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Tumor-associated macrophages in classical Hodgkin lymphoma: hormetic relationship to outcome


Commonly attributed to the prevalence of M2 macrophages, tumor-associated macrophages (TAM) are linked to poor outcome in Hodgkin lymphoma (HL). MYC is supposed to control the expression of M2-specific genes in macrophages, and deficiency in MYC-positive macrophages inhibits tumor growth in mouse models. To verify this hypothesis for HL, seventy-six samples were subjected to immunohistochemical double staining using CD68 or CD163 macrophage-specific antibodies and a reagent detecting MYC. For each cell population, labelled cells were grouped according to low, intermediate and high numbers and related to disease-free survival (DFS) and overall survival (OS). MYC+ cells accounted for 21% and 18% of CD68+ and CD163+ cells, respectively. Numbers of MYC− macrophages were significantly higher in EBV+ cases while no differences were observed for MYC+ macrophages between EBV+ and EBV− cases. Cases with highest numbers of macrophages usually showed worst DFS and OS. In most scenarios, intermediate numbers of macrophages were associated with better outcome than very low or very high numbers. Our observations are reminiscent of the “hormesis hypothesis” and suggest that a relative lack of TAM may allow HL growth while macrophages display an inhibitory effect with increasing numbers. Above a certain threshold, TAM may again support tumor growth.


Hodgkin lymphoma (HL) is classified into two major types: classical HL (cHL), accounting for 95% of all HL cases, and nodular lymphocyte predominant HL (NLPHL)1,2. Histologically, HL is characterized by a small number of neoplastic Hodgkin and Reed-Sternberg (HRS) cells embedded in an abundant inflammatory infiltrate of immune cells, including lymphocytes, macrophages, eosinophils, mast cells, plasma cells and stroma cells. These cells contribute to the microenvironment of the tumor3,4.

In general, macrophages play an important role in tumor microenvironments5. They are commonly defined according to the M1/M2-paradigm6 with M1-polarised macrophages considered as being cytotoxic, and M2-polarised macrophages as promoting wound healing, angiogenesis and defence against parasites. Consequently, M1 macrophages are generally considered to have anti-neoplastic effects while M2 macrophages are believed to support tumor growth. M1/M2-polarisation is influenced by the microenvironment7,8 and the ability of Th1/Th2 cytokines promoting opposing activities in macrophages5,9,10,11. For example, Th1 cells release interferon-γ, which activates the transcription factor STAT112. STAT1 is responsible for the expression of various cytokines such as interleukin(IL)-12 as well as the expression of nitrogen-synthetase13,14. This activation of M1 macrophages is also called classical activation. “Alternative activation” of M2-polarised macrophages is supported by Th2 through release of IL-4, IL-10 and IL-1314,15 which activate transcription factors, such as CMAF, thus stimulating expression of IL-10 and arginase16,17.

Several studies have demonstrated an association of predominantly M2-polarised tumor-associated macrophages (TAM) in different types of tumors with poor prognosis18,19,20,21,22,23,24,25. The M1/M2-paradigm has been confirmed repeatedly but the spectrum of macrophage differentiation is still incompletely understood. A newly identified population of MYC-positive macrophages has recently been found. In macrophages, MYC supports the expression of M2-specific genes like ALOX15, MRC1, SCARB1 and, thus, M2-polarisation26.

MYC is known as a transcription factor, which promotes cell growth and differentiation. MYC also controls the expression of the tumor-promoting factors matrix metallopeptidase 9 (MMP9), vascular endothelial growth factor (VEGF), transforming growth factor (TGF-ß) and hypoxia-inducible factor-1 (HIF-1α)26. The deletion of the MYC gene in macrophages results in a reduced expression of these factors, a defective tumor angiogenesis and a reduced tumor growth in mouse melanoma and fibrosarcoma models27. Thus, the detection of MYC-positive macrophages is associated with increased tumor growth.

Epstein-Barr virus (EBV) infection is associated with the pathogenesis of cHL28,29,30. EBV status of HRS cells effects local immune response and macrophage differentiation. Barros et al.31 described a characteristic Th1 profile in EBV+ pediatric cHL with dominant M1-polarised microenvironment. By contrast, EBV− pediatric cHL display a predominant Th2 immune response, which is associated with a higher number of M2 macrophages32. In pediatric cHL, EBV absence is associated with poor disease outcome, while in adult cHL, conflicting evidence is shown and EBV+ cases are discussed to link to adverse prognosis33,34,35.

Here, we have examined a well characterised series of cHL cases for the presence of CD68+ and CD163+ macrophages with special emphasis on MYC+ macrophages and their impact on disease outcome in relation to EBV-status.

