Characterisation of murine syngeneic tumor models
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB12925 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR1422170_1.fastq.gz | Fastqsanger.gz | |||
| ERR1422170_2.fastq.gz | Fastqsanger.gz | |||
| ERR1422180_1.fastq.gz | Fastqsanger.gz | |||
| ERR1422180_2.fastq.gz | Fastqsanger.gz | |||
| ERR1422171_1.fastq.gz | Fastqsanger.gz |
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Project description:Rational selection of syngeneic preclinical tumor models for immunotherapeutic drug discovery
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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.
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Project description:Rational selection of syngeneic preclinical tumor models for immunotherapeutic drug discovery [aCGH]
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Project description:A common cornerstone of preclinical cancer research is the use of syngeneic orthotopic murine tumors as immunocompetent models of human cancers. For glioblastoma research efforts, the GL261 and CT2A lines are frequently used. We systematically characterized these two lines to decipher the cell-intrinsic mechanisms that drive immuno-resistance in CT2A and to define the aspects of human cancer biology that the lines best model. We show that, despite sharing a few canonical genetic or histologic features of human glioblastoma, the transcriptional profiles of GL261 and CT2A tumours most closely resembled those of glioblastomas. CT2A additionally resembled other cancer types transcriptionally, including melanoma. CT2A displayed mesenchymal differentiation, upregulated angiogenesis, and multiple defects in antigen presentation machinery and interferon response pathways. Loss of MHC class I expression was restored in CT2A by interferon-γ treatment, explaining in part the modest efficacy of some immunotherapy combinations for CT2A. Our findings indicate that CT2A may serve as a robust preclinical solid tumor model of adaptive immune resistance.
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Project description:Mouse syngeneic models serve as indispensable tools for elucidating tumor-immune interactions and assessing immunotherapy efficacy. In this study, we first conducted a comprehensive evaluation of six label-free protein quantification pipelines across 12 mouse syngeneic models, revealing that data-independent acquisition (DIA) significantly outperforms data-dependent acquisition (DDA) in terms of data coverage, reproducibility, and inter-model discrimination. We next performed an integrative multi-omics analysis to uncover molecular mechanisms associated with treatment response. Our analysis identified Dnmt3a and Igf2r, which are correlated with resistance to immune checkpoint inhibitors (ICIs), and highlighted key pathways including interferon signaling and oxidative phosphorylation that distinguish responders from non-responders. To facilitate broader research applications, we have developed an interactive web resource that shares our multi-omics datasets and analytical results, equipped with user-friendly tools for further exploration. It is freely accessible at https://muscreen.crownbio.com/syngeneic_omics. This resource aims to accelerate preclinical research and contribute to the development of personalized cancer therapies.
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