Key Points
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Methods for and applications of phylogenetic tree inference have proliferated in studies of cancer genomics.
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Tumour phylogeny methods have become important tools for making sense of the complexity of emerging tumour genomic data sets, providing new methods for identifying the order and timing of driver mutations. This has led to new insights and controversies about the nature of the evolutionary processes involved in cancer, and has driven novel approaches to prognostic prediction.
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Tumour phylogeny methods can be broadly partitioned into several classes of study design, with variations in data source, evolutionary model, and inference algorithm in each class.
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Productive use of tumour phylogeny methods requires a sophisticated understanding of how to align genomic data sources, evolutionary models, and phylogeny algorithms with research questions about tumour evolution.
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Key problems of the field remain unresolved, including how to make use of various novel and heterogeneous data sources, the development of more sophisticated models and algorithms appropriate to specific mechanisms of tumour evolution, and the generation of methods and standards for statistically rigorous planning and analysis of tumour phylogeny studies.
Abstract
Rapid advances in high-throughput sequencing and a growing realization of the importance of evolutionary theory to cancer genomics have led to a proliferation of phylogenetic studies of tumour progression. These studies have yielded not only new insights but also a plethora of experimental approaches, sometimes reaching conflicting or poorly supported conclusions. Here, we consider this body of work in light of the key computational principles underpinning phylogenetic inference, with the goal of providing practical guidance on the design and analysis of scientifically rigorous tumour phylogeny studies. We survey the range of methods and tools available to the researcher, their key applications, and the various unsolved problems, closing with a perspective on the prospects and broader implications of this field.
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Acknowledgements
This research was supported in part by the Intramural Research Program of the National Library of Medicine (part of the US National Institutes of Health) and by a grant from the Pennsylvania Department of Health (grant number 4100070287). The Pennsylvania Department of Health specifically disclaims responsibility for any analyses, interpretations or conclusions.
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R.S. currently receives research funding from UPMC Enterprises, a division of the University of Pittsburgh Medical Center. A.A.S. declares no competing interests.
Supplementary information
Supplementary information S1 (table)
This Table provides more detailed information on all tools mentioned in Table 1 as well as a few other tools of historical significance to the field or of potential value to readers of this review. (XLSX 23 kb)
Supplementary information S2 (table)
This table provides more detailed information on all case studies mentioned in Table 2, some other case studies that are important to the field and are cited in the main text and a handful of other, related studies that were not cited in the main text but are nonetheless notable in the context of this review. (XLSX 27 kb)
Glossary
- Selection
-
An evolutionary process in which one population (or subclone, in the context of cancer) is favoured for growth or survival over another.
- Cancer progression
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A change of cancer from a less serious to a more serious state, typically in a manner recognizable by pathologists.
- Metastasis
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A progression in which cancer cells spread to a location in the body that is physically distant from the primary tumour site.
- Subclones
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Subpopulations of cells in a tumour; the cells in each subclone are almost or completely genetically identical for all measured cancer-related variants.
- Hypermutability
-
An elevated mitotic mutation rate, relative to that in healthy cells; this is often specific to a given mutation type (for example, a single nucleotide variant or a copy number variant).
- Intra-tumour heterogeneity
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Variation in the genomes of different cells in the same tumour.
- Tumour self-seeding
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A process by which descendants of cells that escaped the primary tumour re-enter circulation and return to the primary site.
- Mathematical model
-
A formal mathematical abstraction of a physical or biological process, such as a set of evolutionary mechanisms.
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Schwartz, R., Schäffer, A. The evolution of tumour phylogenetics: principles and practice. Nat Rev Genet 18, 213–229 (2017). https://doi.org/10.1038/nrg.2016.170
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DOI: https://doi.org/10.1038/nrg.2016.170
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