Colocalisation was performed as a sensitivity analysis to examine whether findings reflected shared causal variants or genomic confounding. In MR analyses, there was evidence for an association of HGF with oestrogen receptor‐negative cancer  but little evidence for associations of other adipokines or CRP with overall or oestrogen receptor‐stratified breast cancer. Colocalisation analysis suggested that the association of HGF with oestrogen receptor‐negative breast cancer was unlikely to reflect a causal association. Collectively, these findings do not support an important aetiological role of various adipokines or CRP in overall or oestrogen receptor‐specific breast cancer risk.Circulating adipokines and C‐reactive protein (CRP) have been linked to breast cancer risk in observational epidemiological studies. The causal nature of these associations is unclear because of the susceptibility of conventional observational designs to residual confounding, reverse causation and other forms of bias. Mendelian randomisation (MR) uses genetic variants as proxies for risk factors to strengthen causal inference in observational settings. We performed a MR analysis to evaluate the causal relevance of six previously reported circulating adipokines  and CRP in risk of overall and oestrogen receptor‐stratified breast cancer in up to 122,977 cases and 105,974 controls of European ancestry. Genetic instruments were constructed from single‐nucleotide polymorphisms robustly ( What's new?Adipokines and C‐reactive protein have been linked to breast cancer risk in observational studies. But while these molecular mediators potentially connect excess adiposity with breast cancer, whether the associations are causal in nature is uncertain. Here, a Mendelian randomization approach was employed to estimate the causal effect of six circulating adipokines and C‐reactive protein on breast cancer risk. Overall, among 122,977 cases and 105,974 controls, analyses provided little evidence supporting a causal role for these molecular markers in breast cancer etiology. The findings warrant investigation of other mediators that could explain apparent links between adiposity and increased breast cancer risk. BCACBreast Cancer Association ConsortiumCRPC‐reactive proteinER−oestrogen receptor‐negativeER+oestrogen receptor‐positiveGWASgenome‐wide association studyHGFhepatocyte growth factorIL‐6interleukin‐6InSIDEInstrument Strength Independent of Direct EffectIVinstrumental variableIVWinverse‐variance weightedMBmegabaseMRMendelian randomisationORodds ratioPAI‐1plasminogen activator inhibitor‐1SDstandard deviationSNPsingle‐nucleotide polymorphismIn vitro studies indicate that leptin may promote human breast cancer cell proliferation and adiponectin may exert antiproliferative effectsin vitro studies to humans, and the susceptibility of conventional observational analyses to residual confounding and reverse causation, all of which undermines causal inference.Elevated body mass index is an important modifiable risk factor for breast cancer.Mendelian randomisation (MR) uses genetic variants as instruments (‘proxies’) for risk factors  to generate more reliable evidence on the causal effects of these factors on disease outcomes and so inform potential intervention strategies.i) the instrument is robustly associated with the exposure of interest, (ii) there is no confounding of the instrument–outcome relationship and (iii) there is no pathway through which an instrument influences an outcome except through the exposure (‘exclusion restriction criterion’).More formally, MR is a form of instrumental variable (IV) analysis that can generate unbiased causal estimates of effects of risk factors on disease and health‐related outcomes if the following conditions are met:  and SNPs with concordance of <98% among 5,280 duplicate sample pairs were removed. An additional 1,128 SNPs where the cluster plot was judged to be inadequate were removed. In imputation, the following SNPs were additionally removed: those with a minor allele frequency of <1%, a call rate of <98%, and those that could not be linked to the 1,000 Genomes Project or differed significantly in frequency from this panel. In total, 469,364 of the 533,631 SNPs that were manufactured on the OncoArray were used in imputation. Results for OncoArray analyses were combined with those from the previous Illumina iSelect Collaborative Oncological Gene‐Environment Study (iCOGS) genotyping project  along with 11 other breast cancer GWAS , using a fixed‐effects meta‐analysis. The OncoArray, iCOGS and individual GWAS were adjusted for principal components of ancestry and OncoArray and iCOGS analyses were further adjusted for country and study, respectively. All participating studies had the approval of their appropriate ethics review board and all participants provided informed consent.Summary genome‐wide association study (GWAS) statistics were obtained from genome‐wide meta‐analyses on 122,977 breast cancer cases and 105,974 controls of European ancestry.cis‐acting variants, i.e. located ≤1 MB of the transcription start site of the protein‐coding gene) or a ‘polygenic’ approach , depending on the number of genome‐wide significant (p < 5 × 10−8) variants available to proxy each respective risk factor. When few SNPs are available as instruments, the restriction of an instrument to cis‐variants can help to minimise horizontal pleiotropy , a violation of the exclusion restriction criterion, as cis‐variants are more likely to have direct effects on protein levels than trans‐variants (i.e. those >1 MB of the transcription start site of the protein‐coding gene).cis‐acting SNPs robustly associated with these markers (p < 5 × 10−8) in GWAS of individuals of European ancestry that were replicated in independent samples. For risk factors with ≥3 independent (r2 < 0.01) cis‐ or trans‐SNPs available as proxies in GWAS of individuals of European ancestry , these SNPs were combined into multi‐allelic instruments to increase the variance in the risk factor explained by the instrument.r2 < 0.15) cis‐variants to investigate horizontal pleiotropy in primary multi‐allelic models.Genetic instruments to proxy risk factors were constructed either using a ‘monogenic’ approach , using previously reported methods.cis‐variants , effect estimates were generated using the Wald ratio and standard errors were approximated using the delta method. For instruments constructed using ≥3 independent variants , effect estimates were generated using inverse‐variance weighted (IVW) random‐effects models to account for overdispersion in models.cis‐variants , effect estimates were generated using IVW random‐effects models with adjustment for correlations between variants.i) neither trait has a genetic association in the region (H0), (ii) only the first trait has a genetic association in the region (H1), (iii) only the second trait has a genetic association in the region (H2), (iv) both traits are associated but have different causal variants (H3) and (v) both traits are associated and share a single causal variant (H4).p < 0.05) by generating windows ±100 kb from the top SNP used to instrument the exposure. As a convention, a posterior probability of ≥0.80 was used to indicate support for a configuration tested. To evaluate assumptions of the coloc package  we also generated regional Manhattan plots for SNP‐risk factor and SNP–breast cancer associations to visually inspect whether there was evidence of multiple independent causal variants within the region examined.As an additional sensitivity analysis, colocalisation was performed to examine whether two traits showing evidence of an association in MR analyses share the same causal variant at a given locus. This is important to perform for analyses in which an instrument is restricted to either a single variant or a single gene region as findings may be more susceptible to bias through genetic confounding . The coloc R package uses approximate Bayes factor computation to generates posterior probabilities that associations between two traits represent each of the following configurations: . All statistical analyses were performed using R version 3.3.1.To account for multiple testing across each breast cancer endpoint , a Bonferroni correction was used to establish a Summary genetic association data for all traits examined in this study were obtained from previously published analyses and can be made available upon reasonable request.R2 and F‐statistics) are presented in Table F‐statistics ranged from 40.1 to 3,872.7, suggesting that analyses were unlikely to suffer from weak instrument bias.For each risk factor, the number of SNPs included in the instrument and estimates of instrument strength . In sensitivity analyses, there was little evidence that this association colocalised (posterior probability H4 = 0.055). Complete colocalisation analysis results are presented in Supporting Information Table In MR analyses, there was little evidence to suggest associations of any of the adipokines or CRP with overall breast cancer risk Table . In ER sIn all other ER‐stratified analyses, there was little evidence of association of other adipokines or CRP with breast cancer outcomes.cis‐SNP instruments for both overall and oestrogen receptor‐stratified breast cancer were consistent with those from the primary analysis .I2  =  93.5%) when comparing breast cancer cases to controls.vs. lower quartile: OR 1.94, 95% CI: 1.37–2.75; upper vs lower tertile: OR 1.98, 95% CI: 1.20–3.29),vs lower quartile: OR 1.39, 95% CI: 0.93–2.09; upper vs. lower tertile: OR 0.83, 95% CI: 0.51–1.37).Our findings are not consistent with some previous conventional observational analyses, notably those examining the association of adiponectin and leptin with breast cancer.Strengths of this analysis include the use of a MR approach to appraise the relationship of adipokines and CRP with breast cancer risk which should be less prone to confounding than conventional observational analyses and cannot be influenced by reverse causation. The use of a two‐sample summary data MR framework afforded these analyses increased statistical power and precision by exploiting summary genetic data from several large genome‐wide association studies. The incorporation of colocalisation analysis permitted evaluation of whether associations present were driven through genetic confounding or shared common causal variants. Finally, through restricting summary genetic association data to that obtained in individuals of European descent and adjusted for principal components of ancestry, these analyses reduced the possibility of confounding through population substructures.cis‐acting variants as primary instruments and in sensitivity analyses, we cannot rule out the possibility that false negative findings may have arisen through horizontally pleiotropic pathways biasing our findings toward the null. An assumption of the colocalisation package employed is that there is at most one causal variant per trait within the region examined. If both traits have a genetic association in the region examined, a low posterior probability generated for H4  could reflect the presence of multiple shared causal variants within that region. However, posterior probabilities generated in this analysis provided evidence that only HGF was likely to