Cargando…

Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data

BACKGROUND: A large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple samples collected either through bulk multi-region sequencing experiments or the sequencing of individual cancer cells....

Descripción completa

Detalles Bibliográficos
Autores principales: Ramazzotti, Daniele, Graudenzi, Alex, De Sano, Luca, Antoniotti, Marco, Caravagna, Giulio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6485126/
https://www.ncbi.nlm.nih.gov/pubmed/31023236
http://dx.doi.org/10.1186/s12859-019-2795-4
_version_ 1783414220440731648
author Ramazzotti, Daniele
Graudenzi, Alex
De Sano, Luca
Antoniotti, Marco
Caravagna, Giulio
author_facet Ramazzotti, Daniele
Graudenzi, Alex
De Sano, Luca
Antoniotti, Marco
Caravagna, Giulio
author_sort Ramazzotti, Daniele
collection PubMed
description BACKGROUND: A large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple samples collected either through bulk multi-region sequencing experiments or the sequencing of individual cancer cells. However, rarely the same method can support both data types. RESULTS: We introduce TRaIT, a computational framework to infer mutational graphs that model the accumulation of multiple types of somatic alterations driving tumour evolution. Compared to other tools, TRaIT supports multi-region and single-cell sequencing data within the same statistical framework, and delivers expressive models that capture many complex evolutionary phenomena. TRaIT improves accuracy, robustness to data-specific errors and computational complexity compared to competing methods. CONCLUSIONS: We show that the application of TRaIT to single-cell and multi-region cancer datasets can produce accurate and reliable models of single-tumour evolution, quantify the extent of intra-tumour heterogeneity and generate new testable experimental hypotheses. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2795-4) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-6485126
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-64851262019-05-03 Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data Ramazzotti, Daniele Graudenzi, Alex De Sano, Luca Antoniotti, Marco Caravagna, Giulio BMC Bioinformatics Methodology Article BACKGROUND: A large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple samples collected either through bulk multi-region sequencing experiments or the sequencing of individual cancer cells. However, rarely the same method can support both data types. RESULTS: We introduce TRaIT, a computational framework to infer mutational graphs that model the accumulation of multiple types of somatic alterations driving tumour evolution. Compared to other tools, TRaIT supports multi-region and single-cell sequencing data within the same statistical framework, and delivers expressive models that capture many complex evolutionary phenomena. TRaIT improves accuracy, robustness to data-specific errors and computational complexity compared to competing methods. CONCLUSIONS: We show that the application of TRaIT to single-cell and multi-region cancer datasets can produce accurate and reliable models of single-tumour evolution, quantify the extent of intra-tumour heterogeneity and generate new testable experimental hypotheses. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2795-4) contains supplementary material, which is available to authorized users. BioMed Central 2019-04-25 /pmc/articles/PMC6485126/ /pubmed/31023236 http://dx.doi.org/10.1186/s12859-019-2795-4 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Ramazzotti, Daniele
Graudenzi, Alex
De Sano, Luca
Antoniotti, Marco
Caravagna, Giulio
Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data
title Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data
title_full Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data
title_fullStr Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data
title_full_unstemmed Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data
title_short Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data
title_sort learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6485126/
https://www.ncbi.nlm.nih.gov/pubmed/31023236
http://dx.doi.org/10.1186/s12859-019-2795-4
work_keys_str_mv AT ramazzottidaniele learningmutationalgraphsofindividualtumourevolutionfromsinglecellandmultiregionsequencingdata
AT graudenzialex learningmutationalgraphsofindividualtumourevolutionfromsinglecellandmultiregionsequencingdata
AT desanoluca learningmutationalgraphsofindividualtumourevolutionfromsinglecellandmultiregionsequencingdata
AT antoniottimarco learningmutationalgraphsofindividualtumourevolutionfromsinglecellandmultiregionsequencingdata
AT caravagnagiulio learningmutationalgraphsofindividualtumourevolutionfromsinglecellandmultiregionsequencingdata