bioRxivCancer BiologyPreprint
Refined USP25/28 inhibitors with enhanced selectivity towards c-Myc driven squamous lung cancer cells
Adán Pinto-FernándeziD, Claire HerideiD, A.P. TurnbulliD, Dora C. S. PedrosoiD, Victoria Smith, Lisa Mullee, Wojciech W. Krajewski, Cameron Bell, Anthony C. VarcaiD, Thomas M. CharltoniD, Dylan T. Jones, Tom E. McAllisteriD, Roman FischeriD, Elena Navarro‐GuerreroiD, Daniel V. EbneriD, Akane KawamuraiD, Sun Kim, Dave GueriniD, Tim R. Hammonds, Jeffrey D. KearnsiD, Christopher J. DinsmoreiD, Neil P. JonesiD, Sara J. BuhrlageiD, David KomanderiD, Sylvie UrbéiD, , Benedikt Mathias KessleriD
Abstract
The ubiquitin specific protease 28 (USP28) is implicated in tumorigenesis by controlling the turnover of substrates including the oncogene c-MYC and the ubiquitin ligase FBW7. Here, we describe small molecule inhibitors of USP25 and USP28, leading to cancer cell cycle arrest and death. However, genetic deletion of USP25/28 does not replicate this effect. An integrated -omics approach revealed off-target effects for thienopyridine and thienopyrazine carboxamide compounds in protein translation. Chemoproteomics and biochemical analyses suggested binding of such compounds to a region near the ribosome complex polypeptide exit tunnel. Structural analysis of a USP28-inhibitor complex enabled the design of modified USP25/28 inhibitor molecules which minimized translation-related off-target effects. In distinction to earlier compounds, the optimized inhibitors were non-toxic to breast cancer cells yet retained potent anti-proliferative activity in squamous lung carcinoma cells, where USP28 is associated with disease progression. Together, our results demonstrate that refined USP25/28 inhibitors can selectively suppress tumor growth by targeting c-MYC driven pathways, offering a more precise therapeutic strategy for treating squamous lung cancers whilst minimizing undesired cytotoxicity.
- organism: human
- assay: proteomics
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
- approach: experimental assay
- Citations
- 0
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