The DOI matches its Crossref record; 137 of the 137 references it cites were found in a registry or an index.
Mouse Models of Cancer
Abstract
Genetically engineered mouse models have significantly contributed to our understanding of cancer biology. They have proven to be useful in validating gene functions, identifying novel cancer genes and tumor biomarkers, gaining insight into the molecular and cellular mechanisms underlying tumor initiation and multistage processes of tumorigenesis, and providing better clinical models in which to test novel therapeutic strategies. However, mice still have significant limitations in modeling human cancer, including species-specific differences and inaccurate recapitulation of de novo human tumor development. Future challenges in mouse modeling include the generation of clinically relevant mouse models that recapitulate the molecular, cellular, and genomic events of human cancers and clinical response as well as the development of technologies that allow for efficient in vivo imaging and high-throughput screening in mice.
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Citations
Cites
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Cited by
Drost, J., & Clevers, H. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0007-6 · Cited by 1,790
Self-Assembled Targeted Nanoparticles: Evolution of Technologies and Bench to Bedside Translation
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Automated microfluidic platform for dynamic and combinatorial drug screening of tumor organoids
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Driver and Passenger Mutations in Cancer
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Influences of diet and the gut microbiome on epigenetic modulation in cancer and other diseases
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Metadata
- Authors
- Cheon, D.-J., & Orsulic, S.
- Venue
- Annual Review of Pathology: Mechanisms of Disease
- Publisher
- Annual Reviews
- Published
- 2011-02-28
- Volume
- 6
- Issue
- 1
- Pages
- 95-119
- ISSN
- 1553-4006 (ISSN-L)
- 1553-4014
- OpenAlex
- W2163067316
- Cited by
- 395
- References
- 137
- FWCI
- 2.99
- Type
- journal article
- Language
- en
- Open access
- closed
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- Cheon, D.-J., & Orsulic, S. (2011). Mouse Models of Cancer. Annual Review of Pathology: Mechanisms of Disease. https://doi.org/10.1146/annurev.pathol.3.121806.154244
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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.
