The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Non-germline genetically engineered mouse models for translational cancer research
Open accessAbstract
Genetically engineered mouse models (GEMMs) of cancer have affected virtually all areas of cancer research. However, the accelerated discovery of new cancer genes emerging from large-scale cancer genomics and new chemical entities pouring from the drug discovery pipeline have strained the capacity of traditional germline mouse models to provide crucial insights. This Review introduces new approaches to modelling cancer, with emphasis on a growing collection of non-germline GEMMs (nGEMMs). These offer flexibility, speed and uniformity at reduced costs, thus paving the way for much needed throughput and practical preclinical therapeutic testing models.
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Cites
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Self-citationMutant p53 gain of function in two mouse models of Li-Fraumeni syndrome
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Self-citationChromosomally unstable mouse tumours have genomic alterations similar to diverse human cancers
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Cited by
Preclinical Mouse Cancer Models: A Maze of Opportunities and Challenges
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Effect of Mutation Order on Myeloproliferative Neoplasms
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Genetically engineered mouse models in oncology research and cancer medicine
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Applications of the CRISPR-Cas9 system in cancer biology
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Driver and Passenger Mutations in Cancer
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Metadata
- Authors
- Heyer, J., Kwong, L. N., Lowe, S. W., & Chin, L.
- Venue
- Nature Reviews Cancer
- Published
- 2010-07
- Volume
- 10
- Issue
- 7
- Pages
- 470-480
- ISSN
- 1474-175X (ISSN-L)
- 1474-1768
- OpenAlex
- W2165474323
- Cited by
- 179
- References
- 7
- FWCI
- 1.35
- Type
- journal article
- Language
- en
- Open access
- green
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- Heyer, J., Kwong, L. N., Lowe, S. W., & Chin, L. (2010). Non-germline genetically engineered mouse models for translational cancer research. Nature Reviews Cancer. https://doi.org/10.1038/nrc2877
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
