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

Authors
  • Taylor, Molly A.1
  • Hughes, Adina M.1
  • Walton, Josephine1
  • Coenen-Stass, Anna M. L.1
  • Magiera, Lukasz1
  • Mooney, Lorraine1, 2
  • Bell, Sigourney1
  • Staniszewska, Anna D.1
  • Sandin, Linda C.1
  • Barry, Simon T.1
  • Watkins, Amanda1
  • Carnevalli, Larissa S.1
  • Hardaker, Elizabeth L.1
  • 1 AstraZeneca, Francis Crick Ave, Cambridge, CB2 0SL, UK , Cambridge (United Kingdom)
  • 2 Present Address: Alderley Park Limited, Preclinical Services, Alderley Park, Macclesfield, SK10 4TG, UK , Macclesfield (United Kingdom)
Type
Published Article
Journal
Journal for ImmunoTherapy of Cancer
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Nov 28, 2019
Volume
7
Issue
1
Identifiers
DOI: 10.1186/s40425-019-0794-7
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundThe 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.MethodsUsing 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.ResultsThis 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.ConclusionsTaken 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.

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