Circulating Immune Cell Populations Related to Primary Breast Cancer, Surgical Removal and Radiotherapy Revealed by Flow Cytometry Analysis

Circulating Immune Cell Populations Related to Primary Breast Cancer, Surgical Removal and Radiotherapy Revealed by Flow Cytometry Analysis | 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 Circulating Immune Cell Populations Related to Primary Breast Cancer, Surgical Removal and Radiotherapy Revealed by Flow Cytometry Analysis Sarah Cattin, Benoît Fellay, Antonello Calderoni, Alexandre Christinat, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-146187/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: Advanced breast cancer (BC) impact immune cells in the blood but whether such effects may reflect the presence of early BC and its therapeutic management remains elusive. Methods: To address this question, we used multiparametric flow cytometry to analyse circulating leukocytes in patients with early BC (n=13) at time of diagnosis, after surgery and after adjuvant radiotherapy, compared to healthy individuals. Data were analysed using a minimally supervised approach based on FlowSOM algorithm and validated manually. Results: At time of diagnosis, BC patients have an increased frequency of CD117 + Granulocytic-Myeloid Derived Suppressor Cells (G-MDSC), which was significantly reduced after tumor removal. Adjuvant radiotherapy increased the frequency of CD45RO + memory CD4 + T cells and CD4 + regulatory T cells. FlowSOM algorithm analysis revealed several unanticipated populations, including cells negative for all markers tested, CD11b + CD15 low , CD3 + CD4 - CD8 - , CD3 + CD4 + CD8 + , CD3 + CD8 + CD127 + CD45RO + cells, associated with BC or radiotherapy. Conclusions: This study revealed changes in blood leukocytes associated with primary BC, surgical removal and adjuvant radiotherapy. Specifically, it identified increased levels of CD117 + G-MDSC, memory and regulatory CD4 + T cells as potential biomarkers of BC and radiotherapy, respectively. Importantly, the study demonstrates the value of unsupervised analysis of complex flow cytometry data to unravel new cell populations of potential clinical relevance. Cancer Biology Breast cancer Radiotherapy MDSC CD117 FlowJo Unsupervised analysis Biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Breast cancer (BC) is the most frequent cancer and main cause of cancer-related mortality for women in industrialized countries (1). Three clinically relevant biological BC subtypes (i.e. Oestrogens/Progesterone Receptor positive, Human Epidermal growth factor Receptor 2 (HER2) amplified and triple negative), and multiple molecular subtypes (e.g. Luminal A/B, HER2, basal like, normal like) with distinct features and clinical outcomes, have been defined and characterized (2–5). Early detection and surgery in combination with adjuvant treatments tailored on biological and molecular subtypes, have improved patients’ survival by about 30% in the past three decades (6). Goal of adjuvant therapy, including radiotherapy, is the eradication of tumor cells that disseminated before diagnosis and surgery. Some of these disseminated tumor cells (DTC), however, will escape therapy and later progress to form metastases, which in most patients represents the main causes of cancer-related death. After breast-conserving surgery, radiotherapy reduces the risk of BC recurrence and death. Among women with operable BC, randomized trials have demonstrated equivalent disease-free and overall survival between mastectomy and breast-conserving surgery followed by radiotherapy alone and/or hormonal, anti-HER2, or chemotherapy (7–16). Mammography is the standard approach for the detection of asymptomatic BC (17). In spite of its benefits in reducing BC specific mortality, mammography has some important limitations (18): low specificity and sensitivity; risk of over-diagnosis; risk of inducing BC due to X-ray exposure, particularly in patients with defective DNA repair genes (19); not recommended before the age of 50 in spite of the fact that 20–25% of all BCs appear before this age. There is therefore an unmet need for complementary or alternative methods for the detection of asymptomatic, early BC (20–22). Circulating tumor cells (CTC), cell free tumor-derived DNA, mRNA and miRNA, proteins, autoantibodies and metabolites are being explored as candidate blood-based biomarkers for BC detection, diagnosis or monitoring, but so far none entered routine clinical practice (23–27). Similarly, there are no effective blood-based biomarkers to actively assess patients’ response to treatment and monitoring disease state after therapy. Also the most used in clinical practice biomarker protein such as CA 15 − 3 is not specific and sensitive in early breast cancer diagnosis (28). Tumors, including BC, mobilize and recruit immuno-inflammatory cells to their microenvironment (29–31). Monocytic and granulocytic cells, mostly immature forms, as well as lymphocytes, contribute to cancer progression by promoting immunosuppression, angiogenesis, cancer cell survival, growth, invasion and metastasis (32,33). We have previously shown that metastatic BC patients have elevated frequencies of TIE2 + CD11b + and CD117 + CD11b + leukocytes circulating in the blood, and that circulating CD11b + cells express higher mRNA levels of the M2 polarization markers CD163, ARG1 and IL-10 (34). Treatment with paclitaxel in combination with bevacizumab decreased the frequency CD117 + CD11b + leukocytes, IL-10 mRNA levels in CD11b + cells and IL-10 protein in plasma. We therefore considered that blood circulating leukocytes, or sub-population thereof, may reflect cancer-relevant immuno-inflammatory events that may be further explored as BC-associated biomarkers. Here, we analysed the phenotype of blood leukocytes of patients with early BC at time of diagnosis, after surgery, and after adjuvant RTX, relative to healthy donors (HD), using flow cytometry and a minimally supervised analytical approach based on FlowSOM algorithm and manual validation. We identified cell populations associated with the presence of a primary BC, tumor removal and adjuvant radiotherapy. Unsupervised clustering analysis approach of the same flow cytometry data allowed discovering unanticipated cell populations associated with BC or adjuvant radiotherapy. These results indicate that phenotypical analysis of peripheral blood leukocytes, with a minimally supervised analytical approach, may be a clinically-relevant strategy for the identification of cellular biomarkers for BC detection and therapy monitoring. Materials And Methods Patients and clinical study The study was approved by the Cantonal ethic commission for human research on Humans of Canton Ticino (CE 2967) and extended to Vaud-Fribourg-Neuchâtel, Switzerland. The study includes 13 female patients (Table 1) who were diagnosed with primary, non-metastatic BC (stage T1-4, N0-N1, M0,). All patients underwent conservative surgery and received standard fractionated adjuvant radiotherapy (2 Gy per session, total dose : 50 + 10 Gy). Blood samples were collected after confirmed diagnosis/before surgery, after surgery/before radiotherapy, at the end of radiotherapy (week 6), and 6–8 weeks after the end of the radiotherapy (week 12–15). All Patients and HDs gave written informed consent before study entry. Patients were recruited before surgery at Clinica Luganese Moncucco, Lugano, and at Hôpital Neuchâtelois, La Chaux-de-Fonds, once diagnosis was histologically confirmed. Mean age for cancer patients was 60.6 years (all patients were between 43 and 73 years old). HDs were recruited along the study, based on the following criteria: age-matched relative to BC patients, no regular medications in the last 6 months, no previous cancer diagnosis, no chronic diseases and normal blood analyses at time of recruitment. Blood processing 20 ml of peripheral venous blood was collected using BD Vacutainer® Blood Collection EDTA Tubes (Becton Dickinson, Franklin Lakes, NJ, USA) following manufacturer’s instructions and immediately shipped by courier at room temperature to the laboratory. All analyses were performed within 24 hours after blood collection. Antibody staining was performed in whole blood. Plasma and total leukocytes were isolated from the remaining blood using BD Vacutainer® CPT™ Cell Preparation Tube (Becton Dickinson) with Sodium Heparin following manufacturer’s instructions. Plasma fraction was frozen at -80 °C and isolated leukocytes were lysed in RA1 lysis buffer (Macherey-Nagel, Düren, Deutschland) and stored at -80 °C. Flow cytometry Whole blood staining’s were performed within 24 hours after blood collection. Leukocytes were counted using Cell-Dyn Sapphire Hematology System (Abbott Diagnostics, Chicago, IL, USA). For staining, 1 million cells per tube were used. Directly labelled antibodies were added to whole blood and incubated for 20 minutes at 4 °C, followed by 10 minutes red-blood-cells lysis (Bühlmann Laboratories, Schönenbuch, Switzerland) and washing using cold PBS. All anti-human antibodies were used at the concentrations recommended by the manufacturer: anti-CD15-PeCy7 (clone HI98), anti-CD14-Pe (clone MφP9), anti-CD163-FITC (clone GHI/61), anti-CD11b-BV510 (clone ICRF44), anti-CD33-V450 (clone WM53), anti-CD64-APCH7 (clone 10.1), anti-CD117-APC (clone YB5.B8), anti-CD45RA-PeCy7 (clone HI100), anti-CD25-Pe (clone M-A251), anti-CD4-FITC (clone RPA-T4), anti-CD8-V500 (clone SK1), anti-CD45RO-BV421 (clone UCHL1), anti-CD3-APCH7 (clone SK7), and CD127-Alexa Fluor 647 (clone HIL-7R-M21) and 7AAD (all from Becton Dickinson). BD FACSCanto II (Becton Dickinson) instrument was used to analyse samples and FlowJo 10.6.2 (Treestar Inc., Ashland, OR, USA) software and several software plugins (FlowCLEAN, downsample_V3, FlowSOM, tSNE) were used to analyse all data. Reverse transcription real-time PCR (RT-qPCR) Total mRNA from total white blood cells was extracted using the NucleoSpin RNA kit from Macherey-Nagel following manufacturer’s instructions (Düren, Germany). The purity and quantity of all RNA samples were examined by NanoDrop (Witec AG, Luzern, Switzerland). Total RNA was retro-transcribed using M-MLV reverse transcriptase kit following manufacturer’s instructions (ThermoFisher Scientific, Waltham, Massachusetts, USA) using 500 ng of total RNA. cDNA was subjected to amplification by real time qPCR with the StepOne SYBR System (Life Technologies) using the following primer pairs (Eurofins Genomics, Huntsville, AL, USA) at the indicated hybridization temperatures: GAPDH 58 °C (Fw-TCTTCTTTTGCGTCGCCAGC, Rev-GATTTTGGAGGGATCTCGCTCCT), ARG1 58 °C (Fw-GGAGTCATCTGGGTGGATGC, Rev-CTGGCACATCGGGAATCTTTC), IL-10 58 °C (Fw-CGAGATGCCTTCAGCAGAGT, Rev-AATCGATGACAGCGCCGTAG), CD117 57 °C (Fw-GATTATCCCAAGTCTGAGAATGAA, Rev-CGTCAGAATTGGACACTAGGA), FN1 52 °C (Fw- ACTTCGACAGGACCACTTGA, Rev-TCAAATTGGAGATTCATGGGA). Real-time PCR data were then analysed using the comparative Ct method [ 55 ]. Statistical analysis Acquired data were analysed and graphics were generated using Prism Software (GraphPad, La Jolla, CA, USA). Statistical comparisons between cancer patients and healthy donors were performed by T-test assuming non-homogenous variance. Normality