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Advances in understanding tumour evolution through single-cell sequencing()

The mutational heterogeneity observed within tumours poses additional challenges to the development of effective cancer treatments. A thorough understanding of a tumour's subclonal composition and its mutational history is essential to open up the design of treatments tailored to individual pat...

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Detalles Bibliográficos
Autores principales: Kuipers, Jack, Jahn, Katharina, Beerenwinkel, Niko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5813714/
https://www.ncbi.nlm.nih.gov/pubmed/28193548
http://dx.doi.org/10.1016/j.bbcan.2017.02.001
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author Kuipers, Jack
Jahn, Katharina
Beerenwinkel, Niko
author_facet Kuipers, Jack
Jahn, Katharina
Beerenwinkel, Niko
author_sort Kuipers, Jack
collection PubMed
description The mutational heterogeneity observed within tumours poses additional challenges to the development of effective cancer treatments. A thorough understanding of a tumour's subclonal composition and its mutational history is essential to open up the design of treatments tailored to individual patients. Comparative studies on a large number of tumours permit the identification of mutational patterns which may refine forecasts of cancer progression, response to treatment and metastatic potential. The composition of tumours is shaped by evolutionary processes. Recent advances in next-generation sequencing offer the possibility to analyse the evolutionary history and accompanying heterogeneity of tumours at an unprecedented resolution, by sequencing single cells. New computational challenges arise when moving from bulk to single-cell sequencing data, leading to the development of novel modelling frameworks. In this review, we present the state of the art methods for understanding the phylogeny encoded in bulk or single-cell sequencing data, and highlight future directions for developing more comprehensive and informative pictures of tumour evolution. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.
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spelling pubmed-58137142018-02-20 Advances in understanding tumour evolution through single-cell sequencing() Kuipers, Jack Jahn, Katharina Beerenwinkel, Niko Biochim Biophys Acta Rev Cancer Article The mutational heterogeneity observed within tumours poses additional challenges to the development of effective cancer treatments. A thorough understanding of a tumour's subclonal composition and its mutational history is essential to open up the design of treatments tailored to individual patients. Comparative studies on a large number of tumours permit the identification of mutational patterns which may refine forecasts of cancer progression, response to treatment and metastatic potential. The composition of tumours is shaped by evolutionary processes. Recent advances in next-generation sequencing offer the possibility to analyse the evolutionary history and accompanying heterogeneity of tumours at an unprecedented resolution, by sequencing single cells. New computational challenges arise when moving from bulk to single-cell sequencing data, leading to the development of novel modelling frameworks. In this review, we present the state of the art methods for understanding the phylogeny encoded in bulk or single-cell sequencing data, and highlight future directions for developing more comprehensive and informative pictures of tumour evolution. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby. Elsevier 2017-04 /pmc/articles/PMC5813714/ /pubmed/28193548 http://dx.doi.org/10.1016/j.bbcan.2017.02.001 Text en © 2017 The Authors. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Kuipers, Jack
Jahn, Katharina
Beerenwinkel, Niko
Advances in understanding tumour evolution through single-cell sequencing()
title Advances in understanding tumour evolution through single-cell sequencing()
title_full Advances in understanding tumour evolution through single-cell sequencing()
title_fullStr Advances in understanding tumour evolution through single-cell sequencing()
title_full_unstemmed Advances in understanding tumour evolution through single-cell sequencing()
title_short Advances in understanding tumour evolution through single-cell sequencing()
title_sort advances in understanding tumour evolution through single-cell sequencing()
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5813714/
https://www.ncbi.nlm.nih.gov/pubmed/28193548
http://dx.doi.org/10.1016/j.bbcan.2017.02.001
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