Research ArticleImmunologyOncology Open Access | 10.1172/jci.insight.184545

David A. Schoenfeld,1 Dijana Djureinovic,1 David G. Su,2 Lin Zhang,1 Benjamin Y. Lu,1 Larisa Kamga,3 Jacqueline E. Mann,1 John D. Huck,4 Michael Hurwitz,1 David A. Braun,1 Lucia Jilaveanu,1 Aaron M. Ring,5 and Harriet M. Kluger1

1Section of Medical Oncology and

2Section of Surgery, Yale School of Medicine, New Haven, Connecticut, USA.

3Department of Internal Medicine, Emory University School of Medicine, Atlanta, Georgia, USA.

4Department of Immunobiology, Yale School of Medicine, New Haven, Connecticut, USA.

5Immunotherapy Integrated Research Center, Fred Hutchinson Cancer Center, Seattle, Washington, USA.

Address correspondence to: Harriet Kluger, 333 Cedar St., PO Box 208028, New Haven, Connecticut 06520, USA. Phone: 203.200.6622; Email: harriet.kluger@yale.edu. Or to: Aaron Ring, 1241 Eastlake Ave. E., S3-204, Seattle, Washington 98102, USA. Phone: 206.667.5001; aaronring@fredutch.org.

Authorship note: AMR and HMK contributed equally to this work.

Find articles by Schoenfeld, D. in: JCI | PubMed | Google Scholar |

1Section of Medical Oncology and

2Section of Surgery, Yale School of Medicine, New Haven, Connecticut, USA.

3Department of Internal Medicine, Emory University School of Medicine, Atlanta, Georgia, USA.

4Department of Immunobiology, Yale School of Medicine, New Haven, Connecticut, USA.

5Immunotherapy Integrated Research Center, Fred Hutchinson Cancer Center, Seattle, Washington, USA.

Address correspondence to: Harriet Kluger, 333 Cedar St., PO Box 208028, New Haven, Connecticut 06520, USA. Phone: 203.200.6622; Email: harriet.kluger@yale.edu. Or to: Aaron Ring, 1241 Eastlake Ave. E., S3-204, Seattle, Washington 98102, USA. Phone: 206.667.5001; aaronring@fredutch.org.

Authorship note: AMR and HMK contributed equally to this work.

Find articles by Djureinovic, D. in: JCI | PubMed | Google Scholar |

1Section of Medical Oncology and

2Section of Surgery, Yale School of Medicine, New Haven, Connecticut, USA.

3Department of Internal Medicine, Emory University School of Medicine, Atlanta, Georgia, USA.

4Department of Immunobiology, Yale School of Medicine, New Haven, Connecticut, USA.

5Immunotherapy Integrated Research Center, Fred Hutchinson Cancer Center, Seattle, Washington, USA.

Address correspondence to: Harriet Kluger, 333 Cedar St., PO Box 208028, New Haven, Connecticut 06520, USA. Phone: 203.200.6622; Email: harriet.kluger@yale.edu. Or to: Aaron Ring, 1241 Eastlake Ave. E., S3-204, Seattle, Washington 98102, USA. Phone: 206.667.5001; aaronring@fredutch.org.

Authorship note: AMR and HMK contributed equally to this work.

Find articles by Su, D. in: JCI | PubMed | Google Scholar

1Section of Medical Oncology and

2Section of Surgery, Yale School of Medicine, New Haven, Connecticut, USA.

3Department of Internal Medicine, Emory University School of Medicine, Atlanta, Georgia, USA.

4Department of Immunobiology, Yale School of Medicine, New Haven, Connecticut, USA.

5Immunotherapy Integrated Research Center, Fred Hutchinson Cancer Center, Seattle, Washington, USA.

Address correspondence to: Harriet Kluger, 333 Cedar St., PO Box 208028, New Haven, Connecticut 06520, USA. Phone: 203.200.6622; Email: harriet.kluger@yale.edu. Or to: Aaron Ring, 1241 Eastlake Ave. E., S3-204, Seattle, Washington 98102, USA. Phone: 206.667.5001; aaronring@fredutch.org.

Authorship note: AMR and HMK contributed equally to this work.

Find articles by Zhang, L. in: JCI | PubMed | Google Scholar

1Section of Medical Oncology and

2Section of Surgery, Yale School of Medicine, New Haven, Connecticut, USA.

3Department of Internal Medicine, Emory University School of Medicine, Atlanta, Georgia, USA.

4Department of Immunobiology, Yale School of Medicine, New Haven, Connecticut, USA.

5Immunotherapy Integrated Research Center, Fred Hutchinson Cancer Center, Seattle, Washington, USA.

Address correspondence to: Harriet Kluger, 333 Cedar St., PO Box 208028, New Haven, Connecticut 06520, USA. Phone: 203.200.6622; Email: harriet.kluger@yale.edu. Or to: Aaron Ring, 1241 Eastlake Ave. E., S3-204, Seattle, Washington 98102, USA. Phone: 206.667.5001; aaronring@fredutch.org.