distribution of the samples was checked in case of significance and, if non-Gaussian, a Mann-Whitney replaced the T-test results. Statistical comparisons of all time points to observe the effect of radiotherapy were performed by one-way ANOVA assuming non-homogenous variance using Tukey correction. Normality distribution of the samples was checked in case of significance and, if non-Gaussian, a Kruskal-Wallis assay replaced the ANOVA results. Highest and lowest values from each group were excluded. Results were considered to be significantly from p < 0.05. In the figures, the various p values thresholds are presented as follow: ≤0.05=*, ≤ 0.01=**, ≤ 0.001=***, ≤ 0.0001=****. Results Increased frequency of CD117 + G-MDSCs in the peripheral blood of patients with newly diagnosed non-metastatic BC Based on previous observations made in metastatic BC patients (34), we hypothesized that an increased frequency of circulating CD11b + cells expressing CD117 and/or displaying a M2 activation phenotype, may also occur in patients with early BC. To test this hypothesis, we monitored the frequency of MDSC cells in the blood of non-metastatic BC patients (cT1-4, N0-1, M0) at time of diagnosis using Flow Cytometry. Aged-matched women without BC served as control population (healthy donors – HDs). Monocytic-MDSC (Mo-MDSC) were defined as CD11b + CD33 + CD14 high CD15 - and granulocytic-MDSC (G-MDSC) as CD11b + CD33 + CD14 low CD15 + cells. In both cell populations we monitored the expression of CD117, the receptor for Kit-ligand/stem cell factor widely present in hematopoietic progenitors cells (35,36), and CD163, a M2 polarization marker in monocytes (37). In order to avoid investigator-associated biases and variability in the results inherent to supervised manual analysis of flow cytometry data, we develop a minimally supervised, standardized analytical workflow based on the FlowSOM algorithm (Supplementary Fig. 1), in complement to conventional manual gating and supervised analysis. Cells clusters revealed by standardized analytical workflow, were considered of interest when their frequency was more than 10% different between HDs and cancer patients. Some of the 14 analysed clusters corresponded to non-standard populations, for example those negative for all tested markers or expressing unanticipated marker combinations (Fig. 1 A-C). These populations would have been missed by conventional supervised gating and analysis driven by the marker combination of interest. Interestingly, we observed a significant increase in the frequency of CD117 + cells among the circulating G-MDSC population in cancer patients relative to HDs (Fig. 1 D). We observed a similar (but non-significant) trend in the frequency of Mo-MDSC cells, albeit at lower frequency. In addition, the frequencies of some non-classical cell populations, such as those expressing none of the markers of interest (Cluster 13 and 3) or a CD11b + CD15 low cell population (Cluster 22 + 9), were significantly different between BC patients and HDs (Fig. 1 E and F). No significant changes were observed for CD163 + cells in both G-MDSC and Mo-MDSC populations (Supplementary Figure S2). Non-standard CD3 expressing cells are present with increased frequency in the peripheral blood of newly diagnosed BC patients In parallel we monitored the presence of selected lymphocyte populations in both groups. By conventional supervised analysis we observed no differences in classical CD3 + CD4 + T cells, CD3 + CD8 + T cells and CD3 + CD4 + CD25 + CD127 - regulatory T cells (Tregs). Likewise, we observed no changes in the frequency of memory (CD45RO + CD45RA - ) or naïve (CD45RA + CD45RO - ) T cells within the same lymphocyte populations (Supplementary Figure S3). In contrast, FlowSOM analysis performed on lymphocytes revealed 21 populations that were more then 10% differentially represented between HDs and cancer patients (Fig. 2 A-C). A population of cells of the size of lymphocytes, but negative for CD3, CD4 or CD8 expression (Cluster 24 + 29) was significantly less represented in cancer patients relative to HDs. CD3 + CD8 + T cells expressing CD127 and CD45RO markers (Cluster 3 + 7) are present at significantly higher frequency in cancer patients. A cell cluster expressing CD3, but not CD4 or CD8 (Cluster 20) was fond more represented in cancer patients relative to HDs (Fig. 2 D-F). Taken together these results reveal an increased frequency of peripheral blood CD117 + G-MDSC in non-metastatic BC patients at time of diagnosis, as well as significant changes in the frequency of myeloid and lymphocytic cell populations expressing unconventional marker combinations. They also demonstrate that FlowSOM-based analysis can identify cell populations that would have been likely missed by supervised analysis. No detectable changes in the expression level of transcripts for M2 polarization markers We previously reported that transcripts of M2-associated genes were expressed at higher levels circulating CD11b + cells in metastatic BC patients compared to HDs (34). We therefore analysed expression of mRNA for CD117 and the M2 markers IL-10, fibronectin-1 (FN1) and arginase 1 (ARG1) in total leukocytes from cancer patient and HDs. No differences in expression levels were observed (Supplementary Figure S4). A proof-of-concept study to monitor the effects of surgical tumor removal and adjuvant radiotherapy on circulating immune cells in BC patients The differences observed in myelomonocytic and lymphocytic populations in BC patients at time of diagnosis relative to HDs, raised the question whether tumor removal and/or adjuvant therapy may reverse these changes, or induce additional ones. To address this question, we performed a proof-of-concept study, by taking advantage of the fact that the investigated patients were scheduled for breast conservative tumor removal and adjuvant radiotherapy as part of their standard treatment. Adjuvant radiotherapy was selected as therapy of choice as systemic effects on the immune system have been reported (38–40), while on the other side chemotherapy was excluded in order to avoid that myelosuppressive effects induced by chemotherapy could non-specifically impact the results (41). To search for potential changes in cell populations in response to surgery and radiotherapy we analysed G-MDSC and Mo-MDSCs as well as lymphocytes at three time points : after surgery/before radiotherapy start (1_PostOP), at the end of radiotherapy (6 weeks; 2_Post_RTX_6w) and at 6–8 weeks after the end of radiotherapy (12–14 weeks; 3_Post_RTX_12w). Results were compared to values obtained at time of diagnosis (0_PreOP) (Fig. 3 ). Tumor removal increased the frequency Mo-MDSCs and G-MDSCs but decreased CD117 + G-MDSCs and radiotherapy induced changes in non-standard myeloid cell populations Using FlowSOM workflow of analysis we observed distinct expression profiles at the four time-points globally visualized by tSNE. 17 cell clusters were found highly differentially represented in one group compared to the other groups. Surprisingly, when looking at the expression profiles of each cluster of interest, the majority of them was lacking CD33 expression, suggesting that this marker may not be suitable to analyse the monocytes fraction (Fig. 4 A-B). After tumor removal and at the end of radiotherapy the frequency of both Mo-MDSCs and G-MDSCs was significantly increased relative to values at time of diagnosis, and returned to pre-therapy levels 6–8 weeks after the end of radiotherapy (Fig. 4 C-D). Strikingly, within the G-MDSCs population, the fractions of CD117 expressing cells significantly decreased after tumor removal and this decrease persisted after the end of radiotherapy (Fig. 4 E). Surgery had no impact on the fraction of CD163 + G-MDSCs population (Fig. 4 F). Radiotherapy itself had an impact on G-MDSC expressing CD163, but not CD117 (Fig. 4 E-F). The presence of one particular cell cluster expressing only CD11b and CD15 (Pop 1, 6 and 7) clearly decreased after radiotherapy. Another population expressing CD11b but lacking expression of all tested markers significantly increased during and after radiotherapy (Pop 16, 18, 12, 15 and 26) (Fig. 4 G-H). The latter observation suggest that some populations defined by non-standard marker combinations may be potentially interesting candidates to investigate further with an extended panel or markers. Analysis of total blood leukocytes for CD117, IL-10, FN1, and ARG1 mRNA expression by RT-qPCR revealed no observable differences in their expression levels (Supplementary Figure S5). Adjuvant radiotherapy increases the frequency of CD4 + memory and regulatory T cells, and induces changes in non-standard lymphocytic populations Likewise, we performed unsupervised analysis of the lymphocyte populations at the three time-points after surgery and radiotherapy. Visualization by tSNE revealed distinctive changes in marker expression profiles. Eleven cells clusters were found highly differentially represented in one group compared to the other ones (Fig. 5 A-B). After tumor removal we observed highly variable effects on the frequency of T lymphocyte subpopulations, most of which were inconsistent and statistically non-significant. Among the stably differentially represented clusters at the various time-points, a CD3 + cell population positive for CD4 and CD8 (Cluster 25), and a CD3 + CD4 + CD127 + CD45RO + population (Cluster 41) appeared at higher frequency after treatment (Fig. 5 C-D). Strikingly, the frequency of this CD45RO + RA - memory subset within the CD3 + CD4 + lymphocyte population was significantly and consistently increased at the end of radiotherapy and this increase was still evident 6–8 weeks later. A similar increase was also present among CD4 + regulatory T cells, corresponding to cluster 23 and 30, which also persisted after the end of radiotherapy (Fig. 5 E-G). Discussion Mammography-based screening significantly reduces BC-related mortality, but intrinsic and practical limitations call for novel screening approaches (17,19,20,42). Blood based biomarkers exploiting cancer-derived CTC, DNA or RNA are being explored, but so far none reached clinical routine practice (22,43). Similarly, there are no valid biomarkers for monitoring patients’ response to treatment or detecting relapses before they become symptomatic. In this study we pursued the use of flow cytometry to analyse the phenotype and frequency of blood leukocytes in patients with non-metastatic BC at time of diagnosis, after surgical tumor removal and after adjuvant radiotherapy. Using a combination of minimally supervised (FlowSOM algorithm), and supervised (manual) analytical approaches, we report that: i) at diagnosis, BC patients have an increased frequency of circulating CD117 + G-MDSC relative to age-matched healthy donors; ii) surgical tumor removal causes transient increase of G-MDSCs and M-MDSCs, and a long-lasting decrease of CD117 + G-MDSC; iii) radiotherapy significantly increases CD45R0 + memory T cells and CD4 + Treg cells; iv) with the FlowSOM algorithm we identified additional unanticipated, non-classical cell populations differentially represented between HD and BC patients and in BC patients in response to therapy. CD117, the receptor for Kit-ligand/stem cell factor, is widely expressed in hematopoietic progenitor cells in the bone marrow, while virtually no CD117 + leukocytes are present in the circulation under homeostatic conditions (35). We have previously reported a role of CD117 + leukocytes in metastasis in the murine 4T1 metastatic BC model (44) and the presence of CD117 + CD11b + cells in the blood of mBC patients (34). Here, we observed an increased frequency of CD117 + cells among total CD11b + G-MDSCs in the peripheral blood of non-metastatic BC patients at time of diagnosis, compared to HD. Interestingly, the frequency of CD117 + G-MDSCs significantly dropped upon tumor removal and remained below pre-treatments levels after radiotherapy. Thus, the increased frequency of CD117 + G-MDSCs may reflect the presence of the primary tumor. No changes were observed in CD117 mRNA expression in total leukocytes. This could be due to that fact that CD117 + cells are lost during leukocyte isolation for RNA extraction (flow cytometry was performed in non-separated total whole blood), or that CD117 mRNA expression has ceased upon cell mobilization (while CD117 protein persisted at the cell surface). The latter possibility is consistent with our previous observation that mobilized CD117 + cells adoptively transferred to a recipient mouse, rapidly became CD117 negative (44). In contrast, the frequency of CD163 + G-MDSCs remained rather constant, with only a transient decrease during radiotherapy. The implication of this decrease is unclear as CD163 expression did not significantly differ between HD and BC patients at time of diagnosis. After surgery and radiotherapy also no change in the mRNA expression of CD117 and ARG1, FN1, IL-10 (i.e., M2 polarization markers) was observed, owing probably to the lack of enrichment of CD11b + cells for PCR analysis. In cancer patients at time of diagnosis we observed a higher frequency of atypical T lymphocytes (CD3 + CD4 - CD8 - ) and of a population of the size of lymphocytes lacking expression of all the tested markers. These observations suggest that some potentially interesting changes may occur in atypical T cells or in non-T cell populations such as B cells or NK cells. Strikingly, after radiotherapy, we observed a steady and significant increase of the fraction of CD45RO + memory T cells within total CD4 + T cells and within CD4 + Treg. We also observed the increased presence of a T cell population expressing both CD4 and CD8 markers. This suggests that radiotherapy may cause T cell activation leading to the subsequent generation of memory T cell subsets. Indeed, there is evidence that radiotherapy exerts its therapeutic effects, not only in the local treatment field, but also outside the irradiated field and at distant sites (i.e. the so called abscopal effect), at least in part, by eliciting a T cell immune response (38). The recent observation that combination of radiotherapy with immune checkpoint inhibitors in experimental models and cancer patients results in potent synergistic therapeutic effects further support the involvement of T cell-dependent events and the therapeutic effects of radiotherapy (45–48). Through experimental work and mathematical modelling, it has been proposed that anti-tumor T cells may by mobilized by radiotherapy toward peripheral tissues to eliminate DTC (49–51). However, to date there is paucity of human data demonstrating specific changes in circulating T lymphocytes to support such a model. Radiotherapy was reported to cause a global reduction in circulating lymphocyte subsets in patients treated for stage I-II prostate cancer (52), or to induce an increase in CD4 + Treg in the peripheral blood of patients with diverse solid cancers (53). Low-dose radon therapy for chronic inflammatory diseases was shown to induce a long-lasting increase in circulating T cells paralleled with a reduced expression of activation markers (54). Thus, the observed effect of adjuvant radiotherapy on memory CD4 + T cells is novel and should be further explored in conjunction with patients’ outcome, as a possible biomarkers of therapy response or efficacy. Conclusion Taken together, this human exploratory study in early, non-metastatic BC revealed changes in blood leukocyte populations associated with the presence of BC, surgical removal and adjuvant radiotherapy. Specifically, we identified CD117 + G-MDSC and CD45RO + CD4 + memory T cells correlating with the presence of the primary tumor and radiotherapy, respectively. Importantly, the study demonstrates that a minimally supervised, algorithm-based analysis of flow cytometry data is a powerful tool to reproducibly detect phenotypical changes in peripheral blood leukocytes in cancer patients. The approach also identifies non-anticipated population correlated with disease state of therapy. These results should instigate the further investigation of peripheral blood leukocytes as source of reliable candidate biomarkers to detect BC, to monitor response to treatment and possibly disease progression. List Of Abbreviations BC Breast Cancer CTC Circulating Tumor Cells DTC Disseminated Tumor Cells G-MDSC Granulocytic-Myeloid Derived Suppressor Cells HD Healthy Donor Mo-MDSC Monocytic- Myeloid Derived Suppressor Cells Treg Regulatory T cell Declarations Ethics approval and consent to participate The study was approved by the Cantonal ethic commission for human research on Humans of Canton Ticino (CE 2967) and extended to Vaud-Fribourg-Neuchâtel, Switzerland. All Patients and HDs gave written informed consent before study entry. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare no conflict of interest. Consent for publication All authors declare that they consent for the publication of the present manuscript. Funding This work was funded by the Swiss National Science Foundation, grant numbers 31003A_159824 and 31003A_179248 and the Medic Foundation to CR. Authors’ contributions Conceptualization, C.R. and A.F.-P.; Methodology, S.C., M.B. C.R. and A.F.-P; Formal Analysis, S.C.; Investigation, S.C., B.F, L.N. and M.B.; Resources, A.B., A.C., A.CH., A.F.-P., P.T., A.S., L.N. M.B., and C.R.; Writing—original draft preparation, S.C. and C.R..; Writing—review and editing, A.F.-P., C.R., S.C. and P.T.; Supervision, S.C.; Project administration, C.R. and A.F.-P..; Funding acquisition, C.R. All authors have read and agreed to the published version of the manuscript. Acknowledgments The authors wish to tank Dr. Chiara Secondini for her help in establishing the clinical protocol and patient forms, Nathalie Duffey for her help in processing the samples and Dr. Med. Sebastiano Semini for his help in identifying healthy donors. The authors wish to thank all patients and healthy donors for participating to this study. References Malvezzi M, Bertuccio P, Levi F, La Vecchia C, Negri E. European cancer mortality predictions for the year 2012. Annals of Oncology. 2012 Apr;23(4):1044–52. Perou CM, Sørlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, et al. Molecular portraits of human breast tumours. Nature. 2000 Aug;406(6797):747–52. Sotiriou C, Neo S-Y, McShane LM, Korn EL, Long PM, Jazaeri A, et al. Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proceedings of the National Academy of Sciences. 2003 Sep 2;100(18):10393–8. Dawson S-J, Rueda OM, Aparicio S, Caldas C. 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Seminars in Cancer Biology. 2012 Jun;22(3):174–86. Cattin S, Fellay B, Pradervand S, Trojan A, Ruhstaller T, Rüegg C, et al. Bevacizumab specifically decreases elevated levels of circulating KIT+CD11b+ cells and IL-10 in metastatic breast cancer patients. Oncotarget. 2016 Mar 8;7(10):11137–50. Broxmeyer HE, Maze R, Miyazawa K, Carow C, Hendrie PC, Cooper S, et al. The kit receptor and its ligand, steel factor, as regulators of hemopoiesis. Cancer Cells. 1991 Dec;3(12):480–7. D’Arena G, Musto P, Cascavilla N, Carotenuto M. Thy-1 (CDw90) and c-kit receptor (CD117) expression on CD34+ hematopoietic progenitor cells: a five dimensional flow cytometric study. Haematologica. 1998 Jul;83(7):587–92. Hou J, Zhang M, Ding Y, Wang X, Li T, Gao P, et al. Circulating CD14 + CD163 + CD206 + M2 Monocytes Are Increased in Patients with Early Stage of Idiopathic Membranous Nephropathy. Mediators of Inflammation. 2018 Jun 21;2018:1–10. Jatoi I, Benson JR, Kunkler I. 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Int J Cancer. 2017 Oct;141(7):1295–306. Nassar FJ, Chamandi G, Tfaily MA, Zgheib NK, Nasr R. Peripheral Blood-Based Biopsy for Breast Cancer Risk Prediction and Early Detection. Front Med. 2020 Feb 11;7:28. Kuonen F, Laurent J, Secondini C, Lorusso G, Stehle J-C, Rausch T, et al. Inhibition of the Kit Ligand/c-Kit Axis Attenuates Metastasis in a Mouse Model Mimicking Local Breast Cancer Relapse after Radiotherapy. Clinical Cancer Research. 2012 Aug 15;18(16):4365–74. Ko EC, Formenti SC. Radiation therapy to enhance tumor immunotherapy: a novel application for an established modality. International Journal of Radiation Biology. 2019 Jul 3;95(7):936–9. Niknam S, Barsoumian HB, Schoenhals JE, Jackson HL, Yanamandra N, Caetano MS, et al. Radiation Followed by OX40 Stimulation Drives Local and Abscopal Antitumor Effects in an Anti–PD1-Resistant Lung Tumor Model. Clin Cancer Res. 2018 Nov 15;24(22):5735–43. Rodriguez-Ruiz ME, Rodriguez I, Garasa S, Barbes B, Solorzano JL, Perez-Gracia JL, et al. Abscopal Effects of Radiotherapy Are Enhanced by Combined Immunostimulatory mAbs and Are Dependent on CD8 T Cells and Crosspriming. Cancer Research. 2016 Oct 15;76(20):5994–6005. Yu W-D, Sun G, Li J, Xu J, Wang X. Mechanisms and therapeutic potentials of cancer immunotherapy in combination with radiotherapy and/or chemotherapy. Cancer Letters. 2019 Jun;452:66–70. Poleszczuk JT, Luddy KA, Prokopiou S, Robertson-Tessi M, Moros EG, Fishman M, et al. Abscopal Benefits of Localized Radiotherapy Depend on Activated T-cell Trafficking and Distribution between Metastatic Lesions. Cancer Research. 2016 Mar 1;76(5):1009–18. Walker R, Poleszczuk J, Pilon-Thomas S, Kim S, Anderson AARA, Czerniecki BJ, et al. Immune interconnectivity of anatomically distant tumors as a potential mediator of systemic responses to local therapy. Sci Rep. 2018 Dec;8(1):9474. Gaipl US, Multhoff G, Scheithauer H, Lauber K, Hehlgans S, Frey B, et al. Kill and spread the word: stimulation of antitumor immune responses in the context of radiotherapy. Immunotherapy. 2014 May;6(5):597–610. Johnke RM, Edwards JM, Kovacs CJ, Evans MJ, Daly BM, Karlsson UL, et al. Response of T lymphocyte populations in prostate cancer patients undergoing radiotherapy: influence of neoajuvant total androgen suppression. Anticancer Res. 2005 Aug;25(4):3159–66. Lissoni P, Brivio F, Fumagalli L, Messina G, Meregalli S, Porro G, et al. Effects of the conventional antitumor therapies surgery, chemotherapy, radiotherapy and immunotherapy on regulatory T lymphocytes in cancer patients. Anticancer Res. 2009 May;29(5):1847–52. Rühle PF, Wunderlich R, Deloch L, Fournier C, Maier A, Klein G, et al. Modulation of the peripheral immune system after low-dose radon spa therapy: Detailed longitudinal immune monitoring of patients within the RAD-ON01 study. Autoimmunity. 