Epigenetic Quantification of Circulating Immune Cells in Peripheral Blood of Triple-negative Breast Cancer Patients

Epigenetic Quantification of Circulating Immune Cells in Peripheral Blood of Triple-negative Breast Cancer Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Epigenetic Quantification of Circulating Immune Cells in Peripheral Blood of Triple-negative Breast Cancer Patients Mehdi Manoochehri, Thomas Hielscher, Nasim Borhani, Clarissa Gerhäuser, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-508197/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Nov, 2021 Read the published version in Clinical Epigenetics → Version 2 posted You are reading this latest preprint version Show more versions Abstract Background: A shift in the proportions of blood immune cells is a hallmark of cancer development. Here, we investigated whether methylation-derived immune cell type ratios and methylation-derived neutrophil-to-lymphocyte ratios (mdNLRs) are associated with triple-negative breast cancer (TNBC). Methods: Leukocyte subtype-specific un/methylated CpG sites were selected and methylation levels at these sites used as proxies for immune cell type proportions and mdNLR estimation in 231 TNBC cases and 231 age-matched controls. Data were validated using the Houseman deconvolution method. Additionally, the natural killer (NK) cell ratio was measured in a prospective sample set of 146 TNBC cases and 146 age-matched controls. Results : The mdNLRs were higher in TNBC cases compared with controls and associated with TNBC (odds ratio (OR) range (2.66-4.29), all P adj. <1e-04). A higher neutrophil ratio and lower ratios of NK cells, CD4+ T cells, CD8+ T cells, monocytes, and B cells were associated with TNBC. The strongest association was observed with decreased NK cell ratio (OR range (1.28-1.42), all P adj. <1e-04). The NK cell ratio was also significantly lower in pre-diagnostic samples of TNBC cases compared with controls ( P =0.019). Conclusion: This immunomethylomic study shows that a shift in the ratios/proportions of leukocyte subtypes is associated with TNBC, with decreased NK cell showing the strongest association. These findings improve our knowledge of the role of the immune system in TNBC and point to the possibility of using NK cell level as a non-invasive molecular marker for TNBC risk assessment, early detection, and prevention. Cancer Biology Immunology Triple negative breast cancer DNA methylation in blood immune cell subtypes TNBC risk Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Inflammation plays an important role in almost every stage of cancer development. Many inflammatory markers have been associated with cancer progression and prognosis (1). Various studies showed that the number and function of blood leukocytes are altered in cancer (2-5). A shift in the number of peripheral immune cells is a predictor of cancer patient survival. For instance, an increased neutrophil-to-lymphocyte ratio (NLR) which is indicative of systemic inflammation, could promote cancer cell proliferation, angiogenesis, cellular migration, and metastasis (6). There is evidence from many studies for a prognostic role of NLR in peripheral blood of various cancer patients (7-10), including breast cancer (8, 11, 12). Triple Negative Breast Cancer (TNBC) accounts for 15-20% of all breast cancers (13). Due to the lack of targeted therapies, chemotherapy still is the main therapeutic strategy. Therefore, many efforts have been conducted to increase the diagnostic and therapeutic opportunities for the TNBC patients (14). TNBC is also the most immunogenic subtype. Higher levels of infiltrated T cells are associated with an improved OS and disease-free survival (DFS) of TNBC patients as compared with those affected by other breast cancer subtypes (15, 16). Further, NLR is associated with survival in a pre-treatment setting and throughout the treatment course and subsequent follow-up (5, 17, 18). In addition, higher peripheral lymphocyte counts are associated with a lower mortality from early-stage TNBC suggesting that immune cell functions improve early TNBC treatment (19). Epigenetic modifications such as DNA methylation play an important role in the cell-specific gene regulation within the hematopoietic system (20, 21). Since DNA methylation signatures are chemically stable and mitotically heritable, they have been successfully applied to quantify leukocyte subtypes accurately in DNA from peripheral blood (22-24). In the present study, we identified and validated associations of methylation-derived leukocyte subtype ratios and methylation-derived neutrophil-to-lymphocyte ratios (mdNLRs) with TNBC using methylation data of 231 TNBC cases and 231 age-matched controls from a retrospective study. We report associations of various leukocyte subtypes ratios with TNBC, with the natural killer (NK) cell ratio showing the strongest association with disease. Further, we provide evidence for an association of the NK cell ratio with TNBC risk in a prospective sample set of 146 TNBC cases and 146 age-matched controls (for a graphical overview of the work, see Figure 1 ). Methods Study Populations In the present study, two TNBC case-control sample sets were analyzed: One set from a retrospective study contains 231 TNBC cases and 231 age-matched controls selected from two different studies: the breast cancer case-only study SKKDKFZS (25) and the breast cancer case-control study GENICA (26). The prospective sample set includes 146 TNBC cases and 146 controls from the Generations Study (GS) (27). All study participants were women of European ethnicity. All studies had local ethical approval and all included individuals gave informed written consent. Details on the study populations can be found in Supplementary Methods (available online). The sample sizes and selected characteristics of study participants are presented in Supplementary Tables 1 and 2 . DNA Methylation Analysis We applied publically available DNA methylation datasets o btained from isolated leukocyte subtypes (NK cells, CD4+ T cells (TCD4+), CD8+ T cells (TCD8+), regulatory T cells (Treg), monocytes, neutrophils, and B cells) for selection of cell-type specific differentially methylated sites. Thereafter, DNA methylation levels (beta values) at selected CpG sites were used as proxies for leukocyte subtypes proportions and mdNLRs. The mdNLR was estimated by dividing the beta value at neutrophil-specific CpGs by the beta value of pan-lymphocyte specific CpGs. Details on the experimental strategies and data analysis are described in Supplementary Methods (available online). In-house genome-wide and locus-specific DNA methylation analysis was performed on the retrospective and prospective sample sets, respectively. Genome-wide DNA methylation profiling was performed on 231 TNBC cases and 231 controls from a retrospective study using the Illumina Infinium HumanMethylation450K BeadChip according to the manufacturer´s instructions. Subsequently, for measurement of the NK cell level in the prospective cohort of 146 TNBC cases and 146 controls, MethyLight droplet digital PCR (ddPCR) assay was carried out on bisulfite converted DNA. More details on the materials and methods are described in Supplementary Methods. For technical validation of our findings, we applied a reference-based method (Houseman algorithm) to statistically deconvolute the proportions of six immune cell subtypes (NK, TCD4+, TCD8+ cells, monocytes, neutrophils, and B cells) in the TNBC cases and controls (28). The mdNLR reference-based method (mdNLR ref ) was calculated by dividing the estimated proportion of neutrophils by the sum of the lymphoid cell proportions (NK, TCD4+, TCD8+, and B cells). Statistical Analyses Associations of TNBC and leukocyte subtype ratios were analyzed using the methylation beta values from the retrospective study. The ratios of the seven leukocyte subtypes in TNBC cases and controls were calculated and compared using methylation levels (beta-value) of 21 selected immune cell specific unmethylated sites (ISUS). A higher level of methylation at each ISUS corresponds to lower ratio of the corresponding leukocyte subtype. In primary analysis, the diagnostic performance of individual CpGs and subtype ratios on TNBC status was assessed with univariable conditional logistic regression accounting for age-matched pairs (age matching +/- 1 year) and Receiver Operating Characteristic (ROC)/Area Under the Curve (AUC) analysis. In supportive analysis, adjusted effects were estimated based on a multivariable conditional logistic regression model additionally accounting for body mass index (BMI) (continuous), menopausal status (pre/peri, post), and smoking status (current) (yes, no). Complete data for 221 matched-pairs was available for the supportive analysis. A multivariable logistic regression model based on all pre-selected CpGs was fitted with backward selection at a significance level of 20% for staying in the model. Internal validation of the AUC for the multivariable model including variable selection was done using bootstrapping with 200 repetitions. To assess the diagnostic performance of subtype ratios, differences in logit-transformed beta values were analyzed. In TNBC cases, the association of methylation levels with histopathological tumor parameters (grade, size, node status, stage) and overall survival (OS) was assessed. OS was defined as the time between TNBC diagnosis and death or last follow-up, whichever occurred first. Mann-Whitney test, Jonckheere-Terpstra trend test, and Spearman’s correlation coefficient were used to assess associations between methylation levels and clinico-pathological parameters. The impact of beta values on OS was analyzed in a Cox regression model. To account for established prognostic factors, a multivariable Cox regression model including age, tumor grade (G1/G2 vs G3), stage (0-4), tumor size (T1, T2, T3, T4) and N status (N0 vs N1) was fitted. Kaplan-Meier estimates and log-rank test were derived for methylation levels at median cut-off. Individual P -values were adjusted for multiple testing using Holm correction to control the family-wise error rate. The obtained “unmethylation“ values from ddPCR experiment were logit-transformed and univariable conditional logistic regression accounting for age-matched and follow-up time matched pairs (age matching +/- 5 years) as well as Wilcoxon signed rank test were used to compare the ratios between cases and controls. All analyses have been done using R 3.6 with add-on packages rms, survival and pROC. Results Associations of mdNLRs with TNBC The methylation levels of the selected neutrophil- and pan-lymphocyte-specific CpG sites are shown in Figure 2A . The neutrophil-specific sites are methylated in the neutrophils and unmethylated in pan-lymphocytes (NK, TCD4+, TCD8+, and B cells) and monocytes. The pan-lymphocyte-specific sites are methylated in the pan-lymphocytes and unmethylated in neutrophils and monocytes. The characteristics of the CpG sites are provided in Table 1. Methylation analysis at the selected CpG sites in 231 TNBC cases and 231 controls revealed a significantly higher ratio of neutrophils and lower ratio of pan-lymphocytes in TNBC cases compared with controls (mean ratios: 49% vs. 45% and 36% vs. 40.3%, respectively; conditional logistic regression: all P adj. <1e-04 ) (Figure 2B ). The neutrophil ratios were associated with a higher likelihood of being a TNBC case (OR range (2.07-3.02); conditional logistic regression: all P adj .<1e-04) and the pan-lymphocyte ratios with a lower likelihood (OR range (0.56-0.61); conditional logistic regression: all P adj .