Authorship note: AMR and HMK contributed equally to this work.

Find articles by Lu, B. in: JCI | PubMed | Google Scholar

1Section of Medical Oncology and

2Section of Surgery, Yale School of Medicine, New Haven, Connecticut, USA.

3Department of Internal Medicine, Emory University School of Medicine, Atlanta, Georgia, USA.

4Department of Immunobiology, Yale School of Medicine, New Haven, Connecticut, USA.

5Immunotherapy Integrated Research Center, Fred Hutchinson Cancer Center, Seattle, Washington, USA.

Address correspondence to: Harriet Kluger, 333 Cedar St., PO Box 208028, New Haven, Connecticut 06520, USA. Phone: 203.200.6622; Email: harriet.kluger@yale.edu. Or to: Aaron Ring, 1241 Eastlake Ave. E., S3-204, Seattle, Washington 98102, USA. Phone: 206.667.5001; aaronring@fredutch.org.

Authorship note: AMR and HMK contributed equally to this work.

Find articles by Kamga, L. in: JCI | PubMed | Google Scholar

1Section of Medical Oncology and

2Section of Surgery, Yale School of Medicine, New Haven, Connecticut, USA.

3Department of Internal Medicine, Emory University School of Medicine, Atlanta, Georgia, USA.

4Department of Immunobiology, Yale School of Medicine, New Haven, Connecticut, USA.

5Immunotherapy Integrated Research Center, Fred Hutchinson Cancer Center, Seattle, Washington, USA.

Address correspondence to: Harriet Kluger, 333 Cedar St., PO Box 208028, New Haven, Connecticut 06520, USA. Phone: 203.200.6622; Email: harriet.kluger@yale.edu. Or to: Aaron Ring, 1241 Eastlake Ave. E., S3-204, Seattle, Washington 98102, USA. Phone: 206.667.5001; aaronring@fredutch.org.

Authorship note: AMR and HMK contributed equally to this work.

Find articles by Mann, J. in: JCI | PubMed | Google Scholar

1Section of Medical Oncology and

2Section of Surgery, Yale School of Medicine, New Haven, Connecticut, USA.

3Department of Internal Medicine, Emory University School of Medicine, Atlanta, Georgia, USA.

4Department of Immunobiology, Yale School of Medicine, New Haven, Connecticut, USA.

5Immunotherapy Integrated Research Center, Fred Hutchinson Cancer Center, Seattle, Washington, USA.

Address correspondence to: Harriet Kluger, 333 Cedar St., PO Box 208028, New Haven, Connecticut 06520, USA. Phone: 203.200.6622; Email: harriet.kluger@yale.edu. Or to: Aaron Ring, 1241 Eastlake Ave. E., S3-204, Seattle, Washington 98102, USA. Phone: 206.667.5001; aaronring@fredutch.org.

Authorship note: AMR and HMK contributed equally to this work.

Find articles by Huck, J. in: JCI | PubMed | Google Scholar

1Section of Medical Oncology and

2Section of Surgery, Yale School of Medicine, New Haven, Connecticut, USA.

3Department of Internal Medicine, Emory University School of Medicine, Atlanta, Georgia, USA.

4Department of Immunobiology, Yale School of Medicine, New Haven, Connecticut, USA.

5Immunotherapy Integrated Research Center, Fred Hutchinson Cancer Center, Seattle, Washington, USA.

Address correspondence to: Harriet Kluger, 333 Cedar St., PO Box 208028, New Haven, Connecticut 06520, USA. Phone: 203.200.6622; Email: harriet.kluger@yale.edu. Or to: Aaron Ring, 1241 Eastlake Ave. E., S3-204, Seattle, Washington 98102, USA. Phone: 206.667.5001; aaronring@fredutch.org.

Authorship note: AMR and HMK contributed equally to this work.

Find articles by Hurwitz, M. in: JCI | PubMed | Google Scholar

1Section of Medical Oncology and

2Section of Surgery, Yale School of Medicine, New Haven, Connecticut, USA.

3Department of Internal Medicine, Emory University School of Medicine, Atlanta, Georgia, USA.

4Department of Immunobiology, Yale School of Medicine, New Haven, Connecticut, USA.

5Immunotherapy Integrated Research Center, Fred Hutchinson Cancer Center, Seattle, Washington, USA.

Address correspondence to: Harriet Kluger, 333 Cedar St., PO Box 208028, New Haven, Connecticut 06520, USA. Phone: 203.200.6622; Email: harriet.kluger@yale.edu. Or to: Aaron Ring, 1241 Eastlake Ave. E., S3-204, Seattle, Washington 98102, USA. Phone: 206.667.5001; aaronring@fredutch.org.

Authorship note: AMR and HMK contributed equally to this work.

