2. Academic Validation
  3. Novel pan-cancer T cell exhaustion signature forecasts immunotherapy response and unveils BCAP31 in macrophages as a therapeutic target in neuroblastoma

Novel pan-cancer T cell exhaustion signature forecasts immunotherapy response and unveils BCAP31 in macrophages as a therapeutic target in neuroblastoma

  • Front Immunol. 2025 Dec 22:16:1709225. doi: 10.3389/fimmu.2025.1709225.
Shan Li # 1 2,  Jianjun Zhu # 3,  Xiang Huang # 3,  Fengming Ji 1,  Jinrong Li 1,  Zhigang Yao 1,  Haoyu Tang 1,  Ling Liu 4,  Bing Yan 1 5,  Chenghao Zhanghuang # 1 2 5
Affiliations

Affiliations

  • 1 Department of Urology, Kunming Children's Hospital (Children's Hospital Affiliated to Kunming Medical University), Kunming, China.
  • 2 Department of Urology, Children's Hospital of Chongqing Medical University, Chongqing, China.
  • 3 Department of Oncology, Kunming Children's Hospital (Children's Hospital Affiliated to Kunming Medical University), Kunming, China.
  • 4 Department of Neonatology, Kunming Children's Hospital (Children's Hospital Affiliated to Kunming Medical University), Kunming, China.
  • 5 Yunnan Key Laboratory of Children's Major Disease Research, Yunnan Clinical Medical Center for Pediatric Diseases, Yunnan Province Clinical Research Center for Children's Health and Disease, Kunming Children's Solid Tumor Diagnosis and Treatment Center, Kunming Children's Hospital (Children's Hospital Affiliated to Kunming Medical University), Kunming, China.
  • # Contributed equally.
PMID: 41438751 DOI: 10.3389/fimmu.2025.1709225
Abstract

Introduction: Gaining insights into the molecular features associated with T cell exhaustion (TEX) can offer fresh perspectives on predicting treatment responses, and we aim to investigate TEX-related tumor associated macrophages (TAM) subset to deeply understand underlying mechanisms of immune exhaustion.

Methods: We performed pan-cancer single-cell RNA Sequencing (scRNA-seq) and spatial transcriptomics RNA Sequencing (stRNA-seq) analyses to investigate the subtype of TEX-associated TAMs, exploring its spatial distribution characteristics in context of immunotherapy. The pan-cancer scRNA-seq and RNA-seq datasets were incorporated to develop the STMN2+ Macrophage Signature (STMN2.SIG), which predicts immunotherapy response based on integrative machine learning techniques. Comprehensive scRNA-seq analysis, with in vitro experiments, investigated the mechanisms by which STMN2+ TAMs influence tumor progression and immune exhaustion.

Results: A macrophage subset, STMN2+ TAMs, and an epithelial subtype, S phase Sympathoblasts were identified as TEX-related cellular subpopulations. A higher proportion of STMN2+ TAMs was observed in non-responders compared to responders in pan-cancer immunotherapy landscape. Pan-cancer STMN2.SIG performed well in predicting immunotherapy response in pan-cancer cohorts, potentially linked to intercellular interactions between STMN2+ TAMs and CD8+ Tex cells. stRNA-seq analysis confirmed that interactions and cellular distances between STMN2+ TAMs and CD8+ Tex cells impact therapy efficacy. In a co-culture system, silencing BCAP31 on TAMs drives CD8+ T cells toward an effector state in NB. And BCAP31 on TAMs is associated with modulation of JAK2-STAT3 pathway in tumor cells.

Conclusion: Our study provides pan-cancer STMN2.SIG as an outperforming approach for patient selection of immunotherapy, and advances our understanding of TAM biology and suggests potential therapeutic strategies for downregulation of BCAP31 in TAMs.

Keywords

T cell exhaustion; machine learning; multi-omics analysis; neuroblastoma; pan-cancer analysis.

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