<1e-04) ( Suppl. Table 3 ). There were no associations of neutrophil and pan-lymphocyte ratios with histopathological characteristics of TNBCs including grade, size, node status, stage, and OS (size/stage: Jonckheere-Terpstra trend test; Grade/Node status: Mann-Whitney test; OS: Cox regression; all P >0.05). The average means of the mdNLR for cases and controls were 1.5 ± 0.63 and 1.18 ± 0.43, respectively. Logistic regression analysis revealed that all mdNLRs were associated with an increased likelihood of being a TNBC case (OR range (2.66-4.29); all P adj. <1e-04) ( Figure 3 ). No associations were observed for any of the nine mdNLRs with tumor histopathological characteristics and OS (size/stage: Jonckheere-Terpstra trend test; Grade/Node status: Mann-Whitney test; OS: Cox regression; all P >0.05). To validate our mdNLR findings, we used the reference-based method (mdNLR ref ), which estimates cell proportions. A higher mdNLR ref was also observed in TNBC cases compared with controls (2.49 ± 1.53 versus 1.81 ± 0.94) (Suppl. Fig. 1) , which was associated with a higher likelihood of being a TNBC case (OR=1.71 [1.38-2.11], conditional logistic regression; P adj. <1e-04) (Suppl. Fig. 2) . The mdNLR and mdNLRs ref were highly correlated (Suppl. Fig. 3), with the mdNLR cg23954655.cg26942829 ratio showing the highest correlation (Spearman’s rank correlation; r=0.97). Adjusting additionally for confounding factors (BMI, menopausal status, and smoking status) in a multivariable conditional logistic regression model, the associations of neutrophil and pan-lymphocyte ratios, mdNLRs, and mdNLR ref with TNBC did not change fundamentally ( Suppl. Tables 3-5 ) . Associations of Leukocyte Subtype Ratios with TNBC The methylation levels of the 21 selected ISUS are shown in Figure 4A . All 21 ISUS were hypomethylated in the target cell types but methylated in the other subtypes. The characteristics of each ISUS are provided in Table 2. In order to investigate the association between leukocyte subtypes ratios with TNBC in the retrospective sample set, the immune cell type ratios of TNBC cases were compared with those in controls using beta values of the specific ISUS. Analysis of these proxies showed that six of seven immune cell type ratios were associated with TNBC. Lower ratios of NK, TCD4+, TCD8+ cells, monocytes, and B cells in cases compared with controls were associated with TNBC ( Figure 4B ), with decreased NK cell ratios showing the strongest association. Further, a higher ratio of neutrophils in TNBC cases compared with controls was associated with TNBC, while no difference between the two groups was found in the Treg cell ratio. To validate our findings obtained with the immune cell methylation proxies, we applied immune cell proportions estimated by reference-based deconvolution method. For each sample, the estimated proportions of the six immune cell types sum to one. Univariable comparison of the various immune cell types between TNBC cases and controls revealed a statistically significant difference in the proportions of neutrophils, NK, TCD4+, and B cells (Suppl. Fig. 4) . No difference was observed in the proportions of TCD8+ cells and monocytes. Logistic regression analysis showed associations of neutrophils, NK, TCD4+, and B cell proportions with TNBC, with a decreased NK cell proportion showing the strongest association signal (Suppl. Fig. 2) . After adjustment for confounding factors, the associations of the leukocyte subtype ratios and proportions remained statistically significant except the association with cg07499259, which was no longer statistically significant ( Suppl. Tables 5 and 6 ). Correlation of Leukocyte Subtype Ratios with Clinical, Epidemiological, and Histopathological Parameters of the Participants of the Retrospective Study Correlation analysis of the immune cell type ratios with selected clinical and epidemiological characteristics of the study participants (age, menopausal status, smoking status, body mass index) revealed correlations with smoking status and age. The neutrophil and B cell ratios correlated with smoking status in controls. Current smokers had a lower neutrophil and a higher B cell ratio compared with non-smokers (Mann-Whitney test; P adj <0.05). Two immune cell type ratios correlated with age. The TCD8+ cell ratio showed an inverse correlation with age in both cases and controls and the NK cell ratio a positive correlation in controls (Spearman’s rank correlation; P adj <0.05). No other correlations were observed. There were no correlations of the immune cell type ratios with selected histopathological tumor characteristics (grade, size, node status, stage). Diagnostic and Prognostic Performance of ISUS AUC analysis showed that NK cells and neutrophils had the highest discriminative capability among all immune cell types ( Figure 4B ). The estimated NK cell-to-neutrophil ratio was higher in controls compared with cases and slightly improved the discrimination performance between cases and controls with AUC values in the range (0.67-0.71) ( Suppl. Fig. 5 ) relative to the values from individual CpGs in the range (0.63-0.67) ( Figure 4B ). The NK cell-to-neutrophil ratios were associated with TNBC: a higher NK cell-to-neutrophil ratio was associated with a lower likelihood of being a TNBC cases (OR range (0.52-0-70); conditional logistic regression; all P adj .<1e-04) ( Suppl. Table 7 ). Next, a diagnostic model was developed by fitting a multivariable logistic regression model based on all 21 ISUS and applying backward variable selection. A bootstrap-adjusted AUC was computed to account for overfitting. The final model contained four ISUS that discriminated cases from controls with an AUC of 72%. Of these four ISUS, two were specific for NK cells, one for monocytes, and one for TCD8+ cells ( Table 3 ). The prognostic performance using log-rank test showed that two probes, cg00219921 and cg08326410, which are specific for TCD8+ and NK cells, were associated with survival when using a median split. Higher ratios of TCD8+ and NK cells were associated with a better patient OS ( Suppl. Fig. 6 ). Using a multivariable Cox regression model including age, tumor grade, stage, tumor size, and lymph node status, only the association with cg00219921 remained statistically significant (Cox regression; P =0.04). However, after adjustment for multiple testing, the association lost statistical significance. Association of the NK Cell Ratio with TNBC in Participants of the Prospective Case-Control Study Since NK cells were the most pronounced immune cell type associated with TNBC in the retrospective sample set, we investigated whether the observed association could be detected in pre-diagnostic DNA samples of TNBC cases compared with controls. In this respect, the NK cell ratio was measured in 146 TNBC cases and 146 controls using a ddPCR TaqMan assay specific for one NK cell-specific unmethylated site (cg23060465). The obtained “unmethylation“ levels from ddPCR were Logit-transformed and the Wilcoxon signed rank test was used to compare the obtained NK cell ratios between cases and controls. A lower NK cell ratio was observed in TNBC cases compared with controls (Wilcoxon signed rank test, P =0.019) ( Figure 5 ). Using conditional logistic regression, a higher NK cell ratio was associated with a reduced TNBC risk at the margin of statistical significance (OR=0.76, 95% CI [0.58-1.00], conditional logistic regression; P =0.052). Heterogeneity due to age at blood draw, age of diagnosis, and interval time between blood draw and reference date was tested. No heterogeneity/subgroup effect was observed (Interaction test based on conditional logistic regression; all P >0.05). Discussion In the present large immunomethylomic study, we showed that methylation-derived leukocyte subtype ratios and mdNLRs at subtype-specific CpG sites are associated with TNBC in peripheral blood of TNBC patients and age-matched controls from a retrospective study. We showed that mdNLRs were higher in TNBC cases compared with controls and associated with TNBC. Further, higher ratios of neutrophils and lower ratios of NK, TCD4+, TCD8+, monocytes, and B cells were associated with TNBC, with a decreased NK cell ratio showing the strongest association. Associations of mdNLRs and neutrophil, NK, TCD4+, and B cell ratios were validated in analysis based on immune cell type proportions. Moreover, we confirmed that the NK cell ratio was significantly lower in pre-diagnostic samples of TNBC cases compared with controls. Overall, a 4% higher ratio of neutrophils and a 4.3% lower ratio of total lymphocytes were observed in TNBC cases compared with controls. It was reported that breast tumors maximize their chance of metastasizing by evoking a systemic inflammatory cascade, which leads to an elevated level of neutrophils. These tumor-induced neutrophils suppress cytotoxic TCD8+ lymphocytes, which ultimately enhancing metastatic seeding in the pre-metastatic lung (29). Furthermore, the interactions between neutrophils and lymphocytes play critical roles in carcinogenesis. As a hallmark of cancer, in this study also an elevated mdNLR was observed in cases compared with controls (1.5 vs. 1.18). With slight difference, this result was repeated also by Houseman estimation method (averaged mdNLR ref for cases and controls were 2.49 and 1.81, respectively). This finding agrees with that from a previous study, which reported mdNLR values of 2.7 in TNBC cases and 2.4 in controls (3). A high NLR was associated with adverse survival of patients affected by various solid tumors, including TNBC (5, 7, 9, 17). Howeverin contrast to the previous studies, mdNLR was not an independent predictor of OS in our study. The discrepant results obtained in these studies may be explained by differences in the study size, study population, and the methods used for mdNLR estimation. Among the major immune cell types, higher neutrophil and lower ratios of B cells, TCD4+, TCD8+, NK cells, and monocytes were associated with TNBC. NK cell ratio showed the strongest association in the large retrospective sample set of 231 TNBC cases and 231 controls. Since the blood samples were drawn after the diagnosis of TNBC, it cannot be excluded that the shift in the NK cell ratio was induced by the tumor. There is evidence from a previous study that the tumor itself could manipulate and decrease the numbers of immune cells, such as NK cells, in the peripheral blood of TNBC patients by secreting cytokines (30). However, a lower NK cell ratio was also observed in the prospective sample set of 146 TNBC cases compared with controls, which was associated with a higher TNBC risk at the margin of statistical significance. This finding suggests that the shift in NK cell ratio has occurred before disease manifestation. Interestingly, a lower NK cell level was also reported in head and neck cancer patients compared with controls with individuals in the lowest NK tertile having over 5-fold risk of being a case (2). There is evidence that NK cell levels are linked with survival of cancer patients. One study demonstrated that the NK cell ratio in the peripheral blood is an independent predictor of survival of colorectal cancer patients; those with a higher percentage of NK cells being associated with a better survival than those with a lower percentage (10). Higher peripheral NK cell counts were also associated with better OS in lymphoma and chronic lymphocytic leukemia patients (31-33). Further, there is some evidence that the number of NK cells in peripheral blood may affect the outcome of B-cell non-Hodgkin lymphoma patients