2017 Feb 17;50(2):133–40. Table Table 1. Clinical data of breast cancer patients included in the study. Patient Number Age ER (%) PR (%) HER2 (+/-) Ki67 (%) Grade Tumor size LN mets Anti-hormonal therapy 1 62 95 70 - 5 1 pT1b pN1a - 2 58 95 95 - 10 2 T1b N0 - 3 50 100 100 + 5 1 pT1 pN0 Tamoxifen 4 73 95 2 - 25 2 pT2 pN1a - 5 49 90 80 - 10 2 T1b pN0 Tamoxifen 6 69 100 100 - 15 2 pT1a pN0 Tamoxifen 7 53 95 60 - 10 2 pT1c pN0 Letrozole 8 73 100 0 - 20 na pT1c pN0 Tamoxifen 9 67 90 80 - 5 1 pT1b pN0 Tamoxifen 10 66 100 100 - 5 2 pT1c pN0 Letrozole 11 64 95 80 - 10 1 pT1b pN0 Letrozole 12 61 80 100 - 10 2 pT1b pN0 Anastrozole 13 43 95 95 - 10 2 pT1c pN0 Tamoxifen Patient’s demographics, tumor subtype, grade, stage (pT and pN) and treatment after conservative surgery. ER (%): oestrogen receptor expression in percent; PR (%): progesterone receptor expression in percent; HER2 (±): overexpression of HER-2; Ki67 (%): fraction of cancer cells positive for Ki67 expression. Supplementary Files BCRSupMaterialCattinRuegg.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Minor Revision 28 Feb, 2021 Review # 2 received at journal 21 Feb, 2021 Review # 1 received at journal 21 Feb, 2021 Reviewer # 2 agreed at journal 16 Feb, 2021 Reviewer # 1 agreed at journal 31 Jan, 2021 Reviewers invited by journal 25 Jan, 2021 First submitted to journal 10 Jan, 2021 Editor assigned by journal 10 Jan, 2021 Submission checks completed at journal 10 Jan, 2021 Editor invited by journal 10 Jan, 2021 You are reading this latest preprint version 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-146187","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":8083471,"identity":"bea9b62c-3441-4e9c-b23e-3c5e4c0bcb65","order_by":0,"name":"Sarah Cattin","email":"","orcid":"","institution":"Université de Fribourg: Universite de Fribourg","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Cattin","suffix":""},{"id":8083472,"identity":"cf2b004f-be06-44b7-b5f0-11b53059f302","order_by":1,"name":"Benoît Fellay","email":"","orcid":"","institution":"HFR Fribourg Cantonal Hospital: HFR Fribourg Hopital cantonal","correspondingAuthor":false,"prefix":"","firstName":"Benoît","middleName":"","lastName":"Fellay","suffix":""},{"id":8083473,"identity":"a48539a8-1b0a-4c7c-8d74-ad9f8661e7cd","order_by":2,"name":"Antonello Calderoni","email":"","orcid":"","institution":"Centro Oncologico Varini-Calderoni-Christinat","correspondingAuthor":false,"prefix":"","firstName":"Antonello","middleName":"","lastName":"Calderoni","suffix":""},{"id":8083474,"identity":"75bb8220-f07c-4a58-9176-d503a5c9c221","order_by":3,"name":"Alexandre Christinat","email":"","orcid":"","institution":"Centro Oncologico Varini-Calderoni-Christinat","correspondingAuthor":false,"prefix":"","firstName":"Alexandre","middleName":"","lastName":"Christinat","suffix":""},{"id":8083475,"identity":"c2dac0fc-ff8a-424a-a63e-5a3efff05bcf","order_by":4,"name":"Laura Negretti","email":"","orcid":"","institution":"Moncucco Lugano Hospital: Clinica Luganese Moncucco","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Negretti","suffix":""},{"id":8083476,"identity":"987acc45-a51d-4d26-9131-08cbcee784ad","order_by":5,"name":"Maira Biggiogero","email":"","orcid":"","institution":"Clinica Luganese Moncucco","correspondingAuthor":false,"prefix":"","firstName":"Maira","middleName":"","lastName":"Biggiogero","suffix":""},{"id":8083477,"identity":"4d709224-f235-43ab-a5b8-c697e506365f","order_by":6,"name":"Alberto Badellino","email":"","orcid":"","institution":"Clinica Luganese Moncucco","correspondingAuthor":false,"prefix":"","firstName":"Alberto","middleName":"","lastName":"Badellino","suffix":""},{"id":8083478,"identity":"124e10e6-7669-4809-99a6-04879b471752","order_by":7,"name":"Anne-Lise Schneider","email":"","orcid":"","institution":"Breast and Oncology center Hôpital Neuchâtelois","correspondingAuthor":false,"prefix":"","firstName":"Anne-Lise","middleName":"","lastName":"Schneider","suffix":""},{"id":8083479,"identity":"2f76c178-e26f-4b03-9908-a88001573eea","order_by":8,"name":"Pelagia Tsoutsou","email":"","orcid":"","institution":"Breast and Onology Center, Hôpital Neuchâtelois","correspondingAuthor":false,"prefix":"","firstName":"Pelagia","middleName":"","lastName":"Tsoutsou","suffix":""},{"id":8083480,"identity":"f5ca3dcf-0dbb-4b47-8965-25156a9ec088","order_by":9,"name":"Alessandra Franzetti Pellanda","email":"","orcid":"","institution":"Clinica Luganese Moncucco","correspondingAuthor":false,"prefix":"","firstName":"Alessandra","middleName":"Franzetti","lastName":"Pellanda","suffix":""},{"id":8083481,"identity":"5ea46365-fd69-4cbb-98cc-b92eb50058fc","order_by":10,"name":"Curzio Rüegg","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie3OMQrCMBSA4RcC7fLQNVIPERAEUelV4lIXi07iWBDq4gF6DI9Q6dAluLh0EBQKXXSouDhaLV1jRsH8kJBAPl4ATKZfTTSHEgDtAIBqExJVBGMd0kSx2r6SduTn5WUFc55ujtdxeOqiE5PHQkFYNuVcSBjspFwO/bBAbAnqRArCmQdiEgLn2czr+WGCLoL1+aGKxB9yvnm9QUVQg5CgnmInOdEhTBYUhGS8I2cW2R7ehKwdFWlvPHp/rka8laZ5+VwmLiLdP1SkGVYt7LP6QoLvoM7OS92nJpPJ9F+9ALZXQzHw5SpcAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-9137-7695","institution":"University of Fribourg, Chair of Pathology","correspondingAuthor":true,"prefix":"","firstName":"Curzio","middleName":"","lastName":"Rüegg","suffix":""}],"badges":[],"createdAt":"2021-01-12 21:28:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-146187/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-146187/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":5054406,"identity":"5ed2e485-fcec-4313-9964-92510ce4569e","added_by":"auto","created_at":"2021-01-18 17:07:59","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1152143,"visible":true,"origin":"","legend":"Altered frequency of circulating monocytic populations in cancer patients. \n(A) Heat map of the FlowSOM clustering between breast cancer patients (BC) and healthy donors (HD). tSNE visualization of (B) the monocytic expression profile and (C) the differentially expressed clusters in the blood of breast cancer patients at time of first diagnosis vs healthy donors. Frequency of (D) CD117+ G-MDSC population, and the atypical populations (E) 22+9 and (F) 13+3 at the same timing. Cell analysis and quantification were performed by flow cytometry with FlowJo software and results are represented as mean values +/- SD.","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-146187/v1/e5430d209572eaabe20748c9.jpg"},{"id":5054261,"identity":"262609af-291c-4b79-ae00-0437f6f55f1a","added_by":"auto","created_at":"2021-01-18 17:04:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1136948,"visible":true,"origin":"","legend":"Altered frequency of circulating lymphocyte populations in cancer patients.\n (A) Heat map of the FlowSOM clustering between breast cancer patients (BC) and healthy donors (HD). tSNE visualization of (B) the lymphocyte expression profile and (C) the differentially expressed clusters in the blood breast cancer patients at time of first diagnosis of healthy donors. Frequency of the atypical populations (D) 29+24, (E) 3+7 and (F) at the same timing. Cell analysis and quantification were performed by flow cytometry with FlowJo software and results are represented as mean values +/- SD.","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-146187/v1/a8aad8b98870d9c209effe2e.jpg"},{"id":5054408,"identity":"fc24d484-f23a-4091-a713-317cb572ec9d","added_by":"auto","created_at":"2021-01-18 17:07:59","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":621423,"visible":true,"origin":"","legend":"Schematic representation of the radiotherapy study.\nPatients were enrolled after confirmed histological diagnosis of breast cancer. All patients underwent conservative surgery and received standard fractionated adjuvant radiotherapy (2 Gy per session, total 50+10 Gy). Blood samples were collected after diagnosis/before surgery (Sample 1), after surgery/before radiotherapy (Sample 2), at the end of radiotherapy (Sample 3), and 6-8 weeks after the end of the radiotherapy (Sample 4).","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-146187/v1/157580f204f0408eaebac8ff.jpg"},{"id":5054265,"identity":"f13455db-db39-4a33-98f3-3a2cc8a9178e","added_by":"auto","created_at":"2021-01-18 17:04:59","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3384298,"visible":true,"origin":"","legend":"Tumor removal reduces the frequency of circulating CD117+ G-MDSC cells.\n(A) Comparative visualization of the expression of surface markers in monocytes at different time-points of treatment by tSNE. (B) Heat map of the FlowSOM clustering of breast cancer patients at indicated time-points. Frequency of (C) Mo-MDSC and (D) G-MDSC cell populations in patients at the indicated time points during treatment. Frequency of (E) CD163+ and (F) CD117+ G-MDSCs population at indicated time-points relative to frequency at 0_PreOp time-point. Frequency of the (G) combined 16+18+21+12+15+26 and (H) 1+6+7 atypical cell populations during treatment. Cell analysis and quantification was performed by flow cytometry with FlowJo software and results are represented as mean values +/- SD.","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-146187/v1/bae9a0ff53a983ff2d9dc36b.jpg"},{"id":5054410,"identity":"26cb9872-d81c-4741-878e-a4893f90bd60","added_by":"auto","created_at":"2021-01-18 17:07:59","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3140456,"visible":true,"origin":"","legend":"Tumor removal and radiotherapy reduce the fraction of CD117+ cells within the G-MDSC population. \n(A) Comparative visualization of the expression of surface markers in lymphocytes at different time-points of treatment by tSNE. (B) Heatmap of the FlowSOM clustering of breast cancer patients at indicated time-points during treatment. Frequency of the atypical populations (C) 25, (D) 41 and (E) 23+30 in patients during treatment. Relative quantification to 0_PreOp time-point of (F) CD4+ CD45RO+ lymphocytes and (G) CD45RO+ regulatory T cells during treatment. Cell analysis and quantification was performed by flow cytometry with FlowJo software and results are represented as mean values +/- SD.","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-146187/v1/de885562dd12ab1e7f381b16.jpg"},{"id":13647303,"identity":"65151558-36c8-4d5a-b1a6-4641d3b5b347","added_by":"auto","created_at":"2021-09-17 09:27:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1095342,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-146187/v1/a4a7f28b-3a27-45fc-b875-8347ac7b9fb7.pdf"},{"id":5054552,"identity":"a5fa5950-312a-4ffe-86a9-98447bbe4306","added_by":"auto","created_at":"2021-01-18 17:10:59","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":648130,"visible":true,"origin":"","legend":"","description":"","filename":"BCRSupMaterialCattinRuegg.pdf","url":"https://assets-eu.researchsquare.com/files/rs-146187/v1/4e63d6b7815a087cae91baa0.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eCirculating Immune Cell Populations Related to Primary Breast Cancer, Surgical Removal and Radiotherapy Revealed by Flow Cytometry Analysis\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eBreast cancer (BC) is the most frequent cancer and main cause of cancer-related mortality for women in industrialized countries (1). Three clinically relevant biological BC subtypes (i.e. Oestrogens/Progesterone Receptor positive, Human Epidermal growth factor Receptor 2 (HER2) amplified and triple negative), and multiple molecular subtypes (e.g. Luminal A/B, HER2, basal like, normal like) with distinct features and clinical outcomes, have been defined and characterized (2\u0026ndash;5).