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What are the best routes to effectively model human colorectal cancer?

Madeleine A. Young, Liliana Ordonez, Alan R. Clarke

Molecular Oncology · 2013 · ▲ 57 citations

Abstract

Colorectal cancer (CRC) is the third most common cancer in the UK, with over 37,500 people being diagnosed every year. Survival rates for CRC have doubled in the last 30 years and it is now curable if diagnosed early, but still over half of all sufferers do not survive for longer than 5 years after diagnosis. The major complication to treating this disease is that of metastasis, specifically to the liver, which is associated with a 5 year survival of less than 5%. These statistics highlight the importance of the development of earlier detection techniques and more targeted therapeutics. The future of treating this disease therefore lies in increasing understanding of the mutations which cause tumourigenesis, and insight into the development and progression of this complex disease. This can only be achieved through the use of functional models which recapitulate all aspects of the human disease. There is a wide range of models of CRC available to researchers, but all have their own strengths and weaknesses. Here we review how CRC can be modelled and discuss the future of modelling this complex disease, with a particular focus on how genetically engineered mouse models have revolutionised this area of research.

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OpenAlex
DOI
10.1016/j.molonc.2013.02.006
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2026-06-29 MST

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APA
Young, M.A., Ordonez, L., &amp; Clarke, A.R. (2013). What are the best routes to effectively model human colorectal cancer?. <em>Molecular Oncology</em>. https://doi.org/10.1016/j.molonc.2013.02.006
Vancouver
Young MA, Ordonez L, Clarke AR. What are the best routes to effectively model human colorectal cancer?. Molecular Oncology. 2013. doi:10.1016/j.molonc.2013.02.006.
BibTeX
@article{madeleine2013Whatar, title = {What are the best routes to effectively model human colorectal cancer?}, author = {Madeleine A. Young and Liliana Ordonez and Alan R. Clarke}, journal = {Molecular Oncology}, year = {2013}, doi = {10.1016/j.molonc.2013.02.006}, }

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