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

• Published in: Mathematics (Basel) Vol. 9; no. 21; p. 2833
• Main Authors: Carbonell-Caballero, José, López-Quílez, Antonio, Conesa, David, Dopazo, Joaquín
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-11-2021
• Subjects: bioinformatics; Biological activity; Biology; Cancer; Cell growth; Composition; Decomposition; Disease; Factorization; Food science; Gene expression; genomic heterogeneity; Heterogeneity; matrix factorisation; Medical research; Mutation; Ontology; Proteins; Stochastic processes; Topology; Tumors; variability
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math9212833

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.