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Inferring parameters of cancer evolution from sequencing and clinical data

Nathan Lee, Ivana Bozic
doi: https://doi.org/10.1101/2020.11.18.387837
Nathan Lee
1Department of Applied Mathematics, University of Washington, Seattle, WA, USA
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Ivana Bozic
1Department of Applied Mathematics, University of Washington, Seattle, WA, USA
2Herbold Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
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  • For correspondence: ibozic@uw.edu
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Abstract

As a cancer develops, its cells accrue new mutations, resulting in a heterogeneous, complex genomic profile. We make use of this heterogeneity to derive simple, analytic estimates of parameters driving carcinogenesis and reconstruct the timeline of selective events following initiation of an individual cancer. Using stochastic computer simulations of cancer growth, we show that we can accurately estimate mutation rate, time before and after a driver event occurred, and growth rates of both initiated cancer cells and subsequently appearing subclones. We demonstrate that in order to obtain accurate estimates of mutation rate and timing of events, observed mutation counts should be corrected to account for clonal mutations that occurred after the founding of the tumor, as well as sequencing coverage. We apply our methodology to reconstruct the individual evolutionary histories of chronic lymphocytic leukemia patients, finding that the parental leukemic clone typically appears within the first fifteen years of life.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 09, 2022.
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Inferring parameters of cancer evolution from sequencing and clinical data
Nathan Lee, Ivana Bozic
bioRxiv 2020.11.18.387837; doi: https://doi.org/10.1101/2020.11.18.387837
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Inferring parameters of cancer evolution from sequencing and clinical data
Nathan Lee, Ivana Bozic
bioRxiv 2020.11.18.387837; doi: https://doi.org/10.1101/2020.11.18.387837

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