Chronic inflammation has been a proposed mechanism of resistance to aromatase inhibitors in breast cancer. Stratifying by HER2 status, a matched case-control study from the Wellness After Breast Cancer-II cohort was performed to assess whether or not elevated serum inflammatory biomarkers  and/or the presence of a high-risk IL-6 promoter genotype were associated with recurrence of hormone receptor positive (HR+) early breast cancer. Estrogen levels were also measured and correlated with biomarkers and disease outcomes. CRP and SAA were significantly associated with an increased risk of recurrence in the HR+/HER2− group, but not the HR+/HER2+ group. Mean serum estrogen levels were non-significantly elevated in patients who relapsed vs. non-relapsed patients. Surprisingly, high-risk IL-6 promoter polymorphisms were strongly associated with HER2+ breast cancer relapse, which has potential therapeutic implications, as elevated intracellular IL-6 has been associated with trastuzumab resistance in pre-clinical models. Aromatase inhibitors (AIs) have revolutionized treatment for this disease by blocking the peripheral conversion of androgens to estrogen, primarily in adipose tissue2 thereby reducing recurrence and improving survival in the adjuvant setting. These benefits extend to both post-menopausal and high-risk premenopausal women (when combined with ovarian suppression). Nevertheless, up to 30% of women will recur during their lifetimes with a persistent and cumulative risk of relapse2.Estrogen receptor positive (ER+) breast cancer represents approximately 60–80% of all breast cancer diagnoses3, but appear to be multifactorial, and there is prior evidence that inflammation may play a significant role4. While there are multiple inflammatory pathways and molecules that have been implicated in ER+ breast cancer recurrence7, the inflammatory cytokine interleukin-6 (IL-6) and its associated pathway has been shown to be associated with poor outcomes in ER+ breast cancer8. Functional polymorphisms in the IL-6 promoter that result in increased transcription of IL-6 are significantly associated with worse prognosis and decreased disease free survival (DFS) in high-risk breast cancer patients9. Increased production of IL-6 has both cellular and microenvironmental effects in the breast, including upregulated activity of aromatase and other enzymes involved in post-menopausal estrogen biosynthesis8. In addition, the soluble IL-6 receptor is expressed at higher levels in many ER+ breast cancers as compared to the triple negative subtype4, enabling IL-6 to impact intracellular signaling through the JAK/STAT pathway.The mechanisms underlying failure of AIs in this setting are poorly understood4. While serum CRP and SAA levels generally rise with increasing serum IL-6 levels, they can be differentially elevated in different disease states10, suggesting somewhat distinct production pathways. High levels of serum CRP and SAA have been associated with increased risk of relapse and decreased survival in large, prospective cohort studies that include all molecular subtypes of breast cancer12.Two downstream products of IL-6 activation, C-reactive protein (CRP) and serum amyloid A (SAA), are mainly produced by the liver, and circulate systemicallyGiven the links between IL-6, its downstream effectors CRP and SAA, and aromatase activity, we hypothesized that patients with high circulating levels of these inflammatory cytokines, as well as patients with high-risk IL-6 promoter genotypes, may be more likely to recur during or after treatment with an aromatase inhibitor compared to those without elevated inflammatory markers. Furthermore, we hypothesized that increased serum estrogen metabolites would be associated with both increased inflammatory markers, as well as increased risk of recurrence. We examined these questions in a cohort of women with ER+ breast cancer who received an AI in the adjuvant setting and for whom long-term disease outcomes were available.14, as a prospective non-intervention cohort study with the specific aims of investigating the risk factors and underlying mechanisms of two categories of outcomes: symptom distress including AI-induced arthralgia, and disease outcomes including failure of AI therapy. To examine early treatment failure specifically, we identified a total of 197 unique cases consisting of those patients with stage I–III ER+ breast cancer who relapsed on or after adjuvant AI and matched controls who remained free of recurrence during or after AI therapy for this study. Of these, 123 tumors were HER2−, while 74 tumors were HER2+. Given the likelihood that risk factors for recurrence differ between HER2+ and HER2− ER+ patients, we analyzed these groups separately.Patients for this nested case-control study were identified from among a total of 1287 patients enrolled to the parent cohort, the Wellness After Breast Cancer-II (WABC-II) cohort study. WABC-II was designed, as previously describedp = 0.009), and while cases were generally of higher grade, this difference was of borderline significance .There were 5 missing samples from among the HER2− subjects (1 case and 4 controls), resulting in 118 analyzable cases in the HR+/HER2− group (38 cases and 80 matched controls) for this analysis. Demographics are reported in Table p = 0.004) and SAA , while serum IL-6 concentrations did not differ . In addition, cases had a higher proportion of subjects with CRP values >ULN (>7.5 mg/L) relative to controls . The proportion of subjects with IL-6 levels >ULN (≥5 pg/mL) did not differ between cases and controls . Univariate conditional logistic regression models (Table p = 0.019) and SAA OR 3.38 . IL-6 levels greater than or equal to the median value were not associated with an increased risk of relapsed breast cancer . Similarly, subjects with CRP levels >ULN were more likely to have relapsed breast cancer , while subjects with IL-6 levels >ULN had no increased likelihood of relapsed breast cancer .Serum inflammatory biomarker results are shown in Table p = 0.001); for SAA, the OR was 1.79 . Increasing IL-6 levels were not associated with increased risk of breast cancer relapse . ROC curves using these continuous measures, as shown in Fig. p < 0.001), with similar results for serum SAA >7.3 mg/L .Continuous measures of inflammatory biomarkers in non-transformed and natural log transformed distributions are shown in Figs p = 0.002) for CRP, 5.62  for SAA, and 0.15  for CRP*SAA. A test for synergy on the additive scale did not suggest a synergistic relationship between CRP and SAA in predicting increased risk of breast cancer relapse. Variables considered in the biologic (additive) analysis are represented as CRP+/SAA+, CRP+/SAA−, and CRP−/SAA+(with CRP−/SAA− as the reference group). Results are shown in Fig. p < 0.001. The RERI was −6.95 (95% CI −25.43 to 11.53).To test for statistical (multiplicative) interaction between CRP and SAA, the individual dichotomous variables of CRP and SAA, as well as the cross-product of CRP*SAA were combined in a multivariate conditional logistic regression model, with ORs (and 95% CIs) of 12.70  and high-grade histology . BMI ≥ 30 kg/m2 was not associated with increased risk of relapsed breast cancer, but was the only covariate significantly associated with any of the elevated serum levels of CRP  and SAA . A significantly higher proportion of patients with a BMI ≥ 30 kg/m2, compared to those with BMIs <30 kg/m2, had elevated levels of CRP  and serum SAA . Based on these analyses, nodal status and high-grade pathology were included as candidate covariates in the final multivariate conditional logistic regression model.To determine whether the clinical covariates listed in Table The significant candidate biomarkers CRP and SAA, the cross-product interaction term of CRP*SAA, and the covariates associated with increased risk of breast cancer relapse were added to a multivariate conditional logistic regression model using both forward and backward variable selection methods. In the final model, nodal status was not significantly associated with relapsed breast cancer after adjusting for the other variables Table . High lep = 0.003), and were more pronounced for elevated SAA .In order to assess for any potential selection bias that may have occurred as a result of extremes of follow up time, a sensitivity analysis was performed for the HER2− group excluding patients with the 5% least amount of follow up time (<6 years) and the 5% greatest amount of follow up time (>18 years). The results of univariate conditional logistic regression remained similar for elevated CRP  and SAA >7.3 mg/L , and overall similar to the results from the univariate conditional logistic regression analyses.To achieve the 1:5 case/control matching ratio for every case, controls were replaced back into the selection pool, with controls being used a median of 3 times (range 1–15). Because this may bias the results in favor of the inflammatory profile of controls used multiple times, a sensitivity analysis was performed using simple logistic regression in the HER2− group without the matching criteria so that each control was only used once. The results were similar for elevated CRP . However, the presence of a high-risk IL-6 promoter genotype was significantly associated with increased risk of relapsed breast cancer in a univariate conditional logistic regression model . Of the clinical covariates significantly associated with relapsed breast cancer , neither were associated with high-risk genotype profiles . Using the cutoff values for CRP and SAA generated by the classification tree analysis, patients with high-risk IL-6 promoter genotypes were significantly more likely to have elevated serum SAA , while likelihood of elevated CRP was of borderline significance  in a univariate conditional logistic regression.The proportion of HER2− subjects with the high-risk IL-6 promoter genotype profile was slightly higher among cases (58% of cases vs. 51% of controls), though this difference was not statistically significant . High serum SAA level (>7.3 mg/L) was not associated with detectable serum E1  or E2 . Results were similarly negative when stratifying by high-risk IL-6 promoter genotype compared to a non-high-risk genotype profile for both E1  and E2 .Detectable levels of serum estrogens were similar between cases and controls, both for E1 (50% of cases and 47% of controls) and E2 (19% of cases and 21% of controls). Among patients receiving standard suppressive doses of an AI, a greater number of subjects with elevated CRP levels (>10.3 mg/L) had detectable levels of serum E1  and E2  compared to those with lower CRP levels, but these differences did not reach statistical significance (p = 0.638 for difference in E1 and p = 0.866 for difference in E2). Differences in detectable estrogen levels stratified by CRP level are shown in Fig. p = 0.182) and E2 . Subjects with high-risk compared to non-high-risk IL-6 promoter genotype profiles had non-significantly lower mean values of E1 , with no significant difference in mean E2 .The mean values were 78.3 pg/mL for E1 and 3.7 pg/mL for E2. Cases relative to controls had higher median values of E1 (104.3 vs. 66.2 pg/mL) and E2 (4.5 vs. 3.3 pg/mL), but these differences were not statistically significant (Wilcoxon rank-sum p = 0.480).To evaluate for adherence to therapy, available serum samples  were analyzed for detection of any AI . 72.8% of patients had detectable levels of an AI, while 27.2% of patients had no detectable level of an AI. Detection of an AI did not significantly reduce the risk of relapse in a univariate conditional logistic regression , resulting in a total of 67 subjects (11 cases and 56 non-matched controls) in this analysis. Demographics are reported in Table p = 0.5423), IL-6 , or SAA . For the HER2+ group, none of the three candidate biomarkers were associated with relapsed breast cancer in a univariate logistic regression of log transformed serum concentrations of CRP , SAA , or IL-6 . Therefore, no further analyses of the inflammatory biomarkers were considered in the HER2+ group.Among patients with HER2+ breast cancer, CRP and SAA levels exhibited a moderate degree of correlation (0.423), while IL-6 levels exhibited little to no correlation with CRP (0.285) or SAA (−0.084). In the HR+/HER2+ group, there were no significant differences between cases and controls in median serum concentrations of CRP . In a univariate logistic regression analysis, a high-risk IL-6 promoter genotype profile was significantly and strongly associated with increased risk of relapsed breast cancer . None of the clinical covariates were associated with an increased risk of HER2+ breast cancer relapse in a multivariate logistic regression analysis, either with or without the inclusion of the genotype profile into the model. Hence, the univariate regression of the genotype profile was considered the final regression model.HER2+ cases and controls differed significantly by the proportion of subjects with a high-risk IL-6 promoter genotype . Cases also had a significantly higher proportion of subjects with detectable E2  as compared to controls . Mean values were 32.3 pg/mL for E1 and 1.7 pg/mL for E2. Cases relative to controls had non-significantly higher mean levels of E1  and E2 . Other results of estrogen analyses stratified by CRP and IL-6 promoter polymorphism status in HER2+ subjects are shown in Supplementary Table p = 0.041). Otherwise, there were no significant differences in proportion of detectable or mean levels of E1 or E2 when stratifying by median CRP level or high-risk IL-6 promoter genotype profile.Of the 66 HR+/HER2+ cases and controls with serum available for estrogen metabolite analysis, cases had a significantly higher proportion of subjects with detectable E1  as compared to controls .After analyzing available serum samples  for detection of any AI , 73.3% of patients had detectable levels of an AI, while 26.7% of patients had no detectable level of an AI. Detection of an AI did not significantly reduce the risk of relapse in a univariate logistic regression  analysis merely facilitates an accurate inclusion of CRP and SAA individually in the multivariate model, and does not itself represent a “synergistic” effect. The interaction and multivariate analyses in this study demonstrate that the knowledge gained by measuring SAA in addition to CRP, if there is any, is minor. Therefore, serum CRP appears to be the most appropriate inflammatory biomarker to identify this subpopulation of high-risk patients.While both elevated serum CRP and SAA were associated with an increased risk of breast cancer relapse, the biologic (additive) interaction analysis between these two biomarkers does not suggest a synergistic effect in which elevated levels of both biomarkers drastically increases one’s risk of relapse. The weakly negative RERI suggests a less than additive effect21. The modified Glasgow Prognostic Scale (mGPS) gives one point for elevated CRP >10.0 mg/L (similar to this study), and one point for serum albumin <35 g/L. Future studies seeking to identify subjects on the basis of an inflammatory mediated risk of relapsed breast cancer should therefore strongly consider checking CRP. While SAA may not provide additional predictive information, hypoalbuminemia may be a more global measure of inflammation, and the mGPS would be a reasonable scale to consider in helping identify this at-risk population.In addition, CRP has already been used as part of an inflammatory risk score in the metastatic setting22 generated trastuzumab resistant HER2+ breast cancer cells by knocking down PTEN, and found that this resistance to trastuzumab was mediated by an IL-6 inflammatory feedback loop, resulting in increased levels of intracellular IL-6 and expansion of a cancer stem cell population. This expansion was reversed by introduction of an IL-6 receptor antibody interrupting the feedback loop. Therefore, the patients in this study with HER2+ tumors and a high-risk IL-6 promoter genotype may have been intrinsically resistant to adjuvant trastuzumab therapy due to increased transcription of intracellular IL-6. While serum inflammatory biomarkers may not be an effective means of identifying or monitoring such patients, these findings lend further support to targeting the IL-6/JAK/STAT pathway in some patients with HER2+ breast cancer who harbor the high-risk IL-6 genotype.While serum CRP, IL-6, and SAA levels were not associated with increased risk of breast cancer recurrence in the HER2+ group, there was a strong association of the presence of a high-risk IL-6 promoter genotype with increased risk of HR+/HER2+ breast cancer relapse. Pre-clinical evidence supports this finding. Korkaya et al.23. Because all patients on this study were prescribed an AI for adjuvant therapy and received trastuzumab as part of their initial chemotherapy regimen if HER2+, high CRP and high-risk IL-6 promoter genotypes might be potential predictive biomarkers in their potential ability to predict response to AIs (in HR+/HER2− subjects) or trastuzumab (in HR+/HER2+subjects). However, a more detailed understanding of these underlying pathways and prospective evaluation is needed before such a classification could be implemented clinically. These data could be further validated in completed clinical trials if there are available blood samples for analysis. In the HR+/HER2− population, adjuvant trials using AIs with adequate follow up would be an ideal validation set. In the HER2+ population, assessment of patients’ IL6 promoter genotype could be performed in either adjuvant studies of HER2 targeted therapy with adequate survival follow up, or neoadjuvant studies with robust pathologic complete response data.Though biomarkers are typically thought of as either being prognostic  or predictive (in determining the sensitivity of the tumor to a particular treatment), few biomarkers are truly one or the other24. When available, this study included clinically derived cutpoints based on clinical lab reference values, which improves the generalizability of the findings. An additional criticism of inflammatory biomarker studies is that the biomarkers can vary significantly throughout the day, depending on the time they are collected24. Though the collection time was not standardized in this study, samples were collected according to clinical convenience prior to relapse, and thus any bias is likely non-differential.There are several strengths and limitations of this study. Because HR+ breast cancer relapse is a relatively uncommon event, a nested case-control design from a large prospective cohort is an appropriate, and feasible, design to measure the effects of exposure to pathways of resistance. Although “risk” is not technically measured in a case-control study, the OR approximates the risk ratio if the rare disease assumption is met in the population . Hence, the term “risk” can be used to describe the associations observed in this study. The use of different cutpoints for inflammatory biomarkers has been a criticism of some cancer epidemiologic studies, as this decreases generalizability25, were not associated with an increased risk of relapse in the present analysis of HR+ breast cancer survivors. This potentially reflects the difference in half-lives between inflammatory cytokines like IL-6, which is measured on the order of hours26, as compared to their downstream acute phase proteins, like CRP and SAA, which can remain elevated for days and be detected more easily18.IL-6 levels, while previously shown to be associated with worse outcomes in patients with active breast cancerSensitivity analyses were performed to protect against some of the limitations of this study. While the long follow-up of this prospective cohort is a strength (median follow up of approximately 10 years from time of breast cancer diagnosis), patients enrolled later in the study had significantly less follow up time (as little as 3.5 years among non-relapsed subjects). The sensitivity analysis suggests that the directionality of the effect of the inflammatory biomarkers was not impacted by follow up time, though the magnitude of the effect was for serum SAA. Because the serum biomarker results were unchanged after excluding relapses that would have occurred in the first 6 years and after 18 years since diagnosis, it is unlikely that additional follow up time would change these findings. In addition, ignoring the matching criteria in the HER2− group did not significantly diminish the effect of the inflammatory biomarkers on the risk of relapsed breast cancer, suggesting that the multiple uses of individual controls did not significantly alter the results.In conclusion, this study demonstrates that elevation in the serum inflammatory biomarkers CRP or SAA drawn randomly during treatment in the adjuvant setting are significantly and independently associated with increased risk of relapsed HR+/HER2− breast cancer, but not HR+/HER2+ breast cancer. This association is amplified in patients with low-intermediate grade tumors, suggesting a stronger association in tumors that are more likely to be strongly hormone receptor positive. The mechanism of inflammatory mediated AI resistance may be more localized at the tissue level, as serum estrogens were not impacted by increased inflammation. The presence of a high-risk IL-6 promoter genotype polymorphism is strongly and significantly associated with relapsed HR+/HER2+ breast cancer, and may have important therapeutic implications. Confirmatory studies are warranted that utilize these biomarkers to identify a subgroup of high-risk HR+/HER2− patients that may benefit from anti-inflammatory/anti-IL-6 therapy in the adjuvant setting. Patients with HER2+ breast cancer at risk for primary trastuzumab resistance could potentially be identified on the basis of a high-risk IL-6 promoter genotype, and future studies could be aimed at overcoming this resistance by inhibiting the IL-6 pathway.This was a nested case-control study, with cases and controls selected from a larger prospective cohort study . WABC-II enrolled 1287 post-menopausal women treated within the University of Pennsylvania Health System for stage I–III, ER+ breast cancer, who had completed definitive surgery and adjuvant chemotherapy and/or radiotherapy as standard of care and were receiving adjuvant hormone therapy with an aromatase inhibitor. Subjects were enrolled from March 2008 through November 2013 and followed for recurrence; a