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Sci Rep. 2017 Dec 5;7(1):16943. doi: 10.1038/s41598-017-16813-4.

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

Author information

1
Department of Genome Sciences, University of Washington, Seattle, WA, USA.
2
Department of Electrical Engineering, University of Washington, Seattle, WA, USA.
3
Center for Cancer Innovation, University of Washington, Seattle, WA, USA.
4
Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA.
5
Department of Medicine/Hematology, University of Washington, Seattle, WA, USA.
6
Department of Pathology, University of Washington, Seattle, WA, USA.
7
Northwest Medical Specialties, Puyallup and Tacoma, WA, USA.
8
Department of Medicine/Oncology, University of Washington, Seattle, WA, USA.
9
Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, 98109, WA, USA.
10
Howard Hughes Medical Institute, Seattle, WA, USA.
11
Center for Cancer Innovation, University of Washington, Seattle, WA, USA. tblau@uw.edu.
12
Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA. tblau@uw.edu.
13
Department of Medicine/Hematology, University of Washington, Seattle, WA, USA. tblau@uw.edu.
14
Department of Genome Sciences, University of Washington, Seattle, WA, USA. william-noble@uw.edu.
15
Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA. william-noble@uw.edu.

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.

PMID:
29208983
PMCID:
PMC5717219
DOI:
10.1038/s41598-017-16813-4
[Indexed for MEDLINE]
Free PMC Article

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