Materials and Methods

Tissue samples

Formalin-fixed paraffin-embedded (FFPE) tissue blocks from 84 patients diagnosed with HL at the Institute of Pathology, University of Erlangen (Germany) and collected between 1991 and 2007 were incorporated, including 53 cases of cHLns (cHL nodular sclerosis), 29 cases of cHLmc (cHL mixed cellularity), one case of cHLlr (cHL lymphocyte-rich) and one case of interfollicular cHL. The collective of 84 patients was partially part of a previous study36. Hodgkin’s lymphomas were classified according to the Ann Arbor classification. Fourteen cases corresponded to stage I, 36 cases to stage II, 17 cases to stage III, 11 cases to stage IV, in 6 cases the stage was unknown. All patients were treated according to the ongoing protocols of the German Hodgkin Lymphoma Study Group.

Fifty-three samples were from men and 31 from women. The age range at diagnosis was between 4 and 84 years, the average age 40 years. Eleven patients (13%) were 18 years old or younger at the time of diagnosis.

After staining the slides, eight cases had to be excluded because of tissue loss. Thus, 76 cases could be used for analysis (Table 1), including 51 cases of cHLns, 23 cases of cHLmc, 1 case of cHLlr and 1 case of interfollicular cHL (Table 1).

Table 1 Overview of the clinicopathological data (cHLns: nodular sclerosis classical Hodgkin lymphoma, cHLmc: mixed cellularity classical Hodgkin lymphoma, cHLlr: lymphocyte-rich classical Hodgkin lymphoma).

All tissue samples had been submitted for diagnostic purposes and were examined for the purpose of this study only after completion of diagnosis. No material was taken for the sole purpose of this study.

Double staining immunohistochemistry

Tissue microarrays were produced for all 84 HL cases with a core diameter of 1.5 mm. Two cores were processed per case.

Paraffin sections were prepared at a thickness of 1 μm, dewaxed and subjected to antigen retrieval in a Leica BOND III System (Leica, Berlin, Germany) by heating with HIER (heat induced epitope retrieval) EDTA buffer (Bond Epitope Retrieval Solution 2, pH 9.0, Leica BOND III System). Next, double staining immunohistochemistry was carried out using antibodies directed against macrophage-specific antigens (CD68, CD163) and against the proto-oncogene-encoded MYC protein using established protocols37.

Briefly, tissue samples were incubated with an appropriately diluted MYC antibody (1:50, monoclonal, rabbit, clone EP121; Biocare Medical, Pacheco, California, USA) as a primary antibody for 30 minutes at room temperature, followed by washing (Bond Wash Solution Leica BOND III System) and detection of immobilised antibodies using the horseradish peroxidase polymer reagent (Bond Polymer Refine Detection Leica BOND III System) and diaminobenzidine (DAB) as chromogen.

After washing (five minutes at room temperature) with Wash Buffer (Zytomed Systems, Berlin, Germany), anti-CD68 (1:800, clone PG-M1; Agilent Dako, Santa Clara, California, USA) or anti-CD163 (1:400, clone 10D6; Leica, Newcastle Upon Tyne, UK) antibodies were added and the slides incubated for 30 minutes at room temperature. Bound antibodies were detected by means of the Alkaline Phosphatase Polymer Kit (Zytomed Systems, Berlin, Germany) and Blue Alkaline Phosphatase (Vector Laboratories, California, USA) as a chromogen, following the protocols described by Barros et al.37. The slides were not counterstained.

Each sample was evaluated for total numbers of CD68+ and CD163+ macrophages, for CD68+/MYC+ and CD163+/MYC+ as well as for CD68+/MYC− and CD163+/MYC− cells.

EBV detection

Samples had been tested previously for EBV infection by EBER-specific in situ hybridization (ISH) and/or by immunohistochemistry (IHC) targeting the EBV latent membrane protein 1 (LMP1) in the Institute of Pathology, University of Erlangen, Germany.

Computer assisted microscopical analysis

Processed samples were visually examined using an Olympus microscope BX53 at 20x objective magnification and photographed at a scale of 1:200 (Olympus DP26). Two representative areas were photographed per case (one for each core). Criteria of image selection were areas around tumor cells with an increased occurrence of macrophages. Areas of sclerosis were excluded.

Labelled cells were counted per mm2, applying the image analysis software ImageJ (Java, Wisconsin, USA). For statistical analysis, the mean of the two cores per case was used.

Statistical analysis

The statistical software SPSS (IBM SPSS STATISTICS 24) was used for all analyses, including statistical evaluation and graphical presentation, except for confidence interval calculation for event estimates which was done by R and R Studio38, package “survival”39,40. The measure of discrepancy between two related samples was evaluated by Wilcoxon signed-rank test. To check for group differences in continuous variables, Mann-Whitney-U test was used. Estimates for disease-free (DFS) and overall survival (OS) were calculated according to Kaplan-Meier. OS was defined as the time interval between initial diagnosis and death, DFS as the time interval between initial diagnosis and relapse. OS or DFS times between patient groups were compared with the log-rank test.

Cox regression analysis was used to additionally adjust for EBV-status. A two-sided significance level of α = 0.05 was used. No adjustment for multiple testing was applied in this exploratory study.

Survival ROC (Receiver operating characteristic) curves for CD163+, CD163+/MYC−, CD163+/MYC+ at time point 2 years after diagnosis were calculated for relapse and death of any cause. For promising cut off values for relapse, sensitivity, specificity and positive and negative predictive values were calculated. Survival ROC analyses was done using R package timeROC41,42.