comprehensive data lock was performed in August 2015. This study was approved by the Institutional Review Board of the University of Pennsylvania and all study procedures were conducted according to the institution’s code of ethics. Informed consent was obtained from all individual participants in the study.27. Cases were defined as having recurred based on the standardized STEEP criteria for recurrence free interval (RFI)28, which includes invasive ipsilateral breast tumor recurrence, local/regional invasive recurrence, distant recurrence, or death from breast cancer. Subjects exited the cohort at the time of relapse. Controls were selected in a 1:5 case/control ratio using cumulative density sampling , and individually matched with replacement on three factors: time since diagnosis, time on aromatase inhibitor, and HER2 status. These matching criteria were chosen to minimize influence of the most important covariates, since increasing time since diagnosis, decreasing time on AI, and HER2+ status are all associated with an increased risk of relapsed breast cancer. Because 38 HER2− cases were available for analysis, a 1:5 case to control ratio was chosen to achieve 94% power to detect an Odds Ratio (OR) of 2.00, assuming an alpha of 0.05 and a prevalence of elevated biomarker levels in the controls of 30% (based on prior data11). In addition to recurrent breast cancer, clinical variables of interest included age, high-grade, >4 positive nodes, race, and obesity at diagnosis, defined as a body mass index (BMI) ≥30 kg/m2. High-grade was distinguished from low-moderate grade. If tumor grade was not specified in the pathology report, the grade was classified as low-moderate.Reporting of all results are in accordance with the REMARK guidelinesn = 74; 38%). These patients were therefore analyzed separately, and all ER+/HER2+ cases and controls were included in the analysis, rather than being individually matched.In March 2018, the relapse status of all controls was updated again to reduce the risk of misclassification, and controls for any new cases that were identified were assigned from the existing pool of controls based on the same matching criteria. The ER+/HER2+ subset of patients in the study represented a minority of the overall population . When available, the initial enrollment sample was used for the present analysis, with additional follow up samples used if the enrollment sample was missing. Subjects with both missing enrollment and follow up samples were excluded from the analysis, as these samples were missing at random.29, analysis of individual serum samples were performed in tandem pairs and back calculated against a standardized curve repeated on each plate analyzed. Laboratory staff performing the analyses were blinded to case/control status. Descriptive results of serum biomarker levels were reported both as median values and proportion of subjects with values greater than the upper limit of normal (>ULN). The ULN for the clinically utilized biomarkers CRP and IL-6 were defined from reference intervals that are based on the distribution in the general population and published online by the HUP lab test services guide, with an elevated CRP being defined as >7.5 mg/L30, and elevated IL-6 being defined as ≥5 pg/mL31. SAA is not currently utilized clinically, therefore no ULN was available for consideration with regard to this variable. Results were compared between cases and controls using the Wilcoxon rank-sum test (for median biomarker levels) and chi squared test .The serum concentrations of CRP, IL-6, and SAA (primary exposure variables) were measured using the commercially available Luminex quantitative multiplex bead assay  and was performed by the Human Immunology Core in the Perelman School of Medicine at the University of Pennsylvania. Because assay validation is key in proper biomarker analysis and reportingBecause the HER2− cases and controls were individually matched, while all HER2+ cases and controls were selected, statistical analyses were performed separately for each cohort. Median serum concentrations of each inflammatory biomarker amongst cases and controls were compared using the Wilcoxon rank-sum test. Because the serum biomarker values were not normally distributed, a natural log transformation was performed to normalize the data Fig.  prior to33. Receiver operating characteristic (ROC) curves were generated based on simple logistic regression to confirm the predictive ability of the biomarkers considered for classification tree analysis. Third, biomarkers found to be significantly associated with relapsed breast cancer were investigated for statistical interaction by creating a multivariate regression model that included the individual candidate biomarkers, as well as a cross-product term. To test for synergy on the additive scale, also known as biologic interaction36, the relative excess risk due to interaction (RERI) was calculated17. The RERI between two dichotomous biomarker variables was calculated by inserting variables that represent one of three scenarios into a  logistic regression model: two variables each representing an instance in which one biomarker is “positive” (above the cutpoint established in the second step above) while the other is negative, and one variable representing an instance in which both biomarkers are positive, with the common reference being subjects who are “negative” for both biomarkers. After this multivariable regression model was generated, beta coefficients and variance/covariance values were used to calculate the RERI37. Fourth, the adjusted association between relapsed breast cancer controlling for the clinical covariates of interest and the significant biomarkers were estimated using multivariate logistic regression. Lastly, a final multivariate regression model was built using the significant individual biomarkers, biomarker interactions, and covariates identified in the prior analyses. Final multivariate logistic regression models were manually constructed using forward selection, and confirmed using backward selection. Sensitivity analyses were performed to account for extremes in follow up time, as well as the use of individual controls multiple times. All descriptive and statistical tests, with the exception of the classification tree analysis, were performed using the Stata Statistical Software Package version 14.2 . Classification tree analysis was performed using CART software .Conditional logistic regression was utilized as the measure of association in the inflammatory biomarker analysis in the HER2− subgroup, and logistic regression was utilized in the HER2+ subgroup in the following steps. First, univariate logistic regression of each biomarker was performed separately for each of the candidate biomarkers. Second, for biomarkers showing a significant association in the univariate analysis, optimal cutpoints were created by performing a classification tree analysis. Classification tree analysis employs a standard algorithm that recursively divides the data into two groups on the basis of a dependent variable that maximizes the difference between the proportion of the event of interest between the two groups38. The forward primer sequence was 5’ AAA AAG GAG TCA CAC ACT CCA CCT 3’ and the reverse primer sequence was 5’ TTG GGC TGA TTG GAA ACC TTA TTA 3’. The enzyme used for the PCR reactions was the Roche Expand High Fidelity PCR system (Cat. No 11 759 078 001) and the cycling conditions were as follows: 95C 5 min; 30 cycles of 95C 15 s, 57C 30 s, and 72C 30 s; 72C 5 min; then held at 4C. PCR products were purified with ExoSap. Sequencing reactions were then assembled using BigDye 3.1 (ThermoScientific) and sequenced on ABI 3730XL sequencer in both forward and reverse directions. DNA was extracted from buffy coat samples drawn at the time of enrollment into the WABC cohort and underwent Sanger sequencing for three functional variants of the IL-6 promoter: −572G>C (rs1800796), −597G>A (rs1800797), and −174G>C (rs1800795). Based on previously published data9, subjects were considered to have a high-risk IL-6 promoter genotype if a “G/G” genotype was present for the −597 and −174 variants. Of all 196 subjects, two samples were missing (one HR+/HER2− case and one HR+/HER2+ control). Conditional and simple logistic regression models were used respectively for the HER2− and HER2+ subgroups to measure the level of association of high-risk IL-6 genotypes with relapsed breast cancer. Confounding effects between clinical covariates and genotype profiles were assessed using logistic regression.IL-6 promoter genotyping methods were adopted from previously described techniques39. Off the clot double charcoal-stripped human serum was purchased from Golden West Biologicals, Inc. . All solvents were HPLC Optima grade unless otherwise noted.Estrone and estradiol standards were purchased from Steraloids Inc. . [13C6]-estrone and [13C6]-estradiol were purchased from Cambridge Isotope Laboratories . [13C3]-exemestane was purchased from Isosciences  and [2H3]-17β-hydroxy-exemestane, [2H4]-letrozole and [2H12]-anastrozole were purchased from Toronto Research Chemicals . β-glucuronidase/arylsulfatase (Helix pomatia) was obtained from Roche . Dry acetonitrile was purchased from Acros Organic . Methyl-tert-butyl-ether (MTBE), 2-fluoro-1-methylpyridinium p-toluenesulfonate (FMP-TS), triethylamine, methanol, acetone, L-ascorbic acid, formic acid, hydrochloric acid (HCl), sodium chloride, sodium acetate and sodium bicarbonate were obtained from Sigma–Aldrich g at 4 °C for 15 min. The upper, organic layer containing extracted estrogens was removed and dried under nitrogen prior to chemical derivatization and LC-HRMS analysis. Formation of methylpyridinium ether derivatives of estrone and estradiol proceeded as follows. 