Find articles by Braun, D. in: JCI | PubMed | Google Scholar

1Section of Medical Oncology and

2Section of Surgery, Yale School of Medicine, New Haven, Connecticut, USA.

3Department of Internal Medicine, Emory University School of Medicine, Atlanta, Georgia, USA.

4Department of Immunobiology, Yale School of Medicine, New Haven, Connecticut, USA.

5Immunotherapy Integrated Research Center, Fred Hutchinson Cancer Center, Seattle, Washington, USA.

Address correspondence to: Harriet Kluger, 333 Cedar St., PO Box 208028, New Haven, Connecticut 06520, USA. Phone: 203.200.6622; Email: harriet.kluger@yale.edu. Or to: Aaron Ring, 1241 Eastlake Ave. E., S3-204, Seattle, Washington 98102, USA. Phone: 206.667.5001; aaronring@fredutch.org.

Authorship note: AMR and HMK contributed equally to this work.

Find articles by Jilaveanu, L. in: JCI | PubMed | Google Scholar

1Section of Medical Oncology and

2Section of Surgery, Yale School of Medicine, New Haven, Connecticut, USA.

3Department of Internal Medicine, Emory University School of Medicine, Atlanta, Georgia, USA.

4Department of Immunobiology, Yale School of Medicine, New Haven, Connecticut, USA.

5Immunotherapy Integrated Research Center, Fred Hutchinson Cancer Center, Seattle, Washington, USA.

Address correspondence to: Harriet Kluger, 333 Cedar St., PO Box 208028, New Haven, Connecticut 06520, USA. Phone: 203.200.6622; Email: harriet.kluger@yale.edu. Or to: Aaron Ring, 1241 Eastlake Ave. E., S3-204, Seattle, Washington 98102, USA. Phone: 206.667.5001; aaronring@fredutch.org.

Authorship note: AMR and HMK contributed equally to this work.

Find articles by Ring, A. in: JCI | PubMed | Google Scholar

1Section of Medical Oncology and

2Section of Surgery, Yale School of Medicine, New Haven, Connecticut, USA.

3Department of Internal Medicine, Emory University School of Medicine, Atlanta, Georgia, USA.

4Department of Immunobiology, Yale School of Medicine, New Haven, Connecticut, USA.

5Immunotherapy Integrated Research Center, Fred Hutchinson Cancer Center, Seattle, Washington, USA.

Address correspondence to: Harriet Kluger, 333 Cedar St., PO Box 208028, New Haven, Connecticut 06520, USA. Phone: 203.200.6622; Email: harriet.kluger@yale.edu. Or to: Aaron Ring, 1241 Eastlake Ave. E., S3-204, Seattle, Washington 98102, USA. Phone: 206.667.5001; aaronring@fredutch.org.

Authorship note: AMR and HMK contributed equally to this work.

Find articles by Kluger, H. in: JCI | PubMed | Google Scholar |

Authorship note: AMR and HMK contributed equally to this work.

Published November 19, 2024 - More info

Published in Volume 10, Issue 1 on January 9, 2025
JCI Insight. 2025;10(1):e184545. https://doi.org/10.1172/jci.insight.184545.
© 2024 Schoenfeld et al. This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Published November 19, 2024 - Version history
Received: July 19, 2024; Accepted: November 13, 2024 View PDF Abstract

The cytokine IL-18 has immunostimulatory effects but is negatively regulated by a secreted binding protein, IL-18BP, that limits IL-18’s anticancer efficacy. A decoy-resistant form of IL-18 (DR-18) that avoids sequestration by IL-18BP while maintaining its immunostimulatory potential has recently been developed. Here, we investigated the therapeutic potential of DR-18 in renal cell carcinoma (RCC). Using pantumor transcriptomic data, we found that clear cell RCC had among the highest expression of IL-18 receptor subunits and IL18BP of tumor types in the database. In samples from patients with RCC treated with immune checkpoint inhibitors, IL-18BP protein expression increased in the tumor microenvironment and in circulation within plasma in nonresponding patients, and it decreased in the majority of responding patients. We used immunocompetent RCC murine models to assess the efficacy of DR-18 in combination with single- and dual-agent anti–PD-1 and anti–CTLA-4. In contrast to preclinical models of other tumor types, in RCC models, DR-18 enhanced the activity of anti–CTLA-4 but not anti–PD-1 treatment. This activity correlated with intratumoral enrichment and clonal expansion of effector CD8+ T cells, decreased Treg levels, and enrichment of proinflammatory antitumor myeloid cell populations. Our findings support further clinical investigation of the combination of DR-18 and anti–CTLA-4 in RCC.

