for cytokine signaling [11]. JAK2 is essential for hormone-like cytokine signaling, including prolactin signaling [11].

This study builds on our previous work that has evaluated breast cancer associations with genetic variants in cytokines among women with diverse genetic ancestry. We have shown that breast cancer risk and mortality as well as risk associated with IL6 and other cytokine SNPs differ by Indigenous American (IA) ancestry [12, 13]. Thus, it is reasonable to hypothesize that breast cancer associations with genetic variation in JAK/STAT/SOCS genes may also vary by IA ancestry. Given previous work that suggest these genes may have unique ER/PR associations, we evaluated associations by ER/PR tumor subtype. Additionally, we evaluated the association of these genes with survival since one of their functions is to promote cell differentiation and metastases [14]. Since diet and lifestyle factors that are associated with inflammation may modify associations with these genes, we evaluate interaction of these genes with a dietary oxidative balance score, body mass index (BMI), cigarette smoking status, and use of aspirin and non-steroidal anti-inflammatory drugs (NSAIDs).

Methods

The Breast Cancer Health Disparities Study includes participants from three population-based case-control studies [15], the 4-Corners Breast Cancer Study (4-CBCS) [16], the Mexico Breast Cancer Study (MBCS)[17], and the San Francisco Bay Area Breast Cancer Study (SFBCS) [18, 19], who completed an in-person interview and who had a blood or mouthwash sample available for DNA extraction. Information on exposures was collected up to the referent year, defined as the calendar year before diagnosis for cases or before selection into the study for controls. 4-CBCS participants were between 25 and 79 years; MBCS participants were between 28 and 74 years; and SFBCS participants were between 35 to 79 years. All participants signed informed written consent prior to participation; the Institutional Review Board for Human Subjects at each institution approved the study.

Data Harmonization

Data were harmonized across all study centers and questionnaires as previously described [15]. Women were classified as either pre-menopausal or post-menopausal based on responses to questions on menstrual history. Pre-menopausal women were those who reported still having periods during the referent year. Post-menopausal women were those who reported either a natural menopause or if they reported taking hormone therapy (HT) 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 4-CBCS and SFBCS were asked to self-identify their race/ethnicity and were classified as non-Hispanic white (NHW), Hispanic, Native American (NA) or a combination of these groups. Women in MBCS were not asked their race or ethnicity and were combined with U.S. Hispanics/NAs in the analyses.

Lifestyle variables included BMI calculated as self-reported weight (kg) during the referent year divided by measured height squared (m2) and categorized as normal (<25 kg/m2), overweight (25–29.9 kg/m2), or obese (≥30 kg/m2). Regular cigarette smoking was evaluated as current, former, or never, where regular was defined as having smoked one or more cigarettes for six months or longer in 4-CBCS and SFBCS (data available for a subset of subjects only) or having smoked 100 or more cigarettes in MCBCS. A dietary oxidative balance score (DOBS) that included nutrients with anti- or pro-oxidative properties was used [20]. Dietary information was collected via a computerized validated diet history questionnaire in 4-CBCS [12, 21], a 104-item semi-quantitative Food Frequency Questionnaire (FFQ) in MBCS [22], and a modified version of the Block Food Frequency Questionnaire in SFBCS [23]. Alcohol consumption was based on long-term use; consumption during the referent year was used for a subset of SFBCS women without information on long-term use. Regular use of aspirin or NSAIDS defined as three or more times a week for at least one month was available for the 4-CBCS only. A history of diabetes was defined as ever being told by a health care provider that you had diabetes or high blood sugar (not available for all SFBCS participants).

Genetic Data

DNA was extracted from either whole blood (n=7287) or mouthwash (n=634) samples. Whole genome amplification (WGA) was applied to the mouthwash-derived DNA samples prior to genotyping. TagSNPs were selected to characterize the genetic variation using the following parameters: linkage disequilibrium (LD) blocks were defined using a Caucasian LD map and an r2=0.8; minor allele frequency (MAF) >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 (AIMs) were used to distinguish European and Indigenous American (IA) ancestry [15]. All markers were genotyped using a multiplexed bead array assay format based on GoldenGate chemistry (Illumina, San Diego, California. In the current analysis we evaluated tagSNPs for JAK1 (10 SNPs), JAK2 (11 SNPs), SOCS1 (2 SNPs), SOCS2 (3 SNPs), STAT1 (15 SNPs), STAT2 (2 SNPs), STAT3 (5 SNPs), STAT4 (20 SNPs), STAT5A (2 SNPs), STAT5B (3 SNPs), STAT6 (6 SNPs), TYK2 (4 SNPs). Online Supplement 1 provides a description of these genes and SNPs; online supplement 2 describes LD structure of these genes.

Tumor Characteristics and Survival

Data on estrogen receptor (ER) and progesterone receptor (PR) tumor status and survival were available for cases from 4-CBCS and SFBCS only. 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. Surveillance Epidemiology and End Results (SEER) disease stage was categorized as local, regional, or distant.

Statistical MethodsGenetic ancestry estimation

The program STRUCTURE was used to estimate individual ancestry for each study participant assuming two founding populations [24, 25]. A three-founding population model was assessed but did not fit the population structure. Participants were classified by level of percent IA ancestry (≤28%, >28–70%, and >70%), based on the distribution of genetic ancestry in the control population [15].

