Deciphering Genomic Heterogeneity and the Internal Composition of Tumour Activities through a Hierarchical Factorisation Model

Genomic heterogeneity constitutes one of the most distinctive features of cancer diseases, limiting the efficacy and availability of medical treatments. Tumorigenesis emerges as a strongly stochastic process, producing a variable landscape of genomic configurations. In this context, matrix factorisa...

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Autores principales:	José Carbonell-Caballero, Antonio López-Quílez, David Conesa, Joaquín Dopazo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	bioinformatics cancer genomic heterogeneity variability matrix factorisation Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/24249b7ddbfb47c494c200edc45bcd30
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spelling	oai:doaj.org-article:24249b7ddbfb47c494c200edc45bcd302021-11-11T18:21:26ZDeciphering Genomic Heterogeneity and the Internal Composition of Tumour Activities through a Hierarchical Factorisation Model10.3390/math92128332227-7390https://doaj.org/article/24249b7ddbfb47c494c200edc45bcd302021-11-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/21/2833https://doaj.org/toc/2227-7390Genomic heterogeneity constitutes one of the most distinctive features of cancer diseases, limiting the efficacy and availability of medical treatments. Tumorigenesis emerges as a strongly stochastic process, producing a variable landscape of genomic configurations. In this context, matrix factorisation techniques represent a suitable approach for modelling such complex patterns of variability. In this work, we present a hierarchical factorisation model conceived from a systems biology point of view. The model integrates the topology of molecular pathways, allowing to simultaneously factorise genes and pathways activity matrices. The protocol was evaluated by using simulations, showing a high degree of accuracy. Furthermore, the analysis with a real cohort of breast cancer patients depicted the internal composition of some of the most relevant altered biological processes in the disease, describing gene and pathway level strategies and their observed combinations in the population of patients. We envision that this kind of approaches will be essential to better understand the hallmarks of cancer.José Carbonell-CaballeroAntonio López-QuílezDavid ConesaJoaquín DopazoMDPI AGarticlebioinformaticscancergenomic heterogeneityvariabilitymatrix factorisationMathematicsQA1-939ENMathematics, Vol 9, Iss 2833, p 2833 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	bioinformatics cancer genomic heterogeneity variability matrix factorisation Mathematics QA1-939
spellingShingle	bioinformatics cancer genomic heterogeneity variability matrix factorisation Mathematics QA1-939 José Carbonell-Caballero Antonio López-Quílez David Conesa Joaquín Dopazo Deciphering Genomic Heterogeneity and the Internal Composition of Tumour Activities through a Hierarchical Factorisation Model
description	Genomic heterogeneity constitutes one of the most distinctive features of cancer diseases, limiting the efficacy and availability of medical treatments. Tumorigenesis emerges as a strongly stochastic process, producing a variable landscape of genomic configurations. In this context, matrix factorisation techniques represent a suitable approach for modelling such complex patterns of variability. In this work, we present a hierarchical factorisation model conceived from a systems biology point of view. The model integrates the topology of molecular pathways, allowing to simultaneously factorise genes and pathways activity matrices. The protocol was evaluated by using simulations, showing a high degree of accuracy. Furthermore, the analysis with a real cohort of breast cancer patients depicted the internal composition of some of the most relevant altered biological processes in the disease, describing gene and pathway level strategies and their observed combinations in the population of patients. We envision that this kind of approaches will be essential to better understand the hallmarks of cancer.
format	article
author	José Carbonell-Caballero Antonio López-Quílez David Conesa Joaquín Dopazo
author_facet	José Carbonell-Caballero Antonio López-Quílez David Conesa Joaquín Dopazo
author_sort	José Carbonell-Caballero
title	Deciphering Genomic Heterogeneity and the Internal Composition of Tumour Activities through a Hierarchical Factorisation Model
title_short	Deciphering Genomic Heterogeneity and the Internal Composition of Tumour Activities through a Hierarchical Factorisation Model
title_full	Deciphering Genomic Heterogeneity and the Internal Composition of Tumour Activities through a Hierarchical Factorisation Model
title_fullStr	Deciphering Genomic Heterogeneity and the Internal Composition of Tumour Activities through a Hierarchical Factorisation Model
title_full_unstemmed	Deciphering Genomic Heterogeneity and the Internal Composition of Tumour Activities through a Hierarchical Factorisation Model
title_sort	deciphering genomic heterogeneity and the internal composition of tumour activities through a hierarchical factorisation model
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/24249b7ddbfb47c494c200edc45bcd30
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Deciphering Genomic Heterogeneity and the Internal Composition of Tumour Activities through a Hierarchical Factorisation Model

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