Introduction

In recent years, the treatment paradigm for advanced renal cell carcinoma (aRCC) has shifted, with the emergence of immune checkpoint inhibitors (ICIs) that target CTLA-4 and PD-1 and newer-generation vascular endothelial growth factor receptor–targeting (VEGFR-targeting) tyrosine kinase inhibitors (TKIs). Combination regimens of dual ICIs targeting CTLA-4 and PD-1, or anti–PD-1 plus a TKI, have significantly extended overall survival compared with previous therapies (1–4). Still, many patients do not respond to front-line therapy, and among initial responders, responses are usually transient (5). There is substantial need for novel therapeutic approaches in RCC beyond traditional ICIs. Given the demonstrated immune responsiveness of RCC, new immunomodulatory agents represent a promising area for investigation (6).

Cytokine-based therapies represent one such approach. High-dose IL-2 and IFN-α have been used for decades in aRCC, albeit with low response rates (7). Other cytokine-based therapies, including IL-12, IL-15, and IL-21, are being explored (7). IL-18 is another potential anticancer cytokine. A member of the IL-1 cytokine family, IL-18 can stimulate innate lymphocytes and activate antigen-experienced T cells, and it is a potent inducer of IFN-γ (8). Due to its immunostimulatory effects, recombinant IL-18 was previously tested in early-phase clinical trials, and while it was safe and well tolerated, it lacked efficacy in melanoma (9, 10). However, IL-18 is negatively regulated by a secreted protein (IL-18 binding protein [IL-18BP]) that binds to IL-18 with high affinity and thus prevents its interaction with the IL-18 receptor (11). Levels of IL-18BP increased in response to administration of recombinant IL-18, suggesting that IL-18BP may have abrogated maximal activity of IL-18 therapy (9).

A recent study demonstrated that IL-18BP is highly expressed in various cancers, including clear cell RCC (ccRCC) and that it functions as a secreted immune checkpoint in cancer (12). Directed evolution was used to engineer a modified version of IL-18, termed “decoy-resistant” IL-18 (DR-18), which avoids neutralization by IL-18BP while maintaining its immune cell–stimulating potential. DR-18 exerted potent antitumor effects in mouse models of melanoma and colon cancer by remodeling the immune tumor microenvironment (TME) and activating antigen-specific CD8+ tumor-infiltrating lymphocytes (TILs), which were sufficient to induce antitumor responses. Anti–PD-1 enhanced the activity of DR-18 in the initial models tested. DR-18 also inhibited tumor growth in MHC class I–deficient tumors, a major mechanism of ICI resistance, through NK cell activity. DR-18 thus represents a promising therapeutic agent with the potential to synergize with ICIs and have activity in ICI-resistant settings. Accordingly, the first-in-human trial of the human version of DR-18 is currently underway to evaluate safety, pharmacokinetics, pharmacodynamics, and clinical activity in patients with relapsed or refractory solid tumors (NCT04787042; https://clinicaltrials.gov/study/NCT04787042).

Based on these preclinical data, the particularly high expression of IL18BP in ccRCC (12), and the demonstrated responsiveness of RCC to ICIs and other cytokine-based immunotherapies, we hypothesized that IL-18 could be an effective cytokine for treating aRCC. Herein, we investigated IL-18BP and the IL-18 receptor in samples from patients with RCC and determined the antitumor activity of DR-18 in RCC murine models and the combined effects with different ICIs.

Results

ccRCC has high expression of IL-18 receptor subunits (IL18R1 and IL18RAP) and IL18BP. We employed The Cancer Genome Atlas (TCGA) PanCancer data to determine mRNA expression of IL-18 receptor subunits (IL18R1 and IL18RAP) and IL18BP in RCC. ccRCC has among the highest expression of both IL-18 receptor subunits and IL18BP relative to 29 other cancer types (Figure 1A and Supplemental Figure 1A; supplemental material available online with this article; https://doi.org/10.1172/jci.insight.184545DS1). Comparing across the 3 most common RCC histologic subtypes (clear cell, papillary, and chromophobe), expression of IL-18 receptor subunits and IL18BP was highest in ccRCC and lowest in chromophobe RCC (Figure 1B and Supplemental Figure 1B). Higher IL18BP but not IL-18 receptor subunit expression was associated with higher disease stage in ccRCC (Figure 1C and Supplemental Figure 1C). Higher IL18BP expression in ccRCC was also associated with higher tumor grade, higher hypoxia signatures scores, and worse survival (Figure 1D and Supplemental Figure 1, D and E). Transcriptional analysis revealed that ccRCC tumors with high IL18BP expression are enriched for markers of cytokine/chemokine signaling, T cell activation, and neutrophil/granulocyte chemotaxis (Figure 1, E and F, Supplemental Figure 2A, Supplemental Table 1). Numerous immune checkpoints were among the most significantly upregulated genes with high IL18BP expression, and IL18BP expression was highly correlated with LAG3, TIGIT, PDCD1, and CTLA4 expression, as well as expression of CD4 and the Treg marker FOXP3 (Supplemental Figure 2B and Supplemental Table 2). IL18BP and IL18 levels were also significantly correlated, although to a lesser degree (Supplemental Figure 2C). Altogether, these findings suggest that the IL-18/IL-18BP axis may play an important role in shaping the TME in at least a subset of ccRCC tumors.

