Related Experiment Video
Updated: May 7, 2025

The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers
Published on: January 22, 2013
Single-cell and spatial transcriptomics reveal SPP1-CD44 signaling drives primary resistance to immune checkpoint
Junfeng Zhang1, Qingyan Peng2, Jin Fan3
1Department of Urology, Xinjiang Medical University Affiliated Cancer Hospital, Urumqi, China.
Background:
Immune checkpoint inhibitors (ICIs) are a cornerstone therapy for advanced renal cell carcinoma (RCC). However, significant rates of primary resistance hinder their efficacy, and the underlying mechanisms remain poorly understood. This study aims to unravel the tumor-immune interactions and signaling pathways driving primary resistance to ICIs in RCC.
Methods:
We integrated single-cell RNA sequencing, spatial transcriptomics, and clinical sample analysis to investigate the tumor microenvironment and intercellular signaling. Advanced computational methods, including cell-cell communication networks, pseudotime trajectories, and gene set enrichment analysis (GSEA), were employed to uncover the underlying resistance mechanisms.
Results:
Compared to the sensitive group, the primary resistance group exhibited a significant increase in SPP1-CD44 signaling-mediated interactions between tumor cells and immune cells. These interactions disrupted antigen presentation in immune effector cells and suppressed key chemokine and cytokine pathways, thereby impairing effective immune responses. In contrast, the sensitive group showed more active antigen presentation and cytokine signaling, which facilitated stronger immune responses. Furthermore, the interaction between SPP1-secreting tumor cells and CD44-expressing exhausted CD8 + T cells activated the MAPK signaling pathway within CD8 + Tex cells, exacerbating T cell exhaustion and driving the development of ICI resistance in RCC.
Conclusion:
Our findings reveal a potential mechanism by which SPP1-CD44 signaling mediates tumor-immune cell interactions leading to ICI resistance, providing a theoretical basis for targeting and disrupting this signaling to overcome primary resistance in RCC.
Insights
Primary resistance to immune checkpoint inhibitors (ICIs) in renal cell carcinoma (RCC) is linked to SPP1-CD44 signaling. This pathway disrupts immune responses and promotes T cell exhaustion, driving resistance.
Area of Science:
- Oncology
- Immunology
- Molecular Biology
Background:
- Immune checkpoint inhibitors (ICIs) are crucial for advanced renal cell carcinoma (RCC) treatment.
- Primary resistance to ICIs significantly limits treatment efficacy in RCC.
- Mechanisms underlying primary ICI resistance in RCC are not fully understood.
Purpose of the Study:
- To investigate tumor-immune interactions driving primary resistance to ICIs in RCC.
- To identify signaling pathways involved in ICI resistance in renal cell carcinoma.
- To unravel the molecular mechanisms of primary ICI resistance in RCC.
Main Methods:
- Integration of single-cell RNA sequencing and spatial transcriptomics.
- Analysis of clinical RCC samples.
- Application of computational methods: cell-cell communication networks, pseudotime, and GSEA.
Main Results:
- Increased SPP1-CD44 signaling in the primary resistance group compared to the sensitive group.
- SPP1-CD44 interactions impaired antigen presentation and suppressed chemokine/cytokine pathways.
- SPP1-CD44 signaling activated MAPK in exhausted CD8+ T cells, exacerbating T cell exhaustion and driving ICI resistance.
Conclusions:
- SPP1-CD44 signaling mediates tumor-immune interactions contributing to ICI resistance in RCC.
- Disruption of SPP1-CD44 signaling may offer a strategy to overcome primary ICI resistance.
- Provides a theoretical basis for novel therapeutic approaches in renal cell carcinoma.
More Related Videos
09:32Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells
Published on: February 8, 2018
06:38A Syngeneic Mouse Model of Metastatic Renal Cell Carcinoma for Quantitative and Longitudinal Assessment of Preclinical Therapies
Published on: April 12, 2017