Myeloid-derived suppressor cells (MDSC) that block antitumor immunity are elevated in glioblastoma (GBM) patient blood and tumors. However, the distinct contributions of monocytic (mMDSC) versus granulocytic (gMDSC) subsets have yet to be determined. In mouse models of GBM, we observed that mMDSCs were enriched in the male tumors, whereas gMDSCs were elevated in the blood of females. Depletion of gMDSCs extended survival only in female mice. Using gene-expression signatures coupled with network medicine analysis, we demonstrated in preclinical models that mMDSCs could be targeted with antiproliferative agents in males, whereas gMDSC function could be inhibited by IL1β blockade in females. Analysis of patient data confirmed that proliferating mMDSCs were predominant in male tumors and that a high gMDSC/IL1β gene signature correlated with poor prognosis in female patients. These findings demonstrate that MDSC subsets differentially drive immune suppression in a sex-specific manner and can be leveraged for therapeutic intervention in GBM.

Significance:

Sexual dimorphism at the level of MDSC subset prevalence, localization, and gene-expression profile constitutes a therapeutic opportunity. Our results indicate that chemotherapy can be used to target mMDSCs in males, whereas IL1 pathway inhibitors can provide benefit to females via inhibition of gMDSCs.

See related commentary by Gabrilovich et al., p. 1100.

This article is highlighted in the In This Issue feature, p. 1079

Glioblastoma (GBM) is the most common primary malignant brain tumor, with a median survival of 20 months postdiagnosis (1). Epidemiologic studies further point to a 1.6-fold higher incidence among men, suggesting a male-dominant sexual dimorphism in GBM (2). In addition, male patients have a worse prognosis than females, underscoring the clinical relevance of studying biological sex in GBM (3). This sex bias in part is dictated by tumor cell–intrinsic mechanisms. In particular, genome-wide association studies led to identification of distinct risk variants associated with GBM in male versus female patients (2). Moreover, differences in tumor mutational profile driving oncogenic transformation and cellular metabolism were linked to the differences observed in GBM manifestation, outcome, and susceptibility to treatment (4–7). In mouse models of pediatric low-grade glioma, differential activation status of tumor-infiltrating myeloid cells and altered cytokine profile were shown to drive female dominance (8). However, the contribution of tumor-extrinsic host factors to sexual dimorphism in GBM is yet to be investigated. Notably, sex differences in host immunity, regulated by the combination of sex chromosomes, hormones, and environmental factors, have been linked to efficacy of vaccine response, susceptibility to autoimmune diseases, and protection against infectious agents (9). Overall, a more active immune response in females, which is driven by a number of innate and adaptive elements, including increased type I IFN signaling, proinflammatory cytokine production, and T-cell activation, is a major factor leading to female predisposition to autoimmune diseases (9). In the context of tumor immunity, this could translate to more robust immunosurveillance and is thus likely to be an important contributor to the increased cancer mortality in males. On the other hand, studies in patients treated with immune checkpoint inhibitors suggested that males might benefit more from these treatment modalities compared with females (10). Nevertheless, it is unclear how sex-dependent immunologic variation affects tumorigenesis or differential immunotherapy response.

Myeloid cells are a major component of the GBM microenvironment and serve as a therapeutic target to reverse the immunosuppression that drives tumor progression (11). Correspondingly, myeloid-derived suppressor cells (MDSC) are more abundant in the peripheral circulation of patients with GBM compared with those with low-grade central nervous system (CNS) tumors, and increased tumor infiltration of MDSCs associates with poor GBM outcome (12–15). Initially recognized for their ability to suppress antitumor immune response, MDSCs are a heterogenous population of bone marrow–derived immature myeloid cells subclassified into monocytic (mMDSC) and granulocytic (gMDSC) subsets. Although both MDSC subsets hinder the activity of T and natural killer (NK) cells, mMDSCs and gMDSCs can undertake additional roles in the maintenance of primary tumors and promotion of metastasis, respectively (16, 17). Despite growing evidence implicating the distinct roles of MDSC subsets in cancer progression, little is known about their differential activity in GBM. Thus, we focused on the sex-specific roles of MDSC subsets as an underlying factor contributing to sexual dimorphism in GBM progression. Our data demonstrate that mMDSCs localize to the tumor microenvironment and support GBM progression in males. In contrast, systemic gMDSC accumulation is the dominant factor regulating the antitumor immune response in females. We further show that unique characteristics of MDSC subsets determine their susceptibility to distinct drug candidates, whose therapeutic efficacy was governed by the sex of the host.

mMDSCs Primarily Localize to GBM Tumors, whereas gMDSCs Accumulate in the Peripheral Circulation in a Sex-Dependent Manner

To determine the dynamics of MDSC subset accumulation and sex-dependent immunologic changes in GBM, we orthotopically implanted GL261 or SB28 cells into immunocompetent male and female C57BL/6 mice via intracranial injection (Fig. 1A). Analysis of the immune profile of the tumor-bearing hemisphere with flow cytometry (Supplementary Fig. S1A) indicated an overall increase in CD45+ cells in both male and female mice 14 days after implantation with SB28 and 21 days after implantation with GL261 cells compared with the contralateral hemisphere and sham-injected controls (Fig. 1B; Supplementary Fig. S1B). Specifically, there was an overall increase in the frequency of mMDSCs with a concomitant decrease in the percentage of gMDSCs in the tumor microenvironment (Fig. 1C; Supplementary Fig. S1C), skewing the mMDSC:gMDSC ratio compared with sham-injected controls (Fig. 1D and E) as a result of accumulation of mMDSCs in the tumor tissue (Supplementary Fig. S1D and S1E). On the basis of recent studies demonstrating sex differences in GBM incidence rates and prognosis (2, 3), we separately assessed MDSC subsets in male and female tumor models and found that mMDSCs accumulated particularly in male tumors, leading to an approximately 5.5- to 6.5-fold increase in the mMDSC/gMDSC ratio, whereas no significant change was observed in females (Fig. 1FH; Supplementary Fig. S1F–S1H). We also analyzed the peripheral immune response, as it has been demonstrated that systemic immunity also plays a critical role in in immunotherapy response (18). gMDSCs were the predominant population in the blood of sham-injected and tumor-bearing animals (Fig. 1I; Supplementary Fig. S1I). In only female mice, there was a 2-fold increase in the peripheral gMDSC frequency post–tumor implantation that was consistent across both GBM models, whereas mMDSCs remained unchanged in the blood of these animals (Fig. 1J and K; Supplementary Fig. S1J and S1K). To determine whether immunologic sex differences were limited to MDSCs, we assessed other immune cell populations, including macrophages, dendritic cells, NK cells, and T lymphocytes from tumors and peripheral blood of GL261- and SB28-bearing animals compared with matched sham-injected controls. The abundance of other tumor-infiltrating immune cell populations was not significantly altered between male and female tumor-bearing mice (Supplementary Fig. S2A–S2L). Furthermore, peripheral differences were limited to the higher frequency of overall T cells and CD4+ T cells in female mice compared with male GL261-bearing mice (Supplementary Fig. S3A–S3L). The observed sexual dimorphism in immune profile of the two mouse GBM models associated with survival differences. As recently reported for patients with GBM (3), female mice experienced longer survival compared with male mice (Supplementary Fig. S4A and S4B). To further evaluate the immunologic basis of this survival difference, we performed bone marrow transplantation with female hosts 4 weeks prior to the implantation of GL261 or SB28 tumors and demonstrated that donor marrows effectively reconstituted the host, generating both MDSC subsets (Supplementary Fig. S4C and S4D). Under these conditions, female hosts reconstituted with male donor bone marrow had decreased survival compared with female-to-female transplant controls (Supplementary Fig. S4E and S4F), demonstrating that the male immune system has a more tumor-promoting role. Collectively, these findings suggest that sex differences in the regulation of antitumor immunity can be driven by the distinct MDSC subset abundance and compartmentalization in GBM, with males having enhanced mMDSC accumulation in the tumor microenvironment and females having elevated gMDSCs in the peripheral circulation.

Figure 1.

mMDSCs accumulate in the tumors of male mice, whereas gMDSCs accumulate systemically in female mice. A, Timeline of immune infiltration analysis. B, Frequency of CD45hi bone marrow–derived immune cells in resected tumors versus the contralateral hemisphere of sham-injected or GL261-implanted mice on day 14 and day 21. Data shown as mean ± SD of n = 10/group from one of independently repeated experiments. *, P < 0.05; **, P < 0.01 as determined by two-way ANOVA. C, Percentage of mMDSCs (CD11b+CD68Ly6C+Ly6GI-A/I-E) and gMDSCs (CD11b+CD68Ly6CLy6G+) in CD45+ cells of the left hemisphere from n = 17 sham-injected and n = 26 GL261-bearing animals. Data shown for individual animals. **, P < 0.01 as calculated by unpaired t test. D, Ratio of mMDSCs to gMDSCs in the left hemisphere from n = 16 sham-injected and n = 26 GL261-bearing animals 21 days post–tumor implantation or sham injection. Data shown for individual mice combined from three independent experiments and ***, P < 0.001 as calculated by unpaired t test. E, Ratio of mMDSC to gMDSC in the left hemisphere from n = 15 sham-injected and n = 19 SB28-bearing animals, 14 days post–tumor implantation or sham injection. Data shown for individual mice combined from two independent experiments. ***, P < 0.001 as calculated by unpaired t test. F, Percentage of mMDSCs in the CD45+ immune cells infiltrating the left hemisphere 21 days post–GL261 implantation or sham injection. Data shown as mean ± SD from three independent experiments. n = 8–9 sham-injected and n = 13 GL261-bearing mice per sex. *, P < 0.05 as determined by unpaired t test. G, Percentage of gMDSCs in the CD45+ immune cells infiltrating the left hemisphere 21 days post–GL261 implantation or sham injection. Data shown as mean ± SD from three independent experiments. n = 8–9 sham-injected and n = 13 GL261-bearing mice per sex. H, Ratio of mMDSCs to gMDSCs in the left hemisphere of sham-injected or tumor-bearing mice 21 days after the procedure. Data shown as mean ± SD from three independent experiments. n = 8–9 sham-injected and n = 13 GL261-bearing mice per sex. **, P < 0.01 as calculated by unpaired t test. I, Frequency of mMDSCs and gMDSCs as a fraction of CD45+ cells in the systemic circulation of sham-injected or GL261-bearing mice. Data shown for individual mice combined from three independent experiments. n = 16 sham-injected and n = 26 GL261-bearing mice. *** P < 0.001 as determined by unpaired t test. J, Percentage of mMDSCs in the circulation of sham-injected and GL261-bearing mice on day 21. Data shown as mean ± SD from three independent experiments. n = 9 sham-injected and n = 13 GL261-bearing mice per sex. *, P < 0.05 as determined by unpaired t test. K, Percentage of gMDSCs in the circulation of sham-injected and GL261-bearing mice on Day 21. Data shown as mean ± s.d. from three independent experiments. n = 9 sham-injected and n = 13 GL261-bearing mice per sex. *, P < 0.05 as determined by unpaired t test.

Figure 1.

mMDSCs accumulate in the tumors of male mice, whereas gMDSCs accumulate systemically in female mice. A, Timeline of immune infiltration analysis. B, Frequency of CD45hi bone marrow–derived immune cells in resected tumors versus the contralateral hemisphere of sham-injected or GL261-implanted mice on day 14 and day 21. Data shown as mean ± SD of n = 10/group from one of independently repeated experiments. *, P < 0.05; **, P < 0.01 as determined by two-way ANOVA. C, Percentage of mMDSCs (CD11b+CD68Ly6C+Ly6GI-A/I-E) and gMDSCs (CD11b+CD68Ly6CLy6G+) in CD45+ cells of the left hemisphere from n = 17 sham-injected and n = 26 GL261-bearing animals. Data shown for individual animals. **, P < 0.01 as calculated by unpaired t test. D, Ratio of mMDSCs to gMDSCs in the left hemisphere from n = 16 sham-injected and n = 26 GL261-bearing animals 21 days post–tumor implantation or sham injection. Data shown for individual mice combined from three independent experiments and ***, P < 0.001 as calculated by unpaired t test. E, Ratio of mMDSC to gMDSC in the left hemisphere from n = 15 sham-injected and n = 19 SB28-bearing animals, 14 days post–tumor implantation or sham injection. Data shown for individual mice combined from two independent experiments. ***, P < 0.001 as calculated by unpaired t test. F, Percentage of mMDSCs in the CD45+ immune cells infiltrating the left hemisphere 21 days post–GL261 implantation or sham injection. Data shown as mean ± SD from three independent experiments. n = 8–9 sham-injected and n = 13 GL261-bearing mice per sex. *, P < 0.05 as determined by unpaired t test. G, Percentage of gMDSCs in the CD45+ immune cells infiltrating the left hemisphere 21 days post–GL261 implantation or sham injection. Data shown as mean ± SD from three independent experiments. n = 8–9 sham-injected and n = 13 GL261-bearing mice per sex. H, Ratio of mMDSCs to gMDSCs in the left hemisphere of sham-injected or tumor-bearing mice 21 days after the procedure. Data shown as mean ± SD from three independent experiments. n = 8–9 sham-injected and n = 13 GL261-bearing mice per sex. **, P < 0.01 as calculated by unpaired t test. I, Frequency of mMDSCs and gMDSCs as a fraction of CD45+ cells in the systemic circulation of sham-injected or GL261-bearing mice. Data shown for individual mice combined from three independent experiments. n = 16 sham-injected and n = 26 GL261-bearing mice. *** P < 0.001 as determined by unpaired t test. J, Percentage of mMDSCs in the circulation of sham-injected and GL261-bearing mice on day 21. Data shown as mean ± SD from three independent experiments. n = 9 sham-injected and n = 13 GL261-bearing mice per sex. *, P < 0.05 as determined by unpaired t test. K, Percentage of gMDSCs in the circulation of sham-injected and GL261-bearing mice on Day 21. Data shown as mean ± s.d. from three independent experiments. n = 9 sham-injected and n = 13 GL261-bearing mice per sex. *, P < 0.05 as determined by unpaired t test.

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gMDSC Depletion Provides Survival Benefit to Females, whereas Tumor-Infiltrating mMDSCs Could Not Be Depleted Effectively

Given these differences in MDSC subset accumulation pattern, we sought to determine the functional contribution of MDSC subsets by utilizing neutralizing antibodies for peripheral depletion. We established GL261 tumors in male and female mice as described previously and then intraperitoneally injected anti-Gr1, anti-Ly6G, or anti-Ly6C antibodies every other day starting 7 days post–tumor implantation (Fig. 2A). Bulk MDSC depletion with anti-Gr1 antibody resulted in a survival extension that was exclusive to females (Fig. 2B and C). MDSC subset–specific depletion confirmed that the increase in survival of female mice with GL261 tumors was due to gMDSC depletion with anti-Ly6G antibody (Fig. 2D and E). Similarly, depletion of gMDSCs provided an 8-day survival advantage to only female SB28-implanted mice (Supplementary Fig. S5A and S5B). However, there was no extension of survival with mMDSC targeting in either sex (Fig. 2F and G; Supplementary Fig. S5C and S5D). To confirm the efficacy of this strategy, we assessed the frequency of MDSC subsets in the blood and tumors of GL261-bearing animals at humane endpoints using flow cytometry. Although gMDSCs could be peripherally reduced with anti-Ly6G treatment, mMDSCs remained unaltered in the tumor microenvironment despite anti-Ly6C antibody injections (Supplementary Fig. S5E). We hypothesized that the lack of systemic mMDSC reduction was due to mMDSCs proliferating and thus repopulating. We directly assessed this by performing intracellular Ki-67 staining of MDSC subsets from tumor-bearing mice on day 21 and observed that mMDSCs, particularly those from blood, highly expressed Ki-67 compared with gMDSCs regardless of sex of the host (Fig. 2H and I; Supplementary Fig. S5F). These findings established that targeting systemic gMDSCs in females could be a relevant therapeutic target to curtail GBM progression, whereas strategies aiming to reduce mMDSC abundance by antibody neutralization might be ineffective due to cellular proliferation (Supplementary Fig. S5G).

Figure 2.

gMDSC depletion extends the survival span of female mice. A, Schematics of MDSC depletion regimen. B–G, Kaplan–Meier curves depicting survival of female and male mice treated with (B and C) anti-Gr-1, (D and E) anti-Ly6G, and (F and G) anti-Ly6C neutralizing antibodies every other day starting 7 days post–GL261 implantation with respect to the isotype-treated mice. For B, n = 9 for isotype- and n = 13 for anti-Gr1–treated female mice; for C, n = 9 for isotype- and n = 11 for anti-Gr1–treated male mice; for D, n = 13 for isotype- and n = 14 for anti-Ly6G–treated female mice; for E, n = 10for isotype- and n = 10 for anti-Ly6G-treated male mice; for F, n = 9 for isotype- and n = 9 for anti-Ly6C–treated female mice; for G, n = 9 for isotype- andn = 10 for anti-Ly6C–treated male mice. Data combined from two to three independent experiments. Significance was determined by Gehan–Breslow–Wilcoxon test with P < 0.05 being considered a significant difference. H, Representative histograms depicting ex vivo intracellular Ki-67 staining of mMDSCs and gMDSCs from blood and tumor. PE, phycoerythrin. I, Quantification of Ki-67 staining as mean fluorescence intensity from n = 4 (2 male and 2 female) animals euthanized on day 21 post–GL261 implantation. Data corrected for background based on fluorescence-minus-one staining and shown as mean ± SD. ***, P < 0.001 as determined by unpaired Student t test.