Ethics declarations

No humans were directly involved in the study. All cases have been included in previous studies36,43 and were available to us only as tissue microarrays in an anonymous fashion. All tissue samples were obtained for diagnostic purposes. The use of archival FFPE tissue blocks and of clinical data has been approved by the Ethics Committee of the Friedrich-Alexander-University of Erlangen-Nuremberg36,43.


Macrophage subsets

76 HL samples were investigated by immunohistochemical double staining for expression of MYC protein and of macrophage-specific antigens CD68 or CD163 (Fig. 1). Numbers of CD68+ and CD163+ cells, double-positive (CD68+/MYC+, CD163+/MYC+) and single-positive (CD68+/MYC−, CD163+/MYC−) cells were analysed for each HL case. Results are summarised in Table 2 and Supplementary Fig. S1.

Table 2 Numbers of macrophages in cHL per mm2 (n = 76) in total and according to EBV status (nEBV− = 50, nEBV+ = 26).
Figure 1

Examples of cHL biopsies stained by immunohistochemical double staining. Blue cytoplasmic/membranous staining indicates CD68 (A) or CD163 (B) expression, brown nuclear staining expression of transcription factor MYC (Fig. 1). Long arrows indicate MYC+ macrophages with blue cyctoplasmic/membraneous staining for CD68 (A) or CD163 (B) and usually moderate nuclear brown staining for MYC. Short arrows indicate macrophages lacking co-expression of MYC. In addition, there are large Hodgkin and Reed-Sternberg cells showing strong brown nuclear staining indicating MYC expression in the absence of macrophage-specific antigens (arrowheads). Finally, there are large numbers of small to medium-sized moderately MYC+ cells lacking expression macrophage antigens probably representing background lymphocytes.

Overall, higher numbers of CD163+ cells were detected compared to CD68+ cells (median 903 CD163+ macrophages/mm2 vs. median 837 CD68+ macrophages/mm2; p < 0.001 Wilcoxon signed-rank test; Table 2).

Median numbers of CD68+/MYC+ and CD163+/MYC+ macrophages were 178 and 166 cells/mm2, and 624 and 754 cells/mm2 for CD68+/MYC− and CD163+/MYC− macrophages (Table 2). Therefore, MYC+ cells accounted for on average 21% of CD68+ and 18% of CD163+ macrophages.

EBV infection status and number of macrophages

EBV+ cases contained higher numbers of CD68+ and CD163+ macrophages/mm2 than EBV− cases (EBV+: median 937 CD68+ macrophages/mm2 and 1246 CD163+ macrophages/mm2, p = 0.012; EBV−: median 747 CD68+ macrophages/mm2 and 840 CD163+ macrophages/mm2; Table 2). On average, 20% more CD68+ and 33% more CD163+ macrophages were detected in EBV+ cases than in EBV− cases.

Higher numbers of CD68+/CMYC− and CD163+/CMYC− macrophages were observed in EBV+ cases than in EBV− cases (EBV+: median 740 CD68+/MYC− macrophages/mm2 and 923 CD163+/CMYC− macrophages/mm2; EBV−: median 596 CD68+/MYC− macrophages/mm2 and 699 CD163+/MYC− macrophages/mm2; pCD68+/CMYC− = 0.002, pCD163+/CMYC− = 0.002; Table 2). By contrast, no statistically significant differences between EBV+ and EBV− cases was observed regarding the numbers of MYC+ cells (Table 2).

Macrophage numbers and survival analysis

To investigate the association of numbers of macrophages and outcome, cases were subdivided into three classes (tertiles) with low, intermediate and high numbers of cells for the total numbers of CD68+ and CD163+ macrophages, the numbers of MYC+ macrophages and the numbers of MYC− macrophages. The definition of classes is given in Supplementary Table S1.

For total CD68+ and CD163+ cells, there were substantial differences in DFS with regards to tertiles of number of macrophages (CD68+: p = 0.03, CD163+: p = 0.07). Cases with highest numbers of macrophages (class 3) consistently displayed shortest DFS (Fig. 2A,B). Similarly, class 3 cases were characterized by shorter OS than class 1 and 2 cases (CD68+: p = 0.23, CD163+: p = 0.07; Fig. 2C,D). Unexpectedly, however, intermediate numbers of macrophages (class 2) were associated best DFS for both CD68+ and CD163+ cells whereas no substantial differences between class 1 and class 2 cases was observed in OS (Fig. 2). Overall, results of survival analyses were comparable for CD68+ and CD163+ groups.

Figure 2

Kaplan-Meier curves in relation to number of macrophages (DFS for (A) CD68+ macrophages/mm2, (B) CD163+ macrophages/mm2, and OS for (C) CD68+ macrophages/mm2, (D) CD163+ macrophages/mm2; n = 76). Kaplan-Meier curves were done using IBM SPSS STATISTICS 24.

