Longitudinal immune characterization of syngeneic tumor models to enable model selection for immune oncology drug discovery.
Animals
Antineoplastic Agents, Immunological
/ pharmacology
Biomarkers, Tumor
Disease Models, Animal
Drug Discovery
Drug Screening Assays, Antitumor
Drug Synergism
Gene Expression Profiling
Humans
Immunomodulation
/ drug effects
Immunophenotyping
Lymphocytes, Tumor-Infiltrating
/ immunology
Mice
Neoplasms
/ drug therapy
Tumor Microenvironment
4 T1
CT-26
Immune checkpoint blockade
MC38
Syngeneic
Journal
Journal for immunotherapy of cancer
ISSN: 2051-1426
Titre abrégé: J Immunother Cancer
Pays: England
ID NLM: 101620585
Informations de publication
Date de publication:
28 11 2019
28 11 2019
Historique:
received:
27
05
2019
accepted:
30
10
2019
entrez:
30
11
2019
pubmed:
30
11
2019
medline:
22
7
2020
Statut:
epublish
Résumé
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. 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. 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. 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.
Sections du résumé
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.
Identifiants
pubmed: 31779705
doi: 10.1186/s40425-019-0794-7
pii: 10.1186/s40425-019-0794-7
pmc: PMC6883640
doi:
Substances chimiques
Antineoplastic Agents, Immunological
0
Biomarkers, Tumor
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
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