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

• Main Authors: Molly A. Taylor, Adina M. Hughes, Josephine Walton, Anna M. L. Coenen-Stass, Lukasz Magiera, Lorraine Mooney, Sigourney Bell, Anna D. Staniszewska, Linda C. Sandin, Simon T. Barry, Amanda Watkins, Larissa S. Carnevalli, Elizabeth L. Hardaker
• Format: Article
• Language: English
• Published: BMJ Publishing Group 2019-11-01
• Series: Journal for ImmunoTherapy of Cancer
• Subjects: CT-26; MC38; 4 T1; Syngeneic; Immune checkpoint blockade
• Online Access: http://link.springer.com/article/10.1186/s40425-019-0794-7

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.