SNP Associations

Genes and SNPs were assessed for their association with breast cancer risk overall, by strata of IA ancestry, and by 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 9.4 (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 in the referent year, and parity as categorical variables and age (five-year categories) and genetic ancestry as continuous variables. Associations with SNPs were assessed assuming a co-dominant model. Based on the initial assessment, SNPs that appeared to have a dominant or recessive mode of inheritance were evaluated with those inheritance models in subsequent analyses. For stratified analyses, the p value was based on the Wald chi-square test comparing the homozygote rare to the homozygote common when presenting the co-dominant model. The multinomial p value reported for ER/PR status using the glogit link in the logistic procedure compares unique associations by tumor phenotype. 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 [26] and modified by Li and Ji [27]. An unadjusted p value of <0.05 was considered statistically significant; results are presented for those where the multiple comparison adjusted p value was <0.15 and are noted as being marginally associated.

Interactions

We assessed gene by environment interactions for lifestyle factors that could influence candidate genes given their potential involvement in inflammation, including BMI (separately for pre- and post-menopausal women given differences in risk associated with BMI by menopausal status), smoking (current, former, or never smokers), dietary oxidative balance score (DOBS), and regular use of aspirin/NSAID (for 4-CBCS participants only). DOBS was based on each individual’s intake of anti-oxidants (vitamin C, vitamin E, beta carotene (data for beta carotene were not available for MBCS), folic acid, and dietary fiber) and pro-oxidants (alcohol). Nutrients were evaluated per 1000 calories and the DOBS was based on study-specific distributions given the different dietary questionnaires used. Alcohol consumption was classified into three levels: the top 25th percentile of consumption, all other drinkers, and non-drinkers. The DOBS ranges from low levels (first quartile) of exposure to anti-oxidants or high exposure to pro-oxidants (fourth quartile) to high levels of anti-oxidants (fourth quartile) and low exposure to pro-oxidants (non-drinkers). Tests for interactions were evaluated using Wald one degree of freedom (1-df) chi-square tests.

Survival Analysis

Survival months were calculated based on month and year of diagnosis and month and year of death or last contact. Survival updates were received in the winter of 2013 that included complete survival surveillance through December of 2012. Associations between SNPs and breast cancer-specific mortality among cases with a first primary invasive breast cancer were evaluated using Cox proportional hazards models to obtain multivariate hazard ratios (HR) and 95% confidence CI. Individuals were censored when they died of causes other than breast cancer or were lost to follow-up. We present Wald p values for all women and by ancestry strata based on the comparison between the homozygote rare and common genotype when presenting the co-dominant inheritance model using models adjusted for age, study center, genetic ancestry, and SEER stage. Since survival data were not available for MBCS, the upper two ancestry strata were combined to evaluate survival by genetic ancestry. Interactions between genetic variants and genetic ancestry, BMI, cigarette smoking, DOBS, and aspirin/NSAID use with survival were assessed using p values from 1-df Wald chi-square tests.

Results

The majority of women were U.S. Hispanic or Mexican and were slightly younger than U.S. NHW women (Table 1). U.S. Hispanic women were more likely to have ER−/PR− tumors than NHW women. Approximately 20% of women had died, with 47.6% of deaths being from breast cancer among NHW and 55.9% of deaths among U.S. Hispanic women.

Few associations were observed between SNPs in our candidate genes and breast cancer risk (Table 2). STAT5B rs6503691 and TYK2 rs280519 were associated with reduced risk among women with high IA ancestry (ORCT/TT = 0.63 95% CI 0.41, 0.97, Phet=0.39 and ORAG/GG = 0.75 95% CI = 0.58, 0.96, Phet=0.46) and STAT6 rs3024974 (ORCT/TT = 1.15 95 %CI 1.02, 1.29, Phet=0.71) was associated with increased breast cancer risk overall. More associations were observed for specific tumor phenotype, with JAK2 rs1536800 being associated with ER−/PR− tumors and STAT3 (4 SNPs) and STAT5A (2 SNPs) and STAT5B (1 SNP) associated with ER−/PR+ tumors. These differences were statistically significant for JAK2 rs1536800 (Phet=0.03), STAT3 rs8069645 (Phet=0.03), STAT5A 7217728 (Phet=0.04), and STAT5B rs7218653 (Phet=0.02). No significant differences in association were detected by menopausal status (data not shown).

BMI was the main lifestyle factor that interacted with these genes to alter risk of breast cancer (Table 3). JAK1 (3 SNPs) and JAK2 (4 SNPs) interacted with BMI among pre-menopausal women, with the majority of the differences observed among those who were obese. Among post-menopausal women, STAT3 (4 SNPs) interacted with BMI to alter breast cancer risk, with the majority of the differences observed among those with normal BMI. Additionally, interactions were seen between STAT1 (2 SNPs) and STAT4 (1 SNP) and DOBS. We also assessed interaction between IL6 and its receptor and JAK/STAT/SOC pathway genes and observed several significant interactions although after adjustment for multiple comparisons none of the associations were statistically significant (data not shown).

STAT6 rs3024979 and TYK2 rs280519 were associated with breast cancer-specific mortality overall (Table 4). However, most associations were restricted to either low or high IA ancestry group. Among women with low IA ancestry, SOCS1 rs193779, STAT3 rs1026916, and STAT4 rs11685878 were associated with breast cancer-specific mortality. Among women with high IA ancestry (JAK1 rs2780890, rs2254002, and rs310245) and STAT1 rs11887698 were associated with breast cancer-specific mortality. Additionally, JAK2 (5 SNPs), SOCS2 (1 SNP), and STAT4 (2 SNPs) interacted with cigarette smoking status to alter breast cancer-specific mortality (Table 5), with associations predominantly observed among current smokers. STAT1 rs2030171 and STAT5B rs9900213 interacted with DOBS to alter breast cancer-specific mortality (Table 5). Interactions also were seen between SOCS2 (1 SNP), STAT3 (5 SNPs), STAT5A (2 SNPs), and STAT5B (3 SNPs) and regular use of aspirin/NSAIDs. No significant interactions with BMI were observed after adjustment for multiple comparisons.

Discussion

Among genes within the JAK/STAT/SOC-signaling pathway, we observed several associations between SNPs and breast cancer mortality, and a few significant associations with risk of breast cancer, irrespective of genetic ancestry or ER/PR tumor subtypes. However, lifestyle factors that influence inflammation interacted with these genes to alter breast cancer risk and mortality. Specifically, BMI and DOBS interacted with these genes to alter breast cancer risk, while cigarette smoking and aspirin/NSAID use interacted with them to influence breast cancer-specific mortality. The proportion of SNPs within a gene that significantly interacted with DOBS and lifestyle factors was considerably greater than by chance, lending support for the observed associations.

Although it is reasonable to genes could help explain differences in breast cancer incidence rates when comparing populations with high vs. low IA ancestry, our results provide little support for that hypothesis. Others have suggested that this pathway has unique associations with tumor phenotype and estrogen [7, 28–30], but we found minimal support for differences in associations by ER/PR subtype. The majority of associations were with ER−/PR+ tumors, and although statistically significant after adjustment for multiple comparisons, there were few individuals with that phenotype and therefore estimates of association were imprecise even though they were statistically significant.

Of interest are the consistent associations observed for the interaction of BMI with JAK1 (3 out of 10 SNPs) and JAK2 (4 out of 11 SNPs) for pre-menopausal breast cancer risk and with STAT3 (4 of 5 SNPs) for post-menopausal breast cancer risk. The number of SNPs within genes that were associated with breast cancer risk was greater than one would expect by chance. One explanation for the interactions between BMI and JAK1, JAK2, and STAT3 could be the strong correlation between leptin and BMI. Although leptin is mainly produced by white adipocytes, it is also produced by mammary epithelium. The leptin receptor is a class I cytokine receptor that acts through JAK and STATs and the JAK/STAT pathway is one of the main signaling cascades activated by leptin [31]. Additionally, STAT3 specifically has been shown to influence energy homeostasis [32, 33]. Activation of the JAK/STAT pathway also can promote tumor growth and induce inflammation as well as regulate other genes that control cell proliferation, differentiation, tumor development, and cell survival.

The JAK/STAT pathway is critical for cell development, cell survival, cell proliferation, and apoptosis; our results suggest genetic variation in these genes is important for breast cancer-specific mortality. We observed stronger estimates of association and more consistent associations across genes, and SNPs within those genes, with breast cancer-specific mortality than with breast cancer risk. Thus, it is possible that these genes function as tumor promoters, as has been suggested [6, 34]. The strongest and most consistent associations were observed for the interaction between cigarette smoking and aspirin/NSAID use with JAK2, STAT3, STAT5a, and STAT5b and to a lesser extent with SOSC2 (rs3816997 which interacted with both cigarette smoking and aspirin/NSAIDs use) to influence breast cancer-specific mortality. The JAK/STAT signaling pathway is activated when cytokines are bound to their receptors while SOCs suppresses the signaling. Nicotine has been shown to activate the JAK2/STAT3 pathway [35], which in this case appears to promote tumor progression depending on JAK2/STAT3 genotype.

Regular use of aspirin/NSAIDs interacted with all SNPs evaluated for STAT3 (5), STAT5A (2), and STAT5B (3) to alter breast cancer-specific mortality. STAT3 has been shown to be a promoter of tumor invasiveness and angiogenesis [6]. Activation of STAT5, which was first recognized as mammary gland factor, results in regulation of several genes involved in cell apoptosis, survival, and proliferation [14]. It has been shown that aspirin regulates apoptosis by down-regulating the IL6-STAT3 pathway [36] and that the epidermal growth factor induces COX2 through STAT5 signaling [37] thus providing biological support for our observations.

We believe that these findings are unique and have found no reference to the importance of these genes in the literature or in GWAS studies of breast cancer. Our candidate pathway approach has enabled us to identified important genes based on their biological function. Furthermore our ability to evaluate interaction with diet and lifestyle factors has enhanced our understanding of these genes and how they influence breast cancer risk and mortality.

This study has both strengths and limitations. The population represents a large genetically diverse population that includes extensive data on diet and lifestyle factors along with genetic data, ER/PR status, and vital status. However, ER/PR status and vital status were available only for the U.S. based studies. We used a tag-SNP approach to characterize genetic variation in these genes, although other SNPs could be important that were not analyzed. We adjusted for multiple comparisons within our candidate genes, although we cannot exclude the possibility of chance observations. However, the number of SNPs within genes for which we observed associations further indicates that these observations may be more than chance findings. Nevertheless, we encourage others to replicate our findings, especially those that pertain to survival, given their implication for treatment modalities as has been suggested [38].

In conclusion, our findings suggest that genetic variation in the JAK/STAT/SOCS signaling pathway is important for breast cancer-specific mortality. Of note is the consistent and stronger interaction observed between JAK2, SOCS2, STAT3 and STAT5 and cigarette smoking and use of aspirin/NSAIDs to modify breast cancer-specific mortality. Additionally JAK1, JAK2, and STAT3 interacted with BMI to modify risk of developing breast cancer. Given the potential importance of these findings on modalities such as aspirin/NSAIDS to improve survival on a subset of women, replication of these findings in other populations is needed.

Supplementary Material

Supplemental Table 1. Summary of genes and tagSNPs analyzed.

Supplemental Table 2. R2 values for SNPs within genes

We would also like to acknowledge the contributions of the following individuals to the study: Sandra Edwards and Jennifer Herrick for data harmonization oversight; Erica Wolff and Michael Hoffman for laboratory support; Carolina Ortega for her assistance with data management for the Mexico Breast Cancer Study, Jocelyn Koo for data management for the San Francisco Bay Area Breast Cancer Study; Dr. Tim Byers, Dr. Kathy Baumgartner, and Dr. Anna Giuliano for their contribution to the 4-Corners Breast Cancer Study; and Dr. Josh Galanter for assistance in selection of AIMs markers.

Funding: The Breast Cancer Health Disparities Study was funded by grant CA14002 from the National Cancer Institute to Dr. Slattery. 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-Corners 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).

Conflict of Interest. The authors have no conflict of interest to report.

Financial Relationships: The authors have no financial relationships with the sponsored research other than through grant funding to MS, EJ, RW, and GTM.

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Description of study population by self-reported race/ethnicity

U.S. non-Hispanic WhiteU. S. Hispanic/Native American or Mexican
ControlsCasesControlsCases
N%N%N%N%
Total158537.9148141.2259762.1211158.8
Study Site
 4-CBCS132183.3122782.872327.859728.3
 MBCS000099438.381638.7
 SFBCS26416.725417.288033.969833.1
Age (years)
 <401167.3896311122009.5
 40–4940825.740927.68313271333.8
 50–5940925.841327.975629.161729.2
 60–693492236124.452620.343020.4
 >7030319.120914.11736.71517.2
 Mean56.65652.352.7
Menopausal Status
 Pre-menopausal49431.548933.5102740.783640.9
 Post-menopausal107568.597066.5149959.3121059.1
Estimated Percent Indigenous American Ancestry
 0–28157799.5147299.427810.727513
 29–7070.470.5168664.9139366
 71–10010.120.163324.444321
ER/PR Status2
 ER+/PR+NA169568.2NA160561.9
 ER+/PR−NA112111.9NA111511.8
 ER−/PR+NA1151.5NA1282.9
 ER−/PR−NA118818.4NA122923.4
SEER Summary Stage2,3
 LocalNA183070.9NA165059.6
 RegionalNA132527.8NA143239.6
 DistantNA1151.3NA190.8
Vital Status2,3
 DeceasedNA125421.4NA122919.8
 AliveNA193578.6NA192980.2
Cause of Death2,3
 Breast CancerNA112147.6NA112855.9
 OtherNA113352.4NA110144.1
Smoking Status4
 Never79460.368856.1161672.1129870.1
 Former36027.338631.534715.532217.4
 Current16312.415212.427812.423112.5
BMI (kg/m2)
 <2569944.467845.945317.649223.5
 25–29.946529.543329.395136.976836.7
 >3041226.136724.8117245.583239.8
NSAID use5
 No70853.767054.744661.739566.2
 Yes61046.355445.327738.320233.8
Dietary Oxidative Balance Score6[mean (SD)]
 4-CBCS6.3 (2.7)6.3 (2.6)6.7 (2.5)6.5 (2.6)
 MCBCSNA1NA15.9 (2.0)5.7 (2.0)
 SFBCS5.6 (2.6)5.7 (2.6)6.9 (2.5)6.1 (2.5)

Data not applicable (NA)

Data unavailable from Mexico Breast Cancer Study (MBCS)

Includes first primary invasive breast cancer cases from the 4-Corners Breast Cancer Study (4-CBCS) and San Francisco Bay Area Breast Cancer Study (SFBCS)

Data unavailable from women using questionnaire’s one and two from SFBCS

Data only available for the 4-CBCS

Dietary Oxidative Balance Score (DOBS) includes alcohol (pro-oxidant), vitamin C, vitamin E, beta carotene (data not available for MCBCS), folic acid, and dietary fiber (anti-oxidants).

Associations between JAK/STAT genes and risk of breast cancer by genetic ancestry and Estrogen and Progesterone Receptor Tumor Phenotype.

Overall≤28% IA Ancestry>28–70% IA Ancestry>70% IA Ancestry
ControlsCasesOR2 (95% CI)ControlsCasesOR (95% CI)ControlsCasesOR (95% CI)ControlsCasesOR (95% CI)
STAT5B (rs6503691)
 CC337828971.00147313831.00135011151.005553991.00
 CT/TT7796720.98 (0.87, 1.10)3743591.01 (0.86, 1.19)3312751.01 (0.84, 1.21)74380.63 (0.41, 0.97)
 P-value (raw; adjusted)0.713, 1.0000.906, 0.9060.938, 1.0000.035, 0.069
STAT6 (rs3024974)
 CC343528681.00150713941.00138410971.005443771.00
 CT/TT7206981.15 (1.02, 1.29)3403461.10 (0.93, 1.30)2952921.25 (1.04, 1.50)85601.01 (0.70, 1.46)
 P-value (raw; adjusted)0.018, 0.0720.268, 1.0000.017, 0.0680.953, 1.000
TYK2 (rs280519)
 AA149912651.004764391.006815621.003422641.00
 AG/GG265122890.96 (0.87, 1.06)137112951.03 (0.89, 1.20)9958210.98 (0.84, 1.13)2851730.75 (0.58, 0.96)
 P-value (raw; adjusted)0.416, 1.0000.704, 1.0000.766, 1.0000.025, 0.093
ER+/PR+ER+/PR−ER−/PR+ER−/PR−
JAK2 (rs1536800)
 CC17987641.001221.00261.002631.00
 CT/TT13745330.92 (0.81, 1.05)1131.22 (0.93, 1.59)170.85 (0.46, 1.57)1520.75 (0.61, 0.93)
 P-value (raw; adjusted)0.225, 1.0000.150, 0.8990.595, 1.0000.009, 0.053
STAT3 (rs8069645)1
 AA18727561.001331.00181.002531.00
 AG11194681.00 (0.87, 1.15)941.16 (0.88, 1.53)191.91 (0.99, 3.68)1440.96 (0.77, 1.20)
 GG181740.94 (0.70, 1.25)80.58 (0.28, 1.21)63.79 (1.46, 9.84)180.74 (0.44, 1.22)
 P-value (raw; adjusted)0.785, 0.9590.831, 1.0000.004, 0.0100.324, 0.882
STAT3 (rs6503695)
 TT16626721.001171.00171.002221.00
 TC12565190.98 (0.85, 1.12)1061.17 (0.88, 1.55)191.63 (0.83, 3.19)1640.99 (0.79, 1.23)
 CC2551060.93 (0.73, 1.19)120.62 (0.33, 1.14)73.10 (1.23, 7.81)280.83 (0.54, 1.26)
 P-value (raw; adjusted)0.564, 0.9590.687, 1.0000.016, 0.0290.507, 0.882
STAT3 (rs12949918)
 TT14285781.001091.00131.001971.00
 TC13715730.98 (0.85, 1.13)970.90 (0.67, 1.21)232.07 (1.03, 4.19)1710.92 (0.73, 1.15)
 CC3721470.88 (0.71, 1.10)290.95 (0.61, 1.47)72.47 (0.94, 6.47)460.91 (0.64, 1.29)
 P-value (raw; adjusted)0.344, 0.9590.631, 1.0000.030, 0.0300.454, 0.882
STAT3 (rs1026916)
 GG15506361.001191.00141.002151.00
 GA/AA16226620.96 (0.84, 1.09)1160.91 (0.69, 1.19)292.13 (1.11, 4.09)2000.90 (0.73, 1.11)
 P-value (raw; adjusted)0.522, 0.9590.488, 1.0000.024, 0.0290.317, 0.882
STAT5A (rs7217728)1
 TT18367401.001301.00161.002471.00
 TC/CC13355570.99 (0.87, 1.13)1051.09 (0.83, 1.43)272.5 (1.32, 4.72)1680.94 (0.76, 1.17)
 P-value (raw; adjusted)0.920, 0.9200.533, 0.8350.005, 0.0070.600, 0.600
STAT5A (rs12601982)
 AA23649451.001741.00261.003201.00
 AG/GG8063531.06 (0.91, 1.22)611.00 (0.74, 1.36)172.03 (1.08, 3.80)950.88 (0.69, 1.12)
 P-value (raw; adjusted)0.479, 0.7500.980, 0.9800.027, 0.0270.294, 0.461
STAT5B (rs7218653)1
 AA18497451.001321.00151.002371.00
 AG/GG13235531.00 (0.87, 1.14)1031.07 (0.81, 1.40)282.85 (1.49, 5.43)1781.07 (0.86, 1.32)
 P-value (raw; adjusted)0.946, 0.9580.631, 0.6310.002, 0.0030.541, 0.541

p values for difference by ER/PR group after adjustment for multiple comparisons were 0.07, 0.05, and 0.04 respectively

Odds ratios (OR) and 95% confidence Intervals (CI) adjusted for age, study center, BMI during referent year, parity, and genetic ancestry. Risk estimates are shown in the table if one or more of the adjusted p values for multiple comparisons is <0.15.

