Usage Information
Abstract
Myeloid cells orchestrate the antitumor immune response and influence the efficacy of immune checkpoint blockade (ICB) therapies. We and others have previously shown that IL-32 mediates DC differentiation and macrophage activation. Here, we demonstrate that IL-32 expression in human melanoma positively correlates with overall survival, response to ICB, and an immune-inflamed tumor microenvironment (TME) enriched in mature DC, M1 macrophages, and CD8+ T cells. Treatment of B16F10 murine melanomas with IL-32 increased the frequencies of activated, tumor-specific CD8+ T cells, leading to the induction of systemic tumor immunity. Our mechanistic in vivo studies revealed a potentially novel role of IL-32 in activating intratumoral DC and macrophages to act in concert to prime CD8+ T cells and recruit them into the TME through CCL5. Thereby, IL-32 treatment reduced tumor growth and rendered ICB-resistant B16F10 tumors responsive to anti–PD-1 therapy without toxicity. Furthermore, increased baseline IL-32 gene expression was associated with response to nivolumab and pembrolizumab in 2 independent cohorts of patients with melanoma, implying that IL-32 is a predictive biomarker for anti–PD-1 therapy. Collectively, this study suggests IL-32 as a potent adjuvant in immunotherapy to enhance the efficacy of ICB in patients with non–T cell–inflamed TME.
Authors
Thomas Gruber, Mirela Kremenovic, Hassan Sadozai, Nives Rombini, Lukas Baeriswyl, Fabienne Maibach, Robert L. Modlin, Michel Gilliet, Diego von Werdt, Robert E. Hunger, S. Morteza Seyed Jafari, Giulia Parisi, Gabriel Abril-Rodriguez, Antoni Ribas, Mirjam Schenk
Usage data is cumulative from July 2025 through July 2026.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 2,583 | 166 |
| 287 | 55 | |
| Figure | 846 | 7 |
| Table | 92 | 0 |
| Supplemental data | 117 | 5 |
| Citation downloads | 289 | 0 |
| Totals | 4,214 | 233 |
| Total Views | 4,447 | |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.
