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Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Tumor Mutational Burden Shapes Success and Resistance in Cancer Immunotherapy
1ISEM, Université de Montpellier, CNRS, Montpellier, France.
Abstract:
Immunotherapy, particularly immune checkpoint blockade, has transformed cancer treatment, yet durable responses remain limited to a subset of patients and cancer types. Many tumors exhibit innate resistance or acquire resistance through immune evasion or neoantigen editing. A central factor in shaping these outcomes is the tumor mutational burden. However, cancer mutations can enhance or impair both cellular replication and immune recognition, reflecting the non-trivial role of mutational load in immunotherapy success and failure. Here, we present a minimal eco-evolutionary model that captures trade-offs between oncogenic and immunogenic mutations in cancer cell replication. Despite its simplicity, the model reveals a rich phase space, including an evolutionary bimodal regime where both immunologically silent and mutationally active strategies are locally optimal for tumor growth. Notably, the model illustrates two key eco-evolutionary mechanisms of resistance to immunotherapy: preexisting resistance, driven by the persistence of silent clones within tumors with high mutational burden; and immunoediting, where immune pressure selects for reduced antigenicity over time.
Insights
Cancer mutations impact immunotherapy success. A new model shows how tumors evolve resistance through silent clones or immune evasion, limiting treatment effectiveness in many patients.
Area of Science:
- Evolutionary biology
- Cancer immunology
- Computational oncology
Background:
- Immune checkpoint blockade has revolutionized cancer therapy but shows limited durable responses.
- Tumor mutational burden (TMB) is crucial for immunotherapy outcomes, yet its role is complex.
- Tumors can develop resistance via immune evasion or neoantigen editing.
Purpose of the Study:
- To model the eco-evolutionary dynamics of oncogenic and immunogenic mutations.
- To understand how tumor mutational burden influences cancer cell replication and immune recognition.
- To identify mechanisms of innate and acquired resistance to cancer immunotherapy.
Main Methods:
- Developed a minimal eco-evolutionary model of cancer cell replication.
- Analyzed trade-offs between oncogenic and immunogenic mutations.
- Simulated tumor evolution under immune pressure.
Main Results:
- Identified an evolutionary bimodal regime where silent and mutationally active tumors coexist.
- Demonstrated how high TMB can harbor pre-existing resistant clones.
- Showed that immune pressure drives immunoediting and selection for reduced antigenicity.
Conclusions:
- Tumor mutational burden plays a dual role in immunotherapy response and resistance.
- Eco-evolutionary dynamics explain key resistance mechanisms like pre-existing resistance and immunoediting.
- Understanding these evolutionary strategies is vital for improving cancer immunotherapy efficacy.
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