ArticleFrontiers in immunology2025
Pre-treatment monocytic myeloid-derived suppressor cells as predictive biomarkers for immune checkpoint inhibitor response in clear cell renal cell carcinoma.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Pembrolizumab plus high-dose IL-2 in advanced clear cell renal cell carcinoma: six-year survival outcomes and molecular signatures from a phase 2 trial.Nature communications · 2026Trial
- Myeloid-derived suppressor cells in cancer: biology, regulatory networks and theranostic prospects.Signal transduction and targeted therapy · 2026Review
- Tumor-Induced Splenic Remodeling: Mechanisms of Systemic Immunosuppression and Emerging Therapeutic Opportunities.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Immune Escape in Renal Cell Carcinoma: Latest Research and Treatment Strategies.International journal of molecular sciences · 2026Review
- TRP channel expression patterns define molecular subtypes, prognosis, and therapeutic targets in gastric cancer.Frontiers in immunology · 2026Article
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Authors and funding
9 authors.
Funding
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Abstract
Background: Immune checkpoint inhibitors (ICIs) are a cornerstone of systemic therapy for clear cell renal cell carcinoma (ccRCC), yet response rates remain variable and predictive biomarkers are lacking. This study aimed to determine whether baseline levels of myeloid-derived suppressor cells (MDSCs), especially monocytic (M-MDSC) and polymorphonuclear (PMN-MDSC) subtypes, could predict ICI response in ccRCC patients. Methods: In this prospective cohort study, 20 ccRCC patients receiving ICI-based therapy for at least 3 months were enrolled. Peripheral blood mononuclear cells were collected before and after treatment to quantify total MDSCs, M-MDSCs, and PMN-MDSCs via flow cytometry. Additional clinical variables, including blood cell counts and metabolic profiles, were assessed. Elastic net regression identified key variables associated with treatment response. Multivariable logistic regression was used to evaluate their predictive value. The primary outcome was objective response (CR/PR) based on RECIST 1.1. Results: Among 47 clinical and laboratory variables with an area under the curve (AUC) greater than 0.6, elastic net regression identified 7 key predictors of immunotherapy response. Notably, higher baseline levels of monocytic MDSCs (M-MDSCs) and their proportion within total MDSCs were independently associated with objective response to immune checkpoint inhibitors (OR = 3.082, p = 0.041 and OR = 5.764, p = 0.036, respectively). Following treatment, responders exhibited a significant decline in circulating M-MDSC levels, whereas non-responders did not. Conclusions: Baseline circulating M-MDSC levels and their relative proportion within total MDSCs may serve as potential predictive biomarkers for response to immune checkpoint inhibitors in ccRCC patients. These findings highlight the role of MDSCs in modulating immunotherapy efficacy and suggest their clinical utility in guiding personalized treatment strategies.
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