Figure 2.

gMDSC depletion extends the survival span of female mice. A, Schematics of MDSC depletion regimen. B–G, Kaplan–Meier curves depicting survival of female and male mice treated with (B and C) anti-Gr-1, (D and E) anti-Ly6G, and (F and G) anti-Ly6C neutralizing antibodies every other day starting 7 days post–GL261 implantation with respect to the isotype-treated mice. For B, n = 9 for isotype- and n = 13 for anti-Gr1–treated female mice; for C, n = 9 for isotype- and n = 11 for anti-Gr1–treated male mice; for D, n = 13 for isotype- and n = 14 for anti-Ly6G–treated female mice; for E, n = 10for isotype- and n = 10 for anti-Ly6G-treated male mice; for F, n = 9 for isotype- and n = 9 for anti-Ly6C–treated female mice; for G, n = 9 for isotype- andn = 10 for anti-Ly6C–treated male mice. Data combined from two to three independent experiments. Significance was determined by Gehan–Breslow–Wilcoxon test with P < 0.05 being considered a significant difference. H, Representative histograms depicting ex vivo intracellular Ki-67 staining of mMDSCs and gMDSCs from blood and tumor. PE, phycoerythrin. I, Quantification of Ki-67 staining as mean fluorescence intensity from n = 4 (2 male and 2 female) animals euthanized on day 21 post–GL261 implantation. Data corrected for background based on fluorescence-minus-one staining and shown as mean ± SD. ***, P < 0.001 as determined by unpaired Student t test.

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Distinct Biologically Active Pathways in mMDSCs and gMDSCs Determine Their Drug Susceptibility

To gain mechanistic insight into the differential roles of MDSC subsets, we generated mMDSCs and gMDSCs from the bone marrow of C57BL/6 mice by adopting previously described polarization methods involving GM-CSF and IL4Rα stimulation by IL13 (19, 20). Both male and female MDSC genes rated via this method were functionally suppressive as assessed by their ability to prevent the proliferation of T cells stimulated with anti-CD3/CD28 and IL2 (Fig. 3AC; Supplementary Fig. S6A). Expression profiling of these flow-sorted MDSC subsets revealed unique gene signatures between mMDSCs and gMDSCs, which translated to the upregulation of distinct biological pathways (Supplementary Fig. S6B and S6C). Although cell proliferation pathways were active in mMDSCs, immune-modulatory pathways were elevated in gMDSCs (Fig. 3D and E; Supplementary Fig. S6D). To identify putative drug targets for these subsets, we leveraged a network medicine approach that takes advantage of reported drug–target interactions (21, 22) using the differentially expressed gene profiles of mMDSCs and gMDSCs. This strategy identified fludarabine, a purine analogue, as a potential drug candidate to target mMDSCs (Fig. 3F). Previous studies have demonstrated conflicting results regarding the effect of chemotherapies on MDSCs. Doxorubicin–cyclophosphamide treatment was shown to increase the frequency of MDSCs in patients with breast cancer (23, 24). In contrast, nucleoside analogues were shown to reduce MDSC frequencies in various tumor types via a mechanism that was not well defined (25–28). Our studies in mouse models and patients with GBM indicated that 5-fluorouracil (5-FU) and its oral formulation capecitabine could be promising candidates to target innate immunosuppression (29, 30), of which fludarabine was predicted to be more effective (Supplementary Fig. S6E). For gMDSC targeting, IL1 pathway inhibitors were enriched among the top targets (Fig. 3F; Supplementary Fig. S6F). Consistent with the predictions of the network medicine analysis and the observation that gMDSC targeting provides survival benefit to females, we detected approximately 3-fold higher IL1β expression in in vitro polarized gMDSCs of female origin compared with those derived from male bone marrows (Supplementary Fig. S6G). To test the therapeutic utility of these predicted drugs, we assessed their efficacy in preclinical models of GL261 and SB28. Fludarabine was intraperitoneally injected for five consecutive days, while anti-IL1β antibody was given every other day in two cycles, starting 7 days after tumor implantation (Fig. 4A). We observed that fludarabine significantly extended survival in male mice, with no significant benefit for female mice, as their survival duration was not improved (Fig. 4B and C; Supplementary Fig. S7A and S7B). In contrast, anti-IL1β treatment significantly prolonged the survival of female mice (Fig. 4D and E; Supplementary Fig. S7C and S7D). To identify immunologic changes associated with treatment response, we analyzed the blood and tumors post–drug treatment. Given the differences in the tumor growth dynamics between the GBM models, we used SB28 to assess earlier immunologic changes after 1 cycle of drug administration, while mice with GL261 were treated for two cycles to analyze the advanced disease scenario. In both GL261- and SB28-bearing male mice, we observed an increase in the frequency of circulating CD8+ T cells with fludarabine treatment, pointing to activation of antitumor immune response (Fig. 4F and G). In SB28-bearing male mice, one cycle of fludarabine was sufficient to decrease mMDSC abundance in tumors by 4-fold (Fig. 4H). Although an approximately 2-fold reduction in mMDSCs was observed in GL261-bearing male mice after two cycles, the effect of the drug was more limited (Supplementary Fig. S7E). Instead, we observed that tumor proliferation rate was reduced in these animals (Fig. 4I), suggesting that inhibition of mMDSC infiltration into the tumors at an early stage can alter the growth dynamics. A similar increase in peripheral CD8+ T cells was also observed in female GL261-bearing animals, but frequency of the proliferating tumor cells or tumor mMDSCs was unaffected by the drug (Supplementary Fig. S7F–S7H). In contrast, completion of two cycles of anti-IL1β reduced systemic gMDSCs specifically in GL261-bearing females (Fig. 4J; Supplementary Fig. S7I). Anti-IL1β also delayed the tumor growth in female mice, as early as after completion of the first cycle, as evidenced by the reduction in the proliferating cell rates based on Ki-67 expression in non-immune tumor tissue (Fig. 4K and L; Supplementary Fig. S7J). Importantly, anti-IL1β had no direct impact on the proliferation of GL261 and SB28 cells in vitro (Supplementary Fig. S7K and S7L), suggesting that it functions by modulating the immune response. These data support that implementation of a sex-specific therapeutic strategy against GBM by targeting MDSC subsets with unique inhibitors provides a survival advantage.

Figure 3.

Proliferation of mMDSCs can be targeted by fludarabine, whereas IL1 pathway inhibition is predicted to counteract gMDSC-mediated immunosuppression. A, Schematics of in vitro MDSC polarization approach. Bone marrow cells from C57BL/6 mice were cocultured with GL261 cells in the presence of GM-CSF/IL13 for 3 days. B, mMDSC, gMDSC, and non-MDSC populations were phenotypically discriminated based on CD11b, Ly6C, and Ly6G expression. C, Proliferation rates of activated T cells cocultured with non-MDSCs, gMDSCs, and mMDSCs generated by cytokine polarization, compared with unstimulated T cells. Data analyzed separately for n = 3 male and female mice and shown as mean ± SD. ***, P < 0.001 as determined by two-way ANOVA. CFSE, carboxyfluorescein succinimidyl ester. D–E, Gene Ontology Enrichment Analysis using differentially expressed genes between mMDSCs versus non-MDSCs (D) and gMDSCs versus non-MDSCs (E) from n = 6 biological replicates based on log(fold change) ≤−1 and adjusted P value < 0.001. F, Potential mechanism of action of fludarabine and rilonacept by network inference. Differentially expressed genes of mMDSCs for fludarabine and gMDSCs for rilonacept were directly connected to the drug targets and/or through one or more common neighbors. For fludarabine, a maximum distance of 2 between drug targets and differentially expressed genes was used to visualize the network. For rilonacept, the distance was set to 4. Node size indicates log2FC, and interaction types are color-coded.

Figure 3.

Proliferation of mMDSCs can be targeted by fludarabine, whereas IL1 pathway inhibition is predicted to counteract gMDSC-mediated immunosuppression. A, Schematics of in vitro MDSC polarization approach. Bone marrow cells from C57BL/6 mice were cocultured with GL261 cells in the presence of GM-CSF/IL13 for 3 days. B, mMDSC, gMDSC, and non-MDSC populations were phenotypically discriminated based on CD11b, Ly6C, and Ly6G expression. C, Proliferation rates of activated T cells cocultured with non-MDSCs, gMDSCs, and mMDSCs generated by cytokine polarization, compared with unstimulated T cells. Data analyzed separately for n = 3 male and female mice and shown as mean ± SD. ***, P < 0.001 as determined by two-way ANOVA. CFSE, carboxyfluorescein succinimidyl ester. D–E, Gene Ontology Enrichment Analysis using differentially expressed genes between mMDSCs versus non-MDSCs (D) and gMDSCs versus non-MDSCs (E) from n = 6 biological replicates based on log(fold change) ≤−1 and adjusted P value < 0.001. F, Potential mechanism of action of fludarabine and rilonacept by network inference. Differentially expressed genes of mMDSCs for fludarabine and gMDSCs for rilonacept were directly connected to the drug targets and/or through one or more common neighbors. For fludarabine, a maximum distance of 2 between drug targets and differentially expressed genes was used to visualize the network. For rilonacept, the distance was set to 4. Node size indicates log2FC, and interaction types are color-coded.

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Figure 4.

