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Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells
Published on: February 8, 2018
Tumour neoantigen heterogeneity thresholds provide a time window for combination immunotherapy
Guim Aguadé-Gorgorió1,2, Ricard Solé1,2,3
1ICREA-Complex Systems Lab, Universitat Pompeu Fabra, 08003 Barcelona, Spain.
Abstract:
Following the advent of cancer immunotherapy, increasing insight has been gained on the role of mutational load and neoantigens as key ingredients in T cell recognition of malignancies. However, not all highly mutational tumours react to immune therapies, and initial success is often followed by eventual relapse. Heterogeneity in the neoantigen landscape of a tumour might be key in the failure of immune surveillance. In this work, we present a mathematical framework to describe how neoantigen distributions shape the immune response. The model predicts the existence of an antigen diversity threshold level beyond which T cells fail at controlling heterogeneous tumours. Incorporating this diversity marker adds predictive value to antigen load for two cohorts of anti-CTLA-4 treated melanoma patients. Furthermore, our analytical approach indicates rapid increases in epitope heterogeneity in early malignancy growth following immune escape. We propose a combination therapy scheme that takes advantage of preexisting resistance to a targeted agent. The model indicates that the selective sweep for a resistant subclone reduces neoantigen heterogeneity, and we postulate the existence of a time window before tumour relapse where checkpoint blockade immunotherapy can become more effective.
Insights
Tumor neoantigen diversity, not just load, impacts cancer immunotherapy success. A new model predicts a diversity threshold beyond which T cells fail, offering insights for melanoma treatment and combination therapy strategies.
Area of Science:
- Immunology
- Computational Biology
- Oncology
Background:
- Cancer immunotherapy has advanced understanding of tumor mutational load and neoantigens in T cell recognition.
- However, not all highly mutated tumors respond to immunotherapy, and relapses occur, suggesting other factors are involved.
- Tumor neoantigen heterogeneity is a potential reason for immune surveillance failure.
Purpose of the Study:
- To develop a mathematical framework modeling how neoantigen distributions influence the immune response.
- To investigate the role of antigen diversity in T cell control of heterogeneous tumors.
- To identify potential therapeutic strategies based on neoantigen landscape dynamics.
Main Methods:
- Developed a mathematical model to describe neoantigen distributions and their impact on immune response.
- Analyzed the relationship between antigen diversity and T cell control efficacy.
- Validated model predictions using patient data from anti-CTLA-4 treated melanoma cohorts.
- Modeled tumor growth dynamics and immune escape mechanisms.
Main Results:
- The model predicts an antigen diversity threshold above which T cells are ineffective against heterogeneous tumors.
- Tumor antigen diversity is a significant predictor of treatment response, complementing antigen load.
- Rapid increases in epitope heterogeneity are observed during early malignancy growth post-immune escape.
- A combination therapy approach leveraging resistance to targeted agents shows promise.
Conclusions:
- Neoantigen heterogeneity is a critical factor in cancer immune evasion and immunotherapy failure.
- Tumor antigen diversity serves as a predictive biomarker for immunotherapy response.
- Therapeutic strategies targeting neoantigen heterogeneity, including combination therapies and optimized immunotherapy timing, may improve patient outcomes.
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