have an association in the HGF region examined, suggesting that this assumption was unlikely to be violated. Fourth, it is not known whether circulating levels of adipokines represent a good surrogate for tissue‐specific levels in breast or adipose tissue. Fifth, we were unable to examine potential nonlinear associations of adipokines or CRP with breast cancer risk.There are several limitations to these analyses. First, since analyses were performed using summary genetic data in aggregate, this precluded stratification according to menopausal status. Around 85% of samples included within the Breast Cancer Association Consortium (BCAC) data used were classified as postmenopausal at diagnosis, suggesting that findings were unlikely to be biased markedly by the presence of premenopausal or perimenopausal participants.N = 98, 842 cases; 83, 464 controls) has suggested that elevated fasting insulin, a consequence of higher adiposity, increases breast cancer risk .Beyond adipokines and inflammatory pathways, there are several other molecular mechanisms through which excess adiposity may influence subsequent risk of breast cancer.In conclusion, our MR analyses suggest that several adipokines and CRP are unlikely to causally influence breast cancer risk. Further exploration of potential molecular mediators linking adiposity to elevated breast cancer risk remains warranted.T.R. has received educational funding from Daiichi‐Sankyo and Amgen. These funding sources have not influenced the authorship or content of this manuscript.AppendixS1: Supporting information tables and figuresTable S1. Estimates of sample overlap across exposure and BCAC datasetsTable S2. Summary genetic association data used across each adipokine and C‐reactive proteinTable S3. Posterior probabilities under differing hypotheses relating the associations between hepatocyte growth factor and oestrogen receptor‐negative breast cancer riskTable S4. Effect estimates per unit increase in adiponectin, C‐reactive protein, and plasminogen activator inhibitor‐1 on overall and oestrogen receptor stratified breast cancer risk using conservative (cis‐SNP) instrumentsTable S5. Sensitivity analyses for adiponectin, C‐reactive protein, and plasminogen activator inhibitor‐1 using MR‐Egger and weighted median estimatesFigure S1. Scatterplot of associations of individual SNPs used to proxy adiponectin with adiponectin and overall breast cancer risk along with slopes obtain from the following models: inverse‐variance weighted, MR‐Egger, weighted medianFigure S2. Scatterplot of associations of individual SNPs used to proxy adiponectin with adiponectin and oestrogen receptor‐positive breast cancer risk along with slopes obtain from the following models: inverse‐variance weighted, MR‐Egger, weighted medianFigure S3. Scatterplot of associations of individual SNPs used to proxy adiponectin with adiponectin and oestrogen receptor‐negative breast cancer risk along with slopes obtain from the following models: inverse‐variance weighted, MR‐Egger, weighted medianFigure S4. Leave‐one‐out analysis iteratively removing one SNP from the instrument used to proxy adiponectin and re‐calculating the association of adiponectin with overall breast cancer riskFigure S5. Leave‐one‐out analysis iteratively removing one SNP from the instrument used to proxy adiponectin and re‐calculating the association of adiponectin with oestrogen receptor‐positive breast cancer riskFigure S6. Leave‐one‐out analysis iteratively removing one SNP from the instrument used to proxy adiponectin and re‐calculating the association of adiponectin with oestrogen receptor‐negative breast cancer riskFigure S7. Forest plot presenting individual SNP estimates and multi‐allelic instrument estimates for the association of adiponectin with overall breast cancer riskFigure S8. Forest plot presenting individual SNP estimates and multi‐allelic instrument estimates for the association of adiponectin with oestrogen receptor‐positive breast cancer riskFigure S9. Forest plot presenting individual SNP estimates and multi‐allelic instrument estimates for the association of adiponectin with oestrogen receptor‐negative breast cancer riskFigure S10. Funnel plot presenting the effect estimate and inverse of the standard error for the causal estimate of each single‐nucleotide polymorphism examining the association of adiponectin with overall breast cancer risk along with slopes obtained from inverse‐variance weighted and MR‐Egger modelsFigure S11. Funnel plot presenting the effect estimate and inverse of the standard error for the causal estimate of each single‐nucleotide polymorphism examining the association of adiponectin with oestrogen receptor‐positive breast cancer risk along with slopes obtained from inverse‐variance weighted and MR‐Egger modelsFigure S12. Funnel plot presenting the effect estimate and inverse of the standard error for the causal estimate of each single‐nucleotide polymorphism examining the association of adiponectin with oestrogen receptor‐negative breast cancer risk along with slopes obtained from inverse‐variance weighted and MR‐Egger modelsFigure S13. Scatterplot of associations of individual SNPs used to proxy Creactive protein with C‐reactive protein and overall breast cancer risk along with slopes obtain from the following models: inverse‐variance weighted, MR‐Egger, weighted medianFigure S14. Scatterplot of associations of individual SNPs used to proxy Creactive protein with C‐reactive protein and oestrogen receptor‐positive breast cancer risk along with slopes obtain from the following models: inverse‐variance weighted, MR‐Egger, weighted medianFigure S15. Scatterplot of associations of individual SNPs used to proxy Creactive protein with C‐reactive protein and oestrogen receptor‐negative breast cancer risk along with slopes obtain from the following models: inverse‐variance weighted, MR‐Egger, weighted medianFigure S16. Leave‐one‐out analysis iteratively removing one SNP from the instrument used to proxy C‐reactive protein and re‐calculating the association of C‐reactive protein with overall breast cancer riskFigure S17. Leave‐one‐out analysis iteratively removing one SNP from the instrument used to proxy C‐reactive protein and re‐calculating the association of C‐reactive protein with oestrogen receptor‐positive breast cancer riskFigure S18. Leave‐one‐out analysis iteratively removing one SNP from the instrument used to proxy C‐reactive protein and re‐calculating the association of C‐reactive protein with oestrogen receptor‐negative breast cancer riskFigure S19. Forest plot presenting individual SNP estimates and multi‐allelic instrument estimates for the association of C‐reactive protein with overall breast cancer riskFigure S20. Forest plot presenting individual SNP estimates and multi‐allelic instrument estimates for the association of C‐reactive protein with oestrogen receptor‐positive breast cancer riskFigure S21. Forest plot presenting individual SNP estimates and multi‐allelic instrument estimates for the association of C‐reactive protein with oestrogen receptornegative breast cancer riskFigure S22. Funnel plot presenting the effect estimate and inverse of the standard error for the causal estimate of each single‐nucleotide polymorphism examining the association of C‐reactive protein with overall breast cancer risk along with slopes obtained from inverse‐variance weighted and MR‐Egger modelsFigure S23. Funnel plot presenting the effect estimate and inverse of the standard error for the causal estimate of each single‐nucleotide polymorphism examining the association of C‐reactive protein with oestrogen receptor‐positive breast cancer risk along with slopes obtained from inverse‐variance weighted and MR‐Egger modelsFigure S24. Funnel plot presenting the effect estimate and inverse of the standard error for the causal estimate of each single‐nucleotide polymorphism examining the association of C‐reactive protein with oestrogen receptor‐negative breast cancer risk along with slopes obtained from inverse‐variance weighted and MR‐Egger modelsFigure S25. Scatterplot of associations of individual SNPs used to proxy plasminogen activator inhibitor‐1 with plasminogen activator inhibitor‐1 and overall breast cancer risk along with slopes obtain from the following models: inverse‐variance weighted, MR‐Egger, weighted medianFigure S26. Scatterplot of associations of individual SNPs used to proxy plasminogen activator inhibitor‐1 with plasminogen activator inhibitor‐1 and oestrogen receptor‐positive breast cancer risk along with slopes obtain from the following models: inverse‐variance weighted, MR‐Egger, weighted medianFigure S27. Scatterplot of associations of individual SNPs used to proxy plasminogen activator inhibitor‐1 with plasminogen activator inhibitor‐1 and oestrogen receptor‐negative breast cancer risk along with slopes obtain from the following models: inverse‐variance weighted, MR‐Egger, weighted medianFigure S28. Leave‐one‐out analysis iteratively removing one SNP from the instrument used to proxy plasminogen activator inhibitor‐1 and re‐calculating the association of plasminogen activator inhibitor‐1 with overall breast cancer riskFigure S29. Leave‐one‐out analysis iteratively removing one SNP from the instrument used to proxy plasminogen activator inhibitor‐1 and re‐calculating the association of plasminogen activator inhibitor‐1 with oestrogen receptor‐positive breast cancer riskFigure S30. Leave‐one‐out analysis iteratively removing one SNP from the instrument used to proxy plasminogen activator inhibitor‐1 and re‐calculating the association of plasminogen activator inhibitor‐1 with oestrogen receptor‐negative breast cancer riskFigure S31. Forest plot presenting individual SNP estimates and multi‐allelic instrument estimates for the association of plasminogen activator inhibitor‐1 with overall breast cancer riskFigure S32. Forest plot presenting individual SNP estimates and multi‐allelic instrument estimates for the association of plasminogen activator inhibitor‐1 with oestrogen receptor‐positive breast cancer riskFigure S33. Forest plot presenting individual SNP estimates and multi‐allelic instrument estimates for the association of plasminogen activator inhibitor‐1 with oestrogen receptor‐negative breast cancer riskFigure S34. Funnel plot presenting the effect estimate and inverse of the standard error for the causal estimate of each single‐nucleotide polymorphism examining the association of plasminogen activator inhibitor‐1 with overall breast cancer risk along with slopes obtained from inverse‐variance weighted and MR‐Egger modelsFigure S35. Funnel plot presenting the effect estimate and inverse of the standard error for the causal estimate of each single‐nucleotide polymorphism examining the association of plasminogen activator inhibitor‐1 with oestrogen receptor‐positive breast cancer risk along with slopes obtained from inverse‐variance weighted and MR‐Egger modelsFigure S36. Funnel plot presenting the effect estimate and inverse of the standard error for the causal estimate of each single‐nucleotide polymorphism examining the association of plasminogen activator inhibitor‐1 with oestrogen receptor‐negative breast cancer risk along with slopes obtained from inverse‐variance weighted and MR‐Egger modelsClick here for additional data file.


