Interferon-γ Driven Differentiation of Monocytes into PD-L1+ and MHC II+ Macrophages and the Frequency of Tim-3+ Tumor-Reactive CD8+ T Cells Within the Tumor Microenvironment Predict a Positive Response to Anti-Pd-1-based Therapy in Tumor-Bearing Mice
biorxiv(2024)
ULB Center for Research in Immunology (U-CRI)
Abstract
While immune checkpoint inhibitors have demonstrated durable responses in various cancer types, a significant proportion of patients do not exhibit favourable responses to these interventions. To uncover potential factors associated with a positive response to immunotherapy, we established a bilateral tumor model using P815 mastocytoma implanted in DBA/2 mice. In this model, only a fraction of tumor-bearing mice responds favourably to anti-PD-1 treatment, thus providing a valuable model to explore the influence of the tumor microenvironment (TME) in determining the efficacy of immune checkpoint blockade (ICB)-based immunotherapies. Moreover, this model allows for the analysis of a pretreatment tumor and inference of its treatment outcome based on the response observed in the contralateral tumor. Here, we demonstrated that tumor-reactive CD8+ T cell clones expressing high levels of Tim-3 were associated to a positive anti-tumor response following anti-PD-1 administration. Our study also revealed distinct differentiation dynamics in tumor-infiltrating myeloid cells in responding and non-responding mice. An IFNγ-enriched TME appeared to promote the differentiation of monocytes into PD-L1pos MHC IIhigh cells in mice responding to immunotherapy. Monocytes present in the TME of non-responding mice failed to reach the same final stage of differentiation trajectory, suggesting that an altered monocyte to macrophage route may hamper the response to ICB. These insights will direct future research towards a temporal analysis of TAMs, aiming to identify factors responsible for transitions between differentiation states within the TME. This approach may potentially pave the way to novel strategies to enhance the efficacy of PD-1 blockade. ### Competing Interest Statement The authors have declared no competing interest.
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