2-fluoro-1-methylpyridinium p-toluenesulfonate (FMP-TS) reagent was freshly prepared at 5 mg/mL in acetonitrile containing 1% triethylamine. Fifty microliter was added to each vial containing extracted estrogens. The mixture was vortexed for 10 s and then incubated at 45 °C for 15 min. The reaction was stopped by the addition of 50 µL water containing 0.1% formic acid and 5 µL of this mixture was directly injected for LC-HRMS analysis.Off the clot double charcoal-stripped human serum was used as an analytical matrix for quantification of estrogen metabolites from human serum. An internal standard mix containing [13C6]-estrone, [13C6]-estradiol, [13C3]-exemestane, [2H3]-17β-hydroxy-exemestane, [2H4]-letrozole and [2H12]-anastrozole was spiked into serum prior to extraction. Calibration curves of estrogens were prepared from standard solutions in the range of 1.56–800 pg/mL. For determination of total estrogens, 10 µL of internal standard working solution was spiked into a 0.1 mL aliquot of serum, followed by the addition of 0.1 mL water, 0.1 mL 0.5% L-ascorbic acid, 0.2 mL sodium acetate buffer , and 20 µL of β-glucuronidase/arylsulfatase. Samples were incubated at 37 °C for 19 h. After hydrolysis, samples were acidified with 15 µL of 1 N HCl followed by addition of 150 µL saturated sodium chloride. Samples underwent liquid–liquid extraction (LLE) with 2.5 mL of MTBE by vortex-mixing for 20 min, followed by centrifugation at 3400 × Separations were performed on a Waters BEH C18 Column (2.1 mm × 50 mm 1.7 μm) using a 7 min gradient starting at 65% methanol w/ 0.1% formic acid. Mobile phase A was water with 0.1% formic acid, and mobile phase B was methanol with 0.1% formic acid. A Thermo QExactive HF instrument was operated in positive ion mode alternating full scan and MS/MS modes at 120,000 resolution. The MS was coupled to an Ultimate 3000 UHPLC interfaced with a heated electrospray ionization (HESI-II) source. Molecular (M+) precursor ions of estrogens were as follows: estrone: 362.2115; [13C6]-estrone: 368.2361; estradiol: 364.2271; and [13C6]-estradiol: 370.2465. The method used the separation of signal from noise based on the molecular ion’s unique stability, by applying extra CID on the parent ion. Aromatase inhibitors and corresponding internal standard molecular ions (M+) were detected in full scan according to accurate mass as follows: letrozole: 286.1087; [2H4]-letrozole: 290.1333; anastrozole: 294.1713; [2H12]-anastrozole: 306.2459; exemestane: 297.1847; [13C3]-exemestane: 300.1948; 17β-hydroxy-exemestane: 299.1996; [2H3]-17β-hydroxy-exemestane: 302.2184. Serum concentrations of estrogens were calculated using Xcalibur software (version 3.0) from Thermo Fisher Scientific. The limit of detection was 1.0 pg/mL for both E1 and E2. Laboratory staff performing the analyses were blinded to case/control status.Serum estrogens were reported in both qualitative (estrogens detected or not) and quantitative fashions. Subjects with serum E1 or E2 levels that were below the limit of detection were reported as 0.2 pg/mL (or the limit of detection/5) so they could be included in the quantitative analysis. Serum estrogen levels and high inflammatory states were compared by measuring the proportion of detectable estrogen metabolites, as well as mean estrogen level within each group. Statistical significance for any differences observed was measured using chi squared (detectable estrogens) and Wilcoxon rank-sum (mean estrogen levels) tests.A total of 12 subjects had hormone serum samples missing at random  and were therefore not included in the analysis. 14 subjects had their serum estrogens measured as part of a pilot project in 2010 to measure serum estrogen levels. Those results are included as part of the qualitative analysis only given a subsequent refinement of laboratory techniques that more precisely quantifies the serum estrogen levels, and were only included if no additional serum samples remained for repeat testing.Further information on experimental design is available in the Supplementary Table 1Reporting Summary Checklist FLAT


Genetic polymorphisms in the methylenetetrahydrofolate reductase (MTHFR) gene may alter the risk of breast cancer. This study aimed to investigate the association of MTHFR C677T and A1298C genetic polymorphisms with breast cancer risk in case-control studies which was followed by stratified analysis. In the case-control study, 300 subjects including 150 women with breast cancer and 150 healthy women were enrolled. After blood sample collection, the C677T and A1298C polymorphisms genotyping were done by the PCR-RFLP method. Our data revealed a significant association between MTHFR C677T and A1298C polymorphisms and breast cancer risk. But, as a preliminary study, stratified analysis revealed no significant association between C677T and A1298C polymorphisms and tumor size and also lymph node metastasis in breast cancer. According to the mentioned findings, the C677T and A1298C polymorphisms in the MTHFR gene could be molecular risk factors for breast cancer in our studied population. However, further studies with larger sample sizes are required to obtain a more accurate conclusion in stratified analysis. Breast cancer is one of the most common causes of death in women worldwide . This malignancy is also considered as the most prevalent cancer in women worldwide . Breast cancer is a complex and multifactorial disease that could be affected by various genetic and environmental factors. For instance, age over 40 years, history of some proliferative breast diseases, history of breast cancer in the first-degree family, early menstruation, late menopause, nulliparity, and higher socioeconomic status . Genetic factors also play an important role in tumor formation and progression. For example, aneusomy of chromosome 17, deletion of loci at 16q, 11q, 6q, and 3p and polymorphisms in genes involved in the key cellular pathways can be the main risk factors for breast cancer . One of the important cellular pathways is the folate metabolism cycle which includes many genes such as methylenetetrahydrofolate reductase (MTHFR), methionine synthase (MTR), and methionine synthase reductase (MTRR). The role of the MTHFR enzyme is to convert 5,10-methylenetetrahydrofolate  to 5-methyltetrahydrofolate (5-MTHF), which is a common substrate for methylation of homocysteine to methionine. The methyl group of methionine is used to form S-adenosyl methionine which is a methyl donor for DNA and protein methylation . This gene consists of numerous single nucleotide polymorphisms (SNPs). The C677T (rs1801133) and A1298C (rs1801131) genetic variations are the most common polymorphisms of the MTHFR gene which may contribute to breast cancer risk. The C677T polymorphism results in alanine to valine replacement at the codon 222. While the A1298C polymorphism replaces glutamate to alanine at codon 429 .There are some studies that investigated the correlation of two common polymorphisms (C677T and A1298C) in MTHFR gene with the breast cancer risk. Kaya et al in 2016 reported no significant association between C677T polymorphism and breast cancer risk . Also, Papandreou et al. in 2012 reported no significant association between A1298C and breast cancer risk . However, Ergul et al., (2003) reported a significant association between two mentioned SNPs and breast cancer risk . Given the above contradictory results, the purpose of this study is to determine the association of C677T and A1298C SNPs of the MTHFR gene with breast cancer risk in a case-control study which is followed by stratified analysis.SubjectsThis study consisted of 150 breast cancer patients and 150 cancer-free and age-matched women. Case individuals were the patients referring to Hospitals in Isfahan province and their breast cancer was confirmed by pathological tests. They completed a questionnaire containing information about their BMI, history of drug, age, and family history of breast cancer. Patients with diseases such as diabetes, other cancers, CHD, and etc. were excluded from the study. The control group was composed of healthy women without a family history of cancer and other disorders who were referring to the same hospitals for routine examinations. Both cases and controls were belonging to a local area with the same ethnicity. All participants signed an informed consent form and this project was approved by the ethics committee of the Kashan University of Medical Sciences (IR.KAUMS.MEDNT.REC.1397.39). Approximately 2 ml of blood was taken from each woman and transferred to an EDTA-coagulated tube and stored at -20°C until DNA extraction. DNA extraction and SNPs genotyping DNA was extracted from peripheral blood samples by a commercial DNA extraction kit . polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method was used to C677T and A1298C SNPs genotyping. For this purpose, at first, forward and reverse oligonucleotide primers were deduced from our previous study  and were ordered from Bioneer Company to amplify fragments containing two mentioned SNPs. Approximately 5 microliters of twice-distilled water were added to 0.2 ml micro-tubes to reach a final volume of 20 µl. Then, 10µl of 2X premix PCR mixture, 0.3 µM of each pair of primers, and 60ng of DNA template were added to the micro-tubes. After a short vortex and then spin the samples, PCR was performed in a Peqlab thermal cycler . All PCR reagents were ordered from Cinnagen company . The thermal cycling conditions for PCR were as follows: initial denaturation at 94°C for 4 min, then 35 cycles of denaturation at 94°C for 30 min, annealing at 63°C for 30 min for C677T polymorphism and 67°C for A1298C, and elongation at 72°C for 30 min, with a final extension at 72°C for 5 min. The PCR products were digested by the HinfI enzyme for the C677T variant and MboII enzyme for A1298C SNP according to manufacturer’s instruction .PCR products were electrophoresed on 1% agarose gel and the fragments containing C677T and A1298C variations produced the bands with a length of 233-bp and 143-bp, respectively. The genotypes of C677T polymorphism was detected by 2% agarose gel electrophoresis. The fragment with TT genotype was digested into two 176-bp and 57-bp fragments, whereas in CT genotype was digested into 233-bp, 176-bp, and 57-bp fragments. In CC genotype, only one band containing the 233-bp fragment was observed because there was no restriction site for HinfI . RegardiStatistical analysisStatistical tests were performed using SPSS 22 software . The Chi-square test was used to compare alleles and genotypes frequencies in case and control groups. This test was also used to evaluate Hardy-Weinberg equilibrium in both the cases and healthy control groups. A binary logistic regression was used to obtain odds ratio (OR) and 95% confidence interval (CI) for investigation of the association between the two polymorphisms and the risk of breast cancer in overall and stratified analyses. A two-tailed p-value of less than 0.05 was considered statistically significant.Association of C677T and A1298C SNPs with breast cancerThe results of genetic association analysis for C677T and A1298C polymorphisms are summarized in Stratified analysisThe genotypes distribution for C677T and A1298C polymorphisms were evaluated for size of tumor and metastasis of lymph node in breast cancer subjects . ConcernIn the present study, we evaluated the association of two common variations in the MTHFR gene with the risk of breast cancer in a case-control study which was followed by stratified analysis. Our case-control study revealed a significant association between A1298C and C677T genetic variation and increased risk of breast cancer in our studied population. But, there are some similar studies with inconsistent results. This phenomenon, may be due to different behavior of polymorphism in various races. However, environmental and nutritional factors are also involved in the effect of this polymorphism.Folate, a form of vitamin B9, is found chiefly in plant diets. The human cell requires folate as a cofactor for numerous reactions, such as DNA synthesis, DNA methylation, and DNA repair . Then diets deficient in fruits and vegetables are generally low in folate, antioxidants, and a lot of other micronutrients which leads to significant amounts of DNA damage and higher cancer rates . Low consumption of folate and related B vitamins plays as one of the few risk factors associated with breast cancer . Some studies revealed that folate intake to be inversely associated with the risk of breast cancer . Wang et al., (2014) and Weiwei et al., (2014) found a significant association of MTHFR-C677T transition and folate intake with risk of breast