학술논문

An integrative epigenome-based strategy for unbiased functional profiling of clinical kinase inhibitors
Document Type
article
Source
Molecular Systems Biology, Vol 20, Iss 6, Pp 626-650 (2024)
Subject
Clinical Kinase Inhibitors
Inflammation
Machine Learning
Epigenome
Drug Repurposing
Biology (General)
QH301-705.5
Medicine (General)
R5-920
Language
English
ISSN
1744-4292
Abstract
Abstract More than 500 kinases are implicated in the control of most cellular process in mammals, and deregulation of their activity is linked to cancer and inflammatory disorders. 80 clinical kinase inhibitors (CKIs) have been approved for clinical use and hundreds are in various stages of development. However, CKIs inhibit other kinases in addition to the intended target(s), causing both enhanced clinical effects and undesired side effects that are only partially predictable based on in vitro selectivity profiling. Here, we report an integrative approach grounded on the use of chromatin modifications as unbiased, information-rich readouts of the functional effects of CKIs on macrophage activation. This approach exceeded the performance of transcriptome-based approaches and allowed us to identify similarities and differences among CKIs with identical intended targets, to recognize novel CKI specificities and to pinpoint CKIs that may be repurposed to control inflammation, thus supporting the utility of this strategy to improve selection and use of CKIs in clinical settings.