One in ten pregnancies is complicated with high blood pressure. Whilst the association between high blood pressure during pregnancy and cardiovascular disease in later life is well-established, its association with cancer is less so. We aimed to determine the association between hypertensive disorders of pregnancy (HDP) and common cancers in a UK population-based longitudinal study. No statistically significant association was found between HDP and the odds of future cancer, and independent site-specific cancers of breast, colorectal, lung, ovarian and endometrial cancers in a UK-based general practice-derived population. Current research underlining HDP and cancer risk is limited to specific populations only. Future work in different populations is required, focusing on complex genetic and hormonal factors involved in these conditions.Purpose: The purpose was to determine the association between HDP and cancer in a UK cohort. Methods: Between 1993 and 1997, participants from the EPIC-Norfolk cohort attended baseline health-checks and completed questionnaires, where a history of HDP was collected. Incident cancer cases were identified through NHS record linkage until March 2016. Univariable and multivariable logistic regression analyses were employed to determine the association between HDP and odds of cancer, with adjustment for potential confounders including co-morbidities, sociodemographic, lifestyle and reproductive factors. Results: 13,562 women were included after excluding prevalent cancer cases and women with no pregnancies. 2919 (21.5%) reported HDP and 2615 incident cancers occurred during mean follow up of 19 years. Median age (IQR) at baseline for incident cancer was 60.8 (±14.8) years. Among incident cancer cases, 578 (22.1%) had HDP. In multivariable analyses, HDP had odds ratio (OR) 1.06; 95% CI 0.95–1.18 for incident cancer. The ORs (95% CIs) for common site-specific cancers including breast, colorectal, lung, ovarian and endometrial cancers were 1.06 (0.88–1.28), 1.15 (0.92–1.45), 0.96 (0.68–1.35), 1.30 (0.93–1.83) and 1.16 (0.80–1.67). Conclusion: We found no association between HDP and cancer risk. Further studies are required to confirm and account for any underlying genetic factors involved in pregnancy-related exposures and cancer risk. Hypertensive disorders of pregnancy (HDP) are a major cause of maternal morbidity and mortality globally ,2, complHowever, less is known regarding HDP and cancer risk. Some studies have shown that HDP, mainly preeclampsia, reduces future breast cancer risk. One recent population-based case-control study of 116,196 breast cancer cases illustrated a decrease in odds of breast cancer, in relation to a history of preeclampsia and gestational hypertension ,11,12,13Most research has focused on preeclampsia alone ,19,20,21We therefore aim to determine the association between HDP and future cancer risk and its independent association in common female cancers: breast, colorectal, lung, ovarian and endometrial, using prospective population-based data from the European Prospective Investigation into Cancer (EPIC)-Norfolk cohort.p-value < 0.001), more likely to have no educational qualification , be physically inactive  and belong to the pre-obesity category .Among 16,740 women, 13,562 women had no prevalent cancer at baseline and had a pregnancy history. Of these, 2615 (19.3%) had incident cancer diagnosis. Among reproductive variables, diabetes during pregnancy had no association with cancer risk . Furthermore, increasing age at first live birth increased odds of cancer. An age of ≥31 years was associated with the greatest odds . Having children was not significantly associated with cancer risk in both univariable and multivariable analyses. Additionally, any stillbirths  and miscarriages and abortions  showed no significant association.As expected, current smokers had greater odds of cancer  compared to ex-smokers . Being physically inactive was also significantly associated with the odds of cancer . After adjustment, this effect was slightly attenuated . Among different body mass index (BMI) categories, belonging to obesity class II or more conferred the greatest odds of cancer . Prevalent stroke significantly decreased the odds of cancer .The sub-analyses confining to those with high BMI did not show any overall significant associations .In this prospective population-based study with 19 years of mean follow-up, we found that HDP was not associated with overall maternal cancer risk. Similarly, the site-specific cancer analyses revealed that HDP was not associated with individual risks of common female cancers including breast, colorectal, lung, ovarian or endometrial cancers. Additionally, our results reinforce that lifestyle factors in the older age are important contributors to cancer risk.p = 0.15, I2 = 43.2%) [These results are supported by findings from previous studies. A systematic review and meta-analysis on preeclampsia and risk of cardiovascular disease and cancer in later life yielded no association between preeclampsia and breast cancer risk with relative risk (RR) 1.04 95% CI 0.78–1.39), although significant heterogeneity was observed. Similarly, analysis on any cancer outcomes revealed no association; RR 0.96 (95% CI 0.73–1.27); (= 43.2%)  and a ri= 43.2%)  and RR 0= 43.2%) ,17,19. A= 43.2%) . Another8–1.39, a= 43.2%) ,11,12,13= 43.2%) ,21,22. T= 43.2%)  and 1.23= 43.2%) . DiffereThere are some theories explaining risk reduction in individuals with HDP—in particular, preeclampsia—and future breast cancer risk. Though the precise sequence of events is still unclear, the current hypothesis states that lower circulating oestrogen levels occur in preeclampsia. Lower oestrogen levels are thought to accompany higher androgen levels in blood due to insufficient conversion of androgens into oestrogen by a dysfunctional placenta . OestrogOur results show that individuals with high BMI  or obesity class II or above ) are at significantly increased risk of cancer. Although it would be expected to observe a dose–response relationship with odds of cancer increasing with each subsequent rise in BMI category, this was not the case. The adjusted OR for the obesity class I category for incident cancer was 1.10 and this result was not statistically significant (95% CI 0.94–1.27). Belonging to the pre-obesity category significantly increased cancer risk for all cancers in the site-specific analyses, except for lung and ovarian cancers. The highest risk observed was between the obesity class II and above category and endometrial cancer . Similar findings have been reported previously . In thisOur study has several strengths. We used a large population-based cohort with adequate follow-up time. Detailed data collection by trained personnel adhering to EPIC protocols increases the reliability of our results. Bias is likely to be low in a prospective study with validated outcomes of cancer. Furthermore, we have adjusted for many of the potential risk factors for cancer. Our results are consistent with the literature in reinforcing many well-established risk factors for cancer.There are some limitations worth acknowledging. Our study relied on self-reported questionnaires for identification of HDP. Maternal recall of this obstetric history has been reported to have high specificity, although sensitivity is low . This maWe cannot account for residual confounding and unknown confounders. Confounders were gathered at baseline, which could vary throughout the time of follow-up. However, this is unlikely to greatly influence the results, as participants were aged 39–79 years at time of study entry and many variables adjusted for are unlikely to change given the age range . It is possible, however, that participants had accrued additional comorbidities. To prevent participants being omitted from analyses which would lower the statistical power of our results, we included missing values as separate categories. Healthy volunteer bias may be a factor in this cohort, as this was a volunteer study requiring long-term follow-up. However, baseline characteristics of the EPIC-Norfolk have been shown to be similar to other UK representative population samples . ParticiThe EPIC-Norfolk cohort is sampled from men and women aged 39–79 years at the time of study entry (1993–1997), in Norfolk, UK. Participants were invited through general practice registers. A total of 16,740 women gave consent and were sent health and lifestyle questionnaires (HLQs). Participants were invited to attend baseline health checks, which 14,030 women attended.Details of data collection and measurement methods have been described in full previously . In brieAdmission and diagnostic episodes for cancer were identified from NHS hospital information system and ENCORE (East Norfolk COmmission Record). Incident cancer was identified from death certificates or hospital discharge codes using ICD-9 codes: 140–208 and ICD-10 codes: C00–C97. The ICD-9 and ICD-10 codes for independent associations between HDP and common female cancers were 174.0 and C50 for breast cancer, 153.0–153.9, 154.0–154.1, 159.0 and C18–C20 for colorectal cancer, 162.0–162.9 and C33–C34 for lung cancer, 183.0 and C56–C57 for ovarian cancer and 182.0 and C54.1 for endometrial cancer. Participants were followed up from date of study enrolment until March 2016 for incident cancer events.U tests to compare medians for non-parametric continuous variables.Baseline characteristics for women with and without incident cancer were analysed using Pearson’s chi-squared test for categorical measures and Mann–Whitney Univariable logistic regression analyses were performed on all variables included in women’s baseline characteristics. A multivariable logistic regression model was used to determine the association between HDP and subsequent cancer. The model adjusted for potential confounders gathered at baseline. These included: age, education level, occupational social class, smoking status, alcohol consumption, physical activity, body mass index (BMI), family history of cancer, prevalent myocardial infarction (MI), prevalent stroke, prevalent diabetes, diabetes during pregnancy, age at first live birth, number of children, number of stillbirths and number of miscarriages or abortions. BMI was categorised according to The World Health Organization BMI categories for Europe . AnalyseSome variables  had missing data. Most missing data was approximately <1%, although the variables alcohol consumption and BMI had ~15–20% missing. Missing values were dealt with using the missing-indicator method, where missing data were re-coded into separate categories within variables on SPSS and were included in statistical analyses . Data weThis study found no association between HDP and future cancer risk and amongst five common cancers in women in a UK-based general practice-derived population. Current research underlining HDP and cancer risk is limited to specific populations only. Future work in different populations focusing on complex genetic and hormonal factors involved in these conditions is required.


