TrAp: a tree approach for fingerprinting subclonal tumor composition

Revealing the clonal composition of a single tumor is essential for identifying cell subpopulations with metastatic potential in primary tumors or with resistance to therapies in metastatic tumors. Sequencing technologies provide only an overview of the aggregate of numerous cells. Computational app...

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Main Authors:	Strino, Francesco, Parisi, Fabio, Micsinai, Mariann, Kluger, Yuval
Format:	Online
Language:	English
Published:	Oxford University Press 2013
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3783191/

id	pubmed-3783191
recordtype	oai_dc
spelling	pubmed-37831912013-09-30 TrAp: a tree approach for fingerprinting subclonal tumor composition Strino, Francesco Parisi, Fabio Micsinai, Mariann Kluger, Yuval Methods Online Revealing the clonal composition of a single tumor is essential for identifying cell subpopulations with metastatic potential in primary tumors or with resistance to therapies in metastatic tumors. Sequencing technologies provide only an overview of the aggregate of numerous cells. Computational approaches to de-mix a collective signal composed of the aberrations of a mixed cell population of a tumor sample into its individual components are not available. We propose an evolutionary framework for deconvolving data from a single genome-wide experiment to infer the composition, abundance and evolutionary paths of the underlying cell subpopulations of a tumor. We have developed an algorithm (TrAp) for solving this mixture problem. In silico analyses show that TrAp correctly deconvolves mixed subpopulations when the number of subpopulations and the measurement errors are moderate. We demonstrate the applicability of the method using tumor karyotypes and somatic hypermutation data sets. We applied TrAp to Exome-Seq experiment of a renal cell carcinoma tumor sample and compared the mutational profile of the inferred subpopulations to the mutational profiles of single cells of the same tumor. Finally, we deconvolve sequencing data from eight acute myeloid leukemia patients and three distinct metastases of one melanoma patient to exhibit the evolutionary relationships of their subpopulations. Oxford University Press 2013-09 2013-07-27 /pmc/articles/PMC3783191/ /pubmed/23892400 http://dx.doi.org/10.1093/nar/gkt641 Text en © The Author(s) 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Strino, Francesco Parisi, Fabio Micsinai, Mariann Kluger, Yuval
spellingShingle	Strino, Francesco Parisi, Fabio Micsinai, Mariann Kluger, Yuval TrAp: a tree approach for fingerprinting subclonal tumor composition
author_facet	Strino, Francesco Parisi, Fabio Micsinai, Mariann Kluger, Yuval
author_sort	Strino, Francesco
title	TrAp: a tree approach for fingerprinting subclonal tumor composition
title_short	TrAp: a tree approach for fingerprinting subclonal tumor composition
title_full	TrAp: a tree approach for fingerprinting subclonal tumor composition
title_fullStr	TrAp: a tree approach for fingerprinting subclonal tumor composition
title_full_unstemmed	TrAp: a tree approach for fingerprinting subclonal tumor composition
title_sort	trap: a tree approach for fingerprinting subclonal tumor composition
description	Revealing the clonal composition of a single tumor is essential for identifying cell subpopulations with metastatic potential in primary tumors or with resistance to therapies in metastatic tumors. Sequencing technologies provide only an overview of the aggregate of numerous cells. Computational approaches to de-mix a collective signal composed of the aberrations of a mixed cell population of a tumor sample into its individual components are not available. We propose an evolutionary framework for deconvolving data from a single genome-wide experiment to infer the composition, abundance and evolutionary paths of the underlying cell subpopulations of a tumor. We have developed an algorithm (TrAp) for solving this mixture problem. In silico analyses show that TrAp correctly deconvolves mixed subpopulations when the number of subpopulations and the measurement errors are moderate. We demonstrate the applicability of the method using tumor karyotypes and somatic hypermutation data sets. We applied TrAp to Exome-Seq experiment of a renal cell carcinoma tumor sample and compared the mutational profile of the inferred subpopulations to the mutational profiles of single cells of the same tumor. Finally, we deconvolve sequencing data from eight acute myeloid leukemia patients and three distinct metastases of one melanoma patient to exhibit the evolutionary relationships of their subpopulations.
publisher	Oxford University Press
publishDate	2013
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3783191/
_version_	1612014258663456768

TrAp: a tree approach for fingerprinting subclonal tumor composition

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