Interaction between BMI, DOBS and JAK/STAT genes and risk of breast cancer

ControlsCasesOR1 (95% CI)ControlsCasesOR (95% CI)ControlsCasesOR (95% CI)Interaction P (raw; adjusted)
Normal (< 25 kg/m2)Overweight (25 to <30 kg/m2)Obese (>= 30 kg/m2)
Pre-menopausal
JAK1 (rs4916005)
 TT3073281.003102370.76 (0.60, 0.96)2772410.89 (0.70, 1.13)0.009, 0.054
 TC1431511.01 (0.76, 1.34)1801470.86 (0.65, 1.14)2011250.66 (0.49, 0.88)
 CC17331.98 (1.07, 3.67)35310.97 (0.57, 1.65)46250.60 (0.35, 1.01)
JAK1 (rs310211)
 AA1882121.001651150.66 (0.48, 0.90)1331320.97 (0.70, 1.33)0.009, 0.054
 AG2052070.93 (0.71, 1.23)2391990.81 (0.61, 1.07)2471870.76 (0.56, 1.01)
 GG74921.15 (0.79, 1.66)121990.85 (0.60, 1.21)143710.50 (0.35, 0.73)
JAK1 (rs2256298)
 CC2252471.002021430.69 (0.52, 0.92)1671520.91 (0.68, 1.22)0.027, 0.106
 CT1871971.02 (0.77, 1.34)2291930.85 (0.65, 1.13)2411790.77 (0.58, 1.02)
 TT55681.19 (0.79, 1.78)94790.91 (0.63, 1.33)116600.54 (0.37, 0.79)
JAK2 (rs10974916)
 GG2282451.002601960.76 (0.58, 0.99)2312020.90 (0.68, 1.18)0.018, 0.074
 GA2012161.01 (0.77, 1.32)2241830.83 (0.63, 1.09)2331590.71 (0.53, 0.94)
 AA38511.31 (0.82, 2.07)41360.90 (0.55, 1.48)60300.51 (0.32, 0.83)
JAK2 (rs7043371)
 AA1201481.001271060.74 (0.51, 1.05)155940.55 (0.39, 0.79)0.024, 0.074
 AT2412480.86 (0.64, 1.16)2802070.66 (0.48, 0.89)2611920.66 (0.48, 0.91)
 TT1061160.88 (0.61, 1.26)1181020.76 (0.53, 1.10)1081050.87 (0.60, 1.26)
JAK2 (rs1536800)
 CC2642831.002952270.77 (0.60, 0.99)2542280.93 (0.72, 1.20)0.001, 0.008
 CT1841910.98 (0.75, 1.28)2031660.83 (0.63, 1.09)2261430.64 (0.49, 0.85)
 TT19381.96 (1.10, 3.50)26210.85 (0.46, 1.56)44200.47 (0.27, 0.83)
JAK2 (rs3780381)
 AA2282561.002741990.70 (0.54, 0.90)2252110.92 (0.70, 1.20)0.009, 0.045
 AC2052060.90 (0.69, 1.17)2161820.81 (0.62, 1.07)2411500.61 (0.46, 0.81)
 CC34501.36 (0.85, 2.19)34340.98 (0.58, 1.64)58300.50 (0.31, 0.82)
Post-Menopause
STAT3 (rs8069645)
 AA3763831.005454680.91 (0.75, 1.10)6404630.78 (0.64, 0.94)0.025, 0.026
 AG2562180.82 (0.65, 1.04)2692560.96 (0.76, 1.20)3402750.85 (0.68, 1.06)
 GG38270.66 (0.40, 1.11)48360.74 (0.47, 1.16)43380.90 (0.57, 1.43)
STAT3 (rs6503695)
 TT3203291.005024300.90 (0.73, 1.10)5924240.76 (0.62, 0.93)0.012, 0.026
 TC2912570.83 (0.66, 1.05)2972840.95 (0.75, 1.19)3712970.82 (0.66, 1.02)
 CC59420.64 (0.41, 0.98)64460.67 (0.44, 1.01)60550.92 (0.62, 1.38)
STAT3 (rs12949918)
 TT2712881.004353720.86 (0.69, 1.07)5313810.74 (0.59, 0.92)0.009, 0.026
 TC3082770.81 (0.64, 1.02)3403160.89 (0.71, 1.12)3983210.79 (0.63, 0.99)
 CC91630.60 (0.42, 0.86)88720.72 (0.51, 1.03)92740.78 (0.55, 1.11)
STAT3 (rs1026916)
 GG3173371.004613900.85 (0.69, 1.05)5494070.76 (0.62, 0.94)0.047, 0.047
 GA/AA3532910.75 (0.60, 0.94)4023700.89 (0.72, 1.10)4733690.78 (0.63, 0.96)
DOBS LowDOBS IntermediateDOBS High
STAT1 (rs1400657)
 AA7848311.00178114880.81 (0.72, 0.91)7845870.72 (0.63, 0.84)0.006, 0.065
 AC/CC1821500.76 (0.60, 0.97)3883060.75 (0.63, 0.90)1541450.90 (0.70, 1.15)
STAT1 (rs3771300)
 CC3213591.007465670.69 (0.57, 0.83)3442400.64 (0.51, 0.80)0.014, 0.126
 CA4504280.81 (0.66, 0.99)10448890.75 (0.63, 0.89)4383490.69 (0.56, 0.85)
 AA1951950.82 (0.63, 1.05)3783370.76 (0.62, 0.94)1531430.80 (0.61, 1.06)
STAT4 (rs925847)
 CC4915611.0012049220.69 (0.59, 0.80)5113740.66 (0.55, 0.80)0.003, 0.035
 CT3903570.80 (0.66, 0.97)8177390.81 (0.69, 0.95)3533010.75 (0.62, 0.92)
 TT81610.63 (0.44, 0.90)1431310.81 (0.62, 1.05)72570.72 (0.50, 1.04)

Odds Ratios (OR) and 95% Confidence Intervals (CI) adjusted for age, study center, BMI during referent year (where appropriate), parity, and genetic ancestry. Risk estimates are shown in the table if the adjusted p value for multiple comparison is <0.15.