The higher OR of breast cancer was associated with the higher adherence to the “Unhealthy/Pro-inflammatory” profile . This study revealed that the consumption of highly processed, high in sugar and animal fat foods should be avoided because this unhealthy diet was positively associated with postmenopausal breast cancer occurrence through its pro-inflammatory potential. Instead, the frequent consumption of low-processed plant foods and fish should be recommended since this pro-healthy diet was inversely associated with the cancer occurrence even though its anti-inflammatory potential has not been confirmed in this study sample.The carcinogenesis process is associated with inflammation, which can be modified by diet. There is limited evidence regarding the inflammatory status and diet in association with breast cancer (BC). The aim of this study was to investigate the association of hybrid dietary-blood inflammatory profiles (HD-BIPs) with postmenopausal breast cancer occurrence. The case-control study was conducted among 420 women  aged 40–79 years from north-eastern Poland. Blood levels of C-reactive protein (CRP), interleukin-6 (IL-6) and leukocyte count were marked in 129 postmenopausal women . The 62-item food frequency questionnaire (FFQ-6) was used to the dietary data collection. Two HD-BIPs were found using the Principal Component Analysis (PCA). The “Pro-healthy/Neutral-inflammatory” profile was characterized by the frequent consumption of wholemeal cereals/coarse groats, legumes, vegetables, fruits, nuts/seeds and fish. The “Unhealthy/Pro-inflammatory” profile was characterized by the frequent consumption of red/processed meats, animal fats, sugar/honey/sweets, refined cereals/fine groats, and an increased concentration of CRP, IL-6 and granulocyte-to-lymphocyte ratio. The lower odds ratio (OR) of breast cancer was associated with the higher adherence to the “Pro-healthy/Neutral-inflammatory” profile (OR = 0.38; 95% Cl: 0.18–0.80;  Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of cancer deaths in women worldwide . Based oBreast cancer aetiology is complex and besides the age and genetic well-known risk factors, there is increasing evidence that inflammation is associated with the cancer development ,5,6. ChrThere are a variety of inflammatory biomarkers, which may be involved in carcinogenesis ,18,19,20Inflammation could be affected by many modifiable lifestyle factors, including smoking, alcohol drinking, physical activity and dietary factors, through the modulation of inflammatory cytokines ,6. ThereDespite the increasing interest in the inflammatory potential of the influence of diet on breast cancer incidence ,33,34,35The aim of this study was to investigate the association of hybrid dietary-blood inflammatory profiles (HD-BIPs) with postmenopausal breast cancer occurrence. In evaluating this association, many potential confounders, including molecular subtypes of cancer, lifestyle and reproductive factors were considered.The present study was approved by the Bioethics Committee of the Faculty of Medical Sciences, University of Warmia and Mazury in Olsztyn on 2 October 2013 (Resolution no. 29/2013) . All subThe total case-control sample obtained 420 women, aged 40.0–79.9 (mean 59.9) years. The general inclusion criteria were: (i) women, (ii) age ≥18 years, (iii) north-eastern Poland , (iv) breast ultrasonography and/or mammography, and (v) consent to participate in the study. Pregnant women were excluded from the study. The inclusion criterion of the cancer sample collection was a primary diagnosis of breast cancer (from 7 to 28 days before recruitment in the study) confirmed in medical records (biopsy and histopathology), before any treatment or surgery. All breast cancer cases were patients of the Warmia and Mazury Cancer Centre of the Ministry of the Interior and Administration’s Hospital. Regarding the hormone receptor status of breast cancer, the most frequently diagnosed (74%) were Luminal A tumours with positive receptor status of oestrogen (ER+) and/or progesterone (PR+), and negative human epidermal growth factor receptor 2 (HER2-). The inclusion criterion of the control sample collection was a lack of any breast pathology confirmed in the ultrasonography and/or mammography screening made within six months before recruitment to the study.Besides the abovementioned basic sample collection criteria, in the next stage of the study  . All sub®, Basel, Switzerland). Serum concentrations of Interleukin-6 (IL-6) were marked with electrochemiluminescence immunoassays (ECLIA) using an automated immune-analyser Cobas e411 . The blood concentration of haematological parameters with the division of leukocytes into granulocytes, including neutrophils and agranulocytes (including lymphocytes), were measured using a haematological analyzer MEK-7300 . The counts of leukocytes and their classes were measured using a detecting electrical resistance method, and light scattering . Details of the laboratory procedures regarding the blood samples collection and serum obtainment were described previously ,42. C-reor laser ), respecThe hybrid dietary-blood inflammatory profiles (HD-BIPs) were derived using a Principal Component Analysis (PCA) with varimax rotation . The inpThe continuous variables  were showed as means and 95% confidence intervals (95% CI). The medians (Me) were used to transform continuous variables into the categorical variables , which were presented in percentages of the sample. Differences between groups were verified with Pearson’s chi-squared test , Student’s t-test for continuous variables with a normal distribution (log-transformed and blood concentration of single inflammatory biomarkers) or Kruskal–Wallis test for continuous data without normal distribution  .2), socioeconomic status .The breast cancer occurrence in associations with the adherence to the HD-BIPs or blood levels of single inflammatory biomarkers was assessed using the logistic regression analysis. The odds ratios (ORs) and 95% CI were calculated. The references (OR = 1.00) were the control sample and the lower level of each HD-BIPs or a lower level of a single biomarker (ref.: < Me). The ORs of breast cancer for a 1-point increase in each HD-BIPs score and blood concentration of single inflammatory biomarkers were also calculated. Three models were created: crude model 1, model 2 adjusted for the confounders, and model 3 adjusted for the same confounders included in model 2 and also for dietary patterns (DPs) scores (for single inflammatory biomarkers only). The set of confounders included: age (years), BMI (kg/mr = 0.68), legumes (r = 0.60), vegetables (r = 0.59), fruits (r = 0.59), nuts and seeds (r = 0.58) and fish (r = 0.34) and was negatively loaded by the consumption frequency of refined cereals and fine groats (r = –0.62). The “Unhealthy/Pro-inflammatory” profile was positively loaded by the consumption frequency of: red and processed meats (r = 0.70), animal fats (r = 0.66), sugar, honey and sweets (r = 0.53) and refined cereals and fine groats (r = 0.30) as well as by the blood concentration of: C-reactive protein (r = 0.65), granulocyte-to-lymphocyte ratio (r = 0.52) and Interleukin-6 , including absolute granulocyte count (4.51 versus 3.26 103 cells/µL), which were mostly neutrophils (4.39 versus 3.02 × 103 cells/µL), as well as the mean ratio of granulocyte-to-lymphocyte , absolute granulocytes ≥3.56 × 103 cells/µL (77.6 versus 33.3%), including neutrophils ≥ 3.31 × 103 cells/µL (78.0 versus 33.3%), and the ratio of granulocyte-to-lymphocyte ≥ 1.75 , leukocyte count , however this association disappeared after adjustment for the set of confounders  and 43% . The odds ratio of breast cancer occurrence was approximately three-times higher in the higher level of the “Unhealthy/Pro-inflammatory” profile  and 22% . The results of the logistic regression analysis of the breast cancer occurrence by adherence to the blood concentration of single inflammatory biomarkers are shown in The odds ratio of breast cancer occurrence was lower in the higher level of the “Pro-healthy/Neutral-inflammatory” profile by 62% , which also includes foods as components of the “Unhealthy/Pro-inflammatory” profile ,29,30. IFindings from the current study strengthen previous studies showing that high adherence to the “Non-Healthy” dietary pattern (DP) increased the odds of breast cancer occurrence by approximately three times . Both thThe positive association of the “Unhealthy/Pro-inflammatory” profile with breast cancer must also consider its inflammatory features. Unfortunately, the few available studies have focused mainly on the single inflammatory markers in association with BC. The “Unhealthy/Pro-inflammatory” profile was positively loaded by the serum concentration of CRP, which has been positively associated with postmenopausal breast cancer in a prospective nested case-control study within the EPIC-Varese cohort study  and seveFor another inflammatory component of the “Unhealthy/Pro-inflammatory” profile, a higher mean serum concentration of IL-6 was observed in BC cases compared to the controls among Polish women. Considering the single inflammatory markers, it was found that a one-unit increase (pg/mL) in serum IL-6 increased the odds of the BC occurrence by 50%. This result is in agreement with the findings of the EPIC-Varese cohort study, where a one standard deviation increase in plasma IL-6 (pg/mL) was associated with an increased risk of BC by 58% . HoweverThe last inflammatory component of the “Unhealthy/Pro-inflammatory” profile