Subgroups of MYC− and MYC+ macrophages were also subdivided into three classes (tertiles) as summarized in Supplementary Table S1. For MYC− macrophages, shorter DFS and OS were observed for class 3 cases (highest numbers of macrophages) than for class 1 and class 2 cases (Fig. 3A–D).

Figure 3

Kaplan-Meier curves in relation to MYC− macrophage distributions (A) CD68+/MYC− macrophages/mm2, (B) CD163+/MYC− macrophages/mm2 associated with DFS, (C) CD68+/MYC− macrophages/mm2, (D) CD163+/MYC− macrophages/mm2 associated with OS; n = 76). Kaplan-Meier curves were done using IBM SPSS STATISTICS 24.

For MYC+ macrophages (Fig. 4A–D), high numbers of CD163+/MYC+ cells (class 3) were associated with shortest OS (Fig. 4D). Interestingly, best DFS was seen for cases with intermediate numbers of CD68+/MYC+ macrophages (class 2) and best OS occurred in class 2 cases with intermediate numbers of CD163+/MYC+ macrophages (Fig. 4A,D).

Figure 4

Kaplan-Meier curves in relation to MYC+ macrophage distributions (A) CD68+/MYC+ macrophages/mm2, (B) CD163+/MYC+ macrophages/mm2 associated with DFS, (C) CD68+/MYC+ macrophages/mm2, (D) CD163+/MYC+ macrophages/mm2 associated with OS; n = 76). Kaplan-Meier curves were done using IBM SPSS STATISTICS 24.

Results ROC analysis

Using ROC analysis we analysed the association between CD163+, CD163+/MYC−, CD163+/MYC+ cells and DFS and OS (Supplementary Fig. S2 and Table S2). Area under the curve (AUC) were smaller than 0.5 for overall survival, however, for DSF AUC were between 0.62 and 0.75 (Supplementary Fig. S2). We also tried to find a cut off for DSF and calculated sensitivity, specificity and predictive values for promising cut offs. Sensitivity was 71%, specificity between 72% and 88%. Positive predictive values were low, between 20% and 39%, negative predictive values were between 85% and 89% (Supplementary Table S2). However, all these results should be interpreted with caution, since sample size is low.

EBV infection status and survival

Overall, EBV infection of HRS cells was detected in 30 of 84 cases (36%).

In the EBV+ group, shorter DFS was observed compared to EBV− patients (Supplementary Fig. S3A). However, EBV status was not substantially associated with OS (Supplementary Fig. S3B).

Exploratory analyses stratified by EBV status suggested, that the association of macrophage numbers with DFS and OS was more pronounced in EBV+ cases than in EBV− cases (Kaplan Meier analyses, data not shown). In EBV+ cases, both highest total numbers of macrophages and of MYC− and MYC+ macrophages (class 3) were associated with shortest survival while cases with intermediate numbers of macrophages displayed best outcome in most scenarios although these effects were not statistically significant (not shown).

In Cox regression models including all cases (EBV+ and EBV−) it could be demonstrated that after adjustment for EBV status highest numbers of macrophages (class 3) were associated with shortest survival (Table 3, DFS and OS, class 3 compared to class 2) and cases with intermediate numbers of macrophages (class 2) displayed best DFS and OS in most scenarios (Table 3).

Table 3 Separate Cox regression analyses of DFS and OS adjusted for EBV infection status. Hazard ratios (HR) and 95% confidence intervals (CI).


Previous studies on the role of macrophages in the pathogenesis of cHL have generally concluded that high numbers of macrophages in the tumor microenvironment are associated with poor outcome in cHL patients18,44,45,46,47,48,49 although this has been disputed by others50,51,52. These studies usually employed CD68 antibodies for the immunohistochemical detection of macrophages. In other studies, this was combined with the detection of CD163, based on the assumption that CD163 is a marker of M2 polarisation48,50,53.

This assumption, however, has been challenged by the demonstration that expression of M1-specific transcription factors such as pSTAT1 can be detected in CD163+ macrophages37,54. Furthermore, we show here that CD163+ cell can outnumber CD68+ cells in cHL and that numbers of CD163+ cells are far greater in EBV+ than in EBV− cHL cases (p = 0.004, Table 2). In view of the prevalence of a Th1-predominant microenvironment in EBV+ cHL32,55, this observation provides further evidence to suggest that CD163+ cells cannot be considered as an M2 subgroup of total CD68+ macrophages. It is not clear why CD163+ cells can outnumber CD68+ cells. This would require an in depth phenotypic analysis of CD163+ cells. One possibility would be expression of CD163 in cells other than macrophages. Indeed, it has been reported that dendritic cells can express CD16356.

