Description
Breast cancer is the most common cancer among women in the European Union and worldwide. Preclinical breast cancer research currently relies on animal models, mostly rodents. However, animal models mimic limited aspects of human breast cancer. The European Commission’s Joint Research Centre (JRC) has carried out an extensive review of advanced non-animal models being used for basic and applied research into breast cancer. Researchers characterised and catalogued 935 models to make them more accessible for human relevant studies that avoid the use of animals. The review has been compiled by the ALTER-N.A.M. consortium for EURL ECVAM (Joint Research Centre, Directorate for Health, Consumers and Reference Materials, Chemicals Safety and Alternative Methods Unit; contract number:JRC/IPR/2018/F.3/0035/OC)
Contact
Contributors
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- Adelaide Dura
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- Ivana Campia
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- Laura Gribaldo
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- Pierre Deceuninck
How to cite
Dura, Adelaide; Campia, Ivana; Gribaldo, Laura; Deceuninck, Pierre (2020): EURL ECVAM Review of non-animal models in biomedical research - Breast Cancer. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/ffebe454-ed9a-47cf-8a33-8cf70c1b7d38
Keywords
biomedical breast cancer non-animal
Data access
This dataset contains 935 non-animal models in the field of Breast Cancer, including information on their applications, biological relevance, diseases area, and level of throughput. This collection aims to cover all existing and in-development non-animal approaches to breast cancer modelling, including
in vitro, in silico and ex vivo approaches.
Publications
Geographic areas
Temporal coverage
From date | To date |
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2014-01-01 | 2018-03-31 |
Additional information
- Published by
- European Commission, Joint Research Centre
- Created date
- 2020-10-27
- Modified date
- 2021-07-27
- Issued date
- 2020-10-27
- Landing page
- https://ec.europa.eu/jrc/en/page/breast-cancer
- Data theme(s)
- Science and technology
- Update frequency
- irregular
- Identifier
- http://data.europa.eu/89h/ffebe454-ed9a-47cf-8a33-8cf70c1b7d38
- Popularity
-