was the ratio of granulocytes-to-lymphocytes. The G/L represents the major classes of leukocytes. Similar to the current study, in the WHI study, the mean leukocyte count was higher in BC cases than in controls  . These rA higher adherence to the “Pro-healthy/Neutral-inflammatory” profile, characterized by the relatively high consumption frequency of wholemeal cereals/coarse groats, legumes, vegetables, fruits, nuts/seeds and fish, was associated with 62% lower odds of breast cancer occurrence among Polish postmenopausal women. This association remained significant after adjustment for potential confounders only in the continuous model, where the one-point increase in the “Pro-healthy/Neutral-inflammatory” profile score decreased the breast cancer occurrence by 39%. In continuous variable modelling, every one-point change is equal and is more neutral, and allows for the more sensitive detection of differences than a dichotomous model . ConsistIn the present study, it was also interesting to note that the “Pro-healthy/Neutral-inflammatory” profile was not significantly related to a lower concentration of inflammatory biomarkers. Thus, this profile was neutral to inflammation. This observation is quite surprising because the main components of the “Pro-healthy/Neutral-inflammatory” profile were plant foods and fish. It is well known that bioactive compounds and phytochemicals in plant food have an anti-inflammatory potential . For exaThe major strength of the current study is its comprehensive examination of the combined association of dietary and inflammatory factors with breast cancer. To the authors’ best knowledge, no previous study has evaluated the combined effect of blood levels of CRP, IL-6 and G/L ratio and food consumption on breast cancer occurrence. It seems that these markers together may better reflect the inflammatory load and provide a more precise insight into breast cancer etiology compared to each biomarker separately. Secondly, when considering the complex association between hybrid dietary-blood inflammatory profiles and postmenopausal breast cancer, a number of potential confounders have been taken into account in the adjusted model of the logistic regression analysis. This study obtained detailed information on socioeconomic lifestyle factors, including vitamin-mineral supplement use, reproduction and clinical data, which allowed an analysis of confounding effects. Moreover, the study adjusted for BMI and physical activity as two energy-balance covariates, in which BMI was calculated based on the measurement of height, and weight and was not self-declared. Thirdly, since inflammation is linked with cancer in both directions , which was limited by the restrictive inclusion criteria, including only primary breast cancer cases, lack of other diseases or infections and no medication use. Using a sample smaller than the ideal increases the chance of obtaining overestimated or underestimated results and assuming a false premise as true. However, the sub-sample size calculated in regard to the main objective of the study was greater than the minimum sample size (at least 42 cases and 42 controls) using the WHO sample size calculator (80% test power and 5% significance level) and was sufficient to detect differences between groups [Some of this study’s limitations are typical of a case-control design, including biases related to recall and selection , which mn groups . Among tn groups ,29,30. Hn groups ,15,18,19This study revealed that the consumption of highly processed, high in sugar and animal fat foods should be avoided because this unhealthy diet was positively associated with postmenopausal breast cancer occurrence through its pro-inflammatory potential. Instead, the frequent consumption of low-processed plant foods and fish should be recommended since this pro-healthy diet was inversely associated with cancer occurrence even though its anti-inflammatory potential has not been confirmed in this study sample. These findings suggest that monitoring both diet and the blood level of inflammatory markers  should be considered in a breast cancer reduction strategy, involving screening programs in the early diagnosis of cancer. Furthermore, large prospective studies are needed to explain the mechanisms by which the dietary inflammatory potential leads to elevated inflammatory markers in the aetiology of breast cancer in Polish women, as well as in other populations.


P < .05) including Ki‐67 proliferation status (P < 5 × 10−07). Our low‐risk TC‐Gx, based on the weighted sum of 37 expression values of genes strongly correlated with TC, was associated with basal‐like (P < 5 × 10−13), HER2‐enriched subtype (P < 5 × 10−07) and worse 10‐year breast cancer‐specific survival (log‐rank P < 5 × 10−04). Associations between low‐risk TC‐Gx and more aggressive molecular subtypes were replicated in an independent cohort from The Cancer Genome Atlas database (n = 975). Gene expression that correlated with low TC was enriched in proliferation and oncogenic signaling pathways (FDR < 0.05). Moreover, higher proliferation was a key factor explaining the association with worse survival. Women who developed breast cancer despite having a low risk were diagnosed with more aggressive tumors and had a worse prognosis, most likely driven by increased proliferation. Our findings imply the need to establish risk factors associated with more aggressive breast cancer subtypes.The association between breast cancer risk defined by the Tyrer‐Cuzick score (TC) and disease prognosis is not well established. Here, we investigated the relationship between 5‐year TC and disease aggressiveness and then characterized underlying molecular processes. In a case‐only study (n = 2474), we studied the association of TC with molecular subtypes and tumor characteristics. In a subset of patients (n = 672), we correlated gene expression to TC and computed a low‐risk TC gene expression (TC‐Gx) profile, that is, a profile expected to be negatively associated with risk, which we used to test for association with disease aggressiveness. We performed enrichment analysis to pinpoint molecular processes likely to be altered in low‐risk tumors. A higher TC was found to be inversely associated with more aggressive surrogate molecular subtypes and tumor characteristics ( What's new?BRCA mutation status. The relationship between Tyrer‐Cuzick score and specific breast cancer subtypes, however, remains unclear. In this investigation, five‐year, low‐risk Tyrer‐Cuzick gene expression profile, expected to be negatively correlated with risk, was associated with certain, more aggressive breast cancer subtypes, including basal‐like and HER2‐enriched subtypes. Analyses further indicate that genes and biological pathways involved in increased proliferation underlie this association. The findings draw attention to factors relevant to aggressive breast cancer subtypes that are not yet captured in risk assessment tools.The Tyrer‐Cuzick score for assessing breast cancer risk integrates information on lifestyle, reproductive, and familial factors, including  AIMSAbsolute Assignment of Breast Cancer Intrinsic Molecular SubtypeClinSeqClinical Sequencing of Cancer in SwedenDGEdifferential gene expressionERestrogen receptorFDRfalse discovery rateGSEAgene set enrichment analysisHER2human epidermal growth factor receptor 2IBISInternational Breast Cancer Intervention StudyKARMAKARolinska MAmmography Project for Risk Prediction of Breast CancerKi‐67marker of proliferation Ki‐67LALBAlactalbumin alphaLIBRO‐1Linné‐Bröst 1low‐risk TC‐Gxlow‐risk Tyrer‐Cuzick gene expressionPGCprogastricsinPHproportional hazardsSCAN‐BThe Sweden Cancerome Analysis Network—BreastTCTyrer‐Cuzick scoreTCGAThe Cancer Genome AtlasTNBCtriple‐negative breast cancers1Breast cancer is a complex disease involving genetic and nongenetic risk factors. Risk assessment tools have been developed to estimate individual breast cancer risk over time.Aggressive tumors are characterized by a faster growth rate, greater capability to invade surrounding tissue and metastazise, leading to poorer survival. More aggressive breast cancers tend to be of basal‐like and human epidermal growth factor receptor 2 (HER2)‐enriched intrinsic molecular subtypes,The goal of this study is to investigate the association between Tyrer‐Cuzick risk score and breast cancer subtypes, tumor characteristics and prognosis, and to gain biological understanding of underlying molecular processes by leverage of gene expression data in samples from a clinically representative study population.2Study population consisted of women under the age of 80, diagnosed with primary invasive breast cancer recruited in the Linné‐Bröst 1 (LIBRO‐1) studyAll LIBRO‐1 and KARMA participants with primary invasive breast cancer diagnosed 2005 to 2015 were considered for inclusion (n = 4598). The cutoff at 2005 was chosen since staining for HER2 and Ki67 immunohistochemistry (IHC) markers was not performed before 2005. In total, 2632 cases with complete information on all the IHC markers, needed to derive surrogate molecular subtypes, were eligible for this study.2.1Information on molecular markers was retrieved from medical and pathology records at treating hospitals. Percent of estrogen receptor (ER) and progesterone receptor (PR) staining was dichotomized into positive or negative status  during this period. HER2 status was dichotomized according to the Swedish Society of Pathology's guidelines, as being negative if protein expression showed 0 or 1+, or higher