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Get Free AccessNeoplasms change over time through a process of cell-level evolution, driven by genetic and epigenetic alterations. However, the ecology of the microenvironment of a neoplastic cell determines which changes provide adaptive benefits. There is widespread recognition of the importance of these evolutionary and ecological processes in cancer, but to date, no system has been proposed for drawing clinically relevant distinctions between how different tumours are evolving. On the basis of a consensus conference of experts in the fields of cancer evolution and cancer ecology, we propose a framework for classifying tumours that is based on four relevant components. These are the diversity of neoplastic cells (intratumoural heterogeneity) and changes over time in that diversity, which make up an evolutionary index (Evo-index), as well as the hazards to neoplastic cell survival and the resources available to neoplastic cells, which make up an ecological index (Eco-index). We review evidence demonstrating the importance of each of these factors and describe multiple methods that can be used to measure them. Development of this classification system holds promise for enabling clinicians to personalize optimal interventions based on the evolvability of the patient's tumour. The Evo- and Eco-indices provide a common lexicon for communicating about how neoplasms change in response to interventions, with potential implications for clinical trials, personalized medicine and basic cancer research.
Carlo C. Maley, Athena Aktipis, Trevor A. Graham, Andrea Sottoriva, Amy M. Boddy, Michalina Janiszewska, Ariosto S. Silva, Marco Gerlinger, Yinyin Yuan, Kenneth J. Pienta, Karen S. Anderson, Robert A. Gatenby, Charles Swanton, David Posada, Chung-I Wu, Joshua D. Schiffman, E. Shelley Hwang, Kornélia Polyák, Alexander R.A. Anderson, Joel S. Brown, Mel Greaves, Darryl Shibata (2017). Classifying the evolutionary and ecological features of neoplasms. , 17(10), DOI: https://doi.org/10.1038/nrc.2017.69.
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Type
Article
Year
2017
Authors
22
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1038/nrc.2017.69
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