An integrative epigenome-based strategy for unbiased functional profiling of clinical kinase inhibitors

• Published in: Molecular systems biology 2024-06, Vol.20 (6), p.626-650
• Main Authors: Gualdrini, Francesco, Rizzieri, Stefano, Polletti, Sara, Pileri, Francesco, Zhan, Yinxiu, Cuomo, Alessandro, Natoli, Gioacchino
• Format: Article
• Language: English
• Subjects: Animals; Biomedical and Life Sciences; Clinical Kinase Inhibitors; Drug Repurposing; EMBO09; EMBO22; Epigenome; Humans; Inflammation; Life Sciences; Machine Learning; Macrophage Activation - drug effects; Macrophage Activation - genetics; Macrophages - drug effects; Macrophages - metabolism; Mice; Protein Kinase Inhibitors - pharmacology; Systems Biology
• ISSN: 1744-4292; 1744-4292
• DOI: 10.1038/s44320-024-00040-x

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. Synopsis Unbiased genome-wide analyses of epigenomic alterations induced by clinical kinase inhibitors (CKIs) in macrophages activated by inflammatory stimuli allow identifying insofar unknown similarities and differences among CKIs, improving their annotations and showing opportunities for repurposing. A genome-wide analysis was developed that exploits epigenetic changes as unbiased, high-content and information-rich read-outs of CKIs’ effects. Effects of CKIs on epigenomic changes induced by macrophage activation can be explained by drug-specific combinations of on-target and off-target effects on different sets of signal-regulated transcription factors. CKIs with similar intended annotated target(s) show only partially overlapping epigenomic effects. Conversely, similarities among CKIs with distinct annotations indicate opportunities for drug repurposing. An epigenomic readout was shown to exceed transcription-based analyses in capturing functional effects of CKIs and similarities among them. Unbiased genome-wide analyses of epigenomic alterations induced by clinical kinase inhibitors (CKIs) in macrophages activated by inflammatory stimuli allow identifying insofar unknown similarities and differences among CKIs, improving their annotations and showing opportunities for repurposing.