Identifying cellular cancer mechanisms through pathway-driven data integration
Visualitza/Obre
10.1093/bioinformatics/btac493
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/371634
Tipus de documentArticle
Data publicació2022-08-02
EditorOxford University Press
Condicions d'accésAccés obert
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Reconeixement-NoComercial 4.0 Internacional
ProjecteICON-BIO - Integrated Connectedness for a New Representation of Biology (EC-H2020-770827)
ANALISIS DE REDES COMPARATIVO E INTEGRATIVO MULTIOMICO MULTIESCALA (AEI-PID2019-105500GB-I00)
ANALISIS DE REDES COMPARATIVO E INTEGRATIVO MULTIOMICO MULTIESCALA (AEI-PID2019-105500GB-I00)
Abstract
Abstract
Motivation
Cancer is a genetic disease in which accumulated mutations of driver genes induce a functional reorganization of the cell by reprogramming cellular pathways. Current approaches identify cancer pathways as those most internally perturbed by gene expression changes. However, driver genes characteristically perform hub roles between pathways. Therefore, we hypothesize that cancer pathways should be identified by changes in their pathway–pathway relationships.
Results
To learn an embedding space that captures the relationships between pathways in a healthy cell, we propose pathway-driven non-negative matrix tri-factorization. In this space, we determine condition-specific (i.e. diseased and healthy) embeddings of pathways and genes. Based on these embeddings, we define our ‘NMTF centrality’ to measure a pathway’s or gene’s functional importance, and our ‘moving distance’, to measure the change in its functional relationships. We combine both measures to predict 15 genes and pathways involved in four major cancers, predicting 60 gene–cancer associations in total, covering 28 unique genes. To further exploit driver genes’ tendency to perform hub roles, we model our network data using graphlet adjacency, which considers nodes adjacent if their interaction patterns form specific shapes (e.g. paths or triangles). We find that the predicted genes rewire pathway–pathway interactions in the immune system and provide literary evidence that many are druggable (15/28) and implicated in the associated cancers (47/60). We predict six druggable cancer-specific drug targets.
DatasetThe code and data are available at: https://gitlab.bsc.es/swindels/pathway_driven_nmtf
CitacióWindels, S.F.L.; Malod Dognin, N.; Przulj, N. Identifying cellular cancer mechanisms through pathway-driven data integration. "Bioinformatics", 2 Agost 2022, btac493.
ISSN1367-4803
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