\u003c/p\u003e \u003cp\u003eEarly detection and surgery in combination with adjuvant treatments tailored on biological and molecular subtypes, have improved patients\u0026rsquo; survival by about 30% in the past three decades (6). Goal of adjuvant therapy, including radiotherapy, is the eradication of tumor cells that disseminated before diagnosis and surgery. Some of these disseminated tumor cells (DTC), however, will escape therapy and later progress to form metastases, which in most patients represents the main causes of cancer-related death. After breast-conserving surgery, radiotherapy reduces the risk of BC recurrence and death. Among women with operable BC, randomized trials have demonstrated equivalent disease-free and overall survival between mastectomy and breast-conserving surgery followed by radiotherapy alone and/or hormonal, anti-HER2, or chemotherapy (7\u0026ndash;16).\u003c/p\u003e \u003cp\u003eMammography is the standard approach for the detection of asymptomatic BC (17). In spite of its benefits in reducing BC specific mortality, mammography has some important limitations (18): low specificity and sensitivity; risk of over-diagnosis; risk of inducing BC due to X-ray exposure, particularly in patients with defective DNA repair genes (19); not recommended before the age of 50 in spite of the fact that 20\u0026ndash;25% of all BCs appear before this age. There is therefore an unmet need for complementary or alternative methods for the detection of asymptomatic, early BC (20\u0026ndash;22). Circulating tumor cells (CTC), cell free tumor-derived DNA, mRNA and miRNA, proteins, autoantibodies and metabolites are being explored as candidate blood-based biomarkers for BC detection, diagnosis or monitoring, but so far none entered routine clinical practice (23\u0026ndash;27). Similarly, there are no effective blood-based biomarkers to actively assess patients\u0026rsquo; response to treatment and monitoring disease state after therapy. Also the most used in clinical practice biomarker protein such as CA 15\u0026thinsp;\u0026minus;\u0026thinsp;3 is not specific and sensitive in early breast cancer diagnosis (28).\u003c/p\u003e \u003cp\u003eTumors, including BC, mobilize and recruit immuno-inflammatory cells to their microenvironment (29\u0026ndash;31). Monocytic and granulocytic cells, mostly immature forms, as well as lymphocytes, contribute to cancer progression by promoting immunosuppression, angiogenesis, cancer cell survival, growth, invasion and metastasis (32,33). We have previously shown that metastatic BC patients have elevated frequencies of TIE2\u003csup\u003e+\u003c/sup\u003eCD11b\u003csup\u003e+\u003c/sup\u003e and CD117\u003csup\u003e+\u003c/sup\u003eCD11b\u003csup\u003e+\u003c/sup\u003e leukocytes circulating in the blood, and that circulating CD11b\u003csup\u003e+\u003c/sup\u003e cells express higher mRNA levels of the M2 polarization markers CD163, ARG1 and IL-10 (34). Treatment with paclitaxel in combination with bevacizumab decreased the frequency CD117\u003csup\u003e+\u003c/sup\u003eCD11b\u003csup\u003e+\u003c/sup\u003e leukocytes, IL-10 mRNA levels in CD11b\u003csup\u003e+\u003c/sup\u003e cells and IL-10 protein in plasma. We therefore considered that blood circulating leukocytes, or sub-population thereof, may reflect cancer-relevant immuno-inflammatory events that may be further explored as BC-associated biomarkers.\u003c/p\u003e \u003cp\u003eHere, we analysed the phenotype of blood leukocytes of patients with early BC at time of diagnosis, after surgery, and after adjuvant RTX, relative to healthy donors (HD), using flow cytometry and a minimally supervised analytical approach based on FlowSOM algorithm and manual validation. We identified cell populations associated with the presence of a primary BC, tumor removal and adjuvant radiotherapy. Unsupervised clustering analysis approach of the same flow cytometry data allowed discovering unanticipated cell populations associated with BC or adjuvant radiotherapy. These results indicate that phenotypical analysis of peripheral blood leukocytes, with a minimally supervised analytical approach, may be a clinically-relevant strategy for the identification of cellular biomarkers for BC detection and therapy monitoring.\u003c/p\u003e "},{"header":"Materials And Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and clinical study\u003c/h2\u003e \u003cp\u003eThe study was approved by the Cantonal ethic commission for human research on Humans of Canton Ticino (CE 2967) and extended to Vaud-Fribourg-Neuch\u0026acirc;tel, Switzerland. The study includes 13 female patients (Table\u0026nbsp;1) who were diagnosed with primary, non-metastatic BC (stage T1-4, N0-N1, M0,). All patients underwent conservative surgery and received standard fractionated adjuvant radiotherapy (2\u0026nbsp;Gy per session, total dose : 50\u0026thinsp;+\u0026thinsp;10\u0026nbsp;Gy). Blood samples were collected after confirmed diagnosis/before surgery, after surgery/before radiotherapy, at the end of radiotherapy (week 6), and 6\u0026ndash;8 weeks after the end of the radiotherapy (week 12\u0026ndash;15). All Patients and HDs gave written informed consent before study entry. Patients were recruited before surgery at Clinica Luganese Moncucco, Lugano, and at H\u0026ocirc;pital Neuch\u0026acirc;telois, La Chaux-de-Fonds, once diagnosis was histologically confirmed. Mean age for cancer patients was 60.6\u0026nbsp;years (all patients were between 43 and 73\u0026nbsp;years old). HDs were recruited along the study, based on the following criteria: age-matched relative to BC patients, no regular medications in the last 6 months, no previous cancer diagnosis, no chronic diseases and normal blood analyses at time of recruitment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBlood processing\u003c/h2\u003e \u003cp\u003e20\u0026nbsp;ml of peripheral venous blood was collected using BD Vacutainer\u0026reg; Blood Collection EDTA Tubes (Becton Dickinson, Franklin Lakes, NJ, USA) following manufacturer\u0026rsquo;s instructions and immediately shipped by courier at room temperature to the laboratory. All analyses were performed within 24 hours after blood collection. Antibody staining was performed in whole blood. Plasma and total leukocytes were isolated from the remaining blood using BD Vacutainer\u0026reg; CPT\u0026trade; Cell Preparation Tube (Becton Dickinson) with Sodium Heparin following manufacturer\u0026rsquo;s instructions. Plasma fraction was frozen at -80\u0026nbsp;\u0026deg;C and isolated leukocytes were lysed in RA1 lysis buffer (Macherey-Nagel, D\u0026uuml;ren, Deutschland) and stored at -80\u0026nbsp;\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFlow cytometry\u003c/h2\u003e \u003cp\u003eWhole blood staining\u0026rsquo;s were performed within 24 hours after blood collection. Leukocytes were counted using Cell-Dyn Sapphire Hematology System (Abbott Diagnostics, Chicago, IL, USA). For staining, 1\u0026nbsp;million cells per tube were used. Directly labelled antibodies were added to whole blood and incubated for 20 minutes at 4\u0026nbsp;\u0026deg;C, followed by 10 minutes red-blood-cells lysis (B\u0026uuml;hlmann Laboratories, Sch\u0026ouml;nenbuch, Switzerland) and washing using cold PBS. All anti-human antibodies were used at the concentrations recommended by the manufacturer: anti-CD15-PeCy7 (clone HI98), anti-CD14-Pe (clone MφP9), anti-CD163-FITC (clone GHI/61), anti-CD11b-BV510 (clone ICRF44), anti-CD33-V450 (clone WM53), anti-CD64-APCH7 (clone 10.1), anti-CD117-APC (clone YB5.B8), anti-CD45RA-PeCy7 (clone HI100), anti-CD25-Pe (clone M-A251), anti-CD4-FITC (clone RPA-T4), anti-CD8-V500 (clone SK1), anti-CD45RO-BV421 (clone UCHL1), anti-CD3-APCH7 (clone SK7), and CD127-Alexa Fluor 647 (clone HIL-7R-M21) and 7AAD (all from Becton Dickinson). BD FACSCanto II (Becton Dickinson) instrument was used to analyse samples and FlowJo 10.6.2 (Treestar Inc., Ashland, OR, USA) software and several software plugins (FlowCLEAN, downsample_V3, FlowSOM, tSNE) were used to analyse all data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eReverse transcription real-time PCR (RT-qPCR)\u003c/h2\u003e \u003cp\u003eTotal mRNA from total white blood cells was extracted using the NucleoSpin RNA kit from Macherey-Nagel following manufacturer\u0026rsquo;s instructions (D\u0026uuml;ren, Germany). The purity and quantity of all RNA samples were examined by NanoDrop (Witec AG, Luzern, Switzerland). Total RNA was retro-transcribed using M-MLV reverse transcriptase kit following manufacturer\u0026rsquo;s instructions (ThermoFisher Scientific, Waltham, Massachusetts, USA) using 500\u0026nbsp;ng of total RNA. cDNA was subjected to amplification by real time qPCR with the StepOne SYBR System (Life Technologies) using the following primer pairs (Eurofins Genomics, Huntsville, AL, USA) at the indicated hybridization temperatures: GAPDH 58\u0026nbsp;\u0026deg;C (Fw-TCTTCTTTTGCGTCGCCAGC, Rev-GATTTTGGAGGGATCTCGCTCCT), ARG1 58\u0026nbsp;\u0026deg;C (Fw-GGAGTCATCTGGGTGGATGC, Rev-CTGGCACATCGGGAATCTTTC), IL-10 58\u0026nbsp;\u0026deg;C (Fw-CGAGATGCCTTCAGCAGAGT, Rev-AATCGATGACAGCGCCGTAG), CD117 57\u0026nbsp;\u0026deg;C (Fw-GATTATCCCAAGTCTGAGAATGAA, Rev-CGTCAGAATTGGACACTAGGA), FN1 52\u0026nbsp;\u0026deg;C (Fw- ACTTCGACAGGACCACTTGA, Rev-TCAAATTGGAGATTCATGGGA). Real-time PCR data were then analysed using the comparative Ct method [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAcquired data were analysed and graphics were generated using Prism Software (GraphPad, La Jolla, CA, USA). Statistical comparisons between cancer patients and healthy donors were performed by T-test assuming non-homogenous variance. Normality distribution of the samples was checked in case of significance and, if non-Gaussian, a Mann-Whitney replaced the T-test results. Statistical comparisons of all time points to observe the effect of radiotherapy were performed by one-way ANOVA assuming non-homogenous variance using Tukey correction. Normality distribution of the samples was checked in case of significance and, if non-Gaussian, a Kruskal-Wallis assay replaced the ANOVA results. Highest and lowest values from each group were excluded. Results were considered to be significantly from p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. In the figures, the various p values thresholds are presented as follow: \u0026le;0.05=*, \u0026le;\u0026thinsp;0.01=**, \u0026le;\u0026thinsp;0.001=***, \u0026le;\u0026thinsp;0.0001=****.