Figure 1

IL18BP and IL18R1 are expressed at high levels in ccRCC and elevated IL18BP is associated with cytokine and T cell activation and worse survival. (A–C) IL18R1 and IL18BP expression from TCGA PanCancer Atlas for all tumors (ccRCC indicated with red asterisk) (A), RCC histologic subtypes (B), and for ccRCC (C), by stage. (D) Kaplan-Meier survival curves based on IL18BP expression in ccRCC, dichotomized by median expression. (E) Volcano plot of transcripts enriched with high versus low IL18BP expression in ccRCC (log2 fold-change thresholds of 1 and –1; P value threshold of 1 × 10–6). (F) The top gene sets from enrichment analysis of transcripts enriched with high IL18BP expression. For B and C, statistical testing was performed using Kruskal-Wallis test with Dunn’s correction for multiple comparisons. **P < 0.01; ***P < 0.001; ****P < 0.0001.

IL-18BP protein expression increases after ICIs in nonresponding patients with RCC. We next quantified IL-18BP protein expression in the TME using a well-established method of quantitative immunofluorescence (qIF) employing tissue microarrays (TMAs) of human RCC samples (Supplemental Table 3) (13, 14). Representative histospot staining patterns are shown in Supplemental Figure 3A. IL-18BP was expressed in both primary RCC tumors and metastases, with lower expression in brain metastases (Supplemental Figure 3B). Among patients treated with ICI-based therapies (treatment regimens shown in Supplemental Table 3), higher IL-18BP expression was associated with worse overall survival (Figure 2A). IL-18BP levels also significantly increased after immunotherapy in nonresponding patients (stable or progressive disease) (Figure 2B).

Figure 2

IL-18BP protein levels increase after immunotherapy in nonresponding patients with RCC. (A) Kaplan-Meier curves of overall survival of patients with RCC after ICIs by IL-18BP protein expression, dichotomized by median qIF levels. (B) IL-18BP protein levels assessed by qIF in the same RCC patient cohort as A, before and after ICIs, in ICI responders/nonresponders. (C) Circulating plasma levels of IL-18BP, as assessed by ELISA, from patient-matched samples before and after ipi + nivo treatment in a different RCC patient cohort from A and B. (D) Circulating plasma levels of IL-18BP from patient-matched samples before- and during ipi + nivo treatment, separated by treatment response. (E) The ratio of post/pretreatment IL-18BP plasma levels by treatment response. (F) The directional change of IL-18BP plasma levels after treatment by response. (G) Kaplan-Meier curves of progression-free survival (PFS) after ipi + nivo by directional change in circulating IL-18BP levels after treatment, in the same RCC cohort as in C–F. Statistical testing was performed using Mann-Whitney U test (B and E), Wilcoxon matched-pairs signed rank test (C and D), and Fisher’s exact test (F). Due to small samples sizes, formal statistical testing was not conducted on G, and the analysis should be viewed as hypothesis generating. *P < 0.05; **P < 0.01.

To determine if these findings extended beyond the TME, we quantified circulating plasma levels of IL-18BP using ELISA in patients with RCC before and after treatment with ipilimumab and nivolumab (ipi + nivo) in the frontline setting (Supplemental Table 4). Patient-matched plasma IL-18BP levels did not significantly change with treatment (Figure 2C). However, when the patients were separated by response to ipi + nivo, a treatment effect was apparent: in responders (complete or partial responses), plasma IL-18BP levels did not significantly change with ipi + nivo, but they increased significantly in nonresponders, consistent with the qIF data (Figure 2, D and E, and Supplemental Figure 3C). Notably, while plasma IL-18BP levels increased in 100% of nonresponders after treatment, they decreased in 67% of responders (Figure 2F). Furthermore, we found that patients whose plasma IL-18BP levels decreased after immunotherapy had longer progression-free survival (Figure 2G). We did not observe the same patterns with circulating plasma levels of IL-18. Patient-matched plasma IL-18 levels increased after treatment with ipi + nivo but did so at equivalent levels between responders and nonresponders (Supplemental Figure 3, D–G). No differences in circulating IL-18 levels were observed between responders and nonresponders before or after treatment, with nearly all patients having increased plasma IL-18 levels after treatment (Supplemental Figure 3H). Interestingly, circulating IL-18 and IL-18BP levels were significantly correlated in responders, particularly before treatment, while levels were not correlated in nonresponders (before or after treatment) (Supplemental Figure 3I).

