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Updated: Nov 8, 2025

Testing Cancer Immunotherapeutics in a Humanized Mouse Model Bearing Human Tumors
Published on: December 16, 2022
Distinct Biomarker Profiles and TCR Sequence Diversity Characterize the Response to PD-L1 Blockade in a Mouse
Rajaa El Meskini1, Devon Atkinson2, Alan Kulaga2
1Center for Advanced Preclinical Research, Frederick National Laboratory for Cancer Research, Frederick, Maryland. zweaverohler@mail.nih.gov elmeskinir@mail.nih.gov.
Abstract:
Only a subset of patients responds to immune checkpoint blockade (ICB) in melanoma. A preclinical model recapitulating the clinical activity of ICB would provide a valuable platform for mechanistic studies. We used melanoma tumors arising from an Hgftg;Cdk4R24C/R24C genetically engineered mouse (GEM) model to evaluate the efficacy of an anti-mouse PD-L1 antibody similar to the anti-human PD-L1 antibodies durvalumab and atezolizumab. Consistent with clinical observations for ICB in melanoma, anti-PD-L1 treatment elicited complete and durable response in a subset of melanoma-bearing mice. We also observed tumor growth delay or regression followed by recurrence. For early treatment assessment, we analyzed gene expression profiles, T-cell infiltration, and T-cell receptor (TCR) signatures in regressing tumors compared with tumors exhibiting no response to anti-PD-L1 treatment. We found that CD8+ T-cell tumor infiltration corresponded to response to treatment, and that anti-PD-L1 gene signature response indicated an increase in antigen processing and presentation, cytokine-cytokine receptor interaction, and natural killer cell-mediated cytotoxicity. TCR sequence data suggest that an anti-PD-L1-mediated melanoma regression response requires not only an expansion of the TCR repertoire that is unique to individual mice, but also tumor access to the appropriate TCRs. Thus, this melanoma model recapitulated the variable response to ICB observed in patients and exhibited biomarkers that differentiate between early response and resistance to treatment, providing a valuable platform for prediction of successful immunotherapy. IMPLICATIONS: Our melanoma model recapitulates the variable response to anti-PD-L1 observed in patients and exhibits biomarkers that characterize early antibody response, including expansion of the TCR repertoire.
Insights
A new melanoma model mimics patient responses to immune checkpoint blockade (ICB) therapy. Biomarkers like T-cell infiltration and TCR repertoire expansion predict early treatment success, aiding immunotherapy development.
Area of Science:
- Immunology
- Oncology
- Genetics
Background:
- Immune checkpoint blockade (ICB) shows variable efficacy in melanoma patients.
- A preclinical model is needed to study ICB mechanisms and predict response.
- Genetically engineered mouse (GEM) models offer platforms for translational research.
Purpose of the Study:
- To develop and validate a GEM model for studying anti-PD-L1 efficacy in melanoma.
- To identify biomarkers predicting response to ICB in melanoma.
- To provide a platform for mechanistic studies of immunotherapy resistance and response.
Main Methods:
- Utilized an Hgftg;Cdk4R24C/R24C GEM model for melanoma.
- Administered anti-mouse PD-L1 antibody therapy, analogous to human ICB treatments.
- Analyzed gene expression, T-cell infiltration, and T-cell receptor (TCR) signatures.
Main Results:
- The model recapitulated variable patient responses to ICB, including complete and durable responses, as well as tumor recurrence.
- CD8+ T-cell infiltration into tumors correlated with treatment response.
- Gene expression signatures indicated increased antigen processing, cytokine interactions, and NK cell activity in responders.
- TCR repertoire expansion and tumor accessibility were crucial for anti-PD-L1 mediated regression.
Conclusions:
- The developed GEM model effectively mimics clinical ICB response variability in melanoma.
- Biomarkers such as CD8+ T-cell infiltration and TCR repertoire expansion can predict early response to anti-PD-L1 therapy.
- This model serves as a valuable platform for investigating immunotherapy resistance and developing predictive biomarkers.
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