MDSC subset targeting with predicted drugs confers sex-specific survival advantage. A, Treatment regimen for testing the predicted drug candidates fludarabine and anti-IL1β neutralizing antibody. Kaplan–Meier curves depicting survival of male (B) and female (C) GL261-bearing mice treated with fludarabine. Data presented from two independent experiments with n = 10 vehicle-treated males and n = 10 fludarabine-treated males (B) and n = 10 vehicle-treated females and n = 10 fludarabine-treated females (C). P < 0.01 as determined by Gehan–Breslow–Wilcoxon test. Kaplan–Meier curves depicting survival of male (D) and female (E) GL261-bearing mice treated with anti-IL1β neutralizing or isotype control antibody. Data presented from three independent experiments with n = 15 isotype control–treated males (D) and n = 15 anti-IL1β antibody–treated males (E), n = 15 isotype control-treated females and n = 15 anti-IL1β antibody–treated females. P < 0.05 as determined by Gehan–Breslow–Wilcoxon test. () F, Frequency of CD8+ T cells in the circulation of n = 5 vehicle- and n = 4 fludarabine-treated SB28-bearing male mice. Data shown as mean ± SD. ***, P < 0.001 as calculated by unpaired t test. G, Frequency of CD8+ T cells in the circulation of n = 8 vehicle- and n = 7 fludarabine-treated GL261-bearing male mice. Data shown from two independent experiments as mean ± SD. ***, P < 0.001 as calculated by unpaired t test. H, Relative percentage of tumor-infiltrating mMDSCs as a fraction of live cells in n = 5 vehicle and n = 5 fludarabine-treated SB28-bearing male mice after one cycle. Data shown as mean ± SD. *, P < 0.05 as calculated by unpaired t test. I, Relative percentage of Ki-67+ cells in the nonimmune cells of the left hemisphere from n = 8 vehicle- and n = 7 fludarabine-treated GL261-bearing male mice after two cycles. Data shown from two independent experiments as mean ± SD. J, Frequency of gMDSCs in the circulation of n = 8 isotype- and n = 8 anti-IL1β–treated GL261-bearing female mice. Data shown as mean ± SD from two independent experiments. *, P < 0.05 as calculated by unpaired t test. K, Relative percentage of Ki-67+ cells in the nonimmune cells of the left hemisphere from n = 5 isotype- and n = 5 anti-IL1β-treated SB28-bearing female mice after one cycle. Data shown as mean ± SD. **, P < 0.01 as calculated by unpaired t test. L, Relative percentage of Ki-67+ cells in the nonimmune cells of the left hemisphere from n = 8 isotype- and n = 8 anti-IL1β-treated GL261-bearing female mice after two cycles. Data shown from two independent experiments as mean ± SD. *, P < 0.05 as calculated by unpaired t test.

Figure 4.

MDSC subset targeting with predicted drugs confers sex-specific survival advantage. A, Treatment regimen for testing the predicted drug candidates fludarabine and anti-IL1β neutralizing antibody. Kaplan–Meier curves depicting survival of male (B) and female (C) GL261-bearing mice treated with fludarabine. Data presented from two independent experiments with n = 10 vehicle-treated males and n = 10 fludarabine-treated males (B) and n = 10 vehicle-treated females and n = 10 fludarabine-treated females (C). P < 0.01 as determined by Gehan–Breslow–Wilcoxon test. Kaplan–Meier curves depicting survival of male (D) and female (E) GL261-bearing mice treated with anti-IL1β neutralizing or isotype control antibody. Data presented from three independent experiments with n = 15 isotype control–treated males (D) and n = 15 anti-IL1β antibody–treated males (E), n = 15 isotype control-treated females and n = 15 anti-IL1β antibody–treated females. P < 0.05 as determined by Gehan–Breslow–Wilcoxon test. () F, Frequency of CD8+ T cells in the circulation of n = 5 vehicle- and n = 4 fludarabine-treated SB28-bearing male mice. Data shown as mean ± SD. ***, P < 0.001 as calculated by unpaired t test. G, Frequency of CD8+ T cells in the circulation of n = 8 vehicle- and n = 7 fludarabine-treated GL261-bearing male mice. Data shown from two independent experiments as mean ± SD. ***, P < 0.001 as calculated by unpaired t test. H, Relative percentage of tumor-infiltrating mMDSCs as a fraction of live cells in n = 5 vehicle and n = 5 fludarabine-treated SB28-bearing male mice after one cycle. Data shown as mean ± SD. *, P < 0.05 as calculated by unpaired t test. I, Relative percentage of Ki-67+ cells in the nonimmune cells of the left hemisphere from n = 8 vehicle- and n = 7 fludarabine-treated GL261-bearing male mice after two cycles. Data shown from two independent experiments as mean ± SD. J, Frequency of gMDSCs in the circulation of n = 8 isotype- and n = 8 anti-IL1β–treated GL261-bearing female mice. Data shown as mean ± SD from two independent experiments. *, P < 0.05 as calculated by unpaired t test. K, Relative percentage of Ki-67+ cells in the nonimmune cells of the left hemisphere from n = 5 isotype- and n = 5 anti-IL1β-treated SB28-bearing female mice after one cycle. Data shown as mean ± SD. **, P < 0.01 as calculated by unpaired t test. L, Relative percentage of Ki-67+ cells in the nonimmune cells of the left hemisphere from n = 8 isotype- and n = 8 anti-IL1β-treated GL261-bearing female mice after two cycles. Data shown from two independent experiments as mean ± SD. *, P < 0.05 as calculated by unpaired t test.

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Male Patients Have an Enhancement in Tumor-Infiltrating mMDSCs, whereas a gMDSC Signature Associates with Poor Prognosis in Female Patients

To validate our preclinical observations in patients, we reanalyzed the immune-inhibitory myeloid cell staining pattern in 188 isocitrate dehydrogenase (IDH)–wild-type GBM specimens, while accounting for patient sex (31). Male patients had more IBA1+ and CD204+ cells in their tumors based on immunofluorescence staining (Fig. 5A), suggesting an increase in the immunosuppressive myeloid cell populations. Analysis of fresh tumor tissue with multicolor flow cytometry from a separate cohort of male patients with IDH–wild-type GBM revealed that mMDSCs were present at significantly higher percentages in human GBM tissue compared with gMDSCs (Fig. 5BD; Supplementary Fig. S8A). Moreover, these tumor-infiltrating mMDSCs were positive for Ki-67, confirming that they are actively proliferating in patient tumors (Fig. 5E and F). To evaluate the prognostic value of gMDSC abundance, we analyzed The Cancer Genome Atlas (TCGA) GBM database for mRNA levels of OLR1 (LOX1), a gMDSC-specific marker (32), and IL1B and found that there were no sex differences in the expression levels of these markers between male and female patients (Supplementary Fig. S8B and S8C). Further investigation of IL1β protein levels by IHC of tumor tissue from patients with IDH–wild-type GBM established that this cytokine was abundant in the tumor microenvironment regardless of patient sex (Fig. 6A and B). Notably, the expression levels of OLR1 and IL1B were associated with patient survival only when stratified on the basis of sex. In both TCGA and the Chinese Glioma Genome Atlas (CGGA), high OLR1 expression inversely correlated with the survival of females but had no effect in male patients with GBM (Fig. 6C; Supplementary Fig. S8D). A similar trend was observed with IL1B levels; high IL1B mRNA portended poor prognosis only in female patients (Fig. 6D; Supplementary Fig. S8E). Importantly, OLR1 expression positively correlated with IL1B expression, suggesting that these genes are part of the same signaling network (Fig. 6E). Collectively, these studies indicated that male tumors have enhanced immunosuppressive myeloid cell infiltration, which includes proliferating mMDSCs, whereas a gMDSC–IL1β axis associates with female disease activity and represents a therapeutic target for female patients (Fig. 6F).

Figure 5.

IDH–wild-type GBM tumors from male patients have more immunosuppressive myeloid cells, which include proliferating mMDSCs. A, Fraction of IBA1+ and CD204+ area of n = 188 patients with IDH–wild-type GBM from ref. 31 was reanalyzed by accounting for biological sex. Data shown from n = 108 males and n = 80 females as their median. **, P < 0.01 as determined by unpaired t test. B, Percentage of CD45hi cells of the viable single cells isolated from GBM tumors. Data shown for n = 8 individual patients. C, The frequency of mMDSCs (CD11b+CD33+CD14+HLA-DRCD68) and gMDSCs (CD11b+CD33+CD66b+LOX1+) in tumor-infiltrating leukocytes from n = 8 male patients with IDH–wild-type GBM. Data shown as mean ± SD. **, P < 0.05 as determined by unpaired Student t test. D, Ratio of mMDSCs to gMDSCs in tumors of n = 8 patients with IDH–wild-type GBM. E, Representative histograms showing Ki-67 expression levels in matched mMDSCs and gMDSCs from the same patient, in comparison with the isotype control staining. F, Mean fluorescence intensity of Ki-67 in tumor-infiltrating mMDSCs and gMDSCs from n = 8 patients with IDH–wild-type GBM. Data shown as mean ± SD. *, P < 0.05 as determined by unpaired Student t test.

Figure 5.

IDH–wild-type GBM tumors from male patients have more immunosuppressive myeloid cells, which include proliferating mMDSCs. A, Fraction of IBA1+ and CD204+ area of n = 188 patients with IDH–wild-type GBM from ref. 31 was reanalyzed by accounting for biological sex. Data shown from n = 108 males and n = 80 females as their median. **, P < 0.01 as determined by unpaired t test. B, Percentage of CD45hi cells of the viable single cells isolated from GBM tumors. Data shown for n = 8 individual patients. C, The frequency of mMDSCs (CD11b+CD33+CD14+HLA-DRCD68) and gMDSCs (CD11b+CD33+CD66b+LOX1+) in tumor-infiltrating leukocytes from n = 8 male patients with IDH–wild-type GBM. Data shown as mean ± SD. **, P < 0.05 as determined by unpaired Student t test. D, Ratio of mMDSCs to gMDSCs in tumors of n = 8 patients with IDH–wild-type GBM. E, Representative histograms showing Ki-67 expression levels in matched mMDSCs and gMDSCs from the same patient, in comparison with the isotype control staining. F, Mean fluorescence intensity of Ki-67 in tumor-infiltrating mMDSCs and gMDSCs from n = 8 patients with IDH–wild-type GBM. Data shown as mean ± SD. *, P < 0.05 as determined by unpaired Student t test.

