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

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 o...

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Autores principales:	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
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c58b18b457184aafaf83bba3e97a5fdf
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spelling	oai:doaj.org-article:c58b18b457184aafaf83bba3e97a5fdf2021-12-02T15:06:16ZComprehensive statistical inference of the clonal structure of cancer from multiple biopsies10.1038/s41598-017-16813-42045-2322https://doaj.org/article/c58b18b457184aafaf83bba3e97a5fdf2017-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-16813-4https://doaj.org/toc/2045-2322Abstract 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.Jie LiuJohn T. HalloranJeffrey A. BilmesRiza M. DazaCholi LeeElisabeth M. MahenDonna PrunkardChaozhong SongSibel BlauMichael O. DorschnerVijayakrishna K. GadiJay ShendureC. Anthony BlauWilliam S. NobleNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-13 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q 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 Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies
description	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.
format	article
author	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
author_facet	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
author_sort	Jie Liu
title	Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies
title_short	Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies
title_full	Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies
title_fullStr	Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies
title_full_unstemmed	Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies
title_sort	comprehensive statistical inference of the clonal structure of cancer from multiple biopsies
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/c58b18b457184aafaf83bba3e97a5fdf
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Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies

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