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Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Current landscape and future directions of biomarkers for predicting responses to immune checkpoint inhibitors
Yingming Zhu1,2, Fen Zhao1, Zhenxiang Li1
1Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong University, Jinan, China, yujinmingsd@126.com.
Abstract:
Immune checkpoint inhibitors (ICIs), represented by anti-CTLA-4 or anti-PD-1/anti-PD-L1 pathway antibodies, have led to a revolution in cancer treatment modalities. ICIs have unique clinical benefits, such as effectiveness against a broad range of tumor types, strong overall impact on survival, and persistent responses after the cessation of therapy. However, only a subset of patients responds to these therapies, and a small proportion of patients even experience rapid progression or an increased risk of death. Therefore, it is imperative to optimize patient selection for treatment. This review focuses on the mechanisms of tumor escape from immune surveillance, the composition and activity of a preexisting immune infiltrate, the degree of tumor foreignness (as reflected by the mutational burden, expression of viral genes, and driver gene mutations), and host factors (including peripheral blood biomarkers, genetic polymorphisms, and gut microbiome) to summarize current evidence on the biomarkers of responses to ICIs and explore the future prospects in this field.
Insights
Immune checkpoint inhibitors (ICIs) revolutionize cancer care but patient selection is key. This review explores biomarkers for predicting response to ICIs, including tumor and host factors, to improve treatment outcomes.
Area of Science:
- Oncology
- Immunology
- Genetics
Background:
- Immune checkpoint inhibitors (ICIs) have transformed cancer therapy, offering significant survival benefits.
- However, response rates vary, necessitating improved patient selection strategies.
- Tumor escape mechanisms and host factors influence ICI efficacy.
Purpose of the Study:
- To review current biomarkers predicting response to ICIs.
- To explore mechanisms of tumor immune evasion.
- To identify host factors influencing ICI therapy.
Main Methods:
- Literature review of studies on ICI biomarkers.
- Analysis of tumor-intrinsic factors (mutational burden, gene expression).
- Evaluation of host-related factors (blood biomarkers, genetics, microbiome).
Main Results:
- Biomarkers include tumor mutational burden, immune infiltrate characteristics, and host genetic factors.
- Gut microbiome composition is emerging as a significant predictor of ICI response.
- Peripheral blood biomarkers and tumor foreignness also play crucial roles.
Conclusions:
- Optimizing patient selection for ICIs requires a multi-faceted biomarker approach.
- Further research into host-tumor interactions is essential for enhancing ICI therapy.
- Integrating diverse biomarkers will improve prediction of treatment success.
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