Journal
ACS PHARMACOLOGY & TRANSLATIONAL SCIENCE
Volume 2, Issue 2, Pages 122-133Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acsptsci.9b00019
Keywords
gene fusions; cancer genomics; machine learning
Categories
Funding
- MRC [MC_U105185859]
- MRC [MC_U105185859] Funding Source: UKRI
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Although gene fusions are recognized as driver mutations in a wide variety of cancers, the general molecular mechanisms underlying oncogenic fusion proteins are insufficiently understood. Here, we employ large-scale data integration and machine learning and (1) identify three functionally distinct subgroups of gene fusions and their molecular signatures; (2) characterize the cellular pathways rewired by fusion events across different cancers; and (3) analyze the relative importance of over 100 structural, functional, and regulatory features of similar to 2200 gene fusions. We report subgroups of fusions that likely act as driver mutations and find that gene fusions disproportionately affect pathways regulating cellular shape and movement. Although fusion proteins are similar across different cancer types, they affect cancer type-specific pathways. Key indicators of fusion-forming proteins include high and nontissue specific expression, numerous splice sites, and higher centrality in protein-interaction networks. Together, these findings provide unifying and cancer typespecific trends across diverse oncogenic fusion proteins.
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