ArticleiScience2023
Metabolic predictors of response to immune checkpoint blockade therapy.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Single-cell meta-analysis of T cells reveals clonal dynamics of response to checkpoint immunotherapy.Cell genomics · 2025Pooled it
- Advanced technologies of single-cell metabolomics unveiling cellular metabolic heterogeneity for biological and biomedical research.Journal of food and drug analysis · 2026Review
- Immune checkpoint inhibitor therapy for gastric cancer: current status, therapeutic challenges, and future prospects.Frontiers in immunology · 2026Review
- Overcoming resistance to immunotherapy by targeting CD38 in human tumor explants.Cell reports. Medicine · 2025Article
- Editorial: Immune-checkpoint inhibitors and immunometabolic reprogramming in cancer immunotherapy.Frontiers in immunology · 2025Article
- Disrupting CD38-driven T cell dysfunction restores sensitivity to cancer immunotherapy.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.