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Figure 6.

gMDSC prevalence predicts poor prognosis of female patients with GBM. A, Representative IL1β IHC tumor sections from male and female patients confirming IL1β protein expression in IDH–wild-type GBM. B, Quantification of IL1β+ cells in tumors from four visual fields of n = 10 male and n = 10 female patients with IDH–wild-type GBM.C, Correlation among OLR1 expression levels, patient sex, and survival duration was analyzed via TCGA GBM dataset. High and low expression levels were determined on the basis of quartiles. Data shown for n = 14 female patients with high OLR1 expression and n = 16 female patients with low OLR1 expression (left) and for n = 29 male patients with high OLR1 expression and n = 25 male patients with low OLR1 expression (right). P < 0.01 as determined by Gehan–Breslow–Wilcoxon test. D, Correlation among IL1B expression levels, patient sex and survival duration was analyzed via TCGA GBM dataset. High and low expression levels were determined on the basis of quartiles. Data shown for n = 15 female patients with high IL1B expression and n = 14 female patients with low IL1B expression (left) and for n = 37 male patients with high IL1B expression and n = 27 male patients with low IL1B expression (right). P < 0.05 as determined by Gehan–Breslow–Wilcoxon test. E, Correlation between OLR1 and IL1B expression levels from n = 538 patients. P < 0.01 based on two-sided t test. F, Proposed model of relative mMDSC abundance and IL1β presence in patients with GBM. RBC, red blood cells.

Figure 6.

gMDSC prevalence predicts poor prognosis of female patients with GBM. A, Representative IL1β IHC tumor sections from male and female patients confirming IL1β protein expression in IDH–wild-type GBM. B, Quantification of IL1β+ cells in tumors from four visual fields of n = 10 male and n = 10 female patients with IDH–wild-type GBM.C, Correlation among OLR1 expression levels, patient sex, and survival duration was analyzed via TCGA GBM dataset. High and low expression levels were determined on the basis of quartiles. Data shown for n = 14 female patients with high OLR1 expression and n = 16 female patients with low OLR1 expression (left) and for n = 29 male patients with high OLR1 expression and n = 25 male patients with low OLR1 expression (right). P < 0.01 as determined by Gehan–Breslow–Wilcoxon test. D, Correlation among IL1B expression levels, patient sex and survival duration was analyzed via TCGA GBM dataset. High and low expression levels were determined on the basis of quartiles. Data shown for n = 15 female patients with high IL1B expression and n = 14 female patients with low IL1B expression (left) and for n = 37 male patients with high IL1B expression and n = 27 male patients with low IL1B expression (right). P < 0.05 as determined by Gehan–Breslow–Wilcoxon test. E, Correlation between OLR1 and IL1B expression levels from n = 538 patients. P < 0.01 based on two-sided t test. F, Proposed model of relative mMDSC abundance and IL1β presence in patients with GBM. RBC, red blood cells.

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Immunotherapy options remain limited for GBM, despite the success of these treatment modalities in preclinical models and other solid tumors (33–35). The immunosuppressive tumor microenvironment mainly consisting of myeloid cells is one of the major factors limiting the efficacy of existing treatment options (11). Thus, understanding the variations in myeloid cell–driven immunosuppression and the associated molecular mechanisms can provide insight into the sexual dimorphism in GBM outcome. Our results demonstrated that MDSC subset heterogeneity could be leveraged for improved immunotherapy response in a sex-specific manner and highlight the importance of assessment of sex as a biological variable in preclinical and clinical immunotherapy studies.

In addition to their suppressive function, MDSC subsets can execute distinct roles during the course of tumorigenesis. It was recently demonstrated in breast cancer models that mMDSCs localize to the primary tumor, where they support cancer stem cells (CSC), whereas gMDSCs promote metastatic spread to distant sites (16). Correspondingly, our group previously reported that MDSC recruitment in GBM is driven by CSCs via the secretion of macrophage-migration inhibitory factor (MIF), suggesting a possible cross-talk between these cell populations in multiple cancers (29). However, this study was conducted only in females and did not distinguish between the MDSC subsets. Consistent with these earlier reports, we observed that mMDSCs were the dominant subset localizing to GBM tissue in both mouse models and patients and that the frequency of tumor-infiltrating mMDSCs was significantly higher in males compared with females. We previously reported that increased MDSC accumulation at the time of recurrence predicts poor GBM outcome in patients (13). In line with this observation, higher mMDSC levels in preclinical models associated with shortened survival duration, and male mice succumbed to disease earlier than female mice. The lack of additional sex differences in the abundance of other tumor-infiltrating immune populations suggested that males have a more immunosuppressive tumor microenvironment, which is reinforced by mMDSC accumulation. Our bone marrow transplantation studies provided an additional level of evidence that sexual dimorphism in immune response drives survival differences in GBM. Future studies will focus on revealing the mechanisms through which these sex differences emerge.

In contrast to the tissue-dominant localization of mMDSCs, we observed that gMDSCs poorly infiltrated into the tumors in preclinical models and patients with GBM. It is noteworthy that gMDSCs constitute the main subpopulation elevated in the circulating blood of patients with GBM (12, 14, 15), an observation recapitulated in murine models. Despite the observation that systemic gMDSC levels were not altered in tumor-bearing male mice compared with the sham-injected controls, we observed that males had overall higher levels of circulating MDSCs at baseline compared with females. In the context of lupus, a higher MDSC frequency was previously linked to the resistance of male (NZB × NZW)F1 mice (36), further suggesting that the differences in systemic MDSC abundance could provide a mechanistic explanation for the increased immunosuppression in males. These findings are supported by the epidemiologic evidence indicating that males are predisposed to malignancies as opposed to inflammatory diseases, which are more prevalent in females (9). However, in the context of GBM prognosis, there was a significant systemic expansion of gMDSCs in female mice implanted with GL261 or SB28 cells. Consistent with earlier reports, depletion of gMDSCs by neutralizing antibody treatment reduced the peripheral gMDSCs without affecting the tumor-infiltrating population in these animals (37). Extension in the survival span of tumor-bearing female animals under these experimental conditions further highlights the importance of the systemic immune response in antitumor immunity in females (18) and establishes that gMDSC expansion in the peripheral blood might serve as a potential therapeutic target.

We also observed MDSC subsets had unique gene-expression signatures and biologically active pathways that could account for the differences in their frequency and compartmentalization. Importantly, along with the modulation of immune response, the top cellular pathways upregulated in mMDSCs were related to cell-cycle regulation. Although low-dose chemotherapies (5-FU, capecitabine, and gemcitabine) were used to deplete MDSCs in preclinical models and patients with various cancers, including GBM, the mechanism by which these drugs have shown efficacy remained unknown (27, 29, 30). Here, we demonstrate that mMDSCs are actively proliferating in vivo and propose that the inhibition of cellular proliferation emerges as a likely explanation for the selective targeting of systemic mMDSCs by chemotherapies, thereby reducing their tissue levels (25). In contrast, gMDSCs were predicted to be more effective modulators of immune response based on pathway analysis. We leveraged these distinct gene-expression signatures associated with this function to identify drug candidates that could be repurposed for cancer immunotherapy. Although IL1 is a prototypical proinflammatory cytokine, recent studies suggest that IL1β can drive tumorigenesis by promoting a CSC phenotype, modulating the immune response and inducing tumorigenic edema (38–41). Importantly, randomized clinical testing of canakinumab, an anti-IL1β antibody, pointed to a reduction in lung cancer incidence of patients with atherosclerosis (42). We observed that IL1β was significantly expressed in the gMDSC subset as opposed to the mMDSC subset, with particular elevation in female gMDSCs. Consistently, blockade of IL1β significantly extended the survival of female mice by delaying tumor growth and subsequently reducing gMDSC frequencies in the circulation. It is conceivable that gMDSCs are not the only cell type that produces IL1β at high levels, and tumor-associated macrophages/microglia comprise additional sources of IL1β (8, 41). Although this line of research warrants further investigation, a lack of sex-specific response to anti-CSF1R antibodies and an absence of changes in macrophage abundance in mice treated with anti-IL1β point to a gMDSC-dominant role in females and suggest that MDSCs are the likely regulators of the sexual dimorphism in anti-GBM immune response (Supplementary Fig. S9A–S9C).

In summary, our findings identify a differential MDSC subset signature and candidate mechanisms that could comprise a therapeutic opportunity for improved immunotherapy of GBM by accounting for patient sex. These results indicate that blockade of mMDSC proliferation with chemotherapies can reprogram the immunosuppressive tumor microenvironment in males. Finally, our study unveils the role of a gMDSC–IL1β axis in systemic GBM immunity in females and further provides the rationale for clinical testing of IL1β inhibitors in patients with GBM.

