Mus musculus
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ABSTRACT:
PROVIDER: PRJNA338686 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Rational selection of syngeneic preclinical tumor models for immunotherapeutic drug discovery
| PRJNA338671 | ENA
Project description:Rational selection of syngeneic preclinical tumor models for immunotherapeutic drug discovery [exon array]
| PRJNA338687 | ENA
Project description:Characterisation of murine syngeneic tumor models enables rational preclinical model selection for immuno-oncology drug discovery
| PRJEB12925 | ENA
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: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
| PRJNA314697 | ENA
Project description:The cell line-derived xenografts and patient derived xenografts have limited use in cancer immunotherapy evaluation because an immune compromised host is required for xenotransplantation. Syngeneic mouse models are derived by transplanting established mouse cell lines or tumor tissues to strain matched mouse hosts, which are better suited to study the interplay between immune and tumor cells. We investigated the differences as well as similarities of a panel of ten mouse syngeneic models to features of human tumors by proteomics, which will provide valuable information to assist experimental biologists in model selection.
2019-12-03 | PXD011885 | Pride
Project description:Many preclinical therapy studies have focused on a small number of well-described mouse allograft or human xenograft models that poorly represent the heterogeneity of human disease. Here we have assembled a panel of mouse mammary cell lines that metastasize in syngeneic mouse hosts and we have assessed gene expression programs in the untreated primary tumors with the goal of generating information that may be useful to the identification of biomarkers that predict response to therapeutic intervention. We used microarrays to assess global gene expression programs in primary tumors from 12 metastatic mouse mammary tumor models transplanted orthotopically into syngeneic, fully immunocompetent mouse hosts. The 12 tumor models used here are based on published cell lines that had been established from either spontaneous mammary tumors or from mammary tumors arising in genetically engineered mouse models. All cell lines were previously described to be metastatic. Cells were surgically implanted in the #4 mammary fat pads of syngeneic mice and primary tumors were harvested when they reached 0.5-1.0 cm diameter and snap-frozen for later RNA extraction. 4 independent tumors were collected for each of the 12 models.
2015-05-19 | E-GEOD-69006 | biostudies-arrayexpress
Project description:Murine syngeneic tumor models have been extensively used for cancer research for several decades. These tumor models are very simplistic cancer models, but recent reports have, however, indicated that the different inoculated cancer cells can lead to the formation of very different tumor microenvironments (TMEs). Importantly, these types of tumor models have been instrumental in driving the discovery and development of cancer immunotherapies. In order to gain more knowledge from studies based on syngeneic tumor models it is essential to know in more details the cellular and molecular composition of the TME in the different models. It is also important to know about other parameters such as the mechanical tumor stiffness of the different models. This type of knowledge can be used for the rational selection of tumor models for specific studies and for studying the correlation between different tumor-promoting parameters. Here, we compare the tumor microenvironment (TME) of tumors derived from six different commonly used syngeneic tumor models. Using flow cytometry and transcriptomic analyses we show that strikingly different TMEs are formed by the different cancer cell lines. The differences are reflected as changes in abundance and phenotype of myeloid, lymphoid and stromal cells in the tumors. The gene expression profile of tumors from the different models supported the different cellular composition of the TMEs and indicate that different mechanisms of immune suppression are employed in the different tumor models. Cancer-associated fibroblasts also acquire very different phenotypes in the different tumor models. These differences include differential expression of genes encoding ECM proteins, MMPs, and immunosuppressive factors. In consistence with these observations, the mechanical stiffness of the tumors from different models do not simply correlate to the number of infiltrating CAFs even though collagen produced by stromal cells is an important reason for the increased stiffness of tumors. The gene expression profiles suggest that CAFs can contribute to the formation of an immunosuppressive TME and flow cytometry analyses show CAFs express high levels of PD-L1 in the immunogenic tumor models MC38 and CT26. Comparison with CAF subsets identified in other studies show that CAFs from the different models are skewed towards specific subsets. CAFs from CT26 tumors show similarities to iCAFs and myCAFs, and in athymic mice without infiltrating cytotoxic T cells, CAFs express lower levels of PD-L1 and lower levels of fibroblast activation markers.
2024-01-29 | GSE245293 | GEO
Project description:Treating unselected cancer patients with new drugs dilutes proof of efficacy when only a fraction of patients respond to therapy. We conducted a meta-analysis on eight primary breast cancer microarray datasets representing diverse breast cancer phenotypes. We present a high-throughput protocol which incorporates drug sensitivity signatures to guide preclinical testing for effective therapeutic agents. Specifically, we focus on drug classes currently undergoing early phase clinical testing. Our genomic and experimental results suggest that the majority of basal-like breast cancers should respond to inhibitors of the phosphatidylinositol-3-kinase pathway, and that a relatively low toxicity histone deacetylase inhibitor, valproic acid, may target aggressive breast cancers. For a subset of drugs, prediction of sensitivity associates with tumor recurrence, suggesting clinical relevance. Preclinical studies using both cell lines and patient tumors grown in 3-dimensional in vitro and orthotopic in vivo preclinical models provide an efficient and highly relevant assessment of drug sensitivity in tumor phenotypes, and validate our genomic analyses. Together, our results show that high-throughput transcriptional profiling can significantly impact drug selection for breast cancer patients. Pre-identification of patient response may not only improve therapeutic response rates, it can also assist in quickly identifying the optimal inclusion criteria for clinical trials. Our model facilitates personalized drug therapy for cancer patients and may be generalized for study of drug efficacy in other diseases. Breast cancer pleural effusion samples from triple negative patients. Compared samples that are computationally predicted to be sensitive to valproic acid and those that are not predicted to be sensitive.
2011-06-01 | E-GEOD-18331 | biostudies-arrayexpress