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Depletion of Mouse Cells from Human Tumor Xenografts Significantly Improves Downstream Analysis of Target Cells
Published on: July 29, 2016
Comparison of the molecular and cellular phenotypes of common mouse syngeneic models with human tumors
Wenyan Zhong1, Jeremy S Myers2, Fang Wang2
1Oncology Research & Development, Pfizer Worldwide Research and Development, New York, Pearl River, 10965, USA. wenyan.zhong@pfizer.com.
Background:
The clinical success of immune checkpoint inhibitors demonstrates that reactivation of the human immune system delivers durable responses for some patients and represents an exciting approach for cancer treatment. An important class of preclinical in vivo models for immuno-oncology is immunocompetent mice bearing mouse syngeneic tumors. To facilitate translation of preclinical studies into human, we characterized the genomic, transcriptomic, and protein expression of a panel of ten commonly used mouse tumor cell lines grown in vitro culture as well as in vivo tumors.
Results:
Our studies identified a number of genetic and cellular phenotypic differences that distinguish commonly used mouse syngeneic models in our study from human cancers. Only a fraction of the somatic single nucleotide variants (SNVs) in these common mouse cell lines directly match SNVs in human actionable cancer genes. Some models derived from epithelial tumors have a more mesenchymal phenotype with relatively low T-lymphocyte infiltration compared to the corresponding human cancers. CT26, a colon tumor model, had the highest immunogenicity and was the model most responsive to CTLA4 inhibitor treatment, by contrast to the relatively low immunogenicity and response rate to checkpoint inhibitor therapies in human colon cancers.
Conclusions:
The relative immunogenicity of these ten syngeneic tumors does not resemble typical human tumors derived from the same tissue of origin. By characterizing the mouse syngeneic models and comparing with their human tumor counterparts, this study contributes to a framework that may help investigators select the model most relevant to study a particular immune-oncology mechanism, and may rationalize some of the challenges associated with translating preclinical findings to clinical studies.
Insights
Mouse syngeneic tumor models show genetic and immune differences from human cancers. Characterizing these models helps select relevant preclinical tools for immuno-oncology research and improve clinical translation.
Area of Science:
- Immunology
- Oncology
- Genomics
Background:
- Immune checkpoint inhibitors show clinical success in cancer treatment by reactivating the immune system.
- Mouse syngeneic tumor models are crucial preclinical tools for immuno-oncology research.
- Translating preclinical findings to human patients requires thorough characterization of these models.
Purpose of the Study:
- To characterize the genomic, transcriptomic, and protein expression of ten common mouse syngeneic tumor models.
- To compare these models with human cancers to identify differences and similarities.
- To provide a framework for selecting appropriate models for immuno-oncology studies.
Main Methods:
- Genomic, transcriptomic, and protein expression analysis of ten mouse tumor cell lines and in vivo tumors.
- Comparison of genetic and cellular phenotypes with human cancer counterparts.
- Assessment of immunogenicity and response to checkpoint inhibitor treatment.
Main Results:
- Significant genetic and phenotypic differences were observed between mouse models and human cancers.
- A small fraction of somatic single nucleotide variants (SNVs) in mouse models matched human actionable cancer genes.
- Some models exhibited mesenchymal phenotypes with low T-lymphocyte infiltration, unlike human cancers. CT26 model showed high immunogenicity and responsiveness to CTLA4 inhibition, contrasting with human colon cancer responses.
Conclusions:
- The immunogenicity of mouse syngeneic models does not always reflect human tumors from the same tissue of origin.
- Characterization and comparison with human counterparts aid in selecting relevant models for specific immuno-oncology mechanisms.
- This study helps rationalize challenges in translating preclinical findings to clinical applications in cancer immunotherapy.