receiving immunochemotherapy (34). NK cells are effector lymphocytes of the innate immune system that control several types of tumors and microbial infections by limiting their spread (35). They have a crucial role in the control of metastasis by virtue of killing circulating tumor cells and acting as the first line of defense against metastasis from circulating tumor cells (36). Therefore, the lower numbers of circulating NK cells in patients with TNBC may be an underlying cause of the disease, and individuals with decreased NK cell levels could be predisposed to develop TNBC. One previous study reported that mice with deficiencies in NK cell number and function are more susceptible to transplanted tumors (37). In addition, it has been shown before that individuals with low cytotoxic activity of peripheral blood lymphocytes, including NK cells, are at higher risk of developing various types of cancer (38). Impairment of NK cell function was also reported to play a role in breast tumorigenesis (39). Breast tumors modify their environment to evade NK cell antitumor immunity (40) and ex vivo -expanded NK cells showed potent antitumor function against breast cancer cell lines and primary cells isolated from patients (41, 42). Another study suggested NK cells are important players in TNBC development and metastasis that could be used as a promising immunotherapeutic against TNBC (43). In this respect, increasing NK cell antitumor activity and ex-vivo expanding NK cell populations is paving the way for a new generation of anticancer immunotherapies (4). Our findings may highlight the value of NK cell-based immunotherapies for TNBC; given that NK-cell induced lysis was significantly higher in TNBC cell lines compared to estrogen receptor positive breast cancer cell lines (44). At present, flow cytometry is the most widely applied analytical approach for immune cell quantification (24). This method, however, is limited to intact cells, but fresh or well-preserved blood samples are not available for many clinical cohorts. Therefore, in various recent studies, including this one, an epigenetic assay has been used (2, 3, 22, 24), which allows cell quantification in samples of limited quality and quantity, is applicable to archival blood specimens, which are available for many clinical cohorts, and is less costly than flow cytometry. It has been shown that the epigenetic assay performs equivalently to flow cytometry for immune cell quantification (22-24). As is the case for all studies, this work is not without some limitations. One limitation is that epigenetic data could not be validated using classical cell counting by flow-cytometry due to the lack of fresh blood samples from TNBC cases and controls. Another limitation is the still relatively limited set of only main immune cell subtypes that were investigated. A further limitation is that the developed diagnostic model with four selected methylation markers, improved the capability to discriminative patients with TNBC from controls to an AUC of 72% (relative to the AUC values from individual CpGs in the range from 55% to 67%), which, however, has no clinical utility based on a threshold AUC of >80%. A higher discriminative capability has to be achieved by integrating other molecular markers of different sources such as cell-free nucleic acids or proteins in future models of non-invasive diagnosis of TNBC. Conclusions In summary, this is the largest study investigating immune cell profiles in TNBC patients and controls using methylation data. We identified and validated associations of mdNLRs and neutrophil, TCD4+, B, and NK cell subtype ratios/proportions with TNBC, with the latter having the strongest association signal. The NK cell ratio was also significantly lower in pre-diagnostic samples of TNBC cases compared with controls. Thus, the NK cell level may be useful as a potential non-invasive blood-based biomarker for TNBC risk assessment, early detection or immunotherapeutic applications. List Of Abbreviations TNBC: triple negative breast cancer NK cell: natural killer cell mdNLR: methylation-derived neutrophil-to-lymphocyte ratio OR: odds ratio ISUS: immune cell specific unmethylated sites ROC: receiver operating characteristic AUC: area under the curve OS: overall survival ddPCR: droplet digital PCR mdNLR ref : methylation-derived neutrophil-to-lymphocyte ratio estimated by Housman method Declarations Ethics approval and consent to participate All procedures performed in studies involving human were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration. Informed consent was obtained for all study participants. Consent for publication Not applicable. Availability of data and material The datasets of sorted immune cells analyzed during the current study are available in the Gene Expression Omnibus repository (GEO; https://www.ncbi.nlm.nih.gov/geo/ ) through GEO accessions: GSE35969, GSE59250, GSE110554, GSE88824, and GSE49667. The in-house generated methylation array data are not publically available. They can be accessed for research purposes upon request. Competing interests The authors declare no conflict of interest. Funding The work was funded by the German Cancer Research Center (DKFZ), Heidelberg. Author Contributions MM and UH participated in the conception and design of the study. OF, AJS, Y-DK, HB, TB provided the study material or patients. CG, TH, NB, MM, and UH carried out the analysis and interpretation of data. MM, TH, CG, and UH wrote the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors thank all study participants, clinicians, family doctors, researchers and technicians for their contributions and commitment to this study. We thank J Hoheisel for critical reading of the manuscript. The GENICA Network: Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany (UH); Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, and University of Tübingen, Tübingen, Germany (HB, W-Y Lo, R Hoppe, S Winter); German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Partner Site Tübingen (HB), funded by the Deutsche Forschungsgemeinschaft (DFG) – EXC 2180-390900677 (HB); Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany (Y-DK, C Baisch); Institute of Pathology, University of Bonn, Germany (H-P Fischer); Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, Germany (TB, B Pesch, S Rabstein, A Lotz); and Institute of Occupational Medicine and Maritime Medicine, University Medical Center Hamburg-Eppendorf, Germany (V Harth). O Fletcher’s work is supported by Programme Grants from Breast Cancer Now as part of Programme Funding to the Breast Cancer Now Toby Robins Research Centre. The Generation Study thanks Breast Cancer Now and the Institute of Cancer Research for funding and support. The Institute of Cancer Research acknowledges NHS funding to the Royal Marsden Foundation Trust and the National Institute for the Health Research (NIHR) Biomedical Research Centre. References Grivennikov SI, Greten FR, Karin M (2010) Immunity, inflammation, and cancer. Cell. 140: 883-99. doi: 10.1016/j.cell.2010.01.025 Accomando WP, Wiencke JK, Houseman EA, Butler RA, Zheng S, Nelson HH, Kelsey KT (2012) Decreased NK cells in patients with head and neck cancer determined in archival DNA. 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Clin Cancer Res. 24: 2851-8. doi: 10.1158/1078-0432.CCR-17-1323 Bocker MT, Hellwig I, Breiling A, Eckstein V, Ho AD, Lyko F (2011) Genome-wide promoter DNA methylation dynamics of human hematopoietic progenitor cells during differentiation and aging. Blood. 117: e182-9. doi: 10.1182/blood-2011-01-331926 Calvanese V, Fernandez AF, Urdinguio RG et al. (2012) A promoter DNA demethylation landscape of human hematopoietic differentiation. Nucleic Acids Res. 40: 116-31. doi: 10.1093/nar/gkr685 Kelsey KT, Wiencke JK (2018) Immunomethylomics: A Novel Cancer Risk Prediction Tool. Ann Am Thorac Soc. 15: S76-S80. doi: 10.1513/AnnalsATS.201706-477MG Accomando WP, Wiencke JK, Houseman EA, Nelson HH, Kelsey KT (2014) Quantitative reconstruction of leukocyte subsets using DNA methylation. Genome Biol. 15: R50. doi: 10.1186/gb-2014-15-3-r50 Baron U, Werner J, Schildknecht K et al. (2018) Epigenetic immune cell counting in human blood samples for immunodiagnostics. 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Ann Oncol. 18: 1209-15. doi: 10.1093/annonc/mdm110 Klanova M, Oestergaard MZ, Trneny M et al. (2019) Prognostic Impact of Natural Killer Cell Count in Follicular Lymphoma and Diffuse Large B-cell Lymphoma Patients Treated with Immunochemotherapy. Clin Cancer Res. 25: 4634-43. doi: 10.1158/1078-0432.CCR-18-3270 Vivier E, Tomasello E, Baratin M, Walzer T, Ugolini S (2008) Functions of natural killer cells. Nat Immunol. 9: 503-10. doi: 10.1038/ni1582 Larsen SK, Gao Y, Basse PH (2014) NK cells in the tumor microenvironment. Crit Rev Oncog. 19: 91-105. Guillerey C, Smyth MJ (2016) NK Cells and Cancer Immunoediting. Curr Top Microbiol Immunol. 395: 115-45. doi: 10.1007/82_2015_446 Imai K, Matsuyama S, Miyake S, Suga K, Nakachi K (2000) Natural cytotoxic activity of peripheral-blood lymphocytes and cancer incidence: an 11-year follow-up study of a general population. Lancet. 356: 1795-9. doi: 10.1016/S0140-6736(00)03231-1 Tsavaris N, Kosmas C, Vadiaka M, Kanelopoulos P, Boulamatsis D (2002) Immune changes in patients with advanced breast cancer undergoing chemotherapy with taxanes. Br J Cancer. 87: 21-7. doi: 10.1038/sj.bjc.6600347 Mamessier E, Sylvain A, Thibult ML et al. (2011) Human breast cancer cells enhance self tolerance by promoting evasion from NK cell antitumor immunity. J Clin Invest. 121: 3609-22. doi: 10.1172/JCI45816 Nham T, Poznanski SM, Fan IY et al. (2018) Ex Vivo-expanded Natural Killer Cells Derived From Long-term Cryopreserved Cord Blood are Cytotoxic Against Primary Breast Cancer Cells. J Immunother. 41: 64-72. doi: 10.1097/CJI.0000000000000192 Shenouda MM, Gillgrass A, Nham T et al. (2017) Ex vivo expanded natural killer cells from breast cancer patients and healthy donors are highly cytotoxic against breast cancer cell lines and patient-derived tumours. Breast Cancer Res. 19: 76. doi: 10.1186/s13058-017-0867-9 Dewan MZ, Terunuma H, Takada M, Tanaka Y, Abe H, Sata T, Toi M, Yamamoto N (2007) Role of natural killer cells in hormone-independent rapid tumor formation and spontaneous metastasis of breast cancer cells in vivo. Breast Cancer Res Treat. 104: 267-75. doi: 10.1007/s10549-006-9416-4 Engel JB, Honig A, Kapp M, Hahne JC, Meyer SR, Dietl J, Segerer SE (2014) Mechanisms of tumor immune escape in triple-negative breast cancers (TNBC) with and without mutated BRCA 1. Arch Gynecol Obstet. 