DR-18 in combination with anti–CTLA-4 demonstrates enhanced in vivo activity in RCC and melanoma murine models. Having seen that the IL-18 pathway may be primed for reactivation in ccRCC, we next performed tumor growth and survival analyses in 2 syngeneic, immunocompetent murine RCC models: Renca and RAG (15, 16). We tested DR-18 monotherapy and combination therapy with single- and dual-agent ICIs, including both anti–PD-1 and anti–CTLA-4 targeting antibodies (Figure 3A). In the Renca model, DR-18 monotherapy modestly inhibited tumor growth and prolonged survival, comparable with ICIs (Figure 3, B and C, and Supplemental Figure 4A). Interestingly, adding PD-1 blockade to DR-18 did not enhance efficacy, whereas the addition of anti–CTLA-4 to DR-18 significantly increased antitumor effects. Triple-therapy (DR-18 + anti–PD-1 + anti–CTLA-4) did not further inhibit tumor growth or prolong survival compared with the doublet (DR-18 + anti–CTLA-4). The RAG model was more sensitive to ICIs but produced similar results, again showing a greater effect of anti–CTLA-4 than anti–PD-1 when combined with DR-18 (Figure 3, D and E, and Supplemental Figure 4, B–D). Immune cell depletion studies in the Renca model demonstrated that CD8+ T cells, NK cells, and IFN-γ, but not CD4+ T cells, are similarly required for activity of DR-18 + anti–CTLA-4 (Figure 3F). We conclude that DR-18 monotherapy has modest activity in murine RCC models, but the combination of DR-18 + anti–CTLA-4 may be particularly effective.

Figure 3

DR-18 combined with anti–CTLA-4 extends survival in murine RCC models. (A) WT immunocompetent balb/c mice were s.c. engrafted with 0.5 × 106 Renca or 1.0 × 106 RAG cells. Starting on day 7–10, mice were treated twice weekly with phosphate buffered saline (PBS), DR-18 (s.c.), and/or ICIs (anti–PD-1/anti–CTLA-4) i.p. Five treatments were given. Red triangles indicate timing of administration of depleting/neutralizing antibodies. (B–E) Kaplan-Meier survival curves and mean tumor growth curves of mice engrafted with Renca (B and C) and RAG (select treatment groups shown) (D and E) cells. Data are shown as mean ± SEM (C and E). (F) Survival of mice engrafted with Renca tumors and treated with control PBS or DR-18 + anti–CTLA-4, either alone (PBS depletion) or with depleting/neutralizing antibodies. Depleting/neutralizing antibodies were given 24 hours prior to treatment and twice weekly thereafter. NK cells were depleted using anti-Asialo GM1. Renca data were combined from 3 independent experiments; RAG data were combined from 2 independent experiments. For Kaplan-Meier curves, statistical testing was performed using the log-rank test with Bonferroni correction in comparison with control-treated mice. *P < 0.05; **P < 0.01; ****P < 0.0001

We then investigated whether the efficacy of DR-18 + anti–CTLA-4 extended beyond RCC models. In the murine melanoma model YUMMER1.7, DR-18 was efficacious as a monotherapy and demonstrated added activity with anti–PD-1 (Supplemental Figure 4, E and F) (12). DR-18 + anti–PD-1 efficacy was comparable with dual-agent ICIs in YUMMER1.7 and was higher than in the RCC models. DR-18 + anti–CTLA-4 was equally as effective as these regimens in the YUMMER1.7 model.

In the RAG and YUMMER1.7 models, where multiple mice treated with various drug regimens had complete tumor regression and prolonged responses, tumor rechallenge studies with twice the initial dose of tumor cells were performed. In all mice tested, no tumors grew out on rechallenge regardless of the initial treatment regimen, indicating prolonged antitumor memory responses.

DR-18 in combination with anti–CTLA-4 induces a broad inflammatory response. We then sought to understand how the combination of DR-18 and anti–CTLA-4 alters the mouse immune system. To start, we profiled circulating cytokines/chemokines in mice with Renca tumors after 2 different time points of treatment with single-agent or combination DR-18 + anti–CTLA-4 (Figure 4A). After the first treatment, DR-18–containing regimens produced increases in multiple inflammatory cytokines, including IFN-γ, IP-10 (CXCL10), MIG (CXCL9), IL-5, G-CSF, and MCP-1 (CCL2) (Figure 4, B–D). Increases in IFN-γ, IP-10, and MIG were particularly pronounced with DR-18 + anti–CTLA-4 treatment (Figure 4, C and D). Of note, IP-10 and MIG are known to be induced by IFN-γ. After the third treatment, these and most of the other cytokines/chemokines profiled were elevated in the DR-18 + anti–CTLA-4–treated mice, suggesting the induction of a broad inflammatory response by this point in the treatment course, including Th1, Th2, and Th17 programs.

