Predicting Response to Immunotherapy in Metastatic Renal Cell Carcinoma
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Updated: Dec 8, 2025
A Syngeneic Mouse Model of Metastatic Renal Cell Carcinoma for Quantitative and Longitudinal Assessment of Preclinical Therapies
Published on: April 12, 2017
Matthew D Tucker1, Brian I Rini1
1Department of Medicine, Division of Hematology and Oncology, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
Abstract:
Immunotherapy-based combinations, driven by PD-1, PD-L1, and CTLA-4 inhibitors, has altered the treatment landscape for metastatic renal cell carcinoma (RCC). Despite significant improvements in clinical outcomes, many patients do not experience deep or lasting benefits. Recent efforts to determine which patients are most likely to benefit from immunotherapy and immunotherapy-based combinations have shown promise but have not yet affected clinical practice. PD-L1 expression via immunohistochemistry (IHC) has shown promise in a few clinical trials, although variations in the IHC assays as well as the use of different values for positivity presents unique challenges for this potential biomarker. Several other candidate biomarkers were investigated including tumor mutational burden, gene expression signatures, single gene mutations, human endogenous retroviruses, the gastrointestinal microbiome, and peripheral blood laboratory markers. While individually these biomarkers have yet to explain the heterogeneity of treatment response to immunotherapy, using aggregate information from these biomarkers may inform clinically useful predictive biomarkers.
Insights
Immunotherapy has changed metastatic renal cell carcinoma (RCC) treatment, but predicting patient response remains challenging. Combining multiple biomarkers may offer a path toward personalized immunotherapy strategies for RCC.
Area of Science:
- Oncology
- Immunology
- Biomarker Discovery
Background:
- Immunotherapy, particularly PD-1, PD-L1, and CTLA-4 inhibitors, has revolutionized metastatic renal cell carcinoma (RCC) treatment.
- Despite advancements, a significant portion of patients do not achieve durable responses to these therapies.
Purpose of the Study:
- To review current and emerging biomarkers for predicting patient response to immunotherapy in metastatic RCC.
- To highlight the challenges and potential of integrating multiple biomarkers for improved clinical decision-making.
Main Methods:
- Literature review of clinical trials and research investigating biomarkers for immunotherapy response in RCC.
- Analysis of candidate biomarkers including PD-L1 expression (IHC), tumor mutational burden, gene expression signatures, and others.
- Evaluation of the potential for aggregate biomarker information.
Main Results:
- PD-L1 expression by immunohistochemistry (IHC) shows promise but faces challenges due to assay variability and differing positivity thresholds.
- Individual biomarkers like tumor mutational burden, gene signatures, and microbiome data have not fully explained treatment response heterogeneity.
- No single biomarker has yet proven sufficient for clinical practice in predicting immunotherapy efficacy.
Conclusions:
- Current predictive biomarkers for immunotherapy in metastatic RCC are limited and require further validation.
- Integrating data from multiple candidate biomarkers may yield more robust predictive models.
- Future research should focus on developing comprehensive biomarker strategies to personalize RCC immunotherapy.
