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.

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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