Associations between breast cancer-specific mortality and JAK/STAT/SOC genes by genetic ancestry

Overall≤28% IA Ancestry>28% IA AncestryInteraction P (raw; adjusted)
Deaths/Person YearsHR (95% CI)Deaths/Person YearsHR (95% CI)Deaths/Person YearsHR (95% CI)
JAK1 (rs2780890)0.031, 0.123
 AA/AG182/178161.0099/96011.0083/82151.00
 GG67/59421.19 (0.89, 1.59)46/46000.96 (0.68, 1.36)21/13421.84 (1.13, 3.00)
 P-value (raw; adjusted)0.237, 0.9500.823, 1.0000.015, 0.058
JAK1 (rs2254002)0.019, 0.116
 GG91/77591.0057/56311.0034/21291.00
 GT/TT158/159870.79 (0.61, 1.03)88/85711.02 (0.73, 1.43)70/74170.54 (0.36, 0.82)
 P-value (raw; adjusted)0.088, 0.5290.911, 1.0000.004, 0.023
JAK1 (rs310245)0.024, 0.119
 CC90/76511.0056/54991.0034/21521.00
 CT/TT159/161070.80 (0.61, 1.04)89/87021.01 (0.72, 1.42)70/74050.55 (0.36, 0.83)
 P-value (raw; adjusted)0.090, 0.5290.948, 1.0000.005, 0.023
SOCS1 (rs193779)0.055, 0.104
 GG164/148411.0093/81351.0071/67061.00
 GA/AA85/88760.87 (0.67, 1.14)52/60400.70 (0.50, 0.99)33/28361.19 (0.78, 1.81)
 P-value (raw; adjusted)0.303, 0.5700.045, 0.0850.431, 0.431
STAT1 (rs11887698)0.025, 0.232
 AA139/128201.0097/100151.0042/28051.00
 AG89/81870.91 (0.69, 1.22)43/36771.17 (0.80, 1.70)46/45090.65 (0.42, 0.99)
 GG21/27410.62 (0.38, 1.03)5/4990.89 (0.36, 2.22)16/22420.46 (0.25, 0.83)
 P-value (raw; adjusted)0.063, 0.5130.799, 0.7990.010, 0.092
STAT3 (rs1026916)0.006, 0.018
 GG121/115841.0051/60441.0070/55401.00
 GA/AA128/121741.04 (0.81, 1.34)94/81571.44 (1.02, 2.03)34/40170.67 (0.45, 1.02)
 P-value (raw; adjusted)0.744, 1.0000.039, 0.1080.062, 0.157
STAT4 (rs11685878)0.011, 0.148
 CC104/89811.0061/46791.0043/43021.00
 CT111/111140.88 (0.67, 1.16)69/71670.76 (0.54, 1.07)42/39471.08 (0.70, 1.65)
 TT34/36510.86 (0.58, 1.27)15/23430.55 (0.31, 0.97)19/13081.49 (0.86, 2.58)
 P-value (raw; adjusted)0.438, 1.0000.039, 0.5390.151, 1.000
STAT6 (rs3024979)0.531, 0.733
 TT195/199831.00109/114771.0086/85061.00
 TA/AA53/37601.52 (1.12, 2.07)35/27081.43 (0.97, 2.10)18/10511.74 (1.04, 2.91)
 P-value (raw; adjusted)0.008, 0.0320.071, 0.1670.033, 0.133
TYK2 (rs280519)0.866, 1.000
 AA93/75111.0041/35051.0052/40061.00
 AG115/109270.97 (0.73, 1.28)73/67530.99 (0.67, 1.46)42/41740.91 (0.60, 1.38)
 GG39/51870.67 (0.46, 0.98)30/38630.69 (0.43, 1.11)9/13240.57 (0.28, 1.17)
 P-value (raw; adjusted)0.040, 0.1470.130, 0.3470.124, 0.455

Hazard Ratio (HR) and 95% Confidence Interval (CI) adjusted for age, study center, BMI during referent year, SEER summary stage, and genetic ancestry.

Risk estimates are shown in the table if one or more of the adjusted p values for multiple comparisons is <0.15.

Interactions between cigarette smoking, DOBS, aspirin/NSAID and JAK/STAT/SOC genes and risk of breast cancer-specific mortality