with no confirmed gene amplification by FISH, and as being positive if FISH showed gene amplification. Proliferation marker Ki67 was measured in hotspot regions according to contemporary guidelines and reported as percent staining (low if <20% and high otherwise). Surrogate molecular subtypes were derived from ER, PR and HER2 status; Ki67 percentage values; age at diagnosis, using a subtype classifier based on a random forest algorithm trained to predict breast cancer molecular subtypes.Data on clinical tumor characteristics and prior breast cancer diagnoses were obtained through the Swedish National Cancer RegisterDate of death was obtained from the Swedish Cause of Death Register (linkage performed on 6 October 2017). Breast cancer‐specific events were identified in cases with cause of death code “C50*.” The quality of the registry is high. A high correlation (95.9%) between hospital discharge diagnosis and underlying cause of death from death certificates for malignant breast neoplasms has been observed.2.2http://www.ems-trials.org/riskevaluator/), based on the Tyrer‐Cuzick model.Individual 5‐year TC was computed using the International Breast Cancer Intervention Study (IBIS) tool version 7  project.An independent RNA‐seq data set consisted of breast cancer expression data from The Cancer Genome Atlas (TCGA).2.4PAM50 molecular subtypes were computed on the discovery, validation and independent data sets from RNA‐seq normalized counts using a research‐based subtype predictor, the Absolute Assignment of Breast Cancer Intrinsic Molecular Subtype (AIMS) method2.5glmQLFit (robust = T) function, and beta coefficients were obtained using the QL F‐test with the glmQLFTest function. Genes with a mean counts per million value of <0.5 were considered weakly expressed and therefore were not included in the analysis. Differences in library composition, for example, total number of counts per sample, were normalized using the trimmed mean of M‐valued method.Regression analyses were used to correlate tumor gene expression with TC. TC score was available for 259 (87.5%) of women in the discovery data set and ranged from 0.1% to 9.5% with a mean of 2.0% in the validation data set, TC was obtained for 313 (83.24%) women and ranged from 0.4% to 7.1% with a mean of 2.1%. In order to capture effects by lower risk, the 5‐year TC risk score was transformed by subtracting its value from zero , so that gene‐level effect sizes (beta coefficients) represent expression changes related to a 1‐percentage decrease on the TC scale. The regression analyses were performed using the quasi‐likelihood (QL) dispersion estimation and hypothesis testing method implemented in the edgeR package2.62[1.5]). Effect size beta estimates, corresponding to a 1% decrease in 5‐year TC risk, were used to weight the normalized and log2‐transformed expression values. The low‐risk TC‐Gx therefore represents a weighted sum of gene expression values, which is expected to be negatively correlated with breast cancer risk. Genes with low expression values  in the validation data set were not included in the final TC‐Gx.A low‐risk Tyrer‐Cuzick gene expression (TC‐Gx) profile was calculated for each individual in the discovery, validation and TCGA expression data sets, as the weighted sum of gene expression values . The profile was based on genes found to be significantly correlated with the TC score through regression analysis in the discovery data set. We controlled the false discovery rate (FDR) to be lower than 0.05, and significantly associated genes were required to have an absolute effect size larger than 1.5‐fold . All statistical tests were two‐sided, with an alpha level set at 0.05, or as specified otherwise. We summarized association between continuous exposures  and outcome variables, one at a time. Binary outcomes such as hormonal status were analyzed using unconditional binomial logistic regression, and categorical outcomes such as molecular subtypes and tumor grade were modeled with unconditional multinomial logistic regressions using the R “nnet” package. The TC score was treated as a continuous linear score, and odds ratios are reported in terms of per one‐percentage increase. The TC‐Gx was treated as a standardized continuous linear score, and odds ratios are reported in terms of per 1 SD increase.2.7.1P value cutoff is applied), using the workflow implemented in the PianoTo test for gene sets enriched for overall gene expression correlated with TC, we performed gene set enrichment analysis (GSEA) methods that do not rely on predefined significance thresholds  and discovery (SCAN‐B) samples with complete information on survival (n = 661). Of these, 416 (62.8%), were prevalent cases. Kaplan‐Meier survival curves were visualized using the “ggkm” R package. Time at risk was considered from date of study entry  until date of breast cancer death, or censoring, due to any cause of death or end of follow‐up (truncated at 10 years), whichever occurred first. For survival analysis, the low‐risk TC‐Gx was dichotomized according to the mean of the distribution . Cox proportional hazards (PH) models were adjusted for data set, year and age at diagnosis. Additional Cox PH models were further adjusted for 33.1P = 1.39 × 10−6) and HER2‐enriched (P = 1.39 × 10−6) surrogate molecular subytpes of breast cancer (P < .05), ER‐negative (P < 1 × 10−3), HER2‐positive (P < .05), lymph‐node positive (P < 1 × 10−3), higher tumor grade (P‐trend < 1 × 10−06) and higher Ki‐67 proliferation status (P < 5 × 10−7)   Figure . Based o) Figure , for eac) Figure . Tumors ) Figure .3.3P < 5 × 10−13) and HER2‐enriched tumors (P < 5 × 10−7). Importantly, the low‐risk TC‐Gx computed in an independent data set from TCGA was associated with more aggressive PAM50 subtypes and tumor characteristics  attenuated the association, similar to adjustment for PAM50 subtypes, while adjustment for estrogen receptor status (defined as log2ESR1 expression levels) did not change, substantially, the survival estimates.We observed 39 events from 661 patients in our discovery‐validation data set. Tumors with an increased low‐risk TC‐Gx were found to be associated with worse survival using Cox models adjusted for data set, age and year of diagnosis  and with worse breast cancer‐specific survival. In addition, differential gene expression associated with low breast cancer risk was found to be related to key biological processes involved in tumor proliferation and oncogenic signaling pathways. This may explain why we observe that some patients, despite having lower risk of breast cancer, tend to develop more aggressive tumors. To our knowledge, this is the first epidemiological study utilizing gene expression data to provide molecular biology insights into the relation between breast cancer risk and disease aggressiveness.The lack of established risk factors associated with more aggressive subtypes could explain why lower TC scores are more frequent in patients with aggressive tumor characteristics. Several of the lifestyle‐ and reproductive risk factors determining the TC risk score have been shown to be positively associated with ER positive and thus less aggressive breast cancer as previously reviewed,LALBA) and progastricsin (PGC) were replicated with strong evidence of association with breast cancer risk. Higher RNA expression of LALBA has been found to be associated with more aggressive breast cancer, such as triple‐negative breast cancers (TNBC),PGC expression has been associated with more favorable tumor characteristics and prognosis related to ER‐positive disease.Our low‐risk TC‐Gx profile included genes known to be biomarkers of specific breast cancer subtypes. In particular, lactalbumin alpha . Adjustment for other proliferation genes, that is, AURKA and PCNA, yielded similar results (data not shown). Also, we lacked information on breast cancer‐specific survival in the TCGA data set; therefore, the negative association of our low‐risk TC‐Gx profile with survival time needs to be further replicated.Some limitations and methodological considerations should be discussed for this study. A considerable proportion of ki67 proliferation data was missing in our validation data set. We addressed this issue by using In conclusion, our results suggest that gene expression patterns associated with low breast cancer risk are related to tumors of more aggressive subtypes, in which deregulation of proliferative and oncogenic signaling pathways can lead to worse prognosis. Importantly, inquiry into molecular and pathological features of breast cancer in relation to known risk factors is an important approach toward better understanding of complex etiology of breast cancer. This is in accordance with the necessity to incorporate subtype‐specific risk factors into current assessment tools in order to identify women at increased risk of aggressive breast cancer and to contribute to effectively decrease disease burden.The authors declare no conflict of interest.Ethical approvals for the LIBRO‐1 and KARMA studies were granted from the regional ethical review board at Karolinska Institutet. All women gave written informed consent to participate in the study, to the retrieval of information from medical records, national registries and mammographic images; donated blood at enrollment for genetic analysis and answered a detailed questionnaire about background and lifestyle risks factors. The study was conducted in accordance with the Declaration of Helsinki.AppendixS1: Supporting informationClick here for additional data file.