Reagents

Fluorophore-conjugated anti-Ly6C (HK1.4, 128024), anti-Ly6G (1A8, 27618, or from BD Biosciences 551460), anti-CD11b (M1/70, 101212), anti-CD68 (FA-11, 137024), anti–I-A/I-E (M5/114.15.2, 107606), anti-CD11c (N418, 117330), anti-CD3 (145-2C11, 100330), anti-CD4 (GK1.5, 100422), anti-CD8 (53-6.7, 100712), anti-NK1.1 (PK136, 108741), anti-F4/80 (BM8, 123118), anti-Ki-67 (16A8, 652404 or 652428), anti-CD45 (30-F11, 103132), anti-CD45.1 (A20, 110728), and anti-CD45.2 (104, 109808) antibodies were obtained from BioLegend for analysis of mouse immune profiles.

InVivoMAb anti-mouse Ly6C (Monts1, BE0203), InVivoMAb anti-mouse Ly6G (1A8, BE0075-1), InVivoMAb anti-mouse Gr1 (RB6-8C5, BE0075), InVivoMAb anti-mouse anti-CSF1R (AFS98, BE0213), InVivoMAb anti-mouse/rat IL1β (B122, BE0246), InVivoMAb rat IgG2a, anti-trinitrophenol (2A3, BE0089), InVivoMAb rat IgGb, anti–keyhole limpet hemocyanin (LTF-2, BE0090), and InVivoMAb polyclonal Armenian hamster IgG (BE0091) were purchased from BioXCell for depletion/targeting of MDSCs.

Fludarabine (Sagent Pharmaceuticals), Sulfatrim (Pharmaceutical Associates Inc.), isoflurane (Piramal Critical Care Inc.), xylazine (Akorn Inc.), and ketamine (Zoetis Inc.) were obtained from the Department of Pharmacy, Cleveland Clinic (Cleveland, OH).

For analysis of human GBM immune profile, anti-CD3 (SP34-2, 557757, BD Biosciences), anti-CD11b (ICRF44, 557743, BD Biosciences), anti-CD33 (WM53, 562492, BD Biosciences), anti-CD66b (G10F5, 561650, BD Biosciences), anti-CD14 (M5E2, 558121, BD Biosciences), anti-HLA-DR (L243, 307638, BioLegend), anti-CD68 (Y1/82A, 333814, BioLegend), anti-LOX1 (15C4, 358606, BioLegend), anti-CD45 (HI30, 560777, BD Biosciences), anti-Ki-67 (B56, 563755, BD Biosciences), and BV711 Mouse IgG1, k isotype control (X40, 563044, BD Biosciences) antibodies were used.

Cell Lines

The GL261 cell line was obtained from the Developmental Therapeutics Program, National Cancer Institute (Bethesda, MD). SB28 cells were gifted by Dr. Hideho Okada (University of California, San Francisco, San Francisco, CA). Both cell lines were karyotyped upon receipt and determined to be desexualized, consistent with previous reports (43). All cell lines were treated with 1:100 MycoRemoval Agent (MP Biomedicals) upon thawing, and routinely tested for Mycoplasma spp. (Lonza). Cells were maintained in RPMI1640 (Media Preparation Core, Cleveland Clinic) supplemented with 10% FBS (Thermo Fisher Scientific) and 1% penicillin/streptomycin (Media Preparation Core). Cells were not grown for more than 10 passages.

For proliferation, 500 GL261 and SB28 cells were cultured in white-walled 96-well plates and treated with 10 μL/mL anti-IL1β or isotype control. Proliferation was measured over the course of 5 days with CellTiter-Glo.

Mice

All experiments were approved by the Cleveland Clinic Institutional Animal Care and Use Committee and performed in accordance with the established guidelines. Four-week-old C57BL/6 (JAX stock #000664) or B6 CD45.1 (B6.SJL-Ptprca Pepcb/BoyJ; JAX Stock #002014) mice were purchased from the Jackson Laboratory as required and housed in the Cleveland Clinic Biological Research Unit Facility under a 12-hour:12-hour light/dark cycle.

Four- to 8-week-old C57BL/6 mice were intracranially injected with 10,000 to 25,000 GL261 cells or 5,000 to 20,000 SB28 cells in 5 μL RPMI-null media into the left hemisphere 2 mm caudal to the coronal suture, 3 mm lateral to the sagittal suture at a 90° angle with the murine skull to a depth of 2.5 mm. A fraction of age- and sex-matched animals were injected with 5 μL RPMI-null media to be used as sham controls. Mice were monitored daily for neurologic symptoms, lethargy, and hunched posture that would qualify as signs of tumor burden.

Seven days post-tumor implantation, mice were randomly assigned to control or treatment groups. For the depletion studies, mice were intraperitoneally injected with 10 mg/kg neutralizing antibodies or isotype control in 100 μL PBS every other day until experimental endpoint, not more than 20 times. For targeting of IL1β, mice were intraperitoneally injected with 10 mg/kg targeting antibody or isotype control three times a week for two cycles. Fludarabine (50 mg/kg) was administered intraperitoneally daily with a regimen of 5 days treatment and 2 days break for two consecutive cycles.

For immune profiling experiments, GL261-bearing mice were treated with anti-IL1β, isotype control, fludarabine, or vehicle for two cycles as described above. The mice were euthanized within 72 hours of treatment completion and only asymptomatic animals that were not at humane endpoints were included for analysis. Samples from vehicle- and drug-treated mice were processed simultaneously. The treatment of SB28-implanted animals was started 3 days after the implantation of tumors. Male mice were injected with fludarabine or vehicle for 5 consecutive days, and mice were euthanized the day after the completion of the treatment regimen. Female mice were administered anti-IL1β or isotype every other day following the same experimental design. Tissue harvesting and flow cytometry were performed as detailed below.

Murine Tissue Harvest

At predefined time points or humane endpoints, tumor-bearing or sham-injected mice were euthanized and samples were processed concurrently. Blood was collected directly into EDTA-coated Safe-T-Fill micro capillary blood collection tubes (RAM Scientific) via cardiac puncture. Tubes were centrifuged at 1,000 × g for 10 minutes to isolate serum, and cell pellets were stained for flow cytometry. Femurs and tibia were flushed with 10 mL of PBS for isolation of bone marrow cells. Single-cell suspensions were prepared from organs by mincing in 10% RPMI, strained on a 40-μm filter (Thermo Fisher Scientific), washed with PBS, counted, and stained for flow cytometry analysis.

Flow Cytometry

Harvested mouse tissue and blood were transferred into 96-well round-bottom plates (Thermo Fisher Scientific) and washed twice with 200 μL PBS. Samples were stained with LIVE/DEAD Fixable Stains (Thermo Fisher Scientific) diluted 1:1,000 for 10 minutes on ice. Following a wash step, cells were resuspended in FcR Blocking Reagent (Miltenyi Biotec) at a 1:25 dilution in PBS/2% BSA (Sigma-Aldrich) for 10 minutes on ice. Fluorophore-conjugated antibodies diluted 1:50 were added at a 1:2 ratio and cells were further incubated for 20 minutes on ice. Samples were washed with PBS/BSA and fixed overnight in eBioscience FOXP3/Transcription Factor Fixation Buffer. For intracellular staining, antibodies were diluted at a ratio of 1:100 in FOXP3/Transcription Factor Permeabilization Buffer and cells were incubated at room temperature for 20 minutes. Samples were acquired with a BD LSR Fortessa (BD Biosciences), and FlowJo (Version 10.5.0, FlowJo LLC) was used for analysis of the staining data. Mouse MDSC subsets were phenotypically defined on the basis of established guidelines (44).

Bone Marrow Transplantation

Four-week-old C57BL/6 female mice were subjected to whole-body irradiation. Radiation (12 Gy) was given in two fractions 3 to 4 hours apart. Reconstitution was achieved by retro-orbital injection of 2 × 106 bone marrow cells from B6 CD45.1 mice. Drinking water was supplemented with Sulfatrim during the first 10 days, and mice were monitored for an additional 3 weeks for weight loss and infection symptoms before tumor implantation. Survival analysis was performed as described above.

MDSC Coculture

Bone marrow was isolated from the femur and tibia of 8- to 12-week-old mice. Two million bone marrow cells were cocultured with 1 × 105 GL261 cells in 6-well plates in 2 mL RPMI/10% FBS supplemented with 40 ng/mL GM-CSF and 80 ng/mL IL13 (PeproTech) for 3 to 4 days. For validation experiments, bone marrow cells were incubated with the cytokine combination alone. Cells were stained for viability, blocked with Fc receptor inhibitor and stained with a combination of CD11b, Ly6C, and Ly6G for sorting of MDSC subsets (mMDSCs: CD11b+Ly6C+Ly6G vs. gMDSCs: CD11b+Ly6CLy6G+) and control (CD11b+Ly6CLy6G) population using a BD FACSAria II (BD Biosciences).

