Related Experiment Video
Updated: Jan 3, 2026
Orthotopic Implantation and Peripheral Immune Cell Monitoring in the II-45 Syngeneic Rat Mesothelioma Model
Published on: October 2, 2015
Longitudinal immune characterization of syngeneic tumor models to enable model selection for immune oncology drug
Molly A Taylor1, Adina M Hughes2, Josephine Walton2
1Oncology R&D, Research and Early Development, Bioscience, AstraZeneca, Francis Crick Ave, Cambridge, CB2 0SL, UK. molly.taylor@astrazeneca.com.
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.
Insights
This study characterizes immune responses in preclinical cancer models to optimize immunotherapy combinations. Understanding these models helps select the best ones and identify biomarkers for patient response to cancer immunotherapies.
Area of Science:
- Immunology
- Oncology
- Preclinical Cancer Models
Background:
- Immune checkpoint blockade antibodies (e.g., anti-PD-1, anti-PD-L1, anti-CTLA-4) have revolutionized cancer therapy.
- Combining small molecules with immunotherapies is a key strategy to improve treatment response rates.
- Murine syngeneic models are crucial for preclinical immunotherapy evaluation but exhibit variable immune responses, complicating model selection.
Purpose of the Study:
- To longitudinally profile immune cell dynamics in common syngeneic models during tumor development.
- To characterize immune population changes following combination immunotherapy (anti-PD-L1 and anti-CTLA-4).
- To establish a framework for selecting optimal preclinical models and identifying biomarkers for immunotherapy response.
Main Methods:
- Flow cytometry
- O-link protein analysis
- RT-PCR
- RNA sequencing
- Longitudinal immune profiling
Main Results:
- Kinetic changes in immune cell populations were characterized throughout tumor development in syngeneic models.
- Pharmacodynamic time points were identified based on specific immune cell populations within each model.
- Immune population shifts after combination anti-PD-L1 and anti-CTLA-4 treatment were documented for benchmarking.
Conclusions:
- The generated dataset provides a framework for characterizing syngeneic models for immunotherapy research.
- This work facilitates the selection of appropriate models for evaluating novel immunotherapy combinations.
- Potential biomarkers for predicting patient response to cancer immunotherapies can be generated from this data.