Epidemiologic data suggest that patients with systemic lupus erythematosus (SLE) have a lower risk of breast cancer than women in the general population. In light of mechanistic studies suggesting that anti-DNA antibodies have anti-cancer effects, we sought to examine breast cancer risk in autoantibody strata in a well-characterized SLE cohort.N = 2431). Overall and site-specific cancer incidence was calculated in racial strata and compared with the US Surveillance, Epidemiology and End Results (SEER) registry. Breast cancer incidence was further examined in autoantibody subsets. Patients were considered positive for an autoantibody if they were ever positive for a specificity during their disease course.SLE patients without a cancer diagnosis prior to entry in the Hopkins Lupus Cohort were studied . The risk of HPV-associated cancers  and thyroid cancer  was increased. Cancer risk varied by race, with breast cancer protection occurring in non-African Americans  and the increased risk of HPV-associated cancers occurring in African Americans . Breast cancer risk was decreased in patients ever positive for anti-dsDNA , anti-La , and lupus anticoagulant . Patients who were positive for fewer (0–2) SLE autoantibodies did not have a lower risk of breast cancer , but patients with 3+ autoantibodies had a 59% decreased risk .Positivity for multiple SLE autoantibodies was associated with a lower risk of breast cancer, supporting the hypothesis that a highly diversified immune response may exert an anti-cancer effect against some cancers. Validation of racial differences in cancer risk in SLE is required to determine whether cancer screening strategies should be targeted to racial subgroups.The online version contains supplementary material available at 10.1186/s13075-021-02449-3. Data from several systemic inflammatory rheumatic diseases suggest that distinct immune responses may be markers of increased cancer risk . For exaIn systemic lupus erythematosus (SLE), analyses of multiple large cohorts and international studies have demonstrated a significantly lower risk of breast cancer than that expected in the general population –9. This In this study, we examined cancer risk in a large, well phenotyped SLE cohort compared to the general population. We investigated whether breast cancer risk differs by autoantibody subtype and whether the presence of multiple lupus autoantibodies confers greater breast cancer protection.Patients seen at the Johns Hopkins Lupus Center for their first visit between 1987 and 2018 were eligible for the study if they consented to participate in the Institutional Review Board (IRB)-approved cohort database and had a diagnosis of SLE by revised American College of Rheumatology (ACR) or Systemic Lupus Collaborating Clinics (SLICC) criteria , 14. TheDemographic, clinical, and laboratory characteristics were compared between patients with a history of cancer after SLE onset and SLE patients without a cancer diagnosis. Continuous variables were assessed by an independent two-sample t-test with pooled variance and dichotomous/categorical variables were compared by chi-square tests.p values. These formulas, which correspond to Fisher’s exact test, have been shown to be conservative, requiring more evidence than is necessary to reject a false null hypothesis. While the less conservative mid P tests have been advocated by statisticians [Cancer risk was calculated by comparing cancer incidence in the Hopkins Lupus Cohort with the Surveillance, Epidemiology and End Results (SEER) registry, which is a nationally representative sample of the US population. Standardized incidence ratios (SIRs) were calculated for cancer overall and individual cancer types. Human papillomavirus (HPV)-associated cancers, including cancers of the cervix, vagina/vulva, and anus, were also examined as a group given prior data suggesting a higher risk of virus-associated cancers in SLE. The observed number of cancers in our cohort was compared with the expected number of cancer cases for the US population by identifying the crude rate of incident cancers corresponding to each patient’s age , sex, race, ethnicity, and the calendar year of exposure in SEER . Due to sticians , we haveCancer risk was assessed after entry into the Hopkins Lupus Cohort. Patients with cancers preceding this time window were excluded from our analysis. Administrative censoring occurred at the cancer diagnosis date or last visit date, whichever came first. The study population for our primary analyses comprised 2431 SLE patients.As our analyses demonstrated that patients with SLE had differential cancer risk by race, cancer incidence data are presented for the overall SLE cohort and in racial strata (African American (AA); non-AA, which includes White, White-Hispanic, Asian and other). Stratified analyses present risk estimates in AA SLE patients compared to AAs in the general population; similarly, risk estimates for non-AA SLE patients are relative to non-AAs in the general population. Given the striking extremes of cancer risk in SLE—that is the markedly lower risk of breast cancer and significantly higher risk of HPV-associated cancers—we graphically examined the age-adjusted incidence of these tumor types in patients with SLE, stratified by race, compared to the general population. Lastly, because we hypothesized that anti-dsDNA antibodies and the presence of multiple immune responses may confer protection for breast cancer in SLE, we further examined the risk of breast cancer in SLE patients in distinct autoantibody strata and by whether patients were positive for few (0–2) or many (3+) autoantibodies.P values ≤ 0.05 were considered to be statistically significant; when appropriate, Bonferroni adjustment for multiple comparisons was performed, as noted in the tables.Analyses were performed using MATLAB R2016b  and R V.3.4.0  to verify SIR calculations. p < 0.001) and at cohort entry , and a longer follow-up duration , than those without cancer. SLE patients with a history of cancer were also more likely to be African-American (AA)  and have ever smoked . After adjusting for multiple comparisons, SLE patients with cancer were more likely to have thrombocytopenia , pulmonary fibrosis , and vasculitis  as features of their SLE  positive, but more likely to have anticardiolipin  antibodies .Among the 2475 patients enrolled in the Hopkins Lupus Cohort, 163 (6.6%) had a history of cancer, occurring at a mean duration of 15.61 ± 9.80 years after SLE onset. Of these 163 cancer cases, 44 cancers occurred prior to entry into our Lupus Cohort, and these patients were not included in our study population. Demographic, social and clinical characteristics of the remaining 119 SLE patients with cancer compared to 2312 SLE patients without a history of cancer are shown in Table Among the 119 patients with SLE and cancer, the most common cancer type was breast, which occurred in 22 patients. This was followed by vagina/vulva (13), cervical (11), lung (11), hematologic (10), colorectal (10), and thyroid (9). A full list of all cancer types observed is provided in Supplemental Table Patients with SLE did not have an increased risk of cancer compared to age, sex, race, ethnicity, and calendar time-matched general population controls , lupus anticoagulant , or ACL  Table , and theIt has long been appreciated that patients with SLE have an increase in cancer risk compared to individuals in the general population, with data suggesting a higher risk of hematologic, lung, thyroid, liver, bladder, pancreatic, kidney, nasopharyngeal and HPV-associated  malignancies and a lower risk of breast, endometrial, and possibly ovarian and prostate cancers –9, 20–23In this study, we utilized the large, well-phenotyped Hopkins Lupus cohort to examine cancer risk in lupus patients compared to the general population, adjusting our risk estimates by age, sex, race, ethnicity, and calendar time. Our data confirm a higher risk of HPV-associated malignancies and thyroid cancer and a lower risk of breast cancer in SLE. To our knowledge, our data demonstrate for the first time that there are striking racial differences in cancer risk and type among SLE patients—notably, AA patients with SLE had a markedly elevated risk of HPV-associated cancers, and non-AA with SLE had a 71% decreased risk in breast cancer. Patients with anti-dsDNA, anti-La, and lupus anticoagulant had a statistically significantly lower breast cancer risk. The presence of 3 or more SLE autoantibodies was associated with a significantly lower risk of breast cancer, a novel finding that was further strengthened among non-AA patients with SLE. These data provide additional support for the hypothesis that a highly diversified immune response may have a cancer-protective effect in the rheumatic diseases.Few studies have investigated risk factors for breast cancer risk or protection in SLE. In a case-cohort analysis with 86 SLE breast cancer cases and 4498 female SLE cancer-free controls, only older age was associated with breast cancer risk in adjusted analyses . There wOnly a few studies have examined racial differences in breast cancer risk in SLE patients, with conflicting results. One study, which examined Medicare claims data and therefore was skewed to an older population, found that breast cancer risk in Caucasian and AA SLE patients was similar to that expected in these racial subgroups in the general population . AnotherPrior studies have suggested that the decrease in breast cancer risk observed in SLE may be due to a lower risk of hormone receptor-negative tumors . It has While not the primary focus of our study, we confirmed that there is an increased risk for HPV-associated cancers in SLE, and to our knowledge noted for the first time that this risk increase is seen primarily in AA patients with SLE. Many studies have investigated the risk of and risk factors for cervical intraepithelial neoplasia (CIN) and cervical cancer in SLE –35. The Our study has important limitations requiring additional research probing the cancer-autoimmunity connection in SLE. Further study in a larger sample size is needed to investigate specific combinations of SLE immune responses that may be breast cancer protective, and mechanistic studies are required to determine if these autoantibodies, alone or in combination, exert an anti-cancer effect. In addition, it will be important to define whether there are interactions between race, genetic framework and autoantibody type. Work is also needed to define whether breast cancer risk varies by anti-DNA antibody titer and subtype over time as there may be differential cancer risk by strength of the immune response and in patients with cell-penetrating anti-DNA antibodies versus other anti-DNA antibodies. Our study was also underpowered to assess whether the decrease in breast cancer risk observed in autoantibody subgroups was due to a decrease in specific breast cancer subtypes, such as hormone receptor-positive versus triple-negative tumors.Our data suggest there may be differential cancer risk and type across racial groups in SLE, and that a greater breadth of the SLE immune response as assessed by autoantibodies may associate with breast cancer protection particularly in non-AA patients. Further study is required to validate these findings and to determine whether cancer screening strategies should be targeted to distinct racial and autoantibody subgroups. Investigating the mechanistic basis for differences in cancer risk in these subgroups has the potential to improve our understanding of SLE autoimmunity and anti-tumor immunity.Additional file 1: Supplemental Table 1. SLE characteristics by cancer status. Supplemental Table 2. Laboratory parameters and serologies by cancer status. Supplemental Table 3. Drug exposure (ever use) by cancer status. Supplemental Table 4. Cancer sites observed in the Hopkins Lupus Cohort (N = 119 cancer cases).


