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Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies

View ORCID ProfileJie Liu, John T. Halloran, Jeffrey A. Bilmes, Riza M. Daza, Choli Lee, Elisabeth M. Mahen, Donna Prunkard, Chaozhong Song, Sibel Blau, Michael O. Dorschner, Vijayakrishna K. Gadi, Jay Shendure, C. Anthony Blau, William S. Noble
doi: https://doi.org/10.1101/125138
Jie Liu
1Department of Genome Sciences, University of Washington, Seattle, WA
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  • ORCID record for Jie Liu
John T. Halloran
2Department of Electrical Engineering, University of Washington, Seattle, WA
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Jeffrey A. Bilmes
2Department of Electrical Engineering, University of Washington, Seattle, WA
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Riza M. Daza
1Department of Genome Sciences, University of Washington, Seattle, WA
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Choli Lee
1Department of Genome Sciences, University of Washington, Seattle, WA
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Elisabeth M. Mahen
3Center for Cancer Innovation, University of Washington, Seattle, WA
4Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA
5Department of Medicine/Hematology, University of Washington, Seattle, WA
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Donna Prunkard
6Department of Pathology, University of Washington, Seattle, WA
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Chaozhong Song
3Center for Cancer Innovation, University of Washington, Seattle, WA
4Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA
5Department of Medicine/Hematology, University of Washington, Seattle, WA
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Sibel Blau
3Center for Cancer Innovation, University of Washington, Seattle, WA
7Northwest Medical Specialties, Puyallup and Tacoma, WA
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Michael O. Dorschner
3Center for Cancer Innovation, University of Washington, Seattle, WA
6Department of Pathology, University of Washington, Seattle, WA
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Vijayakrishna K. Gadi
8Department of Medicine/Oncology, University of Washington, Seattle, WA
9Seattle Cancer Care Alliance, Seattle, WA
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Jay Shendure
1Department of Genome Sciences, University of Washington, Seattle, WA
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C. Anthony Blau
3Center for Cancer Innovation, University of Washington, Seattle, WA
4Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA
5Department of Medicine/Hematology, University of Washington, Seattle, WA
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  • For correspondence: tblau@uw.edu william-noble@uw.edu
William S. Noble
1Department of Genome Sciences, University of Washington, Seattle, WA
10Department of Computer Science and Engineering, University of Washington, Seattle, WA
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  • For correspondence: tblau@uw.edu william-noble@uw.edu
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Abstract

A comprehensive characterization of tumor genetic heterogeneity is critical for understanding how cancers evolve and escape treatment. Although many algorithms have been developed for capturing tumor heterogeneity, they are designed for analyzing either a single type of genomic aberration or individual biopsies. Here we present THEMIS (Tumor Heterogeneity Extensible Modeling via an Integrative System), which allows for the joint analysis of different types of genomic aberrations from multiple biopsies taken from the same patient, using a dynamic graphical model. Simulation experiments demonstrate higher accuracy of THEMIS over its ancestor, TITAN. The heterogeneity analysis results from THEMIS are validated with single cell DNA sequencing from a clinical tumor biopsy. When THEMIS is used to analyze tumor heterogeneity among multiple biopsies from the same patient, it helps to reveal the mutation accumulation history, track cancer progression, and identify the mutations related to treatment resistance. We implement our model via an extensible modeling platform, which makes our approach open, reproducible, and easy for others to extend.

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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. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 06, 2017.
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Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies
Jie Liu, John T. Halloran, Jeffrey A. Bilmes, Riza M. Daza, Choli Lee, Elisabeth M. Mahen, Donna Prunkard, Chaozhong Song, Sibel Blau, Michael O. Dorschner, Vijayakrishna K. Gadi, Jay Shendure, C. Anthony Blau, William S. Noble
bioRxiv 125138; doi: https://doi.org/10.1101/125138
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Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies
Jie Liu, John T. Halloran, Jeffrey A. Bilmes, Riza M. Daza, Choli Lee, Elisabeth M. Mahen, Donna Prunkard, Chaozhong Song, Sibel Blau, Michael O. Dorschner, Vijayakrishna K. Gadi, Jay Shendure, C. Anthony Blau, William S. Noble
bioRxiv 125138; doi: https://doi.org/10.1101/125138

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