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对同源肿瘤模型进行纵向免疫特征分析，以选择用于免疫肿瘤药物发现的模型。

Longitudinal immune characterization of syngeneic tumor models to enable model selection for immune oncology drug discovery.

机构信息

Oncology R&D, Research and Early Development, Bioscience, AstraZeneca, Francis Crick Ave, Cambridge, CB2 0SL, UK.

Present Address: Alderley Park Limited, Preclinical Services, Alderley Park, Macclesfield, SK10 4TG, UK.

出版信息

J Immunother Cancer. 2019 Nov 28;7(1):328. doi: 10.1186/s40425-019-0794-7.

DOI:10.1186/s40425-019-0794-7
PMID:31779705
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6883640/
Abstract

BACKGROUND

The ability to modulate immune-inhibitory pathways using checkpoint blockade antibodies such as αPD-1, αPD-L1, and αCTLA-4 represents a significant breakthrough in cancer therapy in recent years. This has driven interest in identifying small-molecule-immunotherapy combinations to increase the proportion of responses. Murine syngeneic models, which have a functional immune system, represent an essential tool for pre-clinical evaluation of new immunotherapies. However, immune response varies widely between models and the translational relevance of each model is not fully understood, making selection of an appropriate pre-clinical model for drug target validation challenging.

METHODS

Using flow cytometry, O-link protein analysis, RT-PCR, and RNAseq we have characterized kinetic changes in immune-cell populations over the course of tumor development in commonly used syngeneic models.

RESULTS

This longitudinal profiling of syngeneic models enables pharmacodynamic time point selection within each model, dependent on the immune population of interest. Additionally, we have characterized the changes in immune populations in each of these models after treatment with the combination of α-PD-L1 and α-CTLA-4 antibodies, enabling benchmarking to known immune modulating treatments within each model.

CONCLUSIONS

Taken together, this dataset will provide a framework for characterization and enable the selection of the optimal models for immunotherapy combinations and generate potential biomarkers for clinical evaluation in identifying responders and non-responders to immunotherapy combinations.

摘要

背景

近年来，使用检查点阻断抗体（如 αPD-1、αPD-L1 和 αCTLA-4）调节免疫抑制途径的能力代表了癌症治疗的重大突破。这激发了人们对识别小分子免疫疗法组合以提高反应率的兴趣。具有功能免疫系统的鼠同源模型是新免疫疗法临床前评估的重要工具。然而，免疫反应在不同模型之间差异很大，每个模型的转化相关性尚不完全清楚，因此为药物靶点验证选择合适的临床前模型具有挑战性。

方法

我们使用流式细胞术、O 链接蛋白分析、RT-PCR 和 RNAseq 对常用同源模型中肿瘤发展过程中免疫细胞群的动力学变化进行了特征描述。

结果

这种同源模型的纵向分析能够根据感兴趣的免疫群体，在每个模型中选择药效时间点。此外，我们还对这些模型中的每一个模型在接受 α-PD-L1 和 α-CTLA-4 抗体联合治疗后的免疫群体变化进行了特征描述，从而可以在每个模型中对已知的免疫调节治疗进行基准测试。

结论

综上所述，该数据集将提供一个特征描述框架，并能够为免疫疗法组合选择最佳模型，并生成潜在的生物标志物，用于临床评估以识别免疫疗法组合的应答者和无应答者。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ac8/6883640/aab1732ccd27/40425_2019_794_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ac8/6883640/d49e17434ab3/40425_2019_794_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ac8/6883640/fb4a1a86c8b7/40425_2019_794_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ac8/6883640/399025ac180f/40425_2019_794_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ac8/6883640/4895b705693f/40425_2019_794_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ac8/6883640/68f32a1db661/40425_2019_794_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ac8/6883640/aab1732ccd27/40425_2019_794_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ac8/6883640/d49e17434ab3/40425_2019_794_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ac8/6883640/fb4a1a86c8b7/40425_2019_794_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ac8/6883640/399025ac180f/40425_2019_794_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ac8/6883640/4895b705693f/40425_2019_794_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ac8/6883640/68f32a1db661/40425_2019_794_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ac8/6883640/aab1732ccd27/40425_2019_794_Fig6_HTML.jpg

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