289: 141-7. doi: 10.1007/s00404-013-2922-9 Tables Table 1. Characteristics of selected CpG proxies in neutrophils and pan-lymphocytes Cell Probe ID Chromsome: Gene Genic β-value OR [95% CI] b P adj c AUC [95% CI] type Position a name region Cases Controls Neu cg09993145 1:25291905 RUNX3 TSS1500 0.56 0.51 2.07 [1.60-2.67] <1e-04 0.68 [0.64-0.73] cg10825315 14:81425912 TSHR Body 0.43 0.40 2.64 [1.80-3.87] <1e-04 0.67 [0.62-0.71] cg23954655 13:99223562 STK24 Body 0.48 0.44 3.02 [2.07-4.42] <1e-04 0.68 [0.63-0.73] Pan-lym cg04552418 1:31956405 Intergenic --- 0.36 0.41 0.56 [0.44-0.70] <1e-04 0.66 [0.62-0.71] cg13580758 4:57824450 REST Body 0.36 0.40 0.58 [0.46-0.74] <1e-04 0.64 [0.59-0.69] cg26942829 6:13408158 GFOD1 Body 0.36 0.40 0.61 [0.49-0.77] <1e-04 0.65 [0.60-0.70] OR: odds ratio; CI: confidence interval; P adj : adjusted P -value; AUC: area under the curve; Neu: neutrophils; Pan-lym: pan-lymphocytes. a HumanGRCh37/hg19 Assembly. b OR is given for a 10% increase in methylation level. c Adjusted for multiple testing using Holm correction. Table 2 . Characteristics of selected ISUS as proxies and their participation in the diagnostic models Cell type ISUS Probe ID Chromosome: Position a Gene name Genic region LogReg LogReg-VarSel B−cell cg07721872 16:87735256 LOC100129637 Body Yes --- cg04838847 8:110587155 GOLSYN Body Yes --- cg27565966 16:28943198 CD19 TSS200 Yes --- Monocyte cg23244761 6:161796850 PARK2 Body Yes --- cg05923857 10:114911615 TCF7L2 Body Yes --- cg24788483 10:114911652 TCF7L2 Body Yes Yes TCD4+ cg05617307 10:121413182 BAG3 Body Yes --- cg14477767 4:170195438 Intergenic --- Yes --- cg20737812 15:86336631 KLHL25 5'UTR Yes --- TCD8+ cg18857618 2:87048489 CD8B Body Yes --- cg00219921 2:87012810 CD8A 3'UTR Yes --- cg06419846 11:66083697 CD248 1stExon Yes Yes Neu cg25006077 3:152176018 MBNL1 Body Yes --- cg25739938 2:9528072 CPSF3 Body Yes --- cg05398700 14:102677141 WDR20 Body Yes --- NK cg08326410 19:55314884 KIR2DL4 TSS200 Yes Yes cg23855986 11:129980502 APLP2 Body Yes Yes cg23060465 8:141625545 EIF2C2 Body Yes --- Treg cg04920616 X:49121288 FOXP3 TSS200 Yes --- cg02033323 X:49005257 FOXP3 5'UTR Yes --- cg07499259 1:12188502 TNFRSF8 Body Yes --- Neu: neutrophil; NK: natural killer cell; Treg: regulatory T cell; LogReg: logistic regression; LogReg-VarSel: variable selection in logistic regression. a Human GRCh37/hg19) Assembly. Table 3 . Multivariable logistic regression after variable selection CpG ID Cell type OR [95% CI] P cg08326410 NK 1.14 [1.04-1.26] 0.00592 cg23855986 NK 1.31 [1.15-1.49] <1e-04 cg24788483 Monocyte 1.17 [1.09-1.25] <1e-04 cg06419846 TCD8+ 1.17 [1.09-1.26] <1e-04 OR: odds ratio; CI: confidence interval; NK: natural killer cell. Supplementary Files SupplementaryMaterials.docx SupplementaryTable1.docx SupplementaryTable2.docx SupplementaryTable3.docx SupplementaryTable4.docx SupplementaryTable5.docx SupplementaryTable6.docx SupplementaryTable7.docx Cite Share Download PDF Status: Published Journal Publication published 17 Nov, 2021 Read the published version in Clinical Epigenetics → Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-508197","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[{"code":1,"date":"2021-05-17 17:14:06","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research 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04:55:19","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-508197/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-508197/v2","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13148-021-01196-1","type":"published","date":"2021-11-17T12:55:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":9567132,"identity":"b1a36e5d-ccaa-4bba-a319-3d0f72258375","added_by":"auto","created_at":"2021-05-25 17:55:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":142501,"visible":true,"origin":"","legend":"Graphical representation of the analyses performed in the study. ","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-508197/v2/63499c854e5f2e866894a73f.jpg"},{"id":9567131,"identity":"a2b40ff4-1a0b-46df-92a5-75860790342f","added_by":"auto","created_at":"2021-05-25 17:55:56","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62806,"visible":true,"origin":"","legend":"Heat map visualization of DNA methylation levels at the three pan-lymphocyte- and the three neutrophil-specific CpG sites using GEO datasets (A). Box plots of data on the methylation levels of the pan-lymphocyte- and neutrophil-specific CpG sites in 231 TNBC cases and 231 controls from the retrospective study (B). Outliers appear outside of the whiskers.","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-508197/v2/7c7f2365e227d362e072489b.jpg"},{"id":9566820,"identity":"fd5575c3-a7ff-4a53-9df5-5817b3bab34e","added_by":"auto","created_at":"2021-05-25 17:49:57","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":55324,"visible":true,"origin":"","legend":"Forest plot of the associations of the nine mdNLRs with TNBC and estimated areas under the receiver-operating characteristic curves (AUCs) with their corresponding 95% confidence intervals (CIs). OR is given for a 10% increase in methylation level. P-values were adjusted (Padj) for multiple testing using the Holm correction. Horizontal lines indicate 95% CIs. Abbreviations: mdNLR, methylation-derived neutrophil-to-lymphocyte ratio; OR, odds ratio.\n\n","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-508197/v2/24a2a5e0ee2fe59831393ac3.jpg"},{"id":9566815,"identity":"7d55f7ca-e857-4705-8644-4bf390670df5","added_by":"auto","created_at":"2021-05-25 17:49:56","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":137543,"visible":true,"origin":"","legend":"Heat map illustrating the methylation levels at the 21 selected ISUS in target immune cells versus other immune cells (A). Forest plot showing the associations of the leukocyte subtype ratios and TNBC using the ISUS methylation data and estimated areas under the receiver-operating characteristic curves (AUCs) with their corresponding 95% confidence intervals (CIs) (B). P-values were adjusted (Padj) for multiple testing using the Holm correction. Horizontal lines indicate 95% CIs. Abbreviations: ISUS, immune cell specific unmethylated site; OR, odds ratio.","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-508197/v2/96995d8f4bb474ca0b393d87.jpg"},{"id":9566747,"identity":"623eb3dd-12bb-4e66-9a2a-6620300cd7ce","added_by":"auto","created_at":"2021-05-25 17:46:57","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":39722,"visible":true,"origin":"","legend":"Box plot of the DNA “unmethylation” ratio in prediagnosed TNBC cases and controls. The unmethylation level corresponds to the NK cell ratio in sample groups. \n\n","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-508197/v2/08b96d37f61c760a1fd4ff1e.jpg"},{"id":15626829,"identity":"2157feb6-fb41-40eb-bb34-78d903bbfc99","added_by":"auto","created_at":"2021-11-17 12:55:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":870418,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-508197/v2/f19b1ac9-fda8-40a9-9637-ee147dba2a35.pdf"},{"id":9567033,"identity":"2a42adf1-ee4c-4f0d-94e9-76afaf31ff0f","added_by":"auto","created_at":"2021-05-25 17:52:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2248148,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-508197/v2/ead3a11ba8f05e1e25c63bfc.docx"},{"id":9566814,"identity":"df319248-8a46-4ebe-9227-c02750889c93","added_by":"auto","created_at":"2021-05-25 17:49:56","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15638,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-508197/v2/62537b06ab107677866501be.docx"},{"id":9566738,"identity":"b88f1515-d9e9-4356-87cc-8ec3d2f1c8c7","added_by":"auto","created_at":"2021-05-25 17:46:56","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":12189,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-508197/v2/21ef696a3b0b67ab3023ee6a.docx"},{"id":9566750,"identity":"0a125966-44c9-4c8f-8108-395fd5aa881c","added_by":"auto","created_at":"2021-05-25 17:46:57","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":14117,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-508197/v2/b705853f6849ee8ed1673faa.docx"},{"id":9567034,"identity":"5b6b9650-ddff-485c-89a5-10edce59c4f9","added_by":"auto","created_at":"2021-05-25 17:52:57","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":13500,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable4.docx","url":"https://assets-eu.researchsquare.com/files/rs-508197/v2/186a93602d557e27415d99bb.docx"},{"id":9566746,"identity":"e84df7e8-f170-40dc-8031-99e67ad079f4","added_by":"auto","created_at":"2021-05-25 17:46:57","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":13717,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable5.docx","url":"https://assets-eu.researchsquare.com/files/rs-508197/v2/3ce80fa306d4370956396773.docx"},{"id":9566749,"identity":"1ba34a27-52fb-4adb-a22a-4710d231e277","added_by":"auto","created_at":"2021-05-25 17:46:57","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":14351,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable6.docx","url":"https://assets-eu.researchsquare.com/files/rs-508197/v2/6d10f6cdebe5303c027b657d.docx"},{"id":9566821,"identity":"e930df29-027a-4278-9c4c-737fec3dd6d4","added_by":"auto","created_at":"2021-05-25 17:49:57","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":15739,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable7.docx","url":"https://assets-eu.researchsquare.com/files/rs-508197/v2/f7efc93f8cc7cda28e70e9a1.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eEpigenetic Quantification of Circulating Immune Cells in Peripheral Blood of Triple-negative Breast Cancer Patients\u003c/p\u003e","fulltext":[{"header":"Introduction ","content":"\u003cp\u003eInflammation plays an important role in almost every stage of cancer development. Many inflammatory markers have been associated with cancer progression and prognosis (1). Various studies showed that the number and function of blood leukocytes are altered in cancer (2-5). A shift in the number of peripheral immune cells is a predictor of cancer patient survival. For instance, an increased neutrophil-to-lymphocyte ratio (NLR) which is indicative of systemic inflammation, could promote cancer cell proliferation, angiogenesis, cellular migration, and metastasis (6). There is evidence from many studies for a prognostic role of NLR in peripheral blood of various cancer patients (7-10), including breast cancer (8, 11, 12).\u003c/p\u003e\n\u003cp\u003eTriple Negative Breast Cancer (TNBC) accounts for 15-20% of all breast cancers (13). Due to the lack of targeted therapies, chemotherapy still is the main therapeutic strategy. Therefore, many efforts have been conducted to increase the diagnostic and therapeutic opportunities for the TNBC patients (14). TNBC is also the most immunogenic subtype. Higher levels of infiltrated T cells are associated with an improved OS and disease-free survival (DFS) of TNBC patients as compared with those affected by other breast cancer subtypes (15, 16). Further, NLR is associated with survival in a pre-treatment setting and throughout the treatment course and subsequent follow-up (5, 17, 18). In addition, higher peripheral lymphocyte counts are associated with a lower mortality from early-stage TNBC suggesting that immune cell functions improve early TNBC treatment (19).\u003c/p\u003e\n\u003cp\u003eEpigenetic modifications such as DNA methylation play an important role in the cell-specific gene regulation within the hematopoietic system (20, 21). Since DNA methylation signatures are chemically stable and mitotically heritable, they have been successfully applied to quantify leukocyte subtypes accurately in DNA from peripheral blood (22-24).\u003c/p\u003e\n\u003cp\u003eIn the present study, we identified and validated associations of methylation-derived leukocyte subtype ratios and methylation-derived neutrophil-to-lymphocyte ratios (mdNLRs) with TNBC using methylation data of 231 TNBC cases and 231 age-matched controls from a retrospective study. We report associations of various leukocyte subtypes ratios with TNBC, with the natural killer (NK) cell ratio showing the strongest association with disease. Further, we provide evidence for an association of the NK cell ratio with TNBC risk in a prospective sample set of 146 TNBC cases and 146 age-matched controls (for a graphical overview of the work, see \u003cstrong\u003eFigure 1\u003c/strong\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Populations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the present study, two TNBC case-control sample sets were analyzed: One set from a retrospective study contains 231 TNBC cases and 231 age-matched controls selected from two different studies: the breast cancer case-only study SKKDKFZS (25) and the breast cancer case-control study GENICA (26). The prospective sample set includes 146 TNBC cases and 146 controls from the Generations Study (GS) (27). All study participants were women of European ethnicity. All studies had local ethical approval and all included individuals gave informed written consent. Details on the study populations can be found in \u003cstrong\u003eSupplementary Methods\u003c/strong\u003e (available online). The sample sizes and selected characteristics of study participants are presented in \u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003eTables 1 and 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA Methylation Analysis \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe applied publically available DNA methylation datasets o\u003cem\u003ebtained \u003c/em\u003efrom isolated leukocyte subtypes (NK cells, CD4+ T cells (TCD4+), CD8+ T cells (TCD8+), regulatory T cells (Treg), monocytes, neutrophils, and B cells)\u003cem\u003e for selection of cell-type specific differentially methylated sites. Thereafter, DNA methylation levels (beta values) at selected CpG sites were used as proxies for leukocyte subtypes proportions and mdNLRs. The \u003c/em\u003emdNLR was estimated by dividing the beta value at neutrophil-specific CpGs by the beta value of pan-lymphocyte specific CpGs. Details on the experimental strategies and data analysis are described in \u003cstrong\u003eSupplementary Methods\u003c/strong\u003e (available online).\u003c/p\u003e\n\u003cp\u003eIn-house genome-wide and locus-specific DNA methylation analysis was performed on the retrospective and prospective sample sets, respectively. Genome-wide DNA methylation profiling was performed on 231 TNBC cases and 231 controls from a retrospective study using the Illumina Infinium HumanMethylation450K BeadChip according to the manufacturer\u0026acute;s instructions. Subsequently, for measurement of the NK cell level in the prospective cohort of 146 TNBC cases and 146 controls, MethyLight droplet digital PCR (ddPCR) assay was carried out on bisulfite converted DNA. More details on the materials and methods are described in \u003cstrong\u003eSupplementary Methods.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor technical validation of our findings, we applied a reference-based method (Houseman algorithm) to statistically deconvolute the proportions of six immune cell subtypes (NK, TCD4+, TCD8+ cells, monocytes, neutrophils, and B cells) in the TNBC cases and controls (28). The mdNLR\u003csub\u003ereference-based method\u003c/sub\u003e (mdNLR\u003csub\u003eref\u003c/sub\u003e) was calculated by dividing the estimated proportion of neutrophils by the sum of the lymphoid cell proportions (NK, TCD4+, TCD8+, and B cells).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAssociations of TNBC and leukocyte subtype ratios were analyzed using the methylation beta values from the retrospective study. The ratios of the seven leukocyte subtypes in TNBC cases and controls were calculated and compared using methylation levels (beta-value) of 21 selected immune cell specific unmethylated sites (ISUS). A higher level of methylation at each ISUS corresponds to lower ratio of the corresponding leukocyte subtype.\u003c/p\u003e\n\u003cp\u003eIn primary analysis, the diagnostic performance of individual CpGs and subtype ratios on TNBC status was assessed with univariable conditional logistic regression accounting for age-matched pairs (age matching +/- 1 year) and Receiver Operating Characteristic (ROC)/Area Under the Curve (AUC) analysis. In supportive analysis, adjusted effects were estimated based on a multivariable conditional logistic regression model additionally accounting for body mass index (BMI) (continuous), menopausal status (pre/peri, post), and smoking status (current) (yes, no). Complete data for 221 matched-pairs was available for the supportive analysis.\u003c/p\u003e\n\u003cp\u003eA multivariable logistic regression model based on all pre-selected CpGs was fitted with backward selection at a significance level of 20% for staying in the model. Internal validation of the AUC for the multivariable model including variable selection was done using bootstrapping with 200 repetitions. To assess the diagnostic performance of subtype ratios, differences in logit-transformed beta values were analyzed. In TNBC cases, the association of methylation levels with histopathological tumor parameters (grade, size, node status, stage) and overall survival (OS) was assessed. OS was defined as the time between TNBC diagnosis and death or last follow-up, whichever occurred first. Mann-Whitney test, Jonckheere-Terpstra trend test, and Spearman\u0026rsquo;s correlation coefficient were used to assess associations between methylation levels and clinico-pathological parameters. The impact of beta values on OS was analyzed in a Cox regression model. To account for established prognostic factors, a multivariable Cox regression model including age, tumor grade (G1/G2 vs G3), stage (0-4), tumor size (T1, T2, T3, T4) and N status (N0 vs N1) was fitted. Kaplan-Meier estimates and log-rank test were derived for methylation levels at median cut-off. Individual \u003cem\u003eP\u003c/em\u003e-values were adjusted for multiple testing using Holm correction to control the family-wise error rate. The obtained \u0026ldquo;unmethylation\u0026ldquo; values from ddPCR experiment were logit-transformed and univariable conditional logistic regression accounting for age-matched and follow-up time matched pairs (age matching +/- 5 years) as well as Wilcoxon signed rank test were used to compare the ratios between cases and controls. All analyses have been done using R 3.6 with add-on packages rms, survival and pROC.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eAssociations of mdNLRs with TNBC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe methylation levels of the selected neutrophil- and pan-lymphocyte-specific CpG sites are shown in \u003cstrong\u003eFigure 2A\u003c/strong\u003e. The neutrophil-specific sites are methylated in the neutrophils and unmethylated in pan-lymphocytes (NK, TCD4+, TCD8+, and B cells) and monocytes. The pan-lymphocyte-specific sites are methylated in the pan-lymphocytes and unmethylated in neutrophils and monocytes. The characteristics of the CpG sites are provided in \u003cstrong\u003eTable 1. \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMethylation analysis at the selected CpG sites in 231 TNBC cases and 231 controls revealed a significantly higher ratio of neutrophils and lower ratio of pan-lymphocytes in TNBC cases compared with controls (mean ratios: 49% vs. 45% and 36% vs. 40.3%, respectively; conditional logistic regression: all \u003cem\u003eP\u003csub\u003eadj.\u003c/sub\u003e\u003c/em\u003e\u0026lt;1e-04\u003cem\u003e)\u003c/em\u003e\u003cstrong\u003e (Figure 2B\u003c/strong\u003e). The neutrophil ratios were associated with a higher likelihood of being a TNBC case (OR range (2.07-3.02); conditional logistic regression: all \u003cem\u003eP\u003csub\u003eadj\u003c/sub\u003e\u003c/em\u003e.\u0026lt;1e-04) and the pan-lymphocyte ratios with a lower likelihood (OR range (0.56-0.61); conditional logistic regression: all \u003cem\u003eP\u003csub\u003eadj\u003c/sub\u003e\u003c/em\u003e.\u0026lt;1e-04) (\u003cstrong\u003eSuppl. Table 3\u003c/strong\u003e). There were no associations of neutrophil and pan-lymphocyte ratios with histopathological characteristics of TNBCs including grade, size, node status, stage, and OS (size/stage: Jonckheere-Terpstra trend test; Grade/Node status: Mann-Whitney test; OS: Cox regression; all \u003cem\u003eP\u003c/em\u003e\u0026gt;0.05). The average means of the mdNLR for cases and controls were 1.5 \u0026plusmn; 0.63 and 1.18 \u0026plusmn; 0.43, respectively. Logistic regression analysis revealed that all mdNLRs were associated with an increased likelihood of being a TNBC case (OR range (2.66-4.29); all \u003cem\u003eP\u003csub\u003eadj.\u003c/sub\u003e\u003c/em\u003e\u0026lt;1e-04) (\u003cstrong\u003eFigure 3\u003c/strong\u003e). No associations were observed for any of the nine mdNLRs with tumor histopathological characteristics and OS (size/stage: Jonckheere-Terpstra trend test; Grade/Node status: Mann-Whitney test; OS: Cox regression; all \u003cem\u003eP\u003c/em\u003e\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003eTo validate our mdNLR findings, we used the reference-based method (mdNLR\u003csub\u003eref\u003c/sub\u003e), which estimates cell proportions. A higher mdNLR\u003csub\u003eref \u003c/sub\u003ewas also observed in TNBC cases compared with controls (2.49 \u0026plusmn; 1.53 versus 1.81 \u0026plusmn; 0.94) \u003cstrong\u003e(Suppl. Fig. 1)\u003c/strong\u003e, which was associated with a higher likelihood of being a TNBC case (OR=1.71 [1.38-2.11], conditional logistic regression;\u003cem\u003e P\u003csub\u003eadj.\u003c/sub\u003e\u003c/em\u003e\u0026lt;1e-04) \u003cstrong\u003e(Suppl. Fig. 2)\u003c/strong\u003e. The mdNLR and mdNLRs\u003csub\u003eref\u003c/sub\u003e were highly correlated \u003cstrong\u003e(Suppl. Fig. 3),\u003c/strong\u003e with the mdNLR \u003cem\u003ecg23954655.cg26942829\u003c/em\u003e ratio showing the highest correlation (Spearman\u0026rsquo;s rank correlation; r=0.97).\u003c/p\u003e\n\u003cp\u003eAdjusting additionally for confounding factors (BMI, menopausal status, and smoking status) in a multivariable conditional logistic regression model, the associations of neutrophil and pan-lymphocyte ratios, mdNLRs, and mdNLR\u003csub\u003eref\u003c/sub\u003e with TNBC did not change fundamentally (\u003cstrong\u003eSuppl. Tables 3-5\u003c/strong\u003e)\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociations of Leukocyte Subtype Ratios with TNBC \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe methylation levels of the 21 selected ISUS are shown in \u003cstrong\u003eFigure 4A\u003c/strong\u003e. All 21 ISUS were hypomethylated in the target cell types but methylated in the other subtypes. The characteristics of each ISUS are provided in \u003cstrong\u003eTable 2.\u003c/strong\u003e In order to investigate the association between leukocyte subtypes ratios with TNBC in the retrospective sample set, the immune cell type ratios of TNBC cases were compared with those in controls using beta values of the specific ISUS. Analysis of these proxies showed that six of seven immune cell type ratios were associated with TNBC. Lower ratios of NK, TCD4+, TCD8+ cells, monocytes, and B cells in cases compared with controls were associated with TNBC (\u003cstrong\u003eFigure 4B\u003c/strong\u003e), with decreased NK cell ratios showing the strongest association. Further, a higher ratio of neutrophils in TNBC cases compared with controls was associated with TNBC, while no difference between the two groups was found in the Treg cell ratio.