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CNETML: Maximum likelihood inference of phylogeny from copy number profiles of spatio-temporal samples

View ORCID ProfileBingxin Lu, View ORCID ProfileKit Curtius, View ORCID ProfileTrevor A. Graham, View ORCID ProfileZiheng Yang, View ORCID ProfileChris P. Barnes
doi: https://doi.org/10.1101/2022.03.18.484889
Bingxin Lu
1Department of Cell and Developmental Biology, University College London, UK
2UCL Genetics Institute, University College London, UK
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Kit Curtius
3Barts cancer institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
4Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA, USA
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Trevor A. Graham
3Barts cancer institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
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Ziheng Yang
5Department of Genetics, Evolution and Environment, University College London, London, UK
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Chris P. Barnes
1Department of Cell and Developmental Biology, University College London, UK
2UCL Genetics Institute, University College London, UK
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  • For correspondence: christopher.barnes@ucl.ac.uk
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Abstract

Phylogenetic trees based on copy number alterations (CNAs) for multi-region samples of a single cancer patient are helpful to understand the spatio-temporal evolution of cancers, especially in tumours driven by chromosomal instability. Due to the high cost of deep sequencing data, low-coverage data are more accessible in practice, which only allow the calling of (relative) total copy numbers due to the lower resolution. However, methods to reconstruct sample phylogenies from CNAs often use allele-specific copy numbers and those using total copy number are mostly distance matrix or maximum parsimony methods which do not handle temporal data or estimate mutation rates. In this work, we developed a new maximum likelihood method based on a novel evolutionary model of CNAs, CNETML, to infer phylogenies from spatio-temporal samples taken within a single patient. CNETML is the first program to jointly infer the tree topology, node ages, and mutation rates from total copy numbers when samples were taken at different time points. Our extensive simulations suggest CNETML performed well even on relative copy numbers with subclonal whole genome doubling events and under slight violation of model assumptions. The application of CNETML to real data from Barrett’s esophagus patients also generated consistent results with previous discoveries and novel early CNAs for further investigations.

Competing Interest Statement

The authors have declared no competing interest.

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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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Posted March 20, 2022.
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CNETML: Maximum likelihood inference of phylogeny from copy number profiles of spatio-temporal samples
Bingxin Lu, Kit Curtius, Trevor A. Graham, Ziheng Yang, Chris P. Barnes
bioRxiv 2022.03.18.484889; doi: https://doi.org/10.1101/2022.03.18.484889
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CNETML: Maximum likelihood inference of phylogeny from copy number profiles of spatio-temporal samples
Bingxin Lu, Kit Curtius, Trevor A. Graham, Ziheng Yang, Chris P. Barnes
bioRxiv 2022.03.18.484889; doi: https://doi.org/10.1101/2022.03.18.484889

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