cancer, and they reported a significant interaction between folate intake and MTHFR-C677T transition . Also, Ericson et al., (2009) suggested a significant association of high concentration of folate with the risk of postmenopausal breast cancer risk in carriers of the MTHFR-677T allele. Generally, the role of folate to the prevention of tumor development before preneoplastic lesions have been proven, but it conversely increases tumorigenesis once lesions. However, increase the folate intake in women with already-sufficient levels of folate may be harmful . Shrubsole et al., 2004 evaluated C677T and A1298C variations and their impacts on the folate intake and association of breast cancer susceptibility. They reported an inverse correlation of breast cancer risk with the intake of folate for all genotypes, especially among subjects with the 677TT genotype. They suggested that MTHFR-C677T variation might modify the correlation of dietary folate intake with breast cancer risk. But, there was no modifying effect of A1298C genotypes on the correlation of folate intake with the risk of breast cancer . Consequently, the MTHFR enzyme has a balancing role in the pool of methyl groups for DNA methylation, protein methylation, and DNA synthesis . Thus, MTHFR activity may affect both gene expression (by DNA methylation), and genome integrity (by DNA synthesis and repair) . The potential impact of MTHFR enzyme activity on DNA methylation, synthesis, and repair make it a possible candidate in cancer-predisposing . The SNP’s effects on the molecular aspects of a gene were dependent on its gene location ; Mobasseri et al., 2019). Evaluation of these effects by in vitro and in vivo methods is very time and cost consuming . Assessment of the mentioned impacts and other molecular assessments by in silico tools may be too beneficial . A study with bioinformatics analysis revealed that both C677T and A1298C genetic variations made significant changes in the secondary structure of MTHFR-mRNA. Moreover, a structural examination of the A1298C polymorphism showed a significant impact on the function of MTHFR .In conclusion, based on our findings, the MTHFR-A1298C and MTHFR-C677T variations could be genetic risk factors in our studied population. But, these polymorphisms could not be considered as genetic risk factors for tumor size and lymph node metastasis in our studied population. However, there are some limitations in our study, for example, a small sample size in the case-control study especially in the stratified analysis is the main limitation. Also, lack of evaluation of folate and homocysteine level is another limitation of our project. On the other hand, lack of attention to gene-environment and environment-environment interactions are other limitations of our study. However, further studies with a larger sample size regarding the aforementioned interactions could be helpful to obtain more accurate data.


Ataxia telangiectasia-mutated (ATM) gene contributes to repair damaged DNA and to regulate cell cycle; therefore, ATM variants seem to increase breast cancer risk; however, the results are controversial. So we conducted a systematic review and meta-analysis to clarify the pooled association between various ATM variants and the risk of breast cancer.The relevant studies were searched through Scopus, Web of Science, PubMed and Cochrane. Stratified and subgroup analyses were performed to explore heterogeneity between studies and assess effects of study quality. The pooled estimates logarithm with standard error logarithm of odds ratio and relative risk with confidence interval were calculated.This study revealed that there is association between ATM variants and the risk of breast cancer; according to the seven adjusted case-control studies, OR of this association was estimated as 1.67 (95%CI: 0.73–3.82), according to nine unadjusted case-control studies, the crude OR was 2.27 (95% CI: 1.17–4.40) and according to two cohorts, the RR was estimated as 1.68 (95% CI: 1.17–2.40).The ATM variants are associated with an increased risk of breast cancer that ATM V2424G mutation is detected as the most predisposing factor while ATM D1853V, L546V, and S707P variants have the least predictive ability. Breast Cancer is the most common cancer in women and causes the highest mortality rate in developed and developing countries . AnnuallDuring the last decade, BRCA1 and BRCA2 mutations (BRCA1/2) have been screened for hereditary breast cancer and results showed that mutations in BRCA1/2 account for 5% of hereditary breast cancer . BesidesMutations in the ATM gene caused Ataxia Telangiectasia (A-T), an autosomal recessive syndrome that patients have symptoms such as ionizing radiation sensitivity, cerebellar neurodegeneration immunodeficiency and markedly increased risk of cancers like breast cancer –14. The Since the detection of the ATM gene in 1995, several studies have been conducted on the association of mutations of this gene and the risk of breast cancer which had contradictory results and to our knowledge, no systematic study was conducted to examine the association between different variants of ATM and breast cancer. Therefore, the current study aims to clarify the pooled relationship between ATM variants and breast cancer.All methods used in this meta-analysis were according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) . The proTo evaluate the relationship between breast cancer and Ataxia Telangiectasia Mutated Proteins, four English databases including Scopus, Web of Science (EMBASE), PubMed, and Cochrane were browsed up to 20 October 2018. In current study “breast carcinoma”, “breast tumor”, “breast neoplasm”, “breast neoplasms”, “breast cancer”,” breast cancers”, “breast tumors”, “ataxia telangiectasia mutated”, “ataxia telangiectasia mutated proteins”, “ATM”,” mutation”, “mutations”, “variant”, and “variants” keywords were searched in the mentioned databases. Because of avoiding missing any papers, the reference list of primary articles was screened manually. The primary search results were reviewed, and some articles were eliminated after reviewing their title and abstract. Inclusion and exclusion criteria were set by 2 researchers separately .Articles were selected using the following criteria: (1) assessment of the association between ATM variants and breast cancer risk; (2) studies with full text articles; (3) case–control and cohort studies; (4) sufficient data for estimating an odds ratio (OR) with 95% confidence interval (CI).Full texts of each article were sensibly assessed by two independent reviewers (ES and MM) and any probable disagreements with these reviewers were resolved by consultation with another author (YM) to settle the argument, and a concluding deduction was applied. The following data were collected from each study: first author’s name, publication date, type of study, country, study population, age, sample size, type of variant, measurement of association and controlled variables.The qualities of all studies were assessed by Modified Newcastle-Ottawa Scale for Case Control studies . This chp < 0.05.Association between the ATM and breast cancer risk was analyzed by pooling odds ratio (ORs) and risk ratio (RR) with 95% confidence interval (CIs) using STATA metan module. DerSimonian and Laird method were used to compute the pooled estimate of odds ratio (OR) and risk ratio (RR) with a confidence interval (CI 95%) using random models . BecauseThe first step of search in electronic databases yielded 397 publications. In the final step, after removing the duplicates, reviewing by title, abstract and full text and considering the inclusion and exclusion criteria, 18 studies were selected for the meta-analysis of the pooled association between ATM and the risk of breast cancer . The range of OR between these studies was 0.2 to 11.60 .The pooled estimate odds ratio after synthesis results of 7 Adjusted case control studies shows that the association between ATM and risk of breast cancer was 1.67  while the lowest odds ratio was among the European population that was equal to 1.24 . This suggests that ATM variants have a greater impact on the risk of breast cancer in the Asian population than in the European and American populations . Also there is an association between homozygous variants and the risk of breast cancer with an odds ratio (OR) of 1.63 . Meanwhile, the lowest OR belonged to homo or heterozygous which was equal to 1.22 . This statistical analysis indicates that heterozygous ATM variants are the most associated with breast cancer incidence  and D1853V has the least impact on breast cancer incidence   is the most associated with breast cancer incidence    Table .I square: 0.105) and L546V has the lowest association with an OR of 0.37  .The results of two cohort studies showed a risk ratio (RR) range of 0.6 to 6.10. The pooled risk ratio between ATM variants and risk of breast cancer was 1.68  [P < 0.0001). Another meta-analysis of 19 studies by Marabelli M et al. [In the study by Easton DF et al.  in 2015, 0.0001) . Another 0.0001)  in 2016 M et al.  in 2016 M et al. . As the P < 0.0001). Another study by Goldgar DE et al. [P = 0.0005. In addition, another study by Southey MC et al. [p = 0.0012). Also, Mitui M et al. [Of the 29 different variants examined in the current study, V2424G (c.7271 T > G) missense variant had the highest association with breast cancer incidence in different subgroups of adjusted case control, non-adjusted case control and cohort studies. Although this association varied across the three subgroups analyses, all revealed that V2424G missense variant was strongly accompanied by an increased risk of breast cancer. Bernstein JL et al. [E et al.  in 2011 C et al.  in 2016 M et al.  evaluateThe meta-analysis on the adjusted case-control studies revealed that ATM D1853V missense variant has the least association with an increased risk of breast cancer. Gao LB et al.  in 2010 Another objective of our study was to investigate the association of ATM variants with breast cancer in different regions. Although the incidence of breast cancer is higher in the Americas and Europe than in the Asian continent , 39, it The overall risk heterogeneity in our study was 67.4, which could be due to the way we measured, sample-sizes designs, inclusion criteria, family history and BRCA1/2 mutation status and could distort the interpretation of our results, so we performed a sub-group analysis based on variant type, region and heterozygosity to illustrate this heterogeneous factor and thus reduced the heterogeneity up to zero.Since the primary studies used in this meta-analysis did not provide sufficient information about the exact age of the patients, the type of breast cancer, its stage and grade, the hereditary or sporadic type and unilateral or bilateral breast cancer, further subgroup analysis was not possible. Furthermore, there are few cohort studies in this area, and we need more studies.This meta-analysis shows that the pathogenic ATM variants are associated with an increased risk of breast cancer. Accordingly, ATM variants, including V2424G have the highest risk of breast cancer incidence while ATM D1853V, L546V, and S707P variants have the least impact on breast cancer incidence.