\u003c/p\u003e\n\u003cp\u003eTo validate our findings obtained with the immune cell methylation proxies, we applied immune cell proportions estimated by reference-based deconvolution method. For each sample, the estimated proportions of the six immune cell types sum to one. Univariable comparison of the various immune cell types between TNBC cases and controls revealed a statistically significant difference in the proportions of neutrophils, NK, TCD4+, and B cells \u003cstrong\u003e(Suppl. Fig. 4)\u003c/strong\u003e. No difference was observed in the proportions of TCD8+ cells and monocytes. Logistic regression analysis showed associations of neutrophils, NK, TCD4+, and B cell proportions with TNBC, with a decreased NK cell proportion showing the strongest association signal \u003cstrong\u003e(Suppl. Fig. 2)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eAfter adjustment for confounding factors, the associations of the leukocyte subtype ratios and proportions remained statistically significant except the association with cg07499259, which was no longer statistically significant (\u003cstrong\u003eSuppl. Tables 5 and 6\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation of Leukocyte Subtype Ratios with Clinical, Epidemiological, and Histopathological Parameters of the Participants of the Retrospective Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation analysis of the immune cell type ratios with selected clinical and epidemiological characteristics of the study participants (age, menopausal status, smoking status, body mass index) revealed correlations with smoking status and age. The neutrophil and B cell ratios correlated with smoking status in controls. Current smokers had a lower neutrophil and a higher B cell ratio compared with non-smokers (Mann-Whitney test;\u003cem\u003e P\u003csub\u003eadj\u003c/sub\u003e\u003c/em\u003e\u0026lt;0.05). Two immune cell type ratios correlated with age. The TCD8+ cell ratio showed an inverse correlation with age in both cases and controls and the NK cell ratio a positive correlation in controls (Spearman\u0026rsquo;s rank correlation; \u003cem\u003eP\u003csub\u003eadj\u003c/sub\u003e\u003c/em\u003e\u0026lt;0.05). No other correlations were observed. There were no correlations of the immune cell type ratios with selected histopathological tumor characteristics (grade, size, node status, stage).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic and Prognostic Performance of ISUS \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAUC analysis showed that NK cells and neutrophils had the highest discriminative capability among all immune cell types (\u003cstrong\u003eFigure 4B\u003c/strong\u003e). The estimated NK cell-to-neutrophil ratio was higher in controls compared with cases and slightly improved the discrimination performance between cases and controls with AUC values in the range (0.67-0.71) (\u003cstrong\u003eSuppl. Fig. 5\u003c/strong\u003e) relative to the values from individual CpGs in the range (0.63-0.67) (\u003cstrong\u003eFigure 4B\u003c/strong\u003e). The NK cell-to-neutrophil ratios were associated with TNBC: a\u0026nbsp;higher\u0026nbsp;NK cell-to-neutrophil\u0026nbsp;ratio\u0026nbsp;was\u0026nbsp;associated with\u0026nbsp;a\u0026nbsp;lower likelihood of being\u0026nbsp;a\u0026nbsp;TNBC cases\u0026nbsp;(OR range (0.52-0-70); conditional logistic regression; all \u003cem\u003eP\u003csub\u003eadj\u003c/sub\u003e\u003c/em\u003e.\u0026lt;1e-04) (\u003cstrong\u003eSuppl. Table 7\u003c/strong\u003e). Next, a diagnostic model was developed by fitting a multivariable logistic regression model based on all 21 ISUS and applying backward variable selection. A bootstrap-adjusted AUC was computed to account for overfitting. The final model contained four ISUS that discriminated cases from controls with an AUC of 72%. Of these four ISUS, two were specific for NK cells, one for monocytes, and one for TCD8+ cells (\u003cstrong\u003eTable 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe prognostic performance using log-rank test showed that two probes, cg00219921 and cg08326410, which are specific for TCD8+ and NK cells, were associated with survival when using a median split. Higher ratios of TCD8+ and NK cells were associated with a better patient OS (\u003cstrong\u003eSuppl. Fig. 6\u003c/strong\u003e). Using a multivariable Cox regression model including age, tumor grade, stage, tumor size, and lymph node status, only the association with cg00219921 remained statistically significant (Cox regression;\u003cem\u003e P\u003c/em\u003e=0.04). However, after adjustment for multiple testing, the association lost statistical significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of the NK Cell Ratio with TNBC in Participants of the Prospective Case-Control Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince NK cells were the most pronounced immune cell type associated with TNBC in the retrospective sample set, we investigated whether the observed association could be detected in pre-diagnostic DNA samples of TNBC cases compared with controls. In this respect, the NK cell ratio was measured in 146 TNBC cases and 146 controls using a ddPCR TaqMan assay specific for one NK cell-specific unmethylated site (cg23060465). The obtained \u0026ldquo;unmethylation\u0026ldquo; levels from ddPCR were Logit-transformed and the Wilcoxon signed rank test was used to compare the obtained NK cell ratios between cases and controls. A lower NK cell ratio was observed in TNBC cases compared with controls (Wilcoxon signed rank test, \u003cem\u003eP\u003c/em\u003e=0.019) (\u003cstrong\u003eFigure 5\u003c/strong\u003e). Using conditional logistic regression, a higher NK cell ratio was associated with a reduced TNBC risk at the margin of statistical significance (OR=0.76, 95% CI [0.58-1.00], conditional logistic regression; \u003cem\u003eP\u003c/em\u003e=0.052). Heterogeneity due to age at blood draw, age of diagnosis, and interval time between blood draw and reference date was tested. No heterogeneity/subgroup effect was observed (Interaction test based on conditional logistic regression; all \u003cem\u003eP\u003c/em\u003e\u0026gt;0.05).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present large immunomethylomic study, we showed that methylation-derived leukocyte subtype ratios and mdNLRs at subtype-specific CpG sites are associated with TNBC in peripheral blood of TNBC patients and age-matched controls from a retrospective study. We showed that mdNLRs were higher in TNBC cases compared with controls and associated with TNBC. Further, higher ratios of neutrophils and lower ratios of NK, TCD4+, TCD8+, monocytes, and B cells were associated with TNBC, with a decreased NK cell ratio showing the strongest association. Associations of mdNLRs and neutrophil, NK, TCD4+, and B cell ratios were validated in analysis based on immune cell type proportions. Moreover, we confirmed that the NK cell ratio was significantly lower in pre-diagnostic samples of TNBC cases compared with controls.\u003c/p\u003e\n\u003cp\u003eOverall, a 4% higher ratio of neutrophils and a 4.3% lower ratio of total lymphocytes were observed in TNBC cases compared with controls. It was reported that breast tumors maximize their chance of metastasizing by evoking a systemic inflammatory cascade, which leads to an elevated level of neutrophils. These tumor-induced neutrophils suppress cytotoxic TCD8+ lymphocytes, which ultimately enhancing metastatic seeding in the pre-metastatic lung (29). Furthermore, the interactions between neutrophils and lymphocytes play critical roles in carcinogenesis. As a hallmark of cancer, in this study also an elevated mdNLR was observed in cases compared with controls (1.5 vs. 1.18). With slight difference, this result was repeated also by Houseman estimation method (averaged mdNLR\u003csub\u003eref\u003c/sub\u003e for cases and controls were 2.49 and 1.81, respectively). This finding agrees with that from a previous study, which reported mdNLR values of 2.7 in TNBC cases and 2.4 in controls (3). A high NLR was associated with adverse survival of patients affected by various solid tumors, including TNBC (5, 7, 9, 17). Howeverin contrast to the previous studies, mdNLR was not an independent predictor of OS in our study. The discrepant results obtained in these studies may be explained by differences in the study size, study population, and the methods used for mdNLR estimation.\u003c/p\u003e\n\u003cp\u003eAmong the major immune cell types, higher neutrophil and lower ratios of B cells, TCD4+, TCD8+, NK cells, and monocytes were associated with TNBC. NK cell ratio showed the strongest association in the large retrospective sample set of 231 TNBC cases and 231 controls. Since the blood samples were drawn after the diagnosis of TNBC, it cannot be excluded that the shift in the NK cell ratio was induced by the tumor. There is evidence from a previous study that the tumor itself could manipulate and decrease the numbers of immune cells, such as NK cells, in the peripheral blood of TNBC patients by secreting cytokines (30). However, a lower NK cell ratio was also observed in the prospective sample set of 146 TNBC cases compared with controls, which was associated with a higher TNBC risk at the margin of statistical significance. This finding suggests that the shift in NK cell ratio has occurred before disease manifestation. Interestingly, a lower NK cell level was also reported in head and neck cancer patients compared with controls with individuals in the lowest NK tertile having over 5-fold risk of being a case (2).\u003c/p\u003e\n\u003cp\u003eThere is evidence that NK cell levels are linked with survival of cancer patients. One study demonstrated that the NK cell ratio in the peripheral blood is an independent predictor of survival of colorectal cancer patients; those with a higher percentage of NK cells being associated with a better survival than those with a lower percentage (10). Higher peripheral NK cell counts were also associated with better OS in lymphoma and chronic lymphocytic leukemia patients (31-33). Further, there is some evidence that the number of NK cells in peripheral blood may affect the outcome of B-cell non-Hodgkin lymphoma patients receiving immunochemotherapy (34).\u003c/p\u003e\n\u003cp\u003eNK cells are effector lymphocytes of the innate immune system that control several types of tumors and microbial infections by limiting their spread (35). They have a crucial role in the control of metastasis by virtue of killing circulating tumor cells and acting as the first line of defense against metastasis from circulating tumor cells (36). Therefore, the lower numbers of circulating NK cells in patients with TNBC may be an underlying cause of the disease, and individuals with decreased NK cell levels could be predisposed to develop TNBC. One previous study reported that mice with deficiencies in NK cell number and function are more susceptible to transplanted tumors (37). In addition, it has been shown before that individuals with low cytotoxic activity of peripheral blood lymphocytes, including NK cells, are at higher risk of developing various types of cancer (38).\u003c/p\u003e\n\u003cp\u003eImpairment of NK cell function was also reported to play a role in breast tumorigenesis (39). Breast tumors modify their environment to evade NK cell antitumor immunity (40) and \u003cem\u003eex vivo\u003c/em\u003e-expanded NK cells showed potent antitumor function against breast cancer cell lines and primary cells isolated from patients (41, 42). Another study suggested NK cells are important players in TNBC development and metastasis that could be used as a promising immunotherapeutic against TNBC (43). In this respect, increasing NK cell antitumor activity and \u003cem\u003eex-vivo \u003c/em\u003eexpanding NK cell populations is paving the way for a new generation of anticancer immunotherapies (4). Our findings may highlight the value of NK cell-based immunotherapies for TNBC; given that NK-cell induced lysis was significantly higher in TNBC cell lines compared to estrogen receptor positive breast cancer cell lines (44).