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":"\u003cp\u003e\u003cspan class=\"BoldItalic\"\u003eIncreased frequency of CD117\u003c/span\u003e\u003csup\u003e\u003cspan class=\"BoldItalic\"\u003e+\u003c/span\u003e \u003c/sup\u003e \u003cspan class=\"BoldItalic\"\u003eG-MDSCs in the peripheral blood of patients with newly diagnosed non-metastatic BC\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eBased on previous observations made in metastatic BC patients (34), we hypothesized that an increased frequency of circulating CD11b\u003csup\u003e+\u003c/sup\u003e cells expressing CD117 and/or displaying a M2 activation phenotype, may also occur in patients with early BC. To test this hypothesis, we monitored the frequency of MDSC cells in the blood of non-metastatic BC patients (cT1-4, N0-1, M0) at time of diagnosis using Flow Cytometry. Aged-matched women without BC served as control population (healthy donors \u0026ndash; HDs). Monocytic-MDSC (Mo-MDSC) were defined as CD11b\u003csup\u003e+\u003c/sup\u003eCD33\u003csup\u003e+\u003c/sup\u003eCD14\u003csup\u003ehigh\u003c/sup\u003eCD15\u003csup\u003e-\u003c/sup\u003e and granulocytic-MDSC (G-MDSC) as CD11b\u003csup\u003e+\u003c/sup\u003eCD33\u003csup\u003e+\u003c/sup\u003eCD14\u003csup\u003elow\u003c/sup\u003eCD15\u003csup\u003e+\u003c/sup\u003e cells. In both cell populations we monitored the expression of CD117, the receptor for Kit-ligand/stem cell factor widely present in hematopoietic progenitors cells (35,36), and CD163, a M2 polarization marker in monocytes (37). In order to avoid investigator-associated biases and variability in the results inherent to supervised manual analysis of flow cytometry data, we develop a minimally supervised, standardized analytical workflow based on the FlowSOM algorithm (Supplementary Fig.\u0026nbsp;1), in complement to conventional manual gating and supervised analysis.\u003c/p\u003e\n\u003cp\u003eCells clusters revealed by standardized analytical workflow, were considered of interest when their frequency was more than 10% different between HDs and cancer patients. Some of the 14 analysed clusters corresponded to non-standard populations, for example those negative for all tested markers or expressing unanticipated marker combinations (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA-C). These populations would have been missed by conventional supervised gating and analysis driven by the marker combination of interest. Interestingly, we observed a significant increase in the frequency of CD117\u003csup\u003e+\u003c/sup\u003e cells among the circulating G-MDSC population in cancer patients relative to HDs (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). We observed a similar (but non-significant) trend in the frequency of Mo-MDSC cells, albeit at lower frequency. In addition, the frequencies of some non-classical cell populations, such as those expressing none of the markers of interest (Cluster 13 and 3) or a CD11b\u003csup\u003e+\u003c/sup\u003eCD15\u003csup\u003elow\u003c/sup\u003e cell population (Cluster 22\u0026thinsp;+\u0026thinsp;9), were significantly different between BC patients and HDs (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE and F). No significant changes were observed for CD163\u003csup\u003e+\u003c/sup\u003e cells in both G-MDSC and Mo-MDSC populations (Supplementary Figure S2).\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\"\u003eNon-standard CD3 expressing cells are present with increased frequency in the peripheral blood of newly diagnosed BC patients\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eIn parallel we monitored the presence of selected lymphocyte populations in both groups. By conventional supervised analysis we observed no differences in classical CD3\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+\u003c/sup\u003e T cells, CD3\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells and CD3\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003eCD127\u003csup\u003e-\u003c/sup\u003e regulatory T cells (Tregs). Likewise, we observed no changes in the frequency of memory (CD45RO\u003csup\u003e+\u003c/sup\u003eCD45RA\u003csup\u003e-\u003c/sup\u003e) or na\u0026iuml;ve (CD45RA\u003csup\u003e+\u003c/sup\u003eCD45RO\u003csup\u003e-\u003c/sup\u003e) T cells within the same lymphocyte populations (Supplementary Figure S3).\u003c/p\u003e\n\u003cp\u003eIn contrast, FlowSOM analysis performed on lymphocytes revealed 21 populations that were more then 10% differentially represented between HDs and cancer patients (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA-C). A population of cells of the size of lymphocytes, but negative for CD3, CD4 or CD8 expression (Cluster 24\u0026thinsp;+\u0026thinsp;29) was significantly less represented in cancer patients relative to HDs. CD3\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells expressing CD127 and CD45RO markers (Cluster 3\u0026thinsp;+\u0026thinsp;7) are present at significantly higher frequency in cancer patients. A cell cluster expressing CD3, but not CD4 or CD8 (Cluster 20) was fond more represented in cancer patients relative to HDs (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD-F).\u003c/p\u003e\n\u003cp\u003eTaken together these results reveal an increased frequency of peripheral blood CD117\u003csup\u003e+\u003c/sup\u003e G-MDSC in non-metastatic BC patients at time of diagnosis, as well as significant changes in the frequency of myeloid and lymphocytic cell populations expressing unconventional marker combinations. They also demonstrate that FlowSOM-based analysis can identify cell populations that would have been likely missed by supervised analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNo detectable changes in the expression level of transcripts for M2 polarization markers\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003cp\u003eWe previously reported that transcripts of M2-associated genes were expressed at higher levels circulating CD11b\u003csup\u003e+\u003c/sup\u003e cells in metastatic BC patients compared to HDs (34). We therefore analysed expression of mRNA for CD117 and the M2 markers IL-10, fibronectin-1 (FN1) and arginase 1 (ARG1) in total leukocytes from cancer patient and HDs. No differences in expression levels were observed (Supplementary Figure S4).\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\"\u003eA proof-of-concept study to monitor the effects of surgical tumor removal and adjuvant radiotherapy on circulating immune cells in BC patients\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe differences observed in myelomonocytic and lymphocytic populations in BC patients at time of diagnosis relative to HDs, raised the question whether tumor removal and/or adjuvant therapy may reverse these changes, or induce additional ones. To address this question, we performed a proof-of-concept study, by taking advantage of the fact that the investigated patients were scheduled for breast conservative tumor removal and adjuvant radiotherapy as part of their standard treatment. Adjuvant radiotherapy was selected as therapy of choice as systemic effects on the immune system have been reported (38\u0026ndash;40), while on the other side chemotherapy was excluded in order to avoid that myelosuppressive effects induced by chemotherapy could non-specifically impact the results (41). To search for potential changes in cell populations in response to surgery and radiotherapy we analysed G-MDSC and Mo-MDSCs as well as lymphocytes at three time points : after surgery/before radiotherapy start (1_PostOP), at the end of radiotherapy (6 weeks; 2_Post_RTX_6w) and at 6\u0026ndash;8 weeks after the end of radiotherapy (12\u0026ndash;14 weeks; 3_Post_RTX_12w). Results were compared to values obtained at time of diagnosis (0_PreOP) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\"\u003eTumor removal increased the frequency Mo-MDSCs and G-MDSCs but decreased CD117\u003c/span\u003e\u003csup\u003e\u003cspan class=\"BoldItalic\"\u003e+\u003c/span\u003e \u003c/sup\u003e \u003cspan class=\"BoldItalic\"\u003eG-MDSCs and radiotherapy induced changes in non-standard myeloid cell populations\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eUsing FlowSOM workflow of analysis we observed distinct expression profiles at the four time-points globally visualized by tSNE. 17 cell clusters were found highly differentially represented in one group compared to the other groups. Surprisingly, when looking at the expression profiles of each cluster of interest, the majority of them was lacking CD33 expression, suggesting that this marker may not be suitable to analyse the monocytes fraction (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA-B).\u003c/p\u003e\n\u003cp\u003eAfter tumor removal and at the end of radiotherapy the frequency of both Mo-MDSCs and G-MDSCs was significantly increased relative to values at time of diagnosis, and returned to pre-therapy levels 6\u0026ndash;8 weeks after the end of radiotherapy (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC-D). Strikingly, within the G-MDSCs population, the fractions of CD117 expressing cells significantly decreased after tumor removal and this decrease persisted after the end of radiotherapy (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE). Surgery had no impact on the fraction of CD163\u003csup\u003e+\u003c/sup\u003e G-MDSCs population (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF). Radiotherapy itself had an impact on G-MDSC expressing CD163, but not CD117 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE-F).\u003c/p\u003e\n\u003cp\u003eThe presence of one particular cell cluster expressing only CD11b and CD15 (Pop 1, 6 and 7) clearly decreased after radiotherapy. Another population expressing CD11b but lacking expression of all tested markers significantly increased during and after radiotherapy (Pop 16, 18, 12, 15 and 26) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eG-H). The latter observation suggest that some populations defined by non-standard marker combinations may be potentially interesting candidates to investigate further with an extended panel or markers.\u003c/p\u003e\n\u003cp\u003eAnalysis of total blood leukocytes for CD117, IL-10, FN1, and ARG1 mRNA expression by RT-qPCR revealed no observable differences in their expression levels (Supplementary Figure S5).\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\"\u003eAdjuvant radiotherapy increases the frequency of CD4\u003c/span\u003e\u003csup\u003e\u003cspan class=\"BoldItalic\"\u003e+\u003c/span\u003e \u003c/sup\u003e \u003cspan class=\"BoldItalic\"\u003ememory and regulatory T cells, and induces changes in non-standard lymphocytic populations\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eLikewise, we performed unsupervised analysis of the lymphocyte populations at the three time-points after surgery and radiotherapy. Visualization by tSNE revealed distinctive changes in marker expression profiles. Eleven cells clusters were found highly differentially represented in one group compared to the other ones (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA-B). After tumor removal we observed highly variable effects on the frequency of T lymphocyte subpopulations, most of which were inconsistent and statistically non-significant.