Formate Supplementation Enhances Antitumor CD8+ T-cell Fitness and Efficacy of PD-1 Blockade.
Cancer discovery(2023)SCI 1区
Dana Farber Canc Inst | Blavatnik Inst | Princeton Univ | Harvard Med Sch
Abstract
Abstract The tumor microenvironment (TME) restricts antitumor CD8+ T-cell function and immunotherapy responses. Cancer cells compromise the metabolic fitness of CD8+ T cells within the TME, but the mechanisms are largely unknown. Here we demonstrate that one-carbon (1C) metabolism is enhanced in T cells in an antigen-specific manner. Therapeutic supplementation of 1C metabolism using formate enhances CD8+ T-cell fitness and antitumor efficacy of PD-1 blockade in B16-OVA tumors. Formate supplementation drives transcriptional alterations in CD8+ T-cell metabolism and increases gene signatures for cellular proliferation and activation. Combined formate and anti–PD-1 therapy increases tumor-infiltrating CD8+ T cells, which are essential for enhanced tumor control. Our data demonstrate that formate provides metabolic support to CD8+ T cells reinvigorated by anti–PD-1 to overcome a metabolic vulnerability in 1C metabolism in the TME to further improve T-cell function. Significance: This study identifies that deficiencies in 1C metabolism limit the efficacy of PD-1 blockade in B16-OVA tumors. Supplementing 1C metabolism with formate during anti–PD-1 therapy enhances CD8+ T-cell fitness in the TME and CD8+ T-cell–mediated tumor clearance. These findings demonstrate that formate supplementation can enhance exhausted CD8+ T-cell function. See related commentary by Lin et al., p. 2507. This article is featured in Selected Articles from This Issue, p. 2489
MoreTranslated text
Key words
Tumor Microenvironment,Tumor Growth,Cancer Cell Metabolism,Metabolism
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
CELL CHEMICAL BIOLOGY 2024
被引用1
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper