Windels, Sam FL;
Malod-Dognin, Noel;
Przulj, Natasa;
(2022)
Identifying cellular cancer mechanisms through pathway-driven data integration.
Bioinformatics
, 38
(18)
pp. 4344-4351.
10.1093/bioinformatics/btac493.
Preview |
Text
btac493.pdf - Published Version Download (645kB) | Preview |
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.
| Type: | Article |
|---|---|
| Title: | Identifying cellular cancer mechanisms through pathway-driven data integration |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/bioinformatics/btac493 |
| Publisher version: | http://dx.doi.org/10.1093/bioinformatics/btac493 |
| Language: | English |
| Additional information: | © The Author(s) 2022. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://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 |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Technology, Physical Sciences, Biochemical Research Methods, Biotechnology & Applied Microbiology, Computer Science, Interdisciplinary Applications, Mathematical & Computational Biology, Statistics & Probability, Biochemistry & Molecular Biology, Computer Science, Mathematics, ENRICHMENT ANALYSIS, GENE, CELLS |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10187844 |
Archive Staff Only
 |
View Item |