\u003c/p\u003e\n\u003cp\u003eAmong the stably differentially represented clusters at the various time-points, a CD3\u003csup\u003e+\u003c/sup\u003e cell population positive for CD4 and CD8 (Cluster 25), and a CD3\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+\u003c/sup\u003eCD127\u003csup\u003e+\u003c/sup\u003eCD45RO\u003csup\u003e+\u003c/sup\u003e population (Cluster 41) appeared at higher frequency after treatment (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC-D). Strikingly, the frequency of this CD45RO\u003csup\u003e+\u003c/sup\u003eRA\u003csup\u003e-\u003c/sup\u003e memory subset within the CD3\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+\u003c/sup\u003e lymphocyte population was significantly and consistently increased at the end of radiotherapy and this increase was still evident 6\u0026ndash;8 weeks later. A similar increase was also present among CD4\u003csup\u003e+\u003c/sup\u003e regulatory T cells, corresponding to cluster 23 and 30, which also persisted after the end of radiotherapy (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eE-G).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":" \u003cp\u003eMammography-based screening significantly reduces BC-related mortality, but intrinsic and practical limitations call for novel screening approaches (17,19,20,42). Blood based biomarkers exploiting cancer-derived CTC, DNA or RNA are being explored, but so far none reached clinical routine practice (22,43). Similarly, there are no valid biomarkers for monitoring patients\u0026rsquo; response to treatment or detecting relapses before they become symptomatic.\u003c/p\u003e \u003cp\u003eIn this study we pursued the use of flow cytometry to analyse the phenotype and frequency of blood leukocytes in patients with non-metastatic BC at time of diagnosis, after surgical tumor removal and after adjuvant radiotherapy. Using a combination of minimally supervised (FlowSOM algorithm), and supervised (manual) analytical approaches, we report that: i) at diagnosis, BC patients have an increased frequency of circulating CD117\u003csup\u003e+\u003c/sup\u003e G-MDSC relative to age-matched healthy donors; ii) surgical tumor removal causes transient increase of G-MDSCs and M-MDSCs, and a long-lasting decrease of CD117\u003csup\u003e+\u003c/sup\u003e G-MDSC; iii) radiotherapy significantly increases CD45R0\u003csup\u003e+\u003c/sup\u003e memory T cells and CD4\u003csup\u003e+\u003c/sup\u003e Treg cells; iv) with the FlowSOM algorithm we identified additional unanticipated, non-classical cell populations differentially represented between HD and BC patients and in BC patients in response to therapy.\u003c/p\u003e \u003cp\u003eCD117, the receptor for Kit-ligand/stem cell factor, is widely expressed in hematopoietic progenitor cells in the bone marrow, while virtually no CD117\u003csup\u003e+\u003c/sup\u003e leukocytes are present in the circulation under homeostatic conditions (35). We have previously reported a role of CD117\u003csup\u003e+\u003c/sup\u003e leukocytes in metastasis in the murine 4T1 metastatic BC model (44) and the presence of CD117\u003csup\u003e+\u003c/sup\u003eCD11b\u003csup\u003e+\u003c/sup\u003e cells in the blood of mBC patients (34). Here, we observed an increased frequency of CD117\u003csup\u003e+\u003c/sup\u003e cells among total CD11b\u003csup\u003e+\u003c/sup\u003e G-MDSCs in the peripheral blood of non-metastatic BC patients at time of diagnosis, compared to HD. Interestingly, the frequency of CD117\u003csup\u003e+\u003c/sup\u003e G-MDSCs significantly dropped upon tumor removal and remained below pre-treatments levels after radiotherapy. Thus, the increased frequency of CD117\u003csup\u003e+\u003c/sup\u003e G-MDSCs may reflect the presence of the primary tumor. No changes were observed in CD117 mRNA expression in total leukocytes. This could be due to that fact that CD117\u003csup\u003e+\u003c/sup\u003e cells are lost during leukocyte isolation for RNA extraction (flow cytometry was performed in non-separated total whole blood), or that CD117 mRNA expression has ceased upon cell mobilization (while CD117 protein persisted at the cell surface). The latter possibility is consistent with our previous observation that mobilized CD117\u003csup\u003e+\u003c/sup\u003e cells adoptively transferred to a recipient mouse, rapidly became CD117 negative (44). In contrast, the frequency of CD163\u003csup\u003e+\u003c/sup\u003e G-MDSCs remained rather constant, with only a transient decrease during radiotherapy. The implication of this decrease is unclear as CD163 expression did not significantly differ between HD and BC patients at time of diagnosis. After surgery and radiotherapy also no change in the mRNA expression of CD117 and ARG1, FN1, IL-10 (i.e., M2 polarization markers) was observed, owing probably to the lack of enrichment of CD11b\u003csup\u003e+\u003c/sup\u003e cells for PCR analysis.\u003c/p\u003e \u003cp\u003eIn cancer patients at time of diagnosis we observed a higher frequency of atypical T lymphocytes (CD3\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e-\u003c/sup\u003eCD8\u003csup\u003e-\u003c/sup\u003e) and of a population of the size of lymphocytes lacking expression of all the tested markers. These observations suggest that some potentially interesting changes may occur in atypical T cells or in non-T cell populations such as B cells or NK cells. Strikingly, after radiotherapy, we observed a steady and significant increase of the fraction of CD45RO\u003csup\u003e+\u003c/sup\u003e memory T cells within total CD4\u003csup\u003e+\u003c/sup\u003e T cells and within CD4\u003csup\u003e+\u003c/sup\u003e Treg. We also observed the increased presence of a T cell population expressing both CD4 and CD8 markers. This suggests that radiotherapy may cause T cell activation leading to the subsequent generation of memory T cell subsets. Indeed, there is evidence that radiotherapy exerts its therapeutic effects, not only in the local treatment field, but also outside the irradiated field and at distant sites (i.e. the so called abscopal effect), at least in part, by eliciting a T cell immune response (38). The recent observation that combination of radiotherapy with immune checkpoint inhibitors in experimental models and cancer patients results in potent synergistic therapeutic effects further support the involvement of T cell-dependent events and the therapeutic effects of radiotherapy (45\u0026ndash;48). Through experimental work and mathematical modelling, it has been proposed that anti-tumor T cells may by mobilized by radiotherapy toward peripheral tissues to eliminate DTC (49\u0026ndash;51). However, to date there is paucity of human data demonstrating specific changes in circulating T lymphocytes to support such a model. Radiotherapy was reported to cause a global reduction in circulating lymphocyte subsets in patients treated for stage I-II prostate cancer (52), or to induce an increase in CD4\u003csup\u003e+\u003c/sup\u003e Treg in the peripheral blood of patients with diverse solid cancers (53). Low-dose radon therapy for chronic inflammatory diseases was shown to induce a long-lasting increase in circulating T cells paralleled with a reduced expression of activation markers (54). Thus, the observed effect of adjuvant radiotherapy on memory CD4\u003csup\u003e+\u003c/sup\u003e T cells is novel and should be further explored in conjunction with patients\u0026rsquo; outcome, as a possible biomarkers of therapy response or efficacy.\u003c/p\u003e "},{"header":"Conclusion","content":" \u003cp\u003eTaken together, this human exploratory study in early, non-metastatic BC revealed changes in blood leukocyte populations associated with the presence of BC, surgical removal and adjuvant radiotherapy. Specifically, we identified CD117\u003csup\u003e+\u003c/sup\u003e G-MDSC and CD45RO\u003csup\u003e+\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e memory T cells correlating with the presence of the primary tumor and radiotherapy, respectively. Importantly, the study demonstrates that a minimally supervised, algorithm-based analysis of flow cytometry data is a powerful tool to reproducibly detect phenotypical changes in peripheral blood leukocytes in cancer patients. The approach also identifies non-anticipated population correlated with disease state of therapy. These results should instigate the further investigation of peripheral blood leukocytes as source of reliable candidate biomarkers to detect BC, to monitor response to treatment and possibly disease progression.\u003c/p\u003e"},{"header":"List Of Abbreviations","content":"\u003cp\u003eBC Breast Cancer\u003c/p\u003e\n\u003cp\u003eCTC Circulating Tumor Cells\u003c/p\u003e\n\u003cp\u003eDTC Disseminated Tumor Cells\u003c/p\u003e\n\u003cp\u003eG-MDSC Granulocytic-Myeloid Derived Suppressor Cells\u003c/p\u003e\n\u003cp\u003eHD Healthy Donor\u003c/p\u003e\n\u003cp\u003eMo-MDSC Monocytic- Myeloid Derived Suppressor Cells\u003c/p\u003e\n\u003cp\u003eTreg Regulatory T cell\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Cantonal ethic commission for human research on Humans of Canton Ticino (CE 2967) and extended to Vaud-Fribourg-Neuch\u0026acirc;tel, Switzerland. All Patients and HDs gave written informed consent before study entry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they consent for the publication of the present manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Swiss National Science Foundation, grant numbers 31003A_159824 and 31003A_179248 and the Medic Foundation to CR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026rsquo; contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, C.R. and A.F.-P.; Methodology, S.C., M.B. C.R. and A.F.-P; Formal Analysis, S.C.; Investigation, S.C., B.F, L.N. and M.B.; Resources, A.B., A.C., A.CH., A.F.-P., P.T., A.S., L.N. M.B., and C.R.; Writing\u0026mdash;original draft preparation, S.C. and C.R..; Writing\u0026mdash;review and editing, A.F.-P., C.R., S.C. and P.T.; Supervision, S.C.; Project administration, C.R. and A.F.-P..; Funding acquisition, C.R.\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to tank Dr. Chiara Secondini for her help in establishing the clinical protocol and patient forms, Nathalie Duffey for her help in processing the samples and Dr. Med. Sebastiano Semini for his help in identifying healthy donors. The authors wish to thank all patients and healthy donors for participating to this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMalvezzi M, Bertuccio P, Levi F, La Vecchia C, Negri E. European cancer mortality predictions for the year 2012. Annals of Oncology. 2012 Apr;23(4):1044\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003ePerou CM, S\u0026oslash;rlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, et al. Molecular portraits of human breast tumours. Nature. 2000 Aug;406(6797):747\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eSotiriou C, Neo S-Y, McShane LM, Korn EL, Long PM, Jazaeri A, et al. Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proceedings of the National Academy of Sciences. 2003 Sep 2;100(18):10393\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eDawson S-J, Rueda OM, Aparicio S, Caldas C. A new genome-driven integrated classification of breast cancer and its implications. 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Autoimmunity. 2017 Feb 17;50(2):133\u0026ndash;40.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cp\u003eTable 1.\u003c/p\u003e\n\u003cp\u003eClinical data of breast cancer patients included in the study.\u003c/p\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003ePatient Number\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAge\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eER (%)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003ePR (%)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eHER2 (+/-)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eKi67 (%)\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eGrade\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eTumor size\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eLN mets\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAnti-hormonal therapy\u003c/div\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e62\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e95\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e70\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e5\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epT1b\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epN1a\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e58\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e95\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e95\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e10\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eT1b\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eN0\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e50\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e100\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e100\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e+\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e5\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epT1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epN0\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eTamoxifen\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e73\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e95\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e25\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epT2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epN1a\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e5\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e49\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e90\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e80\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e10\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eT1b\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epN0\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eTamoxifen\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e6\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e69\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e100\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e100\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e15\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epT1a\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epN0\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eTamoxifen\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e7\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e53\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e95\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e60\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e10\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epT1c\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epN0\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eLetrozole\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e8\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e73\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e100\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003ena\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epT1c\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epN0\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eTamoxifen\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e9\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e67\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e90\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e80\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e5\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epT1b\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epN0\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eTamoxifen\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e10\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e66\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e100\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e100\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e5\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epT1c\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epN0\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eLetrozole\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e11\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e64\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e95\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e80\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e10\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epT1b\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epN0\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eLetrozole\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e12\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e61\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e80\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e100\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e10\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epT1b\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epN0\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAnastrozole\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e13\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e43\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e95\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e95\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e10\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epT1c\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epN0\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eTamoxifen\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePatient\u0026rsquo;s demographics, tumor subtype, grade, stage (pT and pN) and treatment after conservative surgery. ER (%): oestrogen receptor expression in percent; PR (%): progesterone receptor expression in percent; HER2 (\u0026plusmn;): overexpression of HER-2; Ki67 (%): fraction of cancer cells positive for Ki67 expression.\u003c/p\u003e\n\u003c/div\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":"breast-cancer-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brcr","sideBox":"Learn more about [Breast Cancer Research](http://breast-cancer-research.biomedcentral.com)","snPcode":"13058","submissionUrl":"https://submission.nature.com/new-submission/13058/3","title":"Breast Cancer Research","twitterHandle":"@BCRJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, Radiotherapy, MDSC, CD117, FlowJo, Unsupervised analysis, Biomarker","lastPublishedDoi":"10.21203/rs.3.rs-146187/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-146187/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Advanced breast cancer (BC) impact immune cells in the blood but whether such effects may reflect the presence of early BC and its therapeutic management remains elusive. \u003c/p\u003e\u003cp\u003eMethods: To address this question, we used multiparametric flow cytometry to analyse circulating leukocytes in patients with early BC (n=13) at time of diagnosis, after surgery and after adjuvant radiotherapy, compared to healthy individuals. Data were analysed using a minimally supervised approach based on FlowSOM algorithm and validated manually. \u003c/p\u003e\u003cp\u003eResults: At time of diagnosis, BC patients have an increased frequency of CD117\u003csup\u003e+\u003c/sup\u003e Granulocytic-Myeloid Derived Suppressor Cells (G-MDSC), which was significantly reduced after tumor removal. Adjuvant radiotherapy increased the frequency of CD45RO\u003csup\u003e+\u003c/sup\u003e memory CD4\u003csup\u003e+\u003c/sup\u003e T cells and CD4\u003csup\u003e+\u003c/sup\u003e regulatory T cells. FlowSOM algorithm analysis revealed several unanticipated populations, including cells negative for all markers tested, CD11b\u003csup\u003e+\u003c/sup\u003eCD15\u003csup\u003elow\u003c/sup\u003e, CD3\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e-\u003c/sup\u003eCD8\u003csup\u003e-\u003c/sup\u003e, CD3\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e, CD3\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003eCD127\u003csup\u003e+\u003c/sup\u003eCD45RO\u003csup\u003e+\u003c/sup\u003e cells, associated with BC or radiotherapy. \u003c/p\u003e\u003cp\u003eConclusions: This study revealed changes in blood leukocytes associated with primary BC, surgical removal and adjuvant radiotherapy. Specifically, it identified increased levels of CD117\u003csup\u003e+\u003c/sup\u003e G-MDSC, memory and regulatory CD4\u003csup\u003e+ \u003c/sup\u003eT cells as potential biomarkers of BC and radiotherapy, respectively. Importantly, the study demonstrates the value of unsupervised analysis of complex flow cytometry data to unravel new cell populations of potential clinical relevance.\u003c/p\u003e","manuscriptTitle":"Circulating Immune Cell Populations Related to Primary Breast Cancer, Surgical Removal and Radiotherapy Revealed by Flow Cytometry Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-01-18 17:04:57","doi":"10.21203/rs.3.rs-146187/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor Revision","date":"2021-03-01T00:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-02-22T00:00:00+00:00","index":2,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"editorInvitedReview","content":"","date":"2021-02-22T00:00:00+00:00","index":1,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"reviewerAgreed","content":"","date":"2021-02-17T00:00:00+00:00","index":2,"fulltext":""},{"type":"reviewerAgreed","content":"","date":"2021-02-01T00:00:00+00:00","index":1,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-01-26T00:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"","date":"2021-01-11T00:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-01-11T00:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-01-10T23:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-01-10T23:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brcr","sideBox":"Learn more about [Breast Cancer Research](http://breast-cancer-research.biomedcentral.com)","snPcode":"13058","submissionUrl":"https://submission.nature.com/new-submission/13058/3","title":"Breast Cancer Research","twitterHandle":"@BCRJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bfb65953-2830-431b-af02-32900e9c0ab9","owner":[],"postedDate":"January 18th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":1894812,"name":"Cancer Biology"}],"tags":[],"updatedAt":"2021-05-26T05:49:37+00:00","versionOfRecord":[],"versionCreatedAt":"2021-01-18 17:04:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-146187","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-146187","identity":"rs-146187","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}