To assess the impact of total macrophages on outcome, we adopted a novel approach, assigning cases to three classes with low, intermediate, and high numbers of macrophages (Supplementary Table S1). Although limited by the relatively small case number as well as the small number of events, this analysis allowed a more subtle analysis of the effects of varying macrophages numbers on outcome in cHL than the two-tier system usually employed in previous studies43,47,48,49,50. Using this approach, we confirm previous papers showing that the numbers of TAM are critical for outcome in cHL and that highest numbers of TAM (class 3 in our study) are usually associated with worst outcome18,47,48,49,53. This was true for both CD68+ as well as for CD163+ cells. Studying an overlapping series of cases, Tudor et al. previously obtained similar results for CD163+ macrophages but not for CD68+ cells43. Unexpectedly, however, the three-tier approach adopted by us revealed that cases with intermediate numbers of macrophages (class 2) usually showed the best outcome, ahead of cases with smallest numbers of macrophages (Table 3). This was observed for total numbers of CD68+ and CD163+ macrophages, MYC+ and MYC− macrophages and applied to both DFS and OS, with the exception of CD68+/MYC− macrophages which showed best outcome in the group with lowest macrophage numbers (class 1). Use of a four-tiered system confirmed these observations (not shown). Thus, while confirming the negative prognostic impact of high numbers of macrophages in the cHL microenvironment, our data indicate that there is no linear dose-effect relationship and sheds new light onto the role of macrophages in cHL.

MYC has recently been identified as a key factor in the alternative activation of M2 macrophages26. Pello et al. showed a defective tumor angiogenesis and reduced tumor growth by deletion of the MYC gene in macrophages in mouse models of melanoma and fibrosarcoma27. There are no previous studies addressing the possible role of these MYC+ and MYC− macrophages in human tumor models. Here, we first demonstrate that numbers of MYC+ macrophages account for approximately 20% of all macrophages for both CD68+ and CD163+ cells. Interestingly, in EBV+ cHL cases, more MYC− macrophages were observed (CD68 p = 0.002, CD163 p = 0.002) while numbers of MYC+ macrophages were similar between EBV+ and EBV− cHL cases (Table 2). This is in line with the prevalent Th1 immune response pattern in EBV+ cHL32 and indeed suggests that MYC expression in human macrophages is related to M2 polarisation.

Based on the observations of Pello et al.26,27, we hypothesised that MYC+ macrophages would be associated with poor outcome relative to MYC− macrophages. In our analyses, CD163+/MYC− macrophages behaved similar to overall macrophages. Specifically, worst outcome was associated with highest macrophage numbers (class 3) and best outcome with intermediate numbers (class 2) for both DFS and OS. By contrast for CD68+/MYC− cells, best outcome was observed for cases with lowest numbers (class 1) for both endpoints. For MYC+ macrophages, Kaplan-Meier curves revealed no clear trend, but in Cox regression the pattern of best outcome with intermediate numbers (class 2) again emerged.

EBV is an established co-factor in the pathogenesis of cHL. However, it is uncertain if EBV contributes to outcome of cHL33,34,35,57,58,59, and this may also depend on patient age31,33,34,35,59,60. In our study, EBV+ cases displayed worse DFS and OS (p = 0.06 and p = 0.25, respectively) than EBV− cases. Confirming previous findings43,47,48,49,50,60, EBV+ cases were characterised by higher numbers of tumor-associated macrophages in our study. We therefore hypothesise that EBV-mediated cytokine and chemokine synthesis by HRS cells may contribute to the accumulation of macrophages in cHL microenvironment and through this mechanism to worse outcome.

Our observation that intermediate numbers of macrophages in cHL microenvironment may be associated with better outcome than very low and very high numbers is reminiscent of the “hormesis hypothesis” as proposed by Prehn for tumor infiltrating immune cells61. Prehn suggested that very small numbers of tumor-infiltrating immune cells may be insufficient to support tumor growth and only become supportive of tumor growth above a certain threshold. Further, tumor growth is inhibited by high doses of immune cells61,62.

Our data suggest an inverse situation in which small numbers of tumor-associated macrophages may have a moderate growth promoting effect on cHL (class 1 cases in our study) while with increasing numbers, macrophages display an inhibitory effect on tumor growth (class 2 cases). Finally, very high numbers (class 3 cases) may provide support for tumor growth. This conclusion is supported by the results of Pearce et al.63 who showed that tumor growth is inhibited by high doses of anti-tumor antibodies but stimulated by low antibody doses in animal models of colorectal cancer and Burkitt lymphoma63. Interestingly, in that study, the authors observed high numbers of tumor-associated macrophages in animals treated with the tumor supporting dose while in animals treated with a tumor inhibiting dose, numbers of tumor-associated macrophages were low63. Finally, the authors showed that the tumor promoting effect of antibodies was dependent on the presence of macrophages63.

It is likely, that these effects of tumor-associated macrophages on outcome in cHL may not only be related to total numbers of macrophages but may reflect underlying changes in macrophage polarization. In any case, the notion that macrophages in tumor microenvironments may have a hormetic rather than a linear relationship to outcome parameters merits further investigation and should be considered in studies of other tumor entities.


  1. 1.

    Campo, E. et al. The 2008 WHO classification of lymphoid neoplasms and beyond: evolving concepts and practical applications. Blood 117, 5019–5032, (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Yung, L. & Linch, D. Hodgkin’s lymphoma. Lancet 361, 943–951, (2003).

    Article  PubMed  Google Scholar 

  3. 3.

