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Updated: Jul 11, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Metabolic predictors of response to immune checkpoint blockade therapy
Ofir Shorer1, Keren Yizhak1,2
1Department of Cell Biology and Cancer Science, The Ruth and Bruce Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa 3525422, Israel.
Abstract:
Metabolism of immune cells in the tumor microenvironment (TME) plays a critical role in cancer patient response to immune checkpoint inhibitors (ICI). Yet, a metabolic characterization of immune cells in the TME of patients treated with ICI is lacking. To bridge this gap we performed a semi-supervised analysis of ∼1700 metabolic genes using single-cell RNA-seq data of > 1 million immune cells from ∼230 samples of cancer patients treated with ICI. When clustering cells based on their metabolic gene expression, we found that similar immunological cellular states are found in different metabolic states. Most importantly, we found metabolic states that are significantly associated with patient response. We then built a metabolic predictor based on a dozen gene signature, which significantly differentiates between responding and non-responding patients across different cancer types (AUC = 0.8-0.92). Taken together, our results demonstrate the power of metabolism in predicting patient response to ICI.
Insights
Immune cell metabolism in the tumor microenvironment (TME) impacts cancer patient response to immune checkpoint inhibitors (ICI). This study reveals distinct metabolic states associated with ICI treatment response, enabling a predictor for patient outcomes.
Area of Science:
- Immunology
- Metabolomics
- Cancer Research
Background:
- Immune cell metabolism within the tumor microenvironment (TME) is crucial for predicting patient responses to immune checkpoint inhibitors (ICI).
- A detailed metabolic characterization of immune cells in the TME of patients undergoing ICI therapy is currently lacking.
- Understanding these metabolic profiles is essential for improving cancer treatment strategies.
Purpose of the Study:
- To characterize the metabolism of immune cells in the TME of cancer patients treated with ICI.
- To identify metabolic states associated with patient response to ICI therapy.
- To develop a predictive model for ICI treatment outcomes based on immune cell metabolism.
Main Methods:
- Semi-supervised analysis of approximately 1700 metabolic genes.
- Utilized single-cell RNA sequencing data from over 1 million immune cells across approximately 230 cancer patient samples.
- Clustering of immune cells based on metabolic gene expression profiles.
Main Results:
- Identified distinct metabolic states within immune cells, independent of their immunological states.
- Discovered specific metabolic states significantly correlated with patient response to ICI.
- Developed a predictive model using a 12-gene signature that accurately differentiates responders from non-responders (AUC = 0.8-0.92) across various cancer types.
Conclusions:
- Immune cell metabolism within the TME is a powerful indicator of patient response to ICI.
- Metabolic profiling offers a promising avenue for predicting ICI treatment efficacy.
- This research provides a foundation for developing novel biomarkers and therapeutic strategies targeting cancer metabolism.
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