Figure 4

DR-18 + anti–CTLA-4 potently induces inflammatory cytokines/chemokines. (A) Schematic of treatment and sample collection time points for cytokine/chemokine profiling and scRNA/TCR-Seq in the Renca model. (B) Heatmap of the natural logarithm of circulating cytokine/chemokine levels in mice for the indicated treatments and time points (n = 3 mice/group, with the same mice collected at each time point), with unsupervised hierarchical clustering on the y axis. Data were generated using 31-plex Mouse Cytokine/Chemokine Array from Eve Technologies (MD31). (C) Volcano plots of the same data as in B, comparing circulating cytokine/chemokine levels with DR-18 + anti–CTLA-4 treatment (Combo) to PBS (log2 fold-change thresholds of 0.5 and –0.5; P value threshold of 0.05; cytokine/chemokine changes with FDR < 0.05 highlighted as indicated). (D) Absolute levels of the indicated cytokines/chemokines at each time point for each treatment. Statistical testing performed using 2-way ANOVA with Tukey’s multiple comparisons test comparing all conditions within a given time point; only significant comparisons are shown. Tx, treatment; hr, hours. *P < 0.05; ***P < 0.001; ****P < 0.0001

Enrichment and clonal expansion of effector CD8+ T cells with DR-18 + anti–CTLA-4. To gain insight into global changes to the TME with DR-18, anti–CTLA-4, or the combination, we performed single-cell RNA and T cell receptor (TCR) sequencing (scRNA-Seq and scTCR-Seq) of Renca tumors with and without treatment (Figure 4A). A comparison of the proportion of different infiltrating immune cell types revealed largescale changes in granulocytes and macrophages/monocytes with DR-18 treatment (Figure 5, A and B, and Supplemental Figure 5, A–D), reproducing prior findings (P < 0.0001, control vs. each DR-18 containing regimen, Fisher’s exact test) (12). Only the combination of DR-18 and anti–CTLA-4 led to higher relative CD4+ and CD8+ T cell infiltration compared with every other regimen (P < 0.0001, Fisher’s exact tests) (Figure 5B and Supplemental Figure 5D).

Figure 5

DR-18 alters immune subset composition in Renca tumors, including enrichment and clonal expansion of CD8+ effector T cells. (A) Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction plot of clustering and annotation of all cell populations isolated from Renca tumors treated for 3 cycles with PBS, DR-18, anti–CTLA-4, or DR-18 + anti–CTLA-4 (Combo) (n = 3 mice/group, pooled) based on scRNA-Seq analysis. Annotations were performed using SingleR. (B) Quantification of the proportion of each cell population from A within each of the treatment groups, showing enrichment of granulocytes with DR-18 treatment and CD8+ and CD4+ T cells with DR-18 + anti–CTLA-4. For select cell populations (boxed), the percentages within each treatment group are shown. (C) Neighborhood group plot from Milo analysis of T cell subsets from scRNA-Seq data. (D) Differential abundance fold changes of the neighborhood groups in C, comparing the Combo treatment with control, showing enrichment and deenrichment of certain groups. (E) Heatmap of the top differentially expressed genes between neighborhood group #7, enriched with DR-18 + anti–CTLA-4 treatment and with high expression levels of markers of T cell activation, cytolytic activity, and exhaustion, versus neighborhood group #4, deenriched with combination treatment. (F) Relative proportion of the top 20 clonotypes out of the total for each treatment group based on TCR analysis. (G) Clonotype proportions by size category based on TCR analysis, showing clonal expansion with DR-18 + anti–CTLA-4 (Combo). Statistical testing performed using Fisher’s exact test comparing control with all other treatment conditions, with only significant comparisons shown F, and χ2 test comparing DR-18 + anti–CTLA-4 (Combo) to all other conditions. *P < 0.05; ****P < 0.0001

To probe tumor-infiltrating T cell population differences based on treatment groups, we performed differential abundance testing on the T cell subsets using Milo, which assigns cells to partially overlapping neighborhoods on a k-nearest neighbor graph and then groups neighborhoods (Figure 5C and Supplemental Figure 6A) (17). Comparing the most prominent enriched neighborhood group containing a substantial number of neighborhoods (group #7) to the most deenriched (group #4) with combination treatment, we observed enrichment of numerous markers of CD8+ T cell activation and cytolytic activity, as well as exhaustion markers, including Cd8a, Tox, Klrd1, Klrc1, and Ifng, and the immune checkpoints Tigit, Pdcd1, and Lag3 (Figure 5, D and E, and Supplemental Figure 6B). Similar analyses of other neighborhood groups revealed deenrichment of Tregs (group #1) and mild enrichment of an activated CD4+ T cell population (group #5) (Supplemental Figure 6, C–E).