    Xu, M. L. & Fedoriw, Y. Lymphoma microenvironment and immunotherapy. Surg. Pathol. Clin. 9, 93–100, (2016).

  4. 4.

    Ruella, M. et al. Overcoming the immunosuppressive tumor microenvironment of Hodgkin lymphoma using chimeric antigen receptor T cells. Cancer Discov. 7, 1154–1167, (2017).

  5. 5.

    Mantovani, A. & Sica, A. Macrophages, innate immunity and cancer: balance, tolerance, and diversity. Curr. Opin. Immunol. 22, 231–237, (2010).

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Classen, A., Lloberas, J. & Celada, A. Macrophage activation: classical versus alternative. Methods Mol. Biol. 531, 29–43, (2009).

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Stout, R. D. & Suttles, J. Functional plasticity of macrophages: reversible adaptation to changing microenvironments. J. Leukoc. Biol. 76, 509–513, (2004).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Stout, R. D. et al. Macrophages sequentially change their functional phenotype in response to changes in microenvironmental influences. J. Immunol. 175, 342–349, (2005).

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Monney, L. et al. Th1-specific cell surface protein Tim-3 regulates macrophage activation and severity of an autoimmune disease. Nature 415, 536–541, (2002).

    CAS  Article  PubMed  Google Scholar 

  10. 10.

    Chizzolini, C., Rezzonico, R., De Luca, C., Burger, D. & Dayer, J. M. Th2 cell membrane factors in association with IL-4 enhance matrix metalloproteinase-1 (MMP-1) while decreasing MMP-9 production by granulocyte-macrophage colony-stimulating factor-differentiated human monocytes. J. Immunol. 164, 5952–5960, (2000).

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Anderson, C. F. & Mosser, D. M. A novel phenotype for an activated macrophage: the type 2 activated macrophage. J. Leukoc. Biol. 72, 101–106 (2002).

    CAS  PubMed  Google Scholar 

  12. 12.

    Darnell, J. E. Jr., Kerr, I. M. & Stark, G. R. Jak-STAT pathways and transcriptional activation in response to IFNs and other extracellular signaling proteins. Science 264, 1415–1421, (1994).

    ADS  CAS  Article  PubMed  Google Scholar 

  13. 13.

    Lawrence, T. & Natoli, G. Transcriptional regulation of macrophage polarization: enabling diversity with identity. Nat. Rev. Immunol. 11, 750–761, (2011).

    CAS  Article  PubMed  Google Scholar 

  14. 14.

    Mantovani, A. et al. The chemokine system in diverse forms of macrophage activation and polarization. Trends Immunol. 25, 677–686, (2004).

    CAS  Article  PubMed  Google Scholar 

  15. 15.

    Stein, M., Keshav, S., Harris, N. & Gordon, S. Interleukin 4 potently enhances murine macrophage mannose receptor activity: a marker of alternative immunologic macrophage activation. J. Exp. Med. 176, 287–292, (1992).

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Cao, S. et al. Differential regulation of IL-12 and IL-10 gene expression in macrophages by the basic leucine zipper transcription factor c-Maf fibrosarcoma. J. Immunol. 169, 5715–5725, (2002).

    CAS  Article  PubMed  Google Scholar 

  17. 17.

    Pauleau, A. L. et al. Enhancer-mediated control of macrophage-specific arginase I expression. J. Immunol. 172, 7565–7573, (2004).

    CAS  Article  PubMed  Google Scholar 

  18. 18.

    Steidl, C. et al. Tumor-associated macrophages and survival in classic Hodgkin’s lymphoma. N. Engl. J. Med. 362, 875–885, (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Ryder, M., Ghossein, R. A., Ricarte-Filho, J. C., Knauf, J. A. & Fagin, J. A. Increased density of tumor-associated macrophages is associated with decreased survival in advanced thyroid cancer. Endocr. Relat. Cancer 15, 1069–1074, (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Bronkhorst, I. H. et al. Detection of M2-macrophages in uveal melanoma and relation with survival. Invest. Ophthalmol. Vis. Sci. 52, 643–650, (2011).

    Article  PubMed  Google Scholar 

  21. 21.

    Hasita, H. et al. Significance of alternatively activated macrophages in patients with intrahepatic cholangiocarcinoma. Cancer Sci. 101, 1913–1919, (2010).

    CAS  Article  PubMed  Google Scholar 

  22. 22.

    Kurahara, H. et al. Significance of M2-polarized tumor-associated macrophage in pancreatic cancer. J. Surg. Res. 167, e211–219, (2011).

    Article  PubMed  Google Scholar 

  23. 23.

    Bingle, L., Brown, N. J. & Lewis, C. E. The role of tumour-associated macrophages in tumour progression: implications for new anticancer therapies. J. Pathol. 196, 254–265, (2002).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Chen, J. J. et al. Tumor-associated macrophages: the double-edged sword in cancer progression. J. Clin. Oncol. 23, 953–964, (2005).

    ADS  CAS  Article  PubMed  Google Scholar 

  25. 25.

