Tumor-specific CD8+ Tc9 Cells Activate Host CD4+ T Cells to Control Antigen-Lost Tumors
Cancer Research(2024)
1Houston Methodist Research Institute
Abstract
Abstract Cancer immunotherapies relying on targeted destruction of cancer cells by potent antitumor T cells have achieved unprecedented success in recent years. As a main form of cancer immunotherapies, adoptive T cell therapy (ACT) has shown a durable response in certain cancer patients. However, this response is often short-lived and tumor relapse occurs due to the outgrowth of antigen-lost-variant (ALV) tumors and poor antitumor immune response. Here, We reported that adoptively transfer of murine tumor-specific CD8+ Tc9 but not Tc1/CTL cells achieved long-term control of tumor growth in vivo. Here, we demonstrated that murine tumor-specific Tc9 cells not only killed antigen-expressing primary tumors but also controlled the outgrowth of antigen-lost relapsed tumors by recruiting and activating host effector CD4+ T cells that recognized relapsed tumors. Tc9 cells secreted IL-24 and recruited CCR7-expressing conventional type-2 dendritic cells (cDC2) into tumor-draining lymph nodes to prime host CD4+ T cell response against relapsed tumors. Depleting host CD4+ T cells or deficiency in CCR7 expression impaired Tc9 cell ability to control the outgrowth of relapsed tumor. We also observed that intratumoral IL24 expression was positively correlated with gene signatures of cDC2 and CD4+ T cells in human cancers and expression of IL24 and cDC2 and CD4+ T cell gene signatures were associated with patient’s overall survival. Thus, this study uncovers a novel mechanism underlying activation of tumor-specific CD4+ T cells in vivo. Citation Format: Liuling Xiao, Qing Yi. Tumor-specific CD8+ Tc9 cells activate host CD4+ T cells to control antigen-lost tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3606.
MoreTranslated text
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
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