Most gene-environmental studies have focused on breast cancers generally, the preponderance of which occur after age 50. Young-onset breast cancers (YOBC) tend to be aggressive and may be etiologically different. The goal of this analysis was to assess interactions between an established 77-SNP polygenic risk score (PRS) and non-genetic risk factors for YOBC. We constructed the PRS using a family-based study of 1,291 women diagnosed with breast cancer before age 50 and their parents and unaffected sisters. We used conditional logistic regression to analyze interactions between the PRS and 14 established risk factors. In further analyses we assessed the same interactions, but for invasive cancer, estrogen receptor (ER) positive cancer and with broader inclusion of racial/ethnic groups. Results showed a decreased association between the PRS and YOBC risk for women who had ever used hormonal birth control (odds ratio [OR] = 2.20 versus 3.89) and a stronger association between the PRS and YOBC risk in pre-menopausal women (OR = 2.46 versus 1.23). Restricting the analysis to ER+ cancers or invasive cancers or using samples from all ethnic groups produced similar results. In conclusion, the PRS may interact with hormonal birth control use and with menopausal status on risk of YOBC. Genetic variants contributing to breast cancer risk range from rare variants with moderate to high penetrance to common variants with low penetrance, and GWAS studies have identified hundreds of genetic risk variants1. Many of these variants are also identified in studies of young-onset breast cancer3, which tends to be more aggressive, but some may be predictive of young-onset disease, specifically4. In terms of reproductive and behavioral risk factors, overall breast cancer risk is associated with early age at menarche, late age at first birth, low parity, late age at menopause, hormone replacement therapy, alcohol consumption, recent oral contraceptive use, and low physical activity (https://www.cdc.gov/cancer/breast/basic_info/risk_factors.htm). High body mass index (BMI) is an established risk factor for postmenopausal breast cancer, but has been shown to be inversely associated with premenopausal breast cancer5, another indication of etiologic differences between earlier and later-onset disease.Breast cancer is the most common cancer in women, with an estimated 268,600 new cases in the U.S. in 2019  that was originally proposed by Mavaddat16 and 7 epidemiological factors, using data from the Breast Cancer Association Consortium, and identified interaction between PRS and alcohol consumption, height and hormone therapy17. Case-control studies of gene-by-environment interaction can be vulnerable to exposure-related population stratification if the source population includes subpopulations with differing exposure prevalences, which also differ in their LD between genetic markers and causative variants18. While not fully robust against such bias, family-based studies are far less vulnerable to such effects, because they implicitly match on ancestry. . In this paper we assess interactions between the same 77-SNP PRS3 and 14 established risk factors for breast cancer using data from a family-based genome-wide association study (GWAS) of young-onset (under age 50) breast cancer, the Two Sister Study.Gene-environment interactions can be important to identify, both because they offer clues about etiology and because they can ultimately help us build algorithms through which we can distinguish high risk from low risk women and devise more personalized screening plans based on well-informed risk assessment. Gene-environment interaction effects for overall breast cancer have been assessed by a number of studies, most focusing on the established genetic and reproductive/behavioral risk factors. Overall, the results suggest that the interaction effects are weak4. Briefly, the Sister Study is a prospective breast cancer study that enrolled women (2003–2009) who had one or more sisters diagnosed with breast cancer but had never had breast cancer themselves. Families where the proband sister had been diagnosed with invasive breast cancer or ductal carcinoma in situ (DCIS) before age 50 and within the previous four years were eligible to participate in the ancillary Two Sister Study (2008–2010). The proband case sister as well as any living parents were invited to participate by providing DNA samples. Both the proband case and her breast cancer-free (control) sister completed the same computer-assisted telephone interviews about various health-related and lifestyle factors and Sister Study participants provided blood samples. This genetic substudy included Two Sister Study participants and their parents, and some Sister Study participants who developed incident breast cancer (invasive or DCIS) before age 50 during the follow-up period. All participants provided written informed consent in participating in this study and the study was approved by the National Institute of Environmental Health Sciences and the Copernicus Group Institutional Review Boards and all research was performed in accordance with relevant guidelines/regulations. We note that while the Sister Study is enriched for family history, the add-on Two Sister Study did not require having any breast cancer family history prior to the recruited case.Families were recruited through the Sister Study and the Two Sister Study, as described previously19. To ensure comparable opportunities for exposure between the matched sisters, the time-dependent risk factors that we did assess  were defined as of the proband’s age at breast cancer diagnosis. In other words, risk factor changes that had been experienced by an older control sister at ages after the age at which her case sister had been diagnosed were not considered relevant. All controls had aged past that age without a diagnosis of breast cancer. We used follow-up data from the Sister Study to fill in risk factor information for controls who had been enrolled at an age younger than their case sister’s diagnosis age.We limited our main analysis to the main ethnic subset, i.e. non-Hispanic white women, and assessed interactions with the following risk factors: height , self-reported BMI at 30 , cigarette smoking, alcohol, menopausal status, age at menarche, hormonal birth control use, years of using hormonal birth control, recency of hormonal birth control use (within 5 years), parity, age at first birth, recency of last birth (within 5 years), and breastfeeding (among parous women). We did not include hormone replacement therapy use due to concerns about the abrupt historical shifts over time in the prevalence of use and systematic age differences between cases and controlshttp://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000678.v1.p1)4. To construct the multiplicative 77-SNP polygenic risk score (PRS), originally proposed by Mavaddat et al.16 we used the natural log of the product of the 77 risk-allele-specific odds ratios  to calculate the PRS. We used the published ORs by Mavaddat (2015) in the calculation. Forty of the 77 SNPs were directly genotyped on the Illumina OmniExpress plus HumanExome-8v1-2 arrays used in the GWAS, and for the remaining 37 non-genotyped SNPs we used imputed genotypes. All 40 genotyped SNPs had a high call rate (≥97%) and low discordance rate (≤1%) in 74 study duplicates. “Impute” (v2.3.0) was used to impute the genotypes and the average quality score for the 37 imputed SNPs was 0.99. Details of the risk score calculation are given in Shi et al.3. Individual SNP association results are shown in Table 3 of Shi et al.3. Even though these SNPs were GWAS hits mainly for older onset breast cancer, we have shown previously that many of them were also associated with young-onset breast cancer3 and the 77-SNP PRS is a reasonable measure for young-onset breast cancer.The genotype data were obtained from a previous GWAS study  status from the medical record for most of the women (95% to 97%) and relied on self-report for the remainder. Controls in our study are either the actual sisters who were not diagnosed with breast cancer or the “pseudo-sister” when parents but not the unaffected sister were genotyped. The pseudo-sister is a hypothetical  sister who carries the parental alleles that were not transmitted to the case sister and has the same risk factors as those experienced by the actual unaffected sister21. We assume for this analysis that, conditional on the parents, the alleles that were transmitted do not influence the daughter’s status with respect to the risk factors under study.Breast cancer cases were self-reported, with high confirmation rates seen for those who also provided medical recordsBased on the availability of genotype and risk factor data, families in our study fall into four disjoint groups: case-parents-plus, case-parents, case-sibling-plus and case-plus. All of these families provided both risk factor and genotype data for the cases. The “case”, “parents” or “sibling” in the first part of the name denote that genotype data are available for the corresponding individuals, and the “plus” in the name denotes that data on the control sister’s risk factors was also available. Among non-Hispanic white families, we had 384 case-parents-plus families, 32 case-parents families, 432 case-sibling-plus families and 304 case-plus families.21. This is a weak independence assumption, which only asserts that genetic differences between full sisters do not correspond to differences between the studied nongenetic factors in any systematic way. Conditional on the parents, the alleles that happened to be transmitted are not associated with the risk factors. The matching sets used in CLR for the four family structures in our data are summarized in Table We used conditional logistic regression (CLR) to take advantage of the matched design and enforce within-family gene-by-risk-factor independenceMore specifically, for case-parents-plus and case-sibling-plus families, each case was matched with 3 other hypothetical genotype/risk-factor combinations: (1) her sister’s actual genotypes and risk factors; (2) her own genotypes and her sister’s risk factors and (3) her sister’s genotypes and her own risk factors. Note that here the genotype for the “sister” denotes either the actual control sister in the family or the pseudo-sister as defined above. Case-parents families have a 1:1 matching: each case’s genotypes and risk factors are matched with the pseudo-sister’s genotypes and the case’s risk factors. Case-plus families also have a 1:1 matching with the case’s genotypes and her sister’s risk factors.22. We implicitly matched on age by assessing risk factors for both sisters as of the case’s age at diagnosis. In addition, we adjusted for potential confounders as follows: parity and age at first birth when estimating the effects of breastfeeding, for alcohol when estimating the effects of smoking, for smoking when estimating the effects of alcohol, for age at menarche when estimating the effects of years using hormonal birth control, and for age at first birth when estimating the effects of parity (using parous women). All categorical variables except smoking have a natural ordering, and for those we assumed a linear trend in the interaction term but still treated the risk factor as categorical for its main effects. This ensured that the models were saturated for the risk factor main effect and thus correctly specified under a no-interaction null scenario23. In models targeting PRS-risk-factor interactions, we centered PRS of all individuals by subtracting the mean PRS based on the control sisters. With this centering, the estimated non-genetic risk factor main effect corresponds to the estimated effect when PRS is at the mean of the controls.We estimated risk factor marginal effects as well as multiplicative gene-by-risk-factor interaction effects. For all models, we adjusted for birth order to correct for the fact that because of the sampling design, control sisters were more often older than their case sisterWe performed the main analysis among non-Hispanic white breast cancer families, then further restricted to families with invasive or to families with ER+ breast cancer to examine whether any effect estimates changed substantially. To make use of all available data, we also conducted analyses using samples that included all racial/ethnic groups.The study included risk factor data for a total of 1152 non-Hispanic white cases and their families, which included 1120 controls from the Sister Study. The case sisters had a higher average PRS  than control sisters . The average age at diagnosis of breast cancer was 45. Most of the breast cancers were invasive (85%) and most were ER+ (81%). The distributions of the risk factors in the case and control sisters in non-Hispanic white families are shown in Table Results for risk factor main effects and PRS-risk-factor interaction effects from CLR models based on non-Hispanic white families are shown in Table In interaction analyses, interactions between PRS and both ever use of hormonal birth control and years of use were evident for young-onset breast cancer. The positive association between PRS and breast cancer risk was lower for women who had ever (versus never) used hormonal birth control , where ROR is the ratio of odds ratios of PRS in the exposed group divided by that in the unexposed group. For example, the estimated OR of PRS in women who never used hormonal birth control was 3.89 per unit change, and that for the women who had ever used hormonal birth control was 2.18 ( = 3.89 * 0.56). The positive association between PRS and breast cancer risk decreased with each additional year of hormonal birth control use . The association between PRS and risk of young-onset breast cancer was twice as strong for pre-menopausal woman as for post-menopausal women . In contrast with that interaction with menopausal status, there was no evident interaction with age .Results were similar when analyses were restricted to invasive breast cancer . Their analyses were not focused on young-onset breast cancer, and they did not consider either menopausal status or history of use of hormonal contraception in their meta-analyses. They did report statistically-significant interactions between PRS and both height and alcohol use, where higher alcohol consumption and greater height were both associated with a reduction in the estimated effect of the PRS. The associations were particularly strong for ER+ breast cancer. We did not see compelling evidence for either interaction for the young-onset families in the Two Sister Study. The Rudolph et al. analyses also found some evidence that current post-menopausal users of combined menopausal hormone therapy (compared to never users) may have a stronger effect of PRS on ER+ breast cancer. We did not consider the interaction effects of hormone replacement therapy due to complications in our study design19 and the fact that the large majority of our women were premenopausal.Rudolph 24, with some improvement in risk prediction. Future analyses of gene by environment interactions may benefit by using the more detailed genetic assessments, though the improvements may be modest.More recent analyses based on Breast Cancer Association Consortium have made it possible to include genetic variants with very small effects and have enabled the construction of complex polygenic risk scores that incorporate as many as 313 SNPset al. included up to 23,000 breast cancer cases, versus our 1,291 young-onset cases. Ideally we would find a way to pool our results with other studies that include younger women, but because it is relatively rare, young-onset disease will always be a challenge to study. In terms of generalizability, our sample is predominantly non-Hispanic white women, and the results may not apply to other racial or ethnic groups. Also, our family-based design recruited cases within up to 4 years following their diagnosis, and retrospective studies can be subject to both survival bias and recall bias.The major limitations of the Two Sister Study are its modest sample size and its racially homogenous composition. For comparison, the pooled analysis conducted by Rudolph 25. These covariances can cause artifactual gene-by-environment interactions or can mask true associations. Family-based studies with exposure information from both an affected and an unaffected offspring should not be vulnerable to such distortion25.On the other hand, the family-based design is a notable strength of the Two Sister Study, as family-based methods offer robustness from bias due to population stratification when studying effects of genetic variants. Including risk factor information for both an affected and an unaffected offspring provided further protection from biases for assessment of gene-by-exposure interaction. Case-control studies and cohort studies are both vulnerable to bias in evaluating interaction because subpopulations can differ both in their exposure distribution and also in their genetic architecture  in a way that can cause the true relative risks for marker alleles and exposures to covary across subpopulationsIn this analysis, we carried out a robust family-based analysis to investigate multiplicative interactions between a well-established PRS and nongenetic risk factors for young-onset breast cancer. Most of the measured interactions were compatible with the null, so that our data are consistent with the possibility that the joint effects of those factors and the PRS are multiplicative, but we observed evidence that both menopausal status and use of hormonal birth control modify the association between the PRS and young-onset breast cancer.Supplementary information


TP53. The aim of our study was to evaluate the role of the PIN3 16-bp duplication polymorphism in the susceptibility to breast cancer in the Malian population and to perform a meta-analysis to better understand the correlation with data from other populations.Breast cancer, the most common tumor in women in Mali and worldwide has been linked to several risk factors, including genetic factors, such as the PIN3 16-bp duplication polymorphism of We analyzed the PIN3 16-bp duplication polymorphism in blood samples of 60 Malian women with breast cancer and 60 healthy Malian women using PCR. In addition, we performed a meta-analysis of case-control study data from international databases, including Pubmed, Harvard University Library, Genetics Medical Literature Database, Genesis Library and Web of Science. Overall, odds ratio (OR) with 95% CI from fixed and random effects models were determined. Inconsistency was used to assess heterogeneity between studies and publication bias was estimated using the funnel plot.P = 0.02) and additive  models, but not in the recessive model (P = 0.38). In the meta-analysis, nineteen (19) articles were included with a total of 6018 disease cases and 4456 controls. Except for the dominant model (P = 0.15), an increased risk of breast cancer was detected with the recessive  and additive  models.In the studied Malian patients, a significant association of PIN3 16-bp duplication polymorphism with breast cancer risk was observed in dominant (A1A2 + A2A2 vs. A1A1: OR = 2.26, CI 95% = 1.08–4.73; TP53 is a significant risk factor for breast cancer in Malian women. These findings are supported by data from the meta-analysis carried out on different ethnic groups around the world.The case-control study showed that PIN3 16-bp duplication polymorphism of  BRCA1 and BRCA2), tumor protein p53 (TP53), Checkpoint kinase 2 (CHEK2), methylenetetrahydrofolate reductase (MTHFR), fibroblast growth factor receptor 2 (FGFR2) and glutathione S-transferase mu 1 (GSTM1) [TP53, a tumor suppressor gene, is involved not only in the development of breast cancer, but also in the development of other human cancers. Indeed, this gene plays a significant role in the response to stress. The protein TP53, also called the genome guardian, is a transcription factor that controls the expression of many genes involved in cell cycle regulation, DNA repair, cell death and senescence [TP53 in breast cancer may be linked to the geographic origin and ethnic differences of patients [Breast cancer as a