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Pairtree: fast reconstruction of cancer evolutionary history using pairwise mutation relationships

View ORCID ProfileJeff Wintersinger, Stephanie Dobson, View ORCID ProfileJohn Dick, View ORCID ProfileQuaid Morris
doi: https://doi.org/10.1101/2020.11.06.372219
Jeff Wintersinger
1Department of Computer Science, University of Toronto, Toronto, ON, Canada
2Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
3Ontario Institute for Cancer Research, Toronto, ON, Canada
4Vector Institute, Toronto, ON, Canada
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  • ORCID record for Jeff Wintersinger
Stephanie Dobson
5Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
6Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
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John Dick
5Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
6Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
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Quaid Morris
1Department of Computer Science, University of Toronto, Toronto, ON, Canada
2Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
3Ontario Institute for Cancer Research, Toronto, ON, Canada
4Vector Institute, Toronto, ON, Canada
5Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
7Memorial Sloan Kettering Cancer Center, New York City, NY, USA
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  • For correspondence: morrisq@mskcc.org
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1 Abstract

Cancers are composed of multiple genetically distinct subpopulations of cancer cells. By performing genome sequencing on tissue samples from a cancer, we can infer the existence of these subpopulations, which mutations render them genetically unique, and the evolutionary relationships between subpopulations. This can reveal critical points in disease development and inform treatment.

Here we present Pairtree, a new algorithm for constructing evolutionary trees that reveal relationships between genetically distinct cell subpopulations composing a patient’s cancer. Pairtree focuses on performing these reconstructions using dozens of cancerous tissue samples per patient, which can be taken from different points in space (e.g., primary tumour and metastasis) or in time (e.g., at diagnosis and at relapse). In concert, these can reveal thirty or more distinct subpopulations, and show how their composition changed between tissue samples.

Each additional tissue sample from a patient provides additional constraints on possible evolutionary histories, and so should aid construction of more accurate and precise results. Counterintuitively, we demonstrate using both simulated and real data that existing algorithms actually perform worse as additional tissue samples are provided, often failing to produce any result. Pairtree, conversely, efficiently leverages the information from additional samples to perform progressively better as samples are added. The algorithm’s ability to function in these settings enables new biological and clinical applications, which we demonstrate using data from 14 acute lymphoblastic leukemia cancers, with dozens of tissue samples per cancer. Pairtree also produces a useful visual representation of the degree of support underlying evolutionary relationships present in the user’s data, allowing users to make accurate inferences from its results.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/morrislab/pairtree

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Pairtree: fast reconstruction of cancer evolutionary history using pairwise mutation relationships
Jeff Wintersinger, Stephanie Dobson, John Dick, Quaid Morris
bioRxiv 2020.11.06.372219; doi: https://doi.org/10.1101/2020.11.06.372219
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Pairtree: fast reconstruction of cancer evolutionary history using pairwise mutation relationships
Jeff Wintersinger, Stephanie Dobson, John Dick, Quaid Morris
bioRxiv 2020.11.06.372219; doi: https://doi.org/10.1101/2020.11.06.372219

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