To verify these findings, we performed additional analysis on the T cell subsets. Unsupervised hierarchical clustering revealed an enriched population of activated CD8+ T cells with combination treatment (cluster 0) (Supplemental Figure 7, A–C). Semisupervised analysis with well-annotated reference murine TIL markers similarly demonstrated enrichment of effector CD8+ T cells (both precursor and terminally exhausted CD8+ populations) as well as a concomitant decrease in CD8+ and CD4+ naive–like populations, with the combination regimen (Supplemental Figure 7, D–G). While treatment with DR-18 monotherapy elicited a relative increase in Tregs, with the combination of DR-18 and anti–CTLA-4, the relative proportion of Tregs remained stable (Supplemental Figure 7, F and G). Focused analysis of immune checkpoint expression on T cells revealed strong induction of Ctla4 — and, to a lesser extent, Pdcd1 and Tigit — with DR-18 monotherapy, whereas there was stronger induction of Pdcd1 and Tigit relative to Ctla4 with DR-18 + anti–CTLA-4 (Supplemental Figure 8A).

Single-cell TCR analysis further demonstrated a greater degree of clonal expansion and loss of clonal diversity after treatment with the combination of DR-18 + anti–CTLA-4 relative to either monotherapy (Figure 5, F and G, and Supplemental Figure 8, B and C). While no single clonotype was detected across all 4 treatment groups, a CD8+ clonotype from the DR-18 monotherapy arm expanded to become a dominant effector CD8+ clonotype in the combination arm (Supplemental Figure 8, D and E).

Expansion of proinflammatory myeloid populations with DR-18 + anti–CTLA-4. Given the importance of myeloid populations in immune modulation in RCC, we characterized changes to myeloid populations with DR-18, anti–CTLA-4, and the combination. Unsupervised hierarchal clustering of the monocyte/macrophage subsets suggested population shifts with drug treatment (Figure 6A and Supplemental Figure 9A). To phenotypically classify these clusters, we employed the classification system for murine tumor associated macrophages (TAMs) and tumor-infiltrating monocytes (TIMs) described in Ma et al. (18). This analysis revealed reductions in protumorigenic TAM subtypes, particularly the lipid-associated–TAMs (LA-TAMs), with DR-18 treatment, as well as increased infiltration of classical TIMs, traditionally associated with proinflammatory effects (P < 0.0001, control vs. each DR-18 containing regimen, Fisher’s exact test) (Figure 6, B and C, and Supplemental Figure 9, B and C). The DR-18 + anti–CTLA-4 combination also led to the expansion of a TAM population compared with every other regimen (P < 0.0001, Fisher’s exact tests) defined by markers from multiple phenotypic subtypes, both proinflammatory and protumorigenic (termed “Mixed TAMs”). This finding aligns with the concept that macrophages exist on a phenotypic and functional spectrum (18–20).

Figure 6

DR-18 + anti–CTLA-4 leads to intratumoral expansion of proinflammatory myeloid populations. (A and B) UMAP plots of all macrophages/monocytes identified by scRNA-Seq analysis with overlaid treatment groups (A) and annotated clusters (B). Annotation was performed based on the phenotypic groups and markers described in Ma et al. (18). (C) Quantification of the proportion of each macrophage/monocyte subtype from B within each of the treatment groups, showing relative enrichment of proinflammatory and loss of protumorigenic subtypes. For select cell populations (boxed), the percentages within each treatment group are shown. (D) UMAP plot of all granulocytes identified by scRNA-Seq analysis with overlaid treatment groups. (E) Volcano plot of differential gene expression between granulocytes from tumors treated with combination DR-18 + anti–CTLA-4 (Combo) versus all other treatment groups (Other) (log2 fold-change thresholds of 0.5 and –0.5; P value-adjusted threshold of 1 × 10–6). (F) The top gene sets from enrichment analysis of genes enriched in granulocytes from Combo-treated tumors. (G and H) UMAP plot of all neutrophils from scRNA-Seq analysis with overlaid neutrophil subtype classification based on Zilionis et al. (25) (G), with quantification of the relative proportion of each subtype by treatment group (H). For select cell populations (boxed), the percentages within each treatment group are shown. (I) UMAP plots of neutrophils showing trajectory analysis using Slingshot from the given starting point, with overlaid treatment groups (left) and neutrophil subtypes (right), as in G.

We had also observed increased infiltration of granulocytes after treatment with DR-18, either monotherapy or in combination with anti–CTLA-4, in accord with prior findings (Figure 5B) (12). We hypothesized that phenotypic shifts in granulocyte populations could also be occurring when the combination is given relative to monotherapy, considering the difference in efficacy between the 2 treatments. Unsupervised hierarchal clustering of the granulocyte subsets indeed showed a divergence in granulocyte populations between DR-18 monotherapy and the combination with anti–CTLA-4 (Figure 6D and Supplemental Figure 9D). Differential gene expression analysis revealed enrichment of gene sets associated with type II IFN signaling and cytokines and inflammatory response in granulocytes from combination-treated tumors (Figure 6, E and F).

Recent work has better defined the phenotypic and functional diversity of neutrophils in cancer, which can have both pro- and antitumorigenic roles (21–25). We applied 1 such classification system that has both human and mouse tumor relevance and has been functionally validated in mouse tumor models to our tumor-infiltrating granulocyte population (