\u003c/p\u003e\n\u003cp\u003eAt present, flow cytometry is the most widely applied analytical approach for immune cell quantification (24). This method, however, is limited to intact cells, but fresh or well-preserved blood samples are not available for many clinical cohorts. Therefore, in various recent studies, including this one, an epigenetic assay has been used (2, 3, 22, 24), which allows cell quantification in samples of limited quality and quantity, is applicable to archival blood specimens, which are available for many clinical cohorts, and is less costly than flow cytometry. It has been shown that the epigenetic assay performs equivalently to flow cytometry for immune cell quantification (22-24).\u003c/p\u003e\n\u003cp\u003eAs is the case for all studies, this work is not without some limitations. One limitation is that epigenetic data could not be validated using classical cell counting by flow-cytometry due to the lack of fresh blood samples from TNBC cases and controls. Another limitation is the still relatively limited set of only main immune cell subtypes that were investigated. A further limitation is that the developed diagnostic model with four selected methylation markers, improved the capability to discriminative patients with TNBC from controls to an AUC of 72% (relative to the AUC values from individual CpGs in the range from 55% to 67%), which, however, has no clinical utility based on a threshold AUC of \u0026gt;80%. A higher discriminative capability has to be achieved by integrating other molecular markers of different sources such as cell-free nucleic acids or proteins in future models of non-invasive diagnosis of TNBC.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, this is the largest study investigating immune cell profiles in TNBC patients and controls using methylation data. We identified and validated associations of mdNLRs and neutrophil, TCD4+, B, and NK cell subtype ratios/proportions with TNBC, with the latter having the strongest association signal. The NK cell ratio was also significantly lower in pre-diagnostic samples of TNBC cases compared with controls. Thus, the NK cell level may be useful as a potential non-invasive blood-based biomarker for TNBC risk assessment, early detection or immunotherapeutic applications.\u003c/p\u003e"},{"header":"List Of Abbreviations","content":"\u003cp\u003eTNBC: triple negative breast cancer\u003c/p\u003e\n\u003cp\u003eNK cell: natural killer cell\u003c/p\u003e\n\u003cp\u003emdNLR: methylation-derived neutrophil-to-lymphocyte ratio\u003c/p\u003e\n\u003cp\u003eOR: odds ratio\u003c/p\u003e\n\u003cp\u003eISUS: immune cell specific unmethylated sites\u003c/p\u003e\n\u003cp\u003eROC: receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eAUC: area under the curve\u003c/p\u003e\n\u003cp\u003eOS: overall survival\u003c/p\u003e\n\u003cp\u003eddPCR: droplet digital PCR\u003c/p\u003e\n\u003cp\u003emdNLR\u003csub\u003eref\u003c/sub\u003e: methylation-derived neutrophil-to-lymphocyte ratio estimated by Housman method\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in studies involving human were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration. Informed consent was obtained for all study participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets of sorted immune cells analyzed during the current study are available in the Gene Expression Omnibus repository (GEO;\u0026nbsp;\u003ca href=\"https://www.ncbi.nlm.nih.gov/geo/\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/a\u003e) through GEO accessions: GSE35969, GSE59250, GSE110554, GSE88824, and GSE49667. The in-house generated methylation array data are not publically available. They can be accessed for research purposes upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work was funded by the German Cancer Research Center (DKFZ), Heidelberg.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMM and UH participated\u0026nbsp;in the conception and design of the study. OF, AJS, Y-DK, HB, TB provided the study material or patients. CG, TH, NB, MM, and UH carried out the analysis and interpretation of data. MM, TH, CG, and UH wrote the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all study participants, clinicians, family doctors, researchers and technicians for their contributions and commitment to this study. We thank J Hoheisel for critical reading of the manuscript. The GENICA Network: Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany (UH); Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, and University of T\u0026uuml;bingen, T\u0026uuml;bingen, Germany (HB, W-Y Lo, R Hoppe, S Winter); German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Partner Site T\u0026uuml;bingen (HB), funded by the Deutsche Forschungsgemeinschaft (DFG) \u0026ndash; EXC 2180-390900677 (HB); Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany (Y-DK, C Baisch); Institute of Pathology, University of Bonn, Germany (H-P Fischer); Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, Germany (TB, B Pesch, S Rabstein, A Lotz); and Institute of Occupational Medicine and Maritime Medicine, University Medical Center Hamburg-Eppendorf, Germany (V Harth). O Fletcher\u0026rsquo;s work is supported by Programme Grants from Breast Cancer Now as part of Programme Funding to the Breast Cancer Now Toby Robins Research Centre. The Generation Study thanks Breast Cancer Now and the Institute of Cancer Research for funding and support. The Institute of Cancer Research acknowledges NHS funding to the Royal Marsden Foundation Trust and the National Institute for the Health Research (NIHR) Biomedical Research Centre.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGrivennikov SI, Greten FR, Karin M (2010) Immunity, inflammation, and cancer. Cell. 140: 883-99. doi: 10.1016/j.cell.2010.01.025\u003c/li\u003e\n\u003cli\u003eAccomando WP, Wiencke JK, Houseman EA, Butler RA, Zheng S, Nelson HH, Kelsey KT (2012) Decreased NK cells in patients with head and neck cancer determined in archival DNA. 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Arch Gynecol Obstet. 289: 141-7. doi: 10.1007/s00404-013-2922-9\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Characteristics of selected CpG proxies in neutrophils and pan-lymphocytes\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eCell\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003eProbe ID\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003eChromsome:\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eGene\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003eGenic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"144\"\u003e\n\u003cp\u003e\u0026beta;-value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eOR [95% CI]\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003eAUC [95% CI]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003etype\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003ePosition\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003ename\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003eregion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003eCases\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003eControls\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"76\"\u003e\n\u003cp\u003eNeu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003ecg09993145\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e1:25291905\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u003cem\u003eRUNX3\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003eTSS1500\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e0.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e2.07 [1.60-2.67]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026lt;1e-04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.68 [0.64-0.73]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003ecg10825315\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e14:81425912\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u003cem\u003eTSHR\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003eBody\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e2.64 [1.80-3.87]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026lt;1e-04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.67 [0.62-0.71]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003ecg23954655\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e13:99223562\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u003cem\u003eSTK24\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003eBody\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e0.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e3.02 [2.07-4.42]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026lt;1e-04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.68 [0.63-0.73]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"76\"\u003e\n\u003cp\u003ePan-lym\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003ecg04552418\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e1:31956405\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eIntergenic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e0.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e0.56 [0.44-0.70]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026lt;1e-04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.66 [0.62-0.71]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003ecg13580758\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e4:57824450\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u003cem\u003eREST\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003eBody\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e0.58 [0.46-0.74]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026lt;1e-04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.64 [0.59-0.69]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003ecg26942829\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e6:13408158\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u003cem\u003eGFOD1\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003eBody\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e0.61 [0.49-0.77]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026lt;1e-04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"131\"\u003e\n\u003cp\u003e0.65 [0.60-0.70]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOR: odds ratio; CI: confidence interval; \u003cem\u003eP\u003csub\u003eadj\u003c/sub\u003e\u003c/em\u003e: adjusted \u003cem\u003eP\u003c/em\u003e-value; AUC: area under the curve; Neu: neutrophils; Pan-lym: pan-lymphocytes.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eHumanGRCh37/hg19 Assembly.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eOR is given for a 10% increase in methylation level.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003eAdjusted for multiple testing using Holm correction.