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ToMExO: A probabilistic tree-structured model for cancer progression

Identifying the interrelations among cancer driver genes and the patterns in which the driver genes get mutated is critical for understanding cancer. In this paper, we study cross-sectional data from cohorts of tumors to identify the cancer-type (or subtype) specific process in which the cancer driv...

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Autores principales: Mohaghegh Neyshabouri, Mohammadreza, Lagergren, Jens
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9754607/
https://www.ncbi.nlm.nih.gov/pubmed/36469540
http://dx.doi.org/10.1371/journal.pcbi.1010732
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author Mohaghegh Neyshabouri, Mohammadreza
Lagergren, Jens
author_facet Mohaghegh Neyshabouri, Mohammadreza
Lagergren, Jens
author_sort Mohaghegh Neyshabouri, Mohammadreza
collection PubMed
description Identifying the interrelations among cancer driver genes and the patterns in which the driver genes get mutated is critical for understanding cancer. In this paper, we study cross-sectional data from cohorts of tumors to identify the cancer-type (or subtype) specific process in which the cancer driver genes accumulate critical mutations. We model this mutation accumulation process using a tree, where each node includes a driver gene or a set of driver genes. A mutation in each node enables its children to have a chance of mutating. This model simultaneously explains the mutual exclusivity patterns observed in mutations in specific cancer genes (by its nodes) and the temporal order of events (by its edges). We introduce a computationally efficient dynamic programming procedure for calculating the likelihood of our noisy datasets and use it to build our Markov Chain Monte Carlo (MCMC) inference algorithm, ToMExO. Together with a set of engineered MCMC moves, our fast likelihood calculations enable us to work with datasets with hundreds of genes and thousands of tumors, which cannot be dealt with using available cancer progression analysis methods. We demonstrate our method’s performance on several synthetic datasets covering various scenarios for cancer progression dynamics. Then, a comparison against two state-of-the-art methods on a moderate-size biological dataset shows the merits of our algorithm in identifying significant and valid patterns. Finally, we present our analyses of several large biological datasets, including colorectal cancer, glioblastoma, and pancreatic cancer. In all the analyses, we validate the results using a set of method-independent metrics testing the causality and significance of the relations identified by ToMExO or competing methods.
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spelling pubmed-97546072022-12-16 ToMExO: A probabilistic tree-structured model for cancer progression Mohaghegh Neyshabouri, Mohammadreza Lagergren, Jens PLoS Comput Biol Research Article Identifying the interrelations among cancer driver genes and the patterns in which the driver genes get mutated is critical for understanding cancer. In this paper, we study cross-sectional data from cohorts of tumors to identify the cancer-type (or subtype) specific process in which the cancer driver genes accumulate critical mutations. We model this mutation accumulation process using a tree, where each node includes a driver gene or a set of driver genes. A mutation in each node enables its children to have a chance of mutating. This model simultaneously explains the mutual exclusivity patterns observed in mutations in specific cancer genes (by its nodes) and the temporal order of events (by its edges). We introduce a computationally efficient dynamic programming procedure for calculating the likelihood of our noisy datasets and use it to build our Markov Chain Monte Carlo (MCMC) inference algorithm, ToMExO. Together with a set of engineered MCMC moves, our fast likelihood calculations enable us to work with datasets with hundreds of genes and thousands of tumors, which cannot be dealt with using available cancer progression analysis methods. We demonstrate our method’s performance on several synthetic datasets covering various scenarios for cancer progression dynamics. Then, a comparison against two state-of-the-art methods on a moderate-size biological dataset shows the merits of our algorithm in identifying significant and valid patterns. Finally, we present our analyses of several large biological datasets, including colorectal cancer, glioblastoma, and pancreatic cancer. In all the analyses, we validate the results using a set of method-independent metrics testing the causality and significance of the relations identified by ToMExO or competing methods. Public Library of Science 2022-12-05 /pmc/articles/PMC9754607/ /pubmed/36469540 http://dx.doi.org/10.1371/journal.pcbi.1010732 Text en © 2022 Mohaghegh Neyshabouri, Lagergren https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Mohaghegh Neyshabouri, Mohammadreza
Lagergren, Jens
ToMExO: A probabilistic tree-structured model for cancer progression
title ToMExO: A probabilistic tree-structured model for cancer progression
title_full ToMExO: A probabilistic tree-structured model for cancer progression
title_fullStr ToMExO: A probabilistic tree-structured model for cancer progression
title_full_unstemmed ToMExO: A probabilistic tree-structured model for cancer progression
title_short ToMExO: A probabilistic tree-structured model for cancer progression
title_sort tomexo: a probabilistic tree-structured model for cancer progression
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9754607/
https://www.ncbi.nlm.nih.gov/pubmed/36469540
http://dx.doi.org/10.1371/journal.pcbi.1010732
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