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Updated: May 20, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Overview on biomarkers for immune oncology drugs
Evgeny N Imyanitov1,2, Elena V Preobrazhenskaya1,2, Natalia V Mitiushkina1
1Department of Tumor Growth Biology, N.N. Petrov Institute of Oncology, 197758 St.-Petersburg, Russia.
Abstract:
Although immune checkpoint inhibitors (ICIs) are widely used in clinical oncology, less than half of treated cancer patients derive benefit from this therapy. Both tumor- and host-related variables are implicated in response to ICIs. The predictive value of PD-L1 expression is confined only to several cancer types, so this molecule is not an agnostic biomarker. Highly elevated tumor mutation burden (TMB) caused either by excessive carcinogenic exposure or by a deficiency in DNA repair is a reliable indicator for ICI efficacy, as exemplified by tumors with high-level microsatellite instability (MSI-H). Other potentially relevant tumor-related characteristics include gene expression signatures, pattern of tumor infiltration by immune cells, and, perhaps, some immune-response modifying somatic mutations. Host-related factors have not yet been comprehensively considered in relevant clinical trials. Microbiome composition, markers of systemic inflammation [e.g., neutrophil-to-lymphocyte ratio (NLR)], and human leucocyte antigen (HLA) diversity may influence the efficacy of ICIs. Studies on ICI biomarkers are likely to reveal modifiable tumor or host characteristics, which can be utilized to direct the antitumor immune defense. Examples of the latter approach include tumor priming to immune therapy by cytotoxic drugs and elevation of ICI efficacy by microbiome modification.
Insights
Immune checkpoint inhibitors (ICIs) benefit fewer than half of cancer patients. Biomarkers like tumor mutation burden (TMB) and host factors such as microbiome composition can predict and potentially improve ICI therapy response.
Area of Science:
- Oncology
- Immunotherapy
- Cancer Research
Background:
- Immune checkpoint inhibitors (ICIs) are crucial in cancer treatment but benefit less than 50% of patients.
- Predicting response to ICIs is challenging, as PD-L1 expression is not universally applicable.
- Tumor and host factors significantly influence ICI efficacy.
Purpose of the Study:
- To explore predictive biomarkers for immune checkpoint inhibitor (ICI) therapy response.
- To identify both tumor-intrinsic and host-related factors influencing ICI efficacy.
- To highlight potential strategies for enhancing ICI effectiveness.
Main Methods:
- Review of existing literature on biomarkers for ICI response.
- Analysis of tumor-related factors including PD-L1 expression, tumor mutation burden (TMB), microsatellite instability (MSI-H), gene expression, and immune cell infiltration.
- Evaluation of host-related factors such as microbiome composition, systemic inflammation markers (e.g., neutrophil-to-lymphocyte ratio - NLR), and human leucocyte antigen (HLA) diversity.
Main Results:
- High tumor mutation burden (TMB), particularly in microsatellite instability-high (MSI-H) tumors, is a reliable predictor of ICI efficacy.
- PD-L1 expression has limited predictive value and varies across cancer types.
- Host factors including microbiome, NLR, and HLA diversity show potential influence on ICI response.
- Tumor priming with cytotoxic drugs and microbiome modification can enhance ICI efficacy.
Conclusions:
- Biomarker discovery for ICIs is essential for personalized cancer therapy.
- Both tumor characteristics (TMB, MSI-H) and host factors (microbiome, inflammation, HLA) are critical for ICI response.
- Targeting modifiable tumor and host factors offers promising avenues to improve antitumor immunity and ICI effectiveness.
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