The DOI matches its Crossref record; 142 of the 142 references it cites were found in a registry or an index.
Cellular Heterogeneity and Molecular Evolution in Cancer
Abstract
Intratumor heterogeneity represents a major obstacle to effective cancer treatment and personalized medicine. However, investigators are now elucidating intratumor heterogeneity at the single-cell level due to improvements in technologies. Better understanding of the composition of tumors, and monitoring changes in cell populations during disease progression and treatment, will improve cancer diagnosis and therapeutic design. Measurements of intratumor heterogeneity may also be used as biomarkers to predict the risk of progression and therapeutic resistance. We summarize important considerations related to intratumor heterogeneity during tumor evolution. We also discuss experimental approaches that are commonly used to infer intratumor heterogeneity and describe how these methodologies can be translated into clinical practice.
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Cites
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Glucocorticoids promote breast cancer metastasis
Obradović, M. M. S., Hamelin, B., Manevski, N., Couto, J. P., Sethi, A., Coissieux, M.-M., Münst, S., Okamoto, R., Kohler, H., Schmidt, A., et al. (2019) · Nature · doi:10.1038/s41586-019-1019-4 · Cited by 462
Metadata
- Authors
- Almendro, V., Marusyk, A., & Polyak, K.
- Venue
- Annual Review of Pathology: Mechanisms of Disease
- Publisher
- Annual Reviews
- Published
- 2013-01-24
- Volume
- 8
- Issue
- 1
- Pages
- 277-302
- ISSN
- 1553-4006 (ISSN-L)
- 1553-4014
- OpenAlex
- W2098970540
- Cited by
- 498
- References
- 142
- FWCI
- 2.20
- Type
- journal article
- Language
- en
- Open access
- closed
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- Almendro, V., Marusyk, A., & Polyak, K. (2013). Cellular Heterogeneity and Molecular Evolution in Cancer. Annual Review of Pathology: Mechanisms of Disease. https://doi.org/10.1146/annurev-pathol-020712-163923
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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.