T-cell Proliferation Assay

T cells were isolated from splenocytes of 8- to 12-week-old mice using a Pan T Cell Isolation Kit II (Miltenyi Biotec) with >90% purity. T cells were stained with 1 μmol/L carboxyfluorescein succinimidyl ester (CFSE; BioLegend) for 5 minutes at 37°C and washed with ice-cold 10% RPMI twice. A total of 100,000 T cells were cocultured with 50,000 sorted myeloid cells in the presence of 30 IU recombinant hIL2 plus anti-CD3/CD28 Dynabeads (Thermo Fisher Scientific) for 3 to 4 days in round-bottom plates. Samples were stained with anti-CD11b and anti-CD3. CFSE dilution was analyzed from CD3+CD11b cells using a BD LSR Fortessa.

RNA Sequencing

RNA was isolated from a minimum of 2 × 106 sorted MDSCs or non-MDSCs using an RNeasy Mini Kit (Qiagen). RNA sequencing was performed by GENEWIZ using an Illumina HiSeq, 2 × 150 bp configuration and ≥350 M raw paired-end reads. An average 40.8 M paired-end reads were sequenced across 18 samples. After Illumina universal adapters were trimmed, the average read length was shortened to 136 bp, and 40.6 M reads were kept for the downstream analysis. The RNA-sequencing reads were converted to transcriptome abundant matrix in the format of TPM (transcripts per kilobase million; ref. 45) via kallisto v0.44.0 (46) in default parameters. The kallisto reference genome sequence index was built on a mouse reference transcript sequences (GRCm38.p5) with the gene model annotation gencode.vM16.annotation.gtf. The differential gene expression analysis was conducted with DESeq2 v1.18.1 (47) in a default setting as suggested. Then, we selected genes for which the absolute log2 fold change value was higher than or equal to 1 and Benjamini and Hochberg FDR < 0.001 and queried them into the DAVID website for a functional enrichment analysis (48). The biological processes in both the Gene Ontology and KEGG pathway (P < 0.001) were used for functional annotation. Gene Expression Omnibus accession number: GSE148467.

Network Medicine Analysis

Gene sets for network analysis were generated by determining the overlapping differentially expressed genes between MDSC subsets and for each MDSC subset, in comparison with the non-MDSC fraction from three samples. Reads were quantified using Salmon (v0.9.1; ref. 49) Quasi-Mapping with an index created from ENSEMBL reference noncoding RNA and cDNA transcriptomes (GRCm38 release 95). Differential expression was calculated using DESeq2 (v1.20.0; ref. 47) after collapsing transcript-level quantifications to gene-level quantifications. Secondary elimination was done for mMDSCs based on read count >100 and for gMDSCs using the top 1,500 highly expressed genes based on average read count. The full gene list is provided in Supplementary Table S1. Lineage markers and macrophage and dendritic cell markers were used as negative controls. Positive controls were determined based on previously published profiles (16, 17).

We collected physical drug–target interactions as described in our previous studies (21, 22). We used the human interactome including 351,444 unique protein–protein interactions (edges or links) connecting 17,706 proteins (nodes; ref. 21). The network proximity of the drug targets and differentially expressed genes was computed using the closest method based on the human interactome:

formula

where d(a, b) is the shortest path between gene a and b from gene list A and B, respectively. The proximity was then converted to Z-score using the following method:

formula

where $\overline {{d_r}}$ and σr are the mean and SD of a permutation test repeated 1,000 times, each time using two randomly selected gene lists that have similar degree distributions to the differentially expressed genes and drug targets. A cutoff of −1 was used. Drugs were ranked on the basis of their Z-scores. Networks were visualized using Cytoscape v3.7.1 (https://cytoscape.org/).

Quantitative Reverse Transcriptase PCR

RNA from non-MDSC, mMDSC, and gMDSC fractions was isolated using an RNeasy Mini Kit, and cDNA was synthesized with qSCRIPT cDNA Super-mix (Quanta Biosciences). qPCR reactions were performed using an Applied Biosystems StepOnePlus Real-Time PCR System and Fast SYBR-Green Mastermix (Thermo Fisher Scientific). For qPCR analysis, the threshold cycle (Ct) values for IL1β were normalized to the expression levels of CycloA by non-MDSCs. The following primers (Integrated DNA Technologies) were used:

IL1β: Forward: 5′-ACGGACCCCAAAAGATGAAG-3′ 
 Reverse: 5′-TTCTCCACAGCCACAATGAG-3′ 
CycloA: Forward: 5′-GCGGCAGGTCCATCTACG-3′ 
 Reverse: 5′-GCCATCCAGCCATTCAGTC-3′ 
IL1β: Forward: 5′-ACGGACCCCAAAAGATGAAG-3′ 
 Reverse: 5′-TTCTCCACAGCCACAATGAG-3′ 
CycloA: Forward: 5′-GCGGCAGGTCCATCTACG-3′ 
 Reverse: 5′-GCCATCCAGCCATTCAGTC-3′ 

Immunofluorescence

Specimens from 188 patients with a diagnosis of primary IDH–wild-type GBM were stained with ionized calcium-binding adaptor molecule-1 (IBA1) and cluster of differentiation 204 (CD204; ref. 31). The published dataset was reanalyzed by accounting for patient sex, and the IBA1 and CD204 staining intensities were graphed separately for male versus female patients.

Flow Analysis of GBM Tumors

IDH–wild-type GBM specimens were collected by the Rose Ella Burkhardt Brain Tumor and Neuro-Oncology Center in accordance with the Institutional Review Board (IRB; IRB2559) of Cleveland Clinic. Patient demographic information is provided in Table 1. Tumors were cut into small pieces with a razor blade and incubated with collagenase IV (StemCell Technologies) on a rotator at 37°C for 1 hour. Cells were strained over a 40-μm filter and further minced with a plunger to obtain single-cell suspensions. Samples were washed with 30 mL PBS twice and treated with RBC Lysis Buffer (BioLegend). Samples were stained with LIVE/DEAD Fixable Stains for 10 minutes on ice and incubated with FcR Blocking Reagent for 15 minutes on ice. Staining with fluorophore-conjugated antibodies was performed in Brilliant Stain Buffer (BD Biosciences) for 20 minutes on ice. Cells were fixed overnight in eBioscience FOXP3/Transcription Factor Fixation Buffer. Isotype and Ki-67 staining was performed in eBioscience FOXP3/Transcription Factor Permeabilization Buffer with 20 minutes of incubation at room temperature. Samples were acquired with a BD LSR Fortessa. Human MDSC subsets were phenotypically defined based on established guidelines (44).

Table 1.

