The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Non-cell-autonomous driving of tumour growth supports sub-clonal heterogeneity
Open accessAbstract
Cancers arise through a process of somatic evolution that can result in substantial sub-clonal heterogeneity within tumours. The mechanisms responsible for the coexistence of distinct sub-clones and the biological consequences of this coexistence remain poorly understood. Here we used a mouse xenograft model to investigate the impact of sub-clonal heterogeneity on tumour phenotypes and the competitive expansion of individual clones. We found that tumour growth can be driven by a minor cell subpopulation, which enhances the proliferation of all cells within a tumour by overcoming environmental constraints and yet can be outcompeted by faster proliferating competitors, resulting in tumour collapse. We developed a mathematical modelling framework to identify the rules underlying the generation of intra-tumour clonal heterogeneity. We found that non-cell-autonomous driving of tumour growth, together with clonal interference, stabilizes sub-clonal heterogeneity, thereby enabling inter-clonal interactions that can lead to new phenotypic traits.
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Cites
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Metadata
- Authors
- Marusyk, A., Tabassum, D. P., Altrock, P. M., Almendro, V., Michor, F., & Polyak, K.
- Venue
- Nature
- Published
- 2014-07-30
- Volume
- 514
- Issue
- 7520
- Pages
- 54-58
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2018288777
- Cited by
- 597
- References
- 8
- FWCI
- 21.19
- Type
- journal article
- Language
- en
- Open access
- green
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- Marusyk, A., Tabassum, D. P., Altrock, P. M., Almendro, V., Michor, F., & Polyak, K. (2014). Non-cell-autonomous driving of tumour growth supports sub-clonal heterogeneity. Nature. https://doi.org/10.1038/nature13556
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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.