multifactorial disease is the most diagnosed cancer among women worldwide . The inc (GSTM1) . TP53, anescence . The grepatients –10.TP53 is located on the chromosome 17p13.1 [https://www.ncbi.nlm.nih.gov/gene/7157). It is highly polymorphic both in exonic and intronic regions with more than 200 polymorphisms (http://www-p53.iarc.fr/). Of these, p.Arg72Pro, p.Pro47Ser and PIN3 16-bp duplication of TP53 are the most studied polymorphisms because of their critical roles in modifying the function and/or expression of TP53 [TP53 are at increased risk of breast cancer [The  17p13.1  and cons of TP53 . Sequenc of TP53 , 15. Sev of TP53 –20. It ht cancer , 22. Howt cancer , especiaThe literature review showed that the relationship between PIN3 16-bp duplication polymorphism and the risk of breast cancer has not been evaluated in our population. Therefore, we carried out the present work in order to understand firstly the relation between PIN3 16-bp duplication and the risk of breast cancer in the Malian population and secondly to perform a comparative meta-analysis of different studies around the world better to estimate the risk of breast cancer.The study was approved by the ethics committee of the Faculty of Medicine and Odontostomatology (2018/63/CE/FMPOS) at the University of Sciences, Techniques and Technologies of Bamako (USTTB). The study was explained to each participant prior being asked to sign the approved Informed Consent.TP53.Sixty women (mean age 43.72 ± 3.14) with clinically and histologically diagnosed breast cancer and 60 age-matched apparently healthy women (mean age 43.90 ± 2.92) from the general population were recruited at the University Hospital Center (CHU) of Point G in Bamako, Mali, between July 2018 and July 2019. All cases had early stage cancer (stage II). Clinico-pathological parameters including age at diagnosis, localization, use of contraceptive, menopausal status, parity, breastfeeding, family history of breast cancer, history of benign breast disease, obesity, smoking, histological type, tumor size, nodal involvement and metastasis were collected from each patient’s medical record. In the control group, the inclusion criteria were all Malian women aged of 18 years or over coming from the general population of whom no chronic disease has ever been diagnosed  and having accepted informed consent. Healthy subjects with a history of breast cancer, chronic diseases such as diabetes, or other types of cancer were excluded as controls. A total Five milliliter of peripheral blood was collected from each participant in an EDTA tube for thegenotyping analysis of PIN3 16-bp duplication polymorphism of TP53. PCR amplification conditions were previously described by Maarouf and al [Qiagen’s GentaPuregene Extraction Kit was used to extract the genomic DNA from white blood cells. DNA quantity and quality were determined by spectrophotometer. Genotyping of PIN316-bp duplication polymorphism was performed by allele specific PCR (AS-PCR) using published primers previously described , 23, 24.f and al .. The PCTP53 was tested by Chi2 analysis with exact probability. An odds ratio (OR) test with 95% confidence interval (CI) and P <0.05 was used to determine the association between PIN3 16-bp duplication polymorphism of TP53 and the risk of breast cancer, according to the different genetic models . The P value < 0.05 was considered significant.SPSS 11.0 was used to analyze the data. Chi-square tests (two-sided) were performed to evaluate the correlation between the PIN3 16-bp duplication and the clinical and histological features. Hardy-Weinberg equilibrium for the PIN3 16-bp duplication genotype distribution of TP53”, “Intron 3 Ins16 bp or PIN3 16-bp duplication”; “Polymorphism or mutation or genes” and “breast cancer” were used to perform a literature search of Pubmed, Harvard University Library, Genetics Medical Literature Database, Genesis Library and Web of Science. Only articles published in English were retained. Additional articles were identified by examining the references cited in articles and reviews retained from the search.The keywords “TP53 and the risk of breast cancer; (2) No deviation from Hardy-Weinberg Equilibrium (HWE) in controls; (3) No influence on the pooled odds ratio (OR) and p-values  Results reported about a case-control study, study published as an original study evaluating the association between PIN3 16-bp duplication polymorphism of  Results The following data were extracted from all eligible studies: first author’s name, year of publication, study population, sample size, genotypic and allelic distribution by two independent investigators . These data were compared to find a consensus. A third investigator resolved any conflict.P < 0.05 was carried out to evaluate the Hardy-Weinberg equilibrium in the controls. The association of PIN3 16-bp duplication polymorphism with the risk of breast cancer in the dominant, recessive and additive models was measured by ORs with 95% CI. An inconsistency (I2) test was performed to detect heterogeneity [2 <50% (absence of heterogeneity), the fixed effect model (FEM) was chosen as a pooling method; otherwise, if I2 >50% (presence of heterogeneity), the random effect model (REM) was maintained. The addition and/or deletion of any study that modifies the value of the pooled OR ± 1 was done to assess the sensitivity of the meta-analysis. The funnel curve was used to identify the publication bias.Review Manager Software was used to analyze the data. The Chi-squared test with the value of ogeneity . If I2 <TP53 and the risk of breast cancer in Malian women. The demographic, clinical, and pathological characteristics of the patients are shown in Table TP53 were more likely to have an invasive ductal carcinoma form, T3 stage tumor size, node involvement (N0 and N1), and M0 metastasis status compared to patients with the A1A1 or A1A2 genotype. We found no correlation between the PIN3 16-bp duplication polymorphism and the clinical features of participants except histological type (p = 0.04).We evaluated the association between PIN3 16-bp duplication polymorphism of TP53 in the cases according to the genetic models. The genotypic distribution PIN3 16-bp duplication polymorphism did not deviate from the Hardy-Weinberg equilibrium both in the cases  and in the controls . The heterozygous genotype (A1A2) was associated with an increased risk of breast cancer with . When we extended the analysis to the different genetic models, we noted that the dominant model  and the additive model  of PIN3 16-bp duplication polymorphism was significantly associated with the risk of breast cancer  and additive  models, but not in the dominant model . Figures TP53, respectively. Figure 2.This meta-analysis showed a significant association between PIN3 16-bp duplication polymorphism and breast cancer risk in recessive (Fixed effect model (FEM): OR = 1.46, 95% CI = 1.15–1.85; The stability of the results was assessed by a sensitivity analysis. We have noted a significant association between the PIN3 16-bp duplication polymorphism and the risk of breast cancer in the recessive Fig.  models, I2 = 19%, P = 0.23), recessive  and additive  models between PIN3 16-bp duplication polymorphism and breast cancer risk (Figs. After the non-inclusion of articles with HWE-deviation in controls, we noted a lack of heterogeneity in the dominant (A funnel plot was used to assess publication bias. After the elimination of studies that did not meet the inclusion criteria followed by the sensitivity analysis, no publication bias was observed in the recessive and additive models. However, a slight asymmetry was detected in the dominant model Fig. .Fig. 5FTP53 with the histological type of breast cancer. Similar results have been found in the Iranian population by Faghani et al. who reported a correlation between invasive ductal breast cancer and the PIN3 duplication polymorphism at 16 bp [n the present study, we noted a positive correlation of the PIN3 16-bp duplication polymorphism of at 16 bp . Contrarat 16 bp , 32, 38.Our results show that the PIN3 16-bp duplication polymorphism is significantly linked to the breast cancer risk in the Malian population.TP53 and the risk of breast cancer are contradictory. Similar to our results, Faghani et al. and Wu et al. reported that the A1A2 genotype is associated with the risk of breast cancer [We found that heterozygous, dominant and A1A2 additive models were significantly associated with an increased risk of breast cancer. However, the results of various studies regarding the association between the PIN3 16-bp duplication of t cancer , 41 On tt cancer , 38, 39.t cancer , in Irant cancer , and Polt cancer  but contt cancer . In addit cancer , 40 but t cancer , 36. TheTP53 such as codon 72 or p.Arg72Pro, intron 6 [TP53 haplotype was associated with the ability to repair DNA in lymphoblastic cell lines and apoptic reduction [TP53 could affect the activity of p53 by triggering the process of carcinogenesis.The meta-analysis, which included 6018 breast cancer patients and 4456 controls revealed an increased-risk of breast cancer with the recessive and additive models of PIN3 16-bp duplication. Two previous meta-analyzes, one covering 19 studies with 4479 cases and 4683 controls  and the intron 6 , 45. Theeduction , 46. ThuThis study has some limitations such as small sample size, lack of hormonal receptors tests and subgroup analyzes in the meta-analysis. Another limitation is the collection of data limited to the demographic parameters and history of the disease in controls.TP53 in the Malian population and to understand the relationship between this gene and the risk of breast cancer. Our results have shown that this polymorphism is not only associated with the histological type, but also is with the risk of breast cancer in Malian population. In addition, the meta-analysis carried out confirmed our findings.The present study made it possible to establish for the first time the distribution of alleles and genotypes of PIN3 16-bp duplication polymorphism of Additional file 1. Availability of all data and references with PubMed accession numbers