The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Successful Therapy Must Eradicate Cancer Stem Cells
Open accessAbstract
Despite significant improvements in cancer therapy, tumor recurrence is frequent and can be due to a variety of mechanisms, including the evolution of resistance and tumor progression. Cancer stem cells have been postulated to maintain tumor growth similar to normal stem cells maintaining tissue homeostasis. Recently, the existence of these malignant stem cells has been proven for hematological as well as some solid tumors. Tumor stem cells are not targeted by standard therapy and might be responsible for treatment failure and tumor recurrence in many patients. We designed a simple mathematical model to demonstrate the importance of eliminating tumor stem cells. We explored different therapeutic scenarios to illustrate the properties required from novel therapeutic agents for successful tumor treatment. We show that successful therapy must eradicate tumor stem cells.
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Cites
Zhou, S., Schuetz, J. D., Bunting, K. D., Colapietro, A.-M., Sampath, J., Morris, J. J., Lagutina, I., Grosveld, G. C., Osawa, M., Nakauchi, H., et al. (2001) · Nature Medicine · doi:10.1038/nm0901-1028 · Cited by 2,280
Cited by
Regression of metastatic melanoma by targeting cancer stem cells
Schlaak, M., Schmidt, P., Bangard, C., Kurschat, P., Mauch, C., & Abken, H. (2012) · Oncotarget · doi:10.18632/oncotarget.437 · Cited by 71
Metadata
- Authors
- Dingli, D., & Michor, F.
- Venue
- STEM CELLS
- Publisher
- Oxford University Press (OUP)
- Published
- 2006-12
- Volume
- 24
- Issue
- 12
- Pages
- 2603-2610
- ISSN
- 1066-5099 (ISSN-L)
- 1549-4918
- OpenAlex
- W2083530483
- Cited by
- 241
- References
- 1
- FWCI
- 11.68
- Type
- journal article
- Language
- en
- Open access
- bronze
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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.