Patient demographic information

CCF IDSexAgeRaceEthnicityFinal diagnosisNew diagnosis vs. recurrent diagnosisIDH1 statusATRX expressionp53 stainKi-67 stainMGMT promoter methylation
Patient 1 Male 38 White Nonhispanic GBM New Wild-type Retained 5% 70% Unmethylated 
Patient 2 Male 44 White Nonhispanic GBM New Wild-type Retained 5% 70% Methylated 
Patient 3 Male 60 White Nonhispanic GBM Recurrent Wild-type Retained >80% >50% Unmethylated 
Patient 4 Male 72 White Nonhispanic GBM New Wild-type Retained 80% 60% Unmethylated 
Patient 5 Male 67 White Nonhispanic GBM Recurrent Wild-type Retained 10% 30% Unmethylated 
Patient 6 Male 63 White Nonhispanic GBM New Wild-type Retained 20% 40% Unmethylated 
Patient 7 Male 63 White N/A GBM New Wild-type Retained 10% 50% Unmethylated 
Patient 8 Male 59 White Nonhispanic GBM Recurrent Wild-type Retained >90% 60% Unmethylated 
CCF IDSexAgeRaceEthnicityFinal diagnosisNew diagnosis vs. recurrent diagnosisIDH1 statusATRX expressionp53 stainKi-67 stainMGMT promoter methylation
Patient 1 Male 38 White Nonhispanic GBM New Wild-type Retained 5% 70% Unmethylated 
Patient 2 Male 44 White Nonhispanic GBM New Wild-type Retained 5% 70% Methylated 
Patient 3 Male 60 White Nonhispanic GBM Recurrent Wild-type Retained >80% >50% Unmethylated 
Patient 4 Male 72 White Nonhispanic GBM New Wild-type Retained 80% 60% Unmethylated 
Patient 5 Male 67 White Nonhispanic GBM Recurrent Wild-type Retained 10% 30% Unmethylated 
Patient 6 Male 63 White Nonhispanic GBM New Wild-type Retained 20% 40% Unmethylated 
Patient 7 Male 63 White N/A GBM New Wild-type Retained 10% 50% Unmethylated 
Patient 8 Male 59 White Nonhispanic GBM Recurrent Wild-type Retained >90% 60% Unmethylated 
NU IDSexAgeRaceEthnicityFinal diagnosisNew diagnosis vs. recurrent diagnosisIDH1 statusATRX expressionp53 stainKi-67 stainMGMT promoter methylation
Patient 1 Female 74 White Nonhispanic GBM New Wild-type  25% Unmethylated 
Patient 2 Female 85 White Nonhispanic GBM New Wild-type    Unmethylated 
Patient 3 Female 59 White Nonhispanic GBM New Wild-type Retained 10% 70% Unmethylated 
Patient 4 Female 67 White Nonhispanic GBM New Wild-type Retained  20% Unmethylated 
Patient 5 Female 74 White Nonhispanic GBM New Wild-type    Unmethylated 
Patient 6 Female 53 White Nonhispanic GBM New Wild-type Retained   Unmethylated 
Patient 7 Female 81 White Nonhispanic GBM New Wild-type Retained 5%–7% 50% Unmethylated 
Patient 8 Female 68 White Nonhispanic GBM New Wild-type    Unmethylated 
Patient 9 Female 30 White Nonhispanic GBM New Wild-type Retained 10% 40% Unmethylated 
Patient 10 Female 63 White Nonhispanic GBM New Wild-type    Unmethylated 
Patient 11 Male 69 White Nonhispanic GBM New Wild-type   25%–30% Unmethylated 
Patient 12 Male 56 White Nonhispanic GBM New Wild-type Retained <10% 40% Unmethylated 
Patient 13 Male 63 White Nonhispanic GBM New Wild-type   30% Unmethylated 
Patient 14 Male 77 White Nonhispanic GBM New Wild-type    Unmethylated 
Patient 15 Male 53 White Nonhispanic GBM New Wild-type    Unmethylated 
Patient 16 Male 62 White Nonhispanic GBM New Wild-type Retained 70% 40% Unmethylated 
Patient 17 Male 72 White Nonhispanic GBM New Wild-type    Unmethylated 
Patient 18 Male 80 White Nonhispanic GBM New Wild-type Retained <5% 40% Unmethylated 
Patient 19 Male 61 White Nonhispanic GBM New Wild-type Retained 5% 25% Unmethylated 
Patient 20 Male 54 White Nonhispanic GBM New Wild-type Retained 10% 20% Unmethylated 
NU IDSexAgeRaceEthnicityFinal diagnosisNew diagnosis vs. recurrent diagnosisIDH1 statusATRX expressionp53 stainKi-67 stainMGMT promoter methylation
Patient 1 Female 74 White Nonhispanic GBM New Wild-type  25% Unmethylated 
Patient 2 Female 85 White Nonhispanic GBM New Wild-type    Unmethylated 
Patient 3 Female 59 White Nonhispanic GBM New Wild-type Retained 10% 70% Unmethylated 
Patient 4 Female 67 White Nonhispanic GBM New Wild-type Retained  20% Unmethylated 
Patient 5 Female 74 White Nonhispanic GBM New Wild-type    Unmethylated 
Patient 6 Female 53 White Nonhispanic GBM New Wild-type Retained   Unmethylated 
Patient 7 Female 81 White Nonhispanic GBM New Wild-type Retained 5%–7% 50% Unmethylated 
Patient 8 Female 68 White Nonhispanic GBM New Wild-type    Unmethylated 
Patient 9 Female 30 White Nonhispanic GBM New Wild-type Retained 10% 40% Unmethylated 
Patient 10 Female 63 White Nonhispanic GBM New Wild-type    Unmethylated 
Patient 11 Male 69 White Nonhispanic GBM New Wild-type   25%–30% Unmethylated 
Patient 12 Male 56 White Nonhispanic GBM New Wild-type Retained <10% 40% Unmethylated 
Patient 13 Male 63 White Nonhispanic GBM New Wild-type   30% Unmethylated 
Patient 14 Male 77 White Nonhispanic GBM New Wild-type    Unmethylated 
Patient 15 Male 53 White Nonhispanic GBM New Wild-type    Unmethylated 
Patient 16 Male 62 White Nonhispanic GBM New Wild-type Retained 70% 40% Unmethylated 
Patient 17 Male 72 White Nonhispanic GBM New Wild-type    Unmethylated 
Patient 18 Male 80 White Nonhispanic GBM New Wild-type Retained <5% 40% Unmethylated 
Patient 19 Male 61 White Nonhispanic GBM New Wild-type Retained 5% 25% Unmethylated 
Patient 20 Male 54 White Nonhispanic GBM New Wild-type Retained 10% 20% Unmethylated 

NOTE: Sex, age, race, ethnicity, diagnosis, recurrence, IDH1/ATRX expression, p53/Ki-67 staining and MGMT status of patient specimens used to assess MDSC accumulation [Cleveland Clinic Foundation (CCF) cohort] and IL1β expression [Northwestern University (NU) cohort].

IHC

All samples were collected and processed at the Northwestern University Feinberg School of Medicine (Chicago, IL) in accordance with approved IRB guidelines. Specimens from 10 male and 10 female patients diagnosed with IDH–wild-type primary GBM were stained with anti-IL1β antibody (Clone ab156791, Abcam) diluted 1:250. Patient demographic information and molecular characteristics of the tumors are provided in Table 1.

To quantify IL1β staining, slides were scanned with a Leica SCN400 (Leica) at 40× magnification. Four random 5000 × 5000 μm sections were extracted from each tissue with Aperio ImageScope (Leica). The number of IL1β-positive cells per visual field was counted with Fiji-ImageJ software and normalized to the total number of cells (https://imagej.net/Fiji).

Bioinformatic Analysis

The TCGA GBM dataset was accessed via https://xenabrowser.net/heatmap/ on June 24, 2019, and August 5, 2019, for extraction of patient sex, overall survival, and IL1B and OLR1 (LOX1) expression level. Survival duration was graphed for patients with highest (1st quartile) versus lowest (4th quartile) IL1B and OLR1 expression. Correlation between IL1B and OLR1 expression was determined using the GlioVis database (https://gliovis.shinyapps.io/GlioVis/; ref. 50). The CGGA was accessed via http://www.cgga.org.cn/ on January 29, 2020, for extraction of patient sex, overall survival, and IL1B and OLR1 (LOX1) expression level information from patients with IDH–wild-type primary GBM. Survival duration was graphed for patients separated on the basis of median expression values.

Statistical Analysis

GraphPad PRISM (Version 6, GraphPad Software Inc.) software was used for data presentation and statistical analysis. Unpaired t test, paired t test, and two-way ANOVA were used for comparison of differences among sample groups. The Gehan–Breslow–Wilcoxon test was used to analyze survival data. The specific statistical method employed for individual datasets is listed in the figure legends.

A.M. Khalil is an assistant professor at Case Western Reserve University. T.H. Hwang is a consultant at MEDICALIP. M.S. Ahluwalia is a consultant at AstraZeneca, BMS, Forma Therapeutics, Wiley, Elsevier, Bayer, AbbVie, Varian Medical Systems, Monteris, Karyopharm, Kadmon, VBI Vaccine, Flatrion, and Tocagen, reports receiving commercial research grants from Novartis, AbbVie, Bayer, Pharmacyclics, Novocure, Incyte, Merck, BMS, and AstraZeneca, and has ownership interest (including patents) in Doctible and Mimivax. No potential conflicts of interest were disclosed by the other authors.

Conception and design: D. Bayik, D.C. Watson, M.M. Grabowski, M.A. Vogelbaum, J.D. Lathia

Development of methodology: F. Cheng

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): D. Bayik, D.J. Silver, A. Lauko, G. Roversi, D.C. Watson, A. Lo, T.J. Alban, M. McGraw, M. Sorensen, M.M. Grabowski, B. Otvos, C. Horbinski, B.W. Kristensen

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): D. Bayik, Y. Zhou, C. Park, C. Hong, D. Vail, A. Lauko, A. Lo, T.J. Alban, M. Sorensen, M.M. Grabowski, B. Otvos, M.A. Vogelbaum, C. Horbinski, B.W. Kristensen, A.M. Khalil, T.H. Hwang, M.S. Ahluwalia, F. Cheng, J.D. Lathia

Writing, review, and/or revision of the manuscript: D. Bayik, Y. Zhou, C. Hong, D.J. Silver, A. Lauko, G. Roversi, D.C. Watson, A. Lo, T.J. Alban, M.M. Grabowski, B. Otvos, M.A. Vogelbaum, C. Horbinski, B.W. Kristensen, A.M. Khalil, M.S. Ahluwalia, F. Cheng, J.D. Lathia

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): G. Roversi, M. McGraw, B.W. Kristensen, J.D. Lathia

Study supervision: J.D. Lathia

The authors thank the members of the Lathia Lab for insightful discussions. We would like to acknowledge technical help from Katy McCortney (Northwestern University), Emily Serbinowski and Lexie Trestan (Cleveland Clinic), the Cleveland Clinic Flow Cytometry Core, and Dr. John Peterson from the Cleveland Clinic Imaging Core. We greatly appreciate the editorial assistance of Dr. Erin Mulkearns-Hubert (Cleveland Clinic). We would like to thank Amanda Mendelsohn from the Center for Medical Art and Photography at the Cleveland Clinic for the illustrations. Recombinant IL2 was kindly provided by Dr. Marcela Diaz-Montero (Cleveland Clinic). This work was supported by the NIH grants R01NS109742 (to J.D. Lathia and M.A. Vogelbaum), F32 CA243314 (to D. Bayik), and R01NS102669 (to C. Horbinski), and by the Northwestern SPORE in Brain Cancer P50CA221747 (to C. Horbinski). This work was also supported by the Sontag Foundation (to J.D. Lathia), the American Brain Tumor Association (to J.D. Lathia), Case Comprehensive Cancer Center (to J.D. Lathia), Cleveland Clinic/Lerner Research Institute (to J.D. Lathia) and the Case Comprehensive Cancer Center Cancer Biology Training Award T32 CA059366 (to D. Bayik).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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