    Sica, A., Schioppa, T., Mantovani, A. & Allavena, P. Tumour-associated macrophages are a distinct M2 polarised population promoting tumour progression: potential targets of anti-cancer therapy. Eur. J. Cancer 42, 717–727, (2006).

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Pello, O. M. et al. Role of c-MYC in alternative activation of human macrophages and tumor-associated macrophage biology. Blood 119, 411–421, (2012).

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Pello, O. M. et al. In vivo inhibition of c-MYC in myeloid cells impairs tumor-associated macrophage maturation and pro-tumoral activities. PLoS One 7, e45399, (2012).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Murray, P. G., Young, L. S., Rowe, M. & Crocker, J. Immunohistochemical demonstration of the Epstein-Barr virus-encoded latent membrane protein in paraffin sections of Hodgkin’s disease. J. Pathol. 166, 1–5, (1992).

    CAS  Article  PubMed  Google Scholar 

  29. 29.

    Deacon, E. M. et al. Epstein-Barr virus and Hodgkin’s disease: transcriptional analysis of virus latency in the malignant cells. J. Exp. Med. 177, 339–349, (1993).

    CAS  Article  PubMed  Google Scholar 

  30. 30.

    Pallesen, G., Hamilton-Dutoit, S. J., Rowe, M. & Young, L. S. Expression of Epstein-Barr virus latent gene products in tumour cells of Hodgkin’s disease. Lancet 337, 320–322, (1991).

    CAS  Article  PubMed  Google Scholar 

  31. 31.

    Barros, M. H., Vera-Lozada, G., Soares, F. A., Niedobitek, G. & Hassan, R. Tumor microenvironment composition in pediatric classical Hodgkin lymphoma is modulated by age and Epstein-Barr virus infection. Int. J. Cancer 131, 1142–1152, (2012).

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    Barros, M. H., Segges, P., Vera-Lozada, G., Hassan, R. & Niedobitek, G. Macrophage polarization reflects T cell composition of tumor microenvironment in pediatric classical Hodgkin lymphoma and has impact on survival. PLoS One 10, e0124531, (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Stark, G. L. et al. Hodgkin’s disease in the elderly: a population-based study. Br. J. Haematol. 119, 432–440, (2002).

    Article  PubMed  Google Scholar 

  34. 34.

    Jarrett, R. F. et al. Impact of tumor Epstein-Barr virus status on presenting features and outcome in age-defined subgroups of patients with classic Hodgkin lymphoma: a population-based study. Blood 106, 2444–2451, (2005).

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Diepstra, A. et al. Latent Epstein-Barr virus infection of tumor cells in classical Hodgkin’s lymphoma predicts adverse outcome in older adult patients. J. Clin. Oncol. 27, 3815–3821, (2009).

    Article  PubMed  Google Scholar 

  36. 36.

    Tudor, C. S. et al. B cells in classical Hodgkin lymphoma are important actors rather than bystanders in the local immune reaction. Hum. Pathol. 44, 2475–2486, (2013).

    CAS  Article  PubMed  Google Scholar 

  37. 37.

    Barros, M. H., Hauck, F., Dreyer, J. H., Kempkes, B. & Niedobitek, G. Macrophage polarisation: an immunohistochemical approach for identifying M1 and M2 macrophages. PLoS One 8, e80908, (2013).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    RCore Team R: A language and environment for statistical computing. Vienna, Austria, R Foundation for Statistical Computing (2019).

  39. 39.

    Therneau, T. M. & Lumley, T. Package ‘survival’. CRAN, The Comprehensive R Archive Network (2019).

  40. 40.

    Therneau, T. M. & Grambsch, P. M. Modeling survival data: extending the Cox model. (Springer Science & Business Media, New York, 2013).

    MATH  Google Scholar 

  41. 41.

    Blanche, P., Dartigues, J. F. & Jacqmin‐Gadda, H. Estimating and comparing time‐dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat. Med. 32, 5381–5397, (2013).

    MathSciNet  Article  PubMed  Google Scholar 

  42. 42.

    Eklund, A. Beeswarm: The bee swarm plot, an alternative to stripchart. R package version 0.2.3 (2016).

  43. 43.

    Tudor, C. S. et al. Macrophages and dendritic cells as actors in the immune reaction of classical Hodgkin lymphoma. PLoS One 9, e114345, (2014).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Jakovic, L. R. et al. The prognostic relevance of tumor associated macrophages in advanced stage classical Hodgkin lymphoma. Leuk. Lymphoma 52, 1913–1919, (2011).

    CAS  Article  PubMed  Google Scholar 

  45. 45.

    Panico, L. et al. Prognostic role of tumor-associated macrophages and angiogenesis in classical Hodgkin lymphoma. Leuk. Lymphoma 54, 2418–2425, (2013).

    CAS  Article  PubMed  Google Scholar 

  46. 46.

    Tzankov, A., Matter, M. S. & Dirnhofer, S. Refined prognostic role of CD68-positive tumor macrophages in the context of the cellular micromilieu of classical Hodgkin lymphoma. Pathobiology 77, 301–308, (2010).