Environmental and lifestyle factors are believed to account for >80% of breast cancers; however, it is not well understood how and when these factors affect risk and which exposed individuals will actually develop the disease. While alcohol consumption, obesity, and hormone therapy are some known risk factors for breast cancer, other exposures associated with breast cancer risk have not yet been identified or well characterized. In this paper, it is proposed that the identification of blood epigenetic markers for personal, in utero, and ancestral environmental exposures can help researchers better understand known and potential relationships between exposures and breast cancer risk and may enable personalized prevention strategies. While 5%–10% of breast cancers are due to genetic mutations and an additional ~10% are considered familial, most breast cancers are thought to be due in large part to environmental and lifestyle factors (which will be collectively referred to as “exposures”) ,3,4. HowMore common exposures that have been linked to breast cancer risk, including hormone therapy, obesity, alcohol consumption, and insufficient physical activity , generalDespite our current knowledge about various risk factors and the development of risk models which estimate one’s risk of being diagnosed with breast cancer in the next 5 or 10 years, it is not known which individuals who are exposed to a given exposure will actually develop the disease; that is, the discriminatory accuracies of current risk models, especially those that incorporate modifiable factors, are moderate, at best ,12. ThisAs most breast cancers are not hereditary, identification and characterization of additional risk factors, especially exposures, and their potential interactions with genetic factors, are needed for a better, more comprehensive understanding of breast cancer risk in individuals, which would enable personalized breast cancer prevention. This paper discusses strategies for improving our ability to identify and characterize such risk factors, the first step being the identification of epigenetic biomarkers for exposures that can be used to estimate individuals’ environmental exposures in large cohort studies with archived blood DNA and long follow-up time.a priori, exposure misclassification is still possible, intended or unintended. For example, studies have found that about 20% of pregnant women with cotinine levels  indicatst 144 h ,18. For As a result, there are numerous environmental exposures that have been implicated in breast cancer risk but not well characterized, including bisphenol A (BPA), perfluorooctanoic acid (PFOA), phthalates, other industrial chemicals, light-at-night, hormones in food, medications, personal care products, pesticides, and diet. The mechanisms by which these exposures have been linked to carcinogenesis have mostly been studied in animal or cell culture models and include endocrine disruption, chronic inflammation, and epigenetic dysregulation . HowEnvironmental exposures are usually measured in urine and blood products , with a preference for urine for many chemicals including polycyclic aromatic hydrocarbons, pesticides, and phthalates ,22. HoweGSTP1, BRCA1, CDH1, p16) [DNA methylation is a type of epigenetic mechanism. Epigenetics, or the study of stable but reversible changes in DNA that can affect gene expression but do not involve changes in the DNA sequence, may be key in understanding how environmental exposures affect an exposed individual’s disease susceptibility . For manH1, p16) .AHRR (cg05575921) and F2RL3 (cg03636183) genes, has been consistently associated with cigarette smoking in numerous studies [AHRR hypomethylation predicted current smoking status with an area under the curve (AUC) of 0.99 [AHRR and F2RL3 hypomethylation with both current and lifetime smoking intensity, as well as with time since smoking cessation, and it took more than 20 years for a full “recovery” of methylation levels [epigenetic signatures can represent a biological memory of past exposures and that such signatures may be retained in the exposed stem and progenitor cells [Most studies examining the potential relationships between environmental exposures and DNA methylation in humans have been done using peripheral blood DNA, given that blood draws are generally viewed as non-invasive and routine by the general healthy population compared to tissue sampling, which is more commonly done when specimens are available from biopsies or surgery from patients being tested or treated for illness. Interestingly, peripheral blood DNA methylation at specific loci in the genome have been shown to be associated with specific exposures and can even serve as objective markers of the exposures, which can be especially helpful in studying exposures prone to recall bias or other uncertainties that frequently accompany self-reported questionnaire data . For exaF2RL3 cg036183 gen of 0.99 . There wn levels ,39. Thesor cells . More reor cells . Thus, sCDC42BPB (cg04987734) was shown to be able to classify individuals with respect to heavy alcohol consumption from non-drinkers with an AUC of 0.88. A combination of this marker with one and three additional CpG markers increased the AUC to 0.95 and 0.98, respectively [DNA methylation has been shown to be associated with various other environmental exposures, including aging ,42, BMI ectively . In an iectively . In a seectively . As a biectively .AHRR and F2RL3 methylation had almost 16 and 11 times the risk, respectively, of developing lung cancer [AHRR hypomethylation was also shown to mediate the effects of smoking on bladder cancer risk in postmenopausal women [AHRR and F2RL3 hypomethylation were strongly associated with all-cause, cardiovascular, and cancer mortality, suggesting that methylation at these sites may be potential mediators of the detrimental impact of smoking on mortality [Several exposure-mediated DNA methylation markers have also been shown to be effective markers of disease risk. For example, Zhang et al. showed that individuals in the lowest quartile of g cancer . In addial women . Furtherortality ,66,67. cThe DNA methylation markers described above were identified using white blood cell DNA from peripheral blood samples or saliva samples. From a practicality standpoint, this is important because peripheral blood and saliva DNA samples are non-invasive, generally well accepted and tolerated samples to collect from study participants; furthermore, many cohort studies with long follow-up time already have banked peripheral blood or saliva DNA that can be analyzed for DNA methylation. DNA methylation from archived DNA samples has been shown to be stable even after decades of storage under various conditions ,69. ThusCCDC85A, CYP1A1, and ZFPM2, genes which have been previously implicated in pubertal development and breast cancer susceptibility [It is also possible that epigenetics can help us understand the potential links between an individual’s environmental exposures and breast cancer risk in their children and even grandchildren. For example, using data from a case-control study nested in a prospective 54-year follow-up of 9300 daughters in the Child Health and Development Studies pregnancy cohort, it was shown that women who were prenatally exposed to high levels of the pesticide DDT had 4 times the risk of developing breast cancer as adults . In the tibility . This extibility .Other examples of in utero exposures that are associated with epigenetic differences have been reported in the literature . For exaMaternal exposures are believed to be transferred to the developing fetus primarily through the placenta . HoweverMechanistically, it is thought that germ cells and preimplantation embryos are most susceptible to endogenous and exogenous environmental factors because the epigenome in these cells undergoes dramatic reprogramming ,93. WhilMaternal exposure to a high-fat diet or synthetic estrogen during pregnancy also increased mammary cancer risk in female offspring and even through several generations ,103. ThuFew human studies have data about intrauterine and parental exposures. Thus, there is a large gap in knowledge about the potential role of in utero and parental environmental exposures and breast cancer risk. Epigenetic markers may enable researchers to close this gap. For example, DNA methylation can function as a marker for prenatal smoke exposure in adults ,105,106.Molecular markers are needed to help distinguish which individuals in an exposed population are more likely to develop breast cancer and which individuals would benefit from undertaking risk reduction strategies such as lifestyle modifications. A better understanding of one’s personal breast cancer risk, perhaps informed by one’s DNA methylation profile in addition to one’s genetic profile, would enable targeted risk reduction strategies. Ideally, an individual’s DNA methylation profile would not only reflect risk markers that they acquired from various personal exposures but perhaps also risk markers they inherited from their parents’ and grandparents’ exposures. On the other hand, it is possible that despite being exposed to various insults, an individual has not acquired the methylation risk markers and is thus not at increased risk. For these individuals, it may not be necessary to undertake risk reduction strategies.AHRR and F2RL3 was highly associated with increased lung cancer risk [Risk reduction strategies may include trying to reverse the DNA methylation risk markers by undergoing lifestyle modifications (like quitting smoking), medical procedures, or taking medicine. As mentioned previously, the methylation status of the smoking markers cer risk . Howevercer risk . AlthougLongitudinal epigenetic studies, given their more complicated study design and extended time required, are not as common as cross-sectional ones , but somThe reversal or absence of an epigenetic marker may signify that an individual is no longer at increased risk for the disease due to that specific exposure, whereas the presence of the marker may signify that the individual is at increased risk for the disease. Knowledge of one’s personal risk for breast cancer, especially if the reliability of the risk prediction is high, is a major factor for determining their willingness to undertake lifestyle modifications to lower their risk . KnowledAnimal studies have also shown that epigenetic risk can be reversed by interventions, for example, diet. As a most striking example, it has been shown that maternal supplementation of rats with methyl donors like folic acid or the phytoestrogen genistein can reverse DNA hypomethylation induced by BPA . PharmacCollectively, epigenetic markers of breast cancer risk can be combined with current risk prediction models like the Gail, BCSC, and Tyrer–Cuzick models and their iterations to potentially increase their discriminatory accuracies. Polygenic risk scores have been shown to increase AUCs by up to 0.11 ,172. TheChallenge #1: How can we identify epigenetic markers for additional personal exposures that may be associated with breast cancer risk?Although DNA methylation loci associated with numerous exposures have been identified, most have not been demonstrated to be able to act as markers. The exceptions, CpG markers for current smoking and current heavy alcohol intake, were identified in cohorts that used questionnaires that assessed detailed current and previous use, including type, quantity, frequency, and duration of use, age when first started using, and age when quitting (if applicable) ,40,59,60Given the relative unreliability of self-reported exposures, especially for those that study participants may not be aware of, such as air, food, or water pollution, and other environmental contaminants, supplementing questionnaire data with biochemical analyses of biospecimens such as saliva, urine, or serum or plasma isolated from blood samples that were collected around the time or from the same blood sample as the DNA that is used for the epigenome analysis, would be ideal to minimize measurement error for the levels of exposure. However, this approach would require funding for the significant costs of not only epigenome-wide analysis  but alsChallenge #2: How can we identify epigenetic markers of ancestral exposures that may be associated with breast cancer risk?Doing a prospective study to identify epigenetic risk markers inherited from ancestors would take decades. Fortunately, there are several existing cohorts with incident breast cancers, comprised of individuals from more than one generation whose data and biospecimens may be suitable for examination. Assessing levels of ancestral exposures to act as the independent variable will be difficult, but estimations can be made for some ancestral exposures by accessing data from medical records, government-run registries or monitoring programs, employment records, or, as in the case of the Dutch famine cohort, significant historical events. Characteristics and exposures of the individuals and their ancestors would be needed to adjust for potential confounders. A list of transgenerational cohorts is provided in a review by Pembrey et al. .Challenge #3: Can epigenetic markers be used to predict if an individual will benefit from lifestyle modifications or clinical interventions to decrease their risk?Intervention studies with long enough follow-up time for incident breast cancers to develop, and pre-intervention  DNA samples would be needed to address this challenge. Several cohorts that meet these criteria already exist. Within the group of individuals who completed the intervention, one could compare the DNA methylation profiles between women who developed breast cancer and those who did not.The proposed studies would result in the identification of new personal, in utero, and pre-conception lifestyle factors and environmental exposures associated with breast cancer risk in humans, a better understanding of the molecular mechanisms by which exposures and gene-environment interactions mediate breast cancer risk, and the ability to better predict which exposed individuals will actually develop breast cancer and which would benefit from lifestyle modifications and other risk reduction strategies. This would present significant opportunities for personalized breast cancer prevention not only for individuals but potentially also for their daughters, granddaughters, and future generations.