Deaths/Person YearsHR1 (95% CI)Deaths/Person YearsHR (95% CI)Deaths/Person YearsHR (95% CI)Interaction P (raw; adjusted)
Never SmokerFormer SmokerCurrent Smoker
JAK2 (rs2274471)0.008, 0.024
 TT73/67601.0020/25450.73 (0.44, 1.20)11/10740.99 (0.52, 1.87)
 TC/CC37/42140.84 (0.56, 1.25)14/20430.73 (0.41, 1.30)24/10152.26 (1.41, 3.61)
JAK2 (rs7043371)0.024, 0.047
 AA21/30331.009/11920.96 (0.44, 2.10)15/5263.11 (1.59, 6.11)
 AT/TT90/79351.57 (0.98, 2.54)25/33961.14 (0.64, 2.05)20/15632.05 (1.11, 3.79)
JAK2 (rs10974947)<.001, 0.003
 GG76/67021.0017/25220.63 (0.37, 1.07)11/11660.86 (0.45, 1.62)
 GA/AA35/42750.75 (0.50, 1.13)17/20650.78 (0.46, 1.34)24/9222.43 (1.52, 3.90)
JAK2 (rs3780379)0.002, 0.008
 GG83/74771.0022/29100.73 (0.45, 1.17)14/13330.99 (0.56, 1.75)
 GA/AA28/34910.78 (0.51, 1.19)12/16770.70 (0.38, 1.30)21/7562.65 (1.62, 4.32)
JAK2 (rs10815160)0.002, 0.008
 TT64/56491.0018/23430.70 (0.41, 1.18)8/10550.76 (0.36, 1.59)
 TG/GG47/53280.78 (0.54, 1.14)16/22440.67 (0.39, 1.17)27/10342.16 (1.37, 3.42)
SOCS2 (rs3816997)0.028, 0.057
 TT73/74051.0023/30730.80 (0.50, 1.28)29/13352.27 (1.47, 3.50)
 TG/GG38/35711.09 (0.73, 1.62)11/15140.77 (0.41, 1.45)6/7540.83 (0.36, 1.91)
STAT4 (rs4853546)0.010, 0.137
 GG62/49181.0013/19600.51 (0.28, 0.94)12/9621.06 (0.57, 1.98)
 GA/AA49/60590.64 (0.44, 0.93)21/26270.69 (0.42, 1.14)23/11271.58 (0.97, 2.57)
STAT4 (rs1031508)0.010, 0.137
 CC67/58111.0014/25000.46 (0.26, 0.82)15/11571.20 (0.68, 2.10)
 CT/TT44/51660.73 (0.50, 1.07)20/20870.97 (0.58, 1.60)20/9321.78 (1.07, 2.96)
DOBS LowDOBS IntermediateDOBS High
STAT1 (rs2030171)0.008, 0.069
 GG35/24471.0048/38910.82 (0.53, 1.28)12/15830.53 (0.27, 1.03)
 GA31/28730.70 (0.43, 1.13)62/57740.70 (0.46, 1.07)16/22730.52 (0.28, 0.94)
 AA8/13150.32 (0.15, 0.71)25/25410.66 (0.39, 1.13)11/9480.87 (0.43, 1.75)
STAT5B (rs9900213)0.021, 0.041
 GG54/49981.00100/89441.10 (0.79, 1.54)35/36051.09 (0.71, 1.67)
 GT/TT20/16801.32 (0.79, 2.23)36/32991.14 (0.74, 1.74)4/12060.34 (0.12, 0.93)
Non-Regular Aspirin/NSAID UsersRegular Aspirin/NSAID Users
SOCS2 (rs3816997)0.042, 0.083
 TT59/54101.0040/37900.99 (0.66, 1.48)
 TG/GG28/22831.16 (0.73, 1.83)8/16190.46 (0.22, 0.96)
STAT3 (rs1053005)<.001, <.001
 AA66/51271.0022/36040.45 (0.28, 0.74)
 AG/GG21/25660.57 (0.35, 0.94)26/17931.12 (0.71, 1.79)
STAT3 (rs8069645)0.014, 0.014
 AA53/43281.0020/30090.51 (0.30, 0.86)
 AG/GG34/33650.78 (0.50, 1.20)28/24000.99 (0.62, 1.58)
STAT3 (rs6503695)0.006, 0.006
 TT50/39381.0015/24710.44 (0.24, 0.79)
 TC/CC37/37550.71 (0.46, 1.10)33/29370.90 (0.57, 1.41)
STAT3 (rs12949918)0.003, 0.005
 TT44/32431.0013/22020.40 (0.21, 0.75)
 TC36/36530.75 (0.48, 1.17)24/23970.78 (0.47, 1.30)
 CC7/7960.48 (0.21, 1.10)11/8100.99 (0.50, 1.95)
STAT3 (rs1026916)0.006, 0.006
 GG44/3549114/26050.42 (0.23, 0.77)
 GA/AA43/41440.84 (0.55, 1.29)34/28031.04 (0.65, 1.65)
STAT5A (rs7217728)<.001, <.001
 TT66/43101.0013/27090.28 (0.15, 0.52)
 TC/CC21/33830.37 (0.22, 0.60)35/26990.87 (0.57, 1.33)
STAT5A (rs12601982)<.001, <.001
 AA70/54701.0025/38050.49 (0.30, 0.77)
 AG/GG17/22230.51 (0.30, 0.88)23/16041.12 (0.69, 1.81)
STAT5B (rs9900213)0.050, 0.050
 GG70/55141.0031/38860.64 (0.42, 0.98)
 GT/TT17/21790.66 (0.39, 1.13)17/15220.94 (0.55, 1.63)
STAT5B (rs6503691)0.002, 0.002
 CC78/59661.0033/42150.59 (0.39, 0.89)
 CT/TT9/17270.42 (0.21, 0.84)15/11931.11 (0.64, 1.94)
STAT5B (rs7218653)<.001, <.001
 AA63/43071.0013/26910.30 (0.16, 0.54)
 AG/GG24/33860.43 (0.27, 0.70)35/27180.90 (0.59, 1.37)

Hazard ratio (HR) and 95% confidence intervals (CI) adjusted for age, study center, BMI during referent year (where appropriate), genetic ancestry, and SEER stage.

Risk estimates are shown in the table if the adjusted p value for multiple comparison is <0.15.