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Collaborative intra-tumor heterogeneity detection

MOTIVATION: Despite the remarkable advances in sequencing and computational techniques, noise in the data and complexity of the underlying biological mechanisms render deconvolution of the phylogenetic relationships between cancer mutations difficult. Besides that, the majority of the existing datas...

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Autores principales: Khakabimamaghani, Sahand, Malikic, Salem, Tang, Jeffrey, Ding, Dujian, Morin, Ryan, Chindelevitch, Leonid, Ester, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6612880/
https://www.ncbi.nlm.nih.gov/pubmed/31510674
http://dx.doi.org/10.1093/bioinformatics/btz355
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author Khakabimamaghani, Sahand
Malikic, Salem
Tang, Jeffrey
Ding, Dujian
Morin, Ryan
Chindelevitch, Leonid
Ester, Martin
author_facet Khakabimamaghani, Sahand
Malikic, Salem
Tang, Jeffrey
Ding, Dujian
Morin, Ryan
Chindelevitch, Leonid
Ester, Martin
author_sort Khakabimamaghani, Sahand
collection PubMed
description MOTIVATION: Despite the remarkable advances in sequencing and computational techniques, noise in the data and complexity of the underlying biological mechanisms render deconvolution of the phylogenetic relationships between cancer mutations difficult. Besides that, the majority of the existing datasets consist of bulk sequencing data of single tumor sample of an individual. Accurate inference of the phylogenetic order of mutations is particularly challenging in these cases and the existing methods are faced with several theoretical limitations. To overcome these limitations, new methods are required for integrating and harnessing the full potential of the existing data. RESULTS: We introduce a method called Hintra for intra-tumor heterogeneity detection. Hintra integrates sequencing data for a cohort of tumors and infers tumor phylogeny for each individual based on the evolutionary information shared between different tumors. Through an iterative process, Hintra learns the repeating evolutionary patterns and uses this information for resolving the phylogenetic ambiguities of individual tumors. The results of synthetic experiments show an improved performance compared to two state-of-the-art methods. The experimental results with a recent Breast Cancer dataset are consistent with the existing knowledge and provide potentially interesting findings. AVAILABILITY AND IMPLEMENTATION: The source code for Hintra is available at https://github.com/sahandk/HINTRA.
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spelling pubmed-66128802019-07-12 Collaborative intra-tumor heterogeneity detection Khakabimamaghani, Sahand Malikic, Salem Tang, Jeffrey Ding, Dujian Morin, Ryan Chindelevitch, Leonid Ester, Martin Bioinformatics Ismb/Eccb 2019 Conference Proceedings MOTIVATION: Despite the remarkable advances in sequencing and computational techniques, noise in the data and complexity of the underlying biological mechanisms render deconvolution of the phylogenetic relationships between cancer mutations difficult. Besides that, the majority of the existing datasets consist of bulk sequencing data of single tumor sample of an individual. Accurate inference of the phylogenetic order of mutations is particularly challenging in these cases and the existing methods are faced with several theoretical limitations. To overcome these limitations, new methods are required for integrating and harnessing the full potential of the existing data. RESULTS: We introduce a method called Hintra for intra-tumor heterogeneity detection. Hintra integrates sequencing data for a cohort of tumors and infers tumor phylogeny for each individual based on the evolutionary information shared between different tumors. Through an iterative process, Hintra learns the repeating evolutionary patterns and uses this information for resolving the phylogenetic ambiguities of individual tumors. The results of synthetic experiments show an improved performance compared to two state-of-the-art methods. The experimental results with a recent Breast Cancer dataset are consistent with the existing knowledge and provide potentially interesting findings. AVAILABILITY AND IMPLEMENTATION: The source code for Hintra is available at https://github.com/sahandk/HINTRA. Oxford University Press 2019-07 2019-07-05 /pmc/articles/PMC6612880/ /pubmed/31510674 http://dx.doi.org/10.1093/bioinformatics/btz355 Text en © The Author(s) 2019. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Ismb/Eccb 2019 Conference Proceedings
Khakabimamaghani, Sahand
Malikic, Salem
Tang, Jeffrey
Ding, Dujian
Morin, Ryan
Chindelevitch, Leonid
Ester, Martin
Collaborative intra-tumor heterogeneity detection
title Collaborative intra-tumor heterogeneity detection
title_full Collaborative intra-tumor heterogeneity detection
title_fullStr Collaborative intra-tumor heterogeneity detection
title_full_unstemmed Collaborative intra-tumor heterogeneity detection
title_short Collaborative intra-tumor heterogeneity detection
title_sort collaborative intra-tumor heterogeneity detection
topic Ismb/Eccb 2019 Conference Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6612880/
https://www.ncbi.nlm.nih.gov/pubmed/31510674
http://dx.doi.org/10.1093/bioinformatics/btz355
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