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Characteristics of selected ISUS as proxies and their participation in the diagnostic models\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eCell type\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003eISUS\u003c/p\u003e\n\u003cp\u003eProbe ID\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"141\"\u003e\n\u003cp\u003eChromosome:\u003c/p\u003e\n\u003cp\u003ePosition\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eGene name\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eGenic\u003c/p\u003e\n\u003cp\u003eregion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eLogReg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003eLogReg-VarSel\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"89\"\u003e\n\u003cp\u003eB\u0026minus;cell\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003ecg07721872\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"141\"\u003e\n\u003cp\u003e16:87735256\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u003cem\u003eLOC100129637\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eBody\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003ecg04838847\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"141\"\u003e\n\u003cp\u003e8:110587155\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u003cem\u003eGOLSYN\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eBody\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003ecg27565966\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"141\"\u003e\n\u003cp\u003e16:28943198\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u003cem\u003eCD19\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eTSS200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"89\"\u003e\n\u003cp\u003eMonocyte\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003ecg23244761\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"141\"\u003e\n\u003cp\u003e6:161796850\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u003cem\u003ePARK2\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eBody\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003ecg05923857\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"141\"\u003e\n\u003cp\u003e10:114911615\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u003cem\u003eTCF7L2\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eBody\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003ecg24788483\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"141\"\u003e\n\u003cp\u003e10:114911652\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u003cem\u003eTCF7L2\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eBody\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"89\"\u003e\n\u003cp\u003eTCD4+\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003ecg05617307\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"141\"\u003e\n\u003cp\u003e10:121413182\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u003cem\u003eBAG3\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eBody\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003ecg14477767\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"141\"\u003e\n\u003cp\u003e4:170195438\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eIntergenic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003ecg20737812\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"141\"\u003e\n\u003cp\u003e15:86336631\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u003cem\u003eKLHL25\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e5'UTR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"89\"\u003e\n\u003cp\u003eTCD8+\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003ecg18857618\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"141\"\u003e\n\u003cp\u003e2:87048489\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u003cem\u003eCD8B\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eBody\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003ecg00219921\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"141\"\u003e\n\u003cp\u003e2:87012810\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u003cem\u003eCD8A\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e3'UTR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003ecg06419846\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"141\"\u003e\n\u003cp\u003e11:66083697\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u003cem\u003eCD248\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e1stExon\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"89\"\u003e\n\u003cp\u003eNeu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003ecg25006077\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"141\"\u003e\n\u003cp\u003e3:152176018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u003cem\u003eMBNL1\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eBody\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003ecg25739938\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"141\"\u003e\n\u003cp\u003e2:9528072\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u003cem\u003eCPSF3\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eBody\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003ecg05398700\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"141\"\u003e\n\u003cp\u003e14:102677141\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u003cem\u003eWDR20\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eBody\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"89\"\u003e\n\u003cp\u003eNK\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003ecg08326410\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"141\"\u003e\n\u003cp\u003e19:55314884\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u003cem\u003eKIR2DL4\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eTSS200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003ecg23855986\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"141\"\u003e\n\u003cp\u003e11:129980502\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u003cem\u003eAPLP2\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eBody\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003ecg23060465\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"141\"\u003e\n\u003cp\u003e8:141625545\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u003cem\u003eEIF2C2\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eBody\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"89\"\u003e\n\u003cp\u003eTreg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003ecg04920616\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"141\"\u003e\n\u003cp\u003eX:49121288\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u003cem\u003eFOXP3\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eTSS200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003ecg02033323\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"141\"\u003e\n\u003cp\u003eX:49005257\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u003cem\u003eFOXP3\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e5'UTR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003ecg07499259\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"141\"\u003e\n\u003cp\u003e1:12188502\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u003cem\u003eTNFRSF8\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eBody\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNeu: neutrophil; NK: natural killer cell; Treg: regulatory T cell; LogReg: logistic regression; LogReg-VarSel: variable selection in logistic regression.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eHuman GRCh37/hg19) Assembly.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e. Multivariable logistic regression after variable selection\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003eCpG ID\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003eCell type\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003eOR [95% CI]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003ecg08326410\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003eNK\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e1.14 [1.04-1.26]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e0.00592\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003ecg23855986\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003eNK\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e1.31 [1.15-1.49]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e\u0026lt;1e-04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003ecg24788483\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003eMonocyte\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e1.17 [1.09-1.25]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e\u0026lt;1e-04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003ecg06419846\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003eTCD8+\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e1.17 [1.09-1.26]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e\u0026lt;1e-04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOR: odds ratio; CI: confidence interval; NK: natural killer cell.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Triple negative breast cancer, DNA methylation in blood, immune cell subtypes, TNBC risk","lastPublishedDoi":"10.21203/rs.3.rs-508197/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-508197/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e A shift in the proportions of blood immune cells is a hallmark of cancer development. Here, we investigated whether methylation-derived immune cell type ratios and methylation-derived neutrophil-to-lymphocyte ratios (mdNLRs) are associated with triple-negative breast cancer (TNBC). \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eLeukocyte subtype-specific un/methylated CpG sites were selected and methylation levels at these sites used as proxies for immune cell type proportions and mdNLR estimation in 231 TNBC cases and 231 age-matched controls. Data were validated using the Houseman deconvolution method. Additionally, the natural killer (NK) cell ratio was measured in a prospective sample set of 146 TNBC cases and 146 age-matched controls. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The mdNLRs were higher in TNBC cases compared with controls and associated with TNBC (odds ratio (OR) range (2.66-4.29), all \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj.\u003c/em\u003e\u003c/sub\u003e\u0026lt;1e-04). A higher neutrophil ratio and lower ratios of NK cells, CD4+ T cells, CD8+ T cells, monocytes, and B cells were associated with TNBC. The strongest association was observed with decreased NK cell ratio (OR range (1.28-1.42), all \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj.\u003c/em\u003e\u003c/sub\u003e\u0026lt;1e-04). The NK cell ratio was also significantly lower in pre-diagnostic samples of TNBC cases compared with controls (\u003cem\u003eP\u003c/em\u003e=0.019).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis immunomethylomic study shows that a shift in the ratios/proportions of leukocyte subtypes is associated with TNBC, with decreased NK cell showing the strongest association. These findings improve our knowledge of the role of the immune system in TNBC and point to the possibility of using NK cell level as a non-invasive molecular marker for TNBC risk assessment, early detection, and prevention.\u003c/p\u003e","manuscriptTitle":"Epigenetic Quantification of Circulating Immune Cells in Peripheral Blood of Triple-negative Breast Cancer Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2021-05-25 17:46:54","doi":"10.21203/rs.3.rs-508197/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"921607e5-69cc-4151-b455-5773ca39fba6","owner":[],"postedDate":"May 25th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":4551195,"name":"Cancer Biology"},{"id":4551196,"name":"Immunology"}],"tags":[],"updatedAt":"2021-11-17T12:55:47+00:00","versionOfRecord":{"articleIdentity":"rs-508197","link":"https://doi.org/10.1186/s13148-021-01196-1","journal":{"identity":"clinical-epigenetics","isVorOnly":false,"title":"Clinical Epigenetics"},"publishedOn":"2021-11-17 12:55:47","publishedOnDateReadable":"November 17th, 2021"},"versionCreatedAt":"2021-05-25 17:46:54","video":"","vorDoi":"10.1186/s13148-021-01196-1","vorDoiUrl":"https://doi.org/10.1186/s13148-021-01196-1","workflowStages":[]},"version":"v2","identity":"rs-508197","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-508197","identity":"rs-508197","version":["v2"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}