| Literature DB >> 28923102 |
Andrew Zloza1, A Karolina Palucka2, Lisa M Coussens3, Philip J Gotwals4, Mark B Headley5, Elizabeth M Jaffee6, Amanda W Lund7, Arlene H Sharpe8, Mario Sznol9, Derek A Wainwright10, Kwok-Kin Wong11, Marcus W Bosenberg12.
Abstract
Understanding how murine models can elucidate the mechanisms underlying antitumor immune responses and advance immune-based drug development is essential to advancing the field of cancer immunotherapy. The Society for Immunotherapy of Cancer (SITC) convened a workshop titled, "Challenges, Insights, and Future Directions for Mouse and Humanized Models in Cancer Immunology and Immunotherapy" as part of the SITC 31st Annual Meeting and Associated Programs on November 10, 2016 in National Harbor, MD. The workshop focused on key issues in optimizing models for cancer immunotherapy research, with discussions on the strengths and weaknesses of current models, approaches to improve the predictive value of mouse models, and advances in cancer modeling that are anticipated in the near future. This full-day program provided an introduction to the most common immunocompetent and humanized models used in cancer immunology and immunotherapy research, and addressed the use of models to evaluate immune-targeting therapies. Here, we summarize the workshop presentations and subsequent panel discussion.Entities:
Keywords: Cancer immunotherapy; Humanized mouse; Immunocompetent; Mouse models; Mouse-in-mouse; Tumor microenvironment
Mesh:
Year: 2017 PMID: 28923102 PMCID: PMC5604351 DOI: 10.1186/s40425-017-0278-6
Source DB: PubMed Journal: J Immunother Cancer ISSN: 2051-1426 Impact factor: 13.751
Mouse-in-mouse models
| Model | Examples | Characteristics | Possible Improvements |
|---|---|---|---|
| Genetically engineered (GEMMs) | • Transgenic | • Long latency | • Increasing antigenicity |
| Chemically induced | • 3′methylcholanthrene (MCA) | • Fully penetrant | |
| Syngeneic | • Engraftment of mouse cancer cell lines | • Easy, inexpensive, and fast to use | • Use multiple lines driven by human-relevant genetic changes |
Methods used to generate PDX models
| Dissociation method | |
|---|---|
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| • Unbiased representation/sampling of whole tumor (unlike sectioning) | • Dissociation capabilities and forces may bias the number and type of cells |
| Fragmentation method: | |
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| • TME is maintained (hypoxia, acidity, cell:cell interactions, tissue architecture) | • Not representative of the entire tumor (spatially segregated subclones and immune cells) |