Machine learning derived proliferating T cell-related signature: a novel biomarker for prognosis and treatment efficacy in clear cell renal cell carcinoma

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not specified in abstract

• Primary Outcome: Overall survival and progression-free survival stratification

• Nnt Or Sensitivity: Robust performance across training and validation sets (specific metrics not provided)

• Confidence Interval: Not provided

Clinical Application

Requires transcriptomic analysis capability, may be challenging in routine practice Relevant for Australian oncology practice given PBS coverage of immunotherapy and targeted agents for advanced RCC; would need TGA validation for clinical use Patients with advanced clear cell renal cell carcinoma considering systemic therapy

Abstract

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) exhibits profound molecular heterogeneity, and reliable biomarkers for predicting clinical outcomes are urgently needed. Proliferating T cells (Tprolif) are central to immune system activation but their related signatures in predicting prognosis and therapeutic effect of ccRCC patients remains unexplored. METHODS: We developed a Tprolif-related RCC score (TRRS) using an integrative machine learning framework. Transcriptomic data from the CheckMate025 trial formed the discovery cohort. The model was validated across multiple independent cohorts, including IMmotion151, JAVELIN Renal 101, TCGA-KIRC, and West China Hospital (WCH) cohort from our center. Multi-omics analyses, including spatial and single-cell transcriptomics, were employed to investigate the associated biology and identify key mediators. RESULTS: The final TRRS model, built from 7 genes, demonstrated robust performance in stratifying patients for overall survival (OS) and progression-free survival (PFS) in training and all validation sets. TRRS was a powerful predictor of improved outcomes not only for immune checkpoint inhibitor (ICI) monotherapy but also for ICI-based combination therapy and targeted therapies. Biologically, a high TRRS was associated with aggressive tumor hallmarks, a distinct metabolic profile favoring aerobic glycolysis and glutamine metabolism, and an immunosuppressive tumor microenvironment despite high immune cell infiltration based on WCH cohort transcriptome expression profile. Through multi-omics screening, we identified CST3 as a key target, with spatial and single-cell analyses confirming its role in promoting tumor malignancy and enhancing cell-cell communication among microenvironment components. CONCLUSION: The TRRS is a novel, validated biomarker that effectively predicts prognosis and therapeutic responses in advanced ccRCC. It reflects critical biological features of tumor aggressiveness and immune evasion, with CST3 emerging as a potential central mediator and therapeutic target.

References

1. 1.Liu, D., Wang, L., Pan, X., Zhao, J., Tang, Y., Liang, J., & Zeng, H. (2026). Machine learning derived proliferating T cell-related signature: a novel biomarker for prognosis and treatment efficacy in clear cell renal cell carcinoma. International Immunopharmacology, 116361. https://doi.org/10.1016/j.intimp.2026.116361