Related Experiment Video
Updated: Aug 29, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
An immunotherapy response prediction model derived from proliferative CD4+ T cells and antigen-presenting monocytes
Kun Zheng1, Lianchong Gao2, Jie Hao3
1Department of Urology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Abstract:
Most patients with clear cell renal cell carcinoma (ccRCC) have an impaired response to immune checkpoint blockade (ICB) therapy. Few biomarkers can predict responsiveness, and there is insufficient evidence to extend them to ccRCC clinical use. To explore subtypes and signatures of immunocytes with good predictive performance for ICB outcomes in the ccRCC context, we reanalyzed two ccRCC single-cell RNA sequencing (scRNA-seq) datasets from patients receiving ICB treatment. A subtype of proliferative CD4+ T cells and regulatory T cells and a subtype of antigen-presenting monocytes that have good predictive capability and are correlated with ICB outcomes were identified. These findings were corroborated in independent ccRCC ICB pretreatment bulk RNA-seq datasets. By incorporating the cluster-specific marker genes of these three immunocyte subtypes, we developed a prediction model, which reached an AUC of 93% for the CheckMate cohort (172 samples). Our study shows that the ICB response prediction model can serve as a valuable clinical decision-making tool for guiding ICB treatment of ccRCC patients.
Insights
New biomarkers predict immune checkpoint blockade (ICB) therapy response in clear cell renal cell carcinoma (ccRCC). A novel model identifies predictive immunocyte subtypes, aiding clinical decisions for ccRCC patients receiving ICB treatment.
Area of Science:
- Oncology
- Immunology
- Genomics
Background:
- Clear cell renal cell carcinoma (ccRCC) patients often show poor response to immune checkpoint blockade (ICB).
- Limited biomarkers exist to predict ICB responsiveness in ccRCC, hindering clinical application.
Purpose of the Study:
- To identify immunocyte subtypes and gene signatures that predict ICB treatment outcomes in ccRCC.
- To develop a predictive model for ICB response in ccRCC patients.
Main Methods:
- Reanalysis of two ccRCC single-cell RNA sequencing (scRNA-seq) datasets from patients undergoing ICB therapy.
- Identification and validation of predictive immunocyte subtypes using independent ccRCC bulk RNA-sequencing data.
- Development of a prediction model based on cluster-specific marker genes.
Main Results:
- Identified a subtype of proliferative CD4+ T cells and regulatory T cells with predictive capability.
- Discovered a subtype of antigen-presenting monocytes correlated with ICB outcomes.
- Developed a prediction model achieving 93% AUC in the CheckMate cohort for ICB response.
Conclusions:
- Specific immunocyte subtypes can predict ICB response in ccRCC.
- The developed prediction model shows potential as a clinical decision-making tool for ccRCC patients receiving ICB therapy.
More Related Videos
Related Concept Videos
01:22T Cell Activation and Clonal Selection
Naive T cells that have not yet encountered an antigen express two primary CD...
01:27Tumor Immunotherapy
01:27Cytotoxic T Cells-mediated Immune Response
Immunological surveillance is the ability of immune cells to monitor and eliminate infected cells with intracellular pathogens, neoplastically transformed cells, and cells with non-self antigens. Cytotoxic T cells and NK...