Rational selection of syngeneic preclinical tumor models for immunotherapeutic drug discovery
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE85509 | GEO | 2016/12/07
SECONDARY ACCESSION(S): PRJNA338671
REPOSITORIES: GEO
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Project description:Murine syngeneic tumor models are the cornerstone of novel immuno-oncology (IO)-based therapy development but the molecular and immunological features of these models are still not clearly defined. The translational relevance of differences between the models is not fully understood, impeding appropriate preclinical model selection for target validation, and ultimately hindering drug development. Within a panel of commonly-used murine syngeneic tumor models, we showed variable responsiveness to IO-therapies. We employed aCGH, whole-exome sequencing, exon microarray analysis and flow cytometry to extensively characterise these models and revealed striking differences that may underlie these contrasting response profiles. We identified strong differential gene expression in immune-related pathways and changes in immune cell-specific genes that suggested differences in tumor immune infiltrates between models. We further investigated this using flow cytometry, which showed differences in both the composition and magnitude of the tumor immune infiltrates, identifying models that harbor ‘inflamed’ and ‘non-inflamed’ tumor immune infiltrate phenotypes. Moreover, we found that immunosuppressive cell types predominated in syngeneic mouse tumor models that did not respond to immune-checkpoint blockade, whereas cytotoxic effector immune cells were enriched in responsive models. A cytotoxic cell-rich tumor immune infiltrate has been correlated with increased efficacy of IO-therapy in the clinic and these differences could underlie the varying response profiles to IO-therapy between the syngeneic models. This characterisation highlighted the importance of extensive profiling and will enable investigators to select appropriate models to interrogate the activity of IO-therapies as well as combinations with targeted therapies in vivo.
2016-12-07 | GSE85508 | GEO
Project description:Murine syngeneic tumor models are the cornerstone of novel immuno-oncology (IO)-based therapy development but the molecular and immunological features of these models are still not clearly defined. The translational relevance of differences between the models is not fully understood, impeding appropriate preclinical model selection for target validation, and ultimately hindering drug development. Within a panel of commonly-used murine syngeneic tumor models, we showed variable responsiveness to IO-therapies. We employed aCGH, whole-exome sequencing, exon microarray analysis and flow cytometry to extensively characterise these models and revealed striking differences that may underlie these contrasting response profiles. We identified strong differential gene expression in immune-related pathways and changes in immune cell-specific genes that suggested differences in tumor immune infiltrates between models. We further investigated this using flow cytometry, which showed differences in both the composition and magnitude of the tumor immune infiltrates, identifying models that harbor ‘inflamed’ and ‘non-inflamed’ tumor immune infiltrate phenotypes. Moreover, we found that immunosuppressive cell types predominated in syngeneic mouse tumor models that did not respond to immune-checkpoint blockade, whereas cytotoxic effector immune cells were enriched in responsive models. A cytotoxic cell-rich tumor immune infiltrate has been correlated with increased efficacy of IO-therapy in the clinic and these differences could underlie the varying response profiles to IO-therapy between the syngeneic models. This characterisation highlighted the importance of extensive profiling and will enable investigators to select appropriate models to interrogate the activity of IO-therapies as well as combinations with targeted therapies in vivo.
2016-12-07 | GSE85507 | GEO
Project description:Rational selection of syngeneic preclinical tumor models for immunotherapeutic drug discovery
| PRJNA338671 | ENA
Project description:BACKGROUND:The ability to modulate immune-inhibitory pathways using checkpoint blockade antibodies such as ?PD-1, ?PD-L1, and ?CTLA-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 ?-PD-L1 and ?-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.
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Project description:Understanding the glioblastoma (GBM) immune microenvironment and development of clinical treatment drugs rely on suitable preclinical GBM models. Here, we present a protocol to establish syngeneic orthotopic glioma mouse models. We also describe the steps to intracranially deliver immunotherapeutic peptides and monitor the treatment response. Finally, we show how to assess the tumor immune microenvironment with treatment outcomes. For complete details on the use and execution of this protocol, please refer to Chen et al. (2021).1.
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