    CAS  Article  PubMed  Google Scholar 

  47. 47.

    Kamper, P. et al. Tumor-infiltrating macrophages correlate with adverse prognosis and Epstein-Barr virus status in classical Hodgkin’s lymphoma. Haematologica 96, 269–276, (2011).

    Article  PubMed  Google Scholar 

  48. 48.

    Yoon, D. H. et al. CD68 and CD163 as prognostic factors for Korean patients with Hodgkin lymphoma. Eur. J. Haematol. 88, 292–305, (2012).

    CAS  Article  PubMed  Google Scholar 

  49. 49.

    Tan, K. L. et al. Tumor-associated macrophages predict inferior outcomes in classic Hodgkin lymphoma: a correlative study from the E2496 Intergroup trial. Blood 120, 3280–3287, (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Azambuja, D. et al. Lack of association of tumor-associated macrophages with clinical outcome in patients with classical Hodgkin’s lymphoma. Ann. Oncol. 23, 736–742, (2012).

    CAS  Article  PubMed  Google Scholar 

  51. 51.

    Harris, J. A. et al. CD163 versus CD68 in tumor associated macrophages of classical Hodgkin lymphoma. Diagn. Pathol. 7, 12, (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Sanchez-Espiridion, B. et al. Immunohistochemical markers for tumor associated macrophages and survival in advanced classical Hodgkin’s lymphoma. Haematologica 97, 1080–1084, (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Zaki, M. A. et al. Prognostic implication of types of tumor-associated macrophages in Hodgkin lymphoma. Virchows Arch. 459, 361–366, (2011).

    CAS  Article  PubMed  Google Scholar 

  54. 54.

    Pettersen, J. S. et al. Tumor-associated macrophages in the cutaneous SCC microenvironment are heterogeneously activated. J Invest Dermatol 131, 1322–1330, (2011).

  55. 55.

    Chetaille, B. et al. Molecular profiling of classical Hodgkin lymphoma tissues uncovers variations in the tumor microenvironment and correlations with EBV infection and outcome. Blood 113, 2765–3775, (2009).

    CAS  Article  PubMed  Google Scholar 

  56. 56.

    Comi, M. et al. Coexpression of CD163 and CD141 identifies human circulating IL-10-producing dendritic cells (DC-10). Cell. Mol. Immunol. 17, 95–107, (2020).

  57. 57.

    Enblad, G. et al. Lack of correlation between EBV serology and presence of EBV in the Hodgkin and Reed-Sternberg cells of patients with Hodgkin’s disease. Int. J. Cancer 72, 394–397,<394::aid-ijc3>;2-k (1997).

  58. 58.

    Engel, M. et al. Improved prognosis of Epstein-Barr virus associated childhood Hodgkin’s lymphoma: study of 47 South African cases. J. Clin. Pathol. 53, 182–186, (2000).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Keegan, T. H. et al. Epstein-Barr virus as a marker of survival after Hodgkin’s lymphoma: a population-based study. J. Clin. Oncol. 23, 7604–7613, (2005).

    Article  PubMed  Google Scholar 

  60. 60.

    Barros, M. H., Hassan, R. & Niedobitek, G. Tumor-associated macrophages in pediatric classical Hodgkin lymphoma: association with Epstein-Barr virus, lymphocyte subsets, and prognostic impact. Clin. Cancer Res. 18, 3762–3771, (2012).

    CAS  Article  PubMed  Google Scholar 

  61. 61.

    Prehn, R. T. The immune reaction as a stimulator of tumor growth. Science 176, 170–171, (1972).

    ADS  CAS  Article  PubMed  Google Scholar 

  62. 62.

    Prehn, R. T. The initial immune reaction to a new tumor antigen is always stimulatory and probably necessary for the tumor’s growth. Clin. Dev. Immunol. 2010, (2010).

  63. 63.

    Pearce, O. M. et al. Inverse hormesis of cancer growth mediated by narrow ranges of tumor-directed antibodies. Proc. Natl Acad. Sci. USA 111, 5998–6003, (2014).

    ADS  CAS  Article  PubMed  Google Scholar 

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The authors are grateful to Katja Wolf, Pamela Ritter, Sabine Näthe und Ann-Kathrin Höls for excellent technical assistance.

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Conception and design: G. Niedobitek. Development of methodology: M. Barros, G. Niedobitek. Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): L. Werner, M. Büttner, G. Niedobitek. Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): L. Werner, U. Grittner, D. Hartmann, Johannes Dreyer, G. Niedobitek. Writing, review, and/or revision of the manuscript: L. Werner, G. Niedobitek, J. Dreyer, U. Grittner, D. Hartmann, M. Barros, M. Büttner. Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): L. Werner, M. Büttner, G. Niedobitek. Study supervision: G. Niedobitek.

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Correspondence to Laura Werner.

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Werner, L., Dreyer, J.H., Hartmann, D. et al. Tumor-associated macrophages in classical Hodgkin lymphoma: hormetic relationship to outcome. Sci Rep 10, 9410 (2020).

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