bioRxivCancer BiologyPreprint
Anticipating on-target resistance to WRN inhibitors in microsatellite unstable cancers
Mark E. OrcholskiiD, Nancy LaterreuriD, Wardah MasudiD, Shamika Shenoy, Vincent ChapdelaineiD, Julian Bowlan, Amisha Minju-OP, Júlia-Jié Cabré-Romans, Tanja SackiD, Chris Fiore, Jordan T.F. Young, Alejandro Álvarez-QuilóniD, Raquel Cuella-MartiniD
- Citations
- 2
Abstract
Leveraging WRN helicase dependency in microsatellite instability (MSI) cancers offers a synthetic lethal (SL) therapeutic opportunity, with several WRN inhibitors in development. However, the hypermutator nature of MSI tumors creates strong evolutionary pressure for rapid resistance. Here, we apply a multimodal functional genomics framework integrating base editing screens and deep mutational scanning to map on-target resistance to two clinical WRN inhibitors, HRO761 and VVD-214. We identify discrete resistance hotspots within WRN and demonstrate that single-allele (heterozygous) mutations at the drug-binding site are sufficient to abrogate WRN inhibitor-induced cytotoxicity. Resistance profiles diverged between HRO761 and VVD-214, revealing mutations that impair one but preserve sensitivity to the other. Genome-wide CRISPR screens further identified non-homologous end joining (NHEJ) factors and the checkpoint phosphatase WIP1 as tractable synthetic vulnerabilities that potentiate WRN inhibition. Together, these findings establish a framework for resistance-aware deployment of WRN inhibitors through rational drug selection, therapeutic switching, and combination strategies. Statement of SignificanceResistance to WRN inhibitors threatens the clinical durability of synthetic lethal therapies in microsatellite-instable cancers. Using multimodal functional genomics, we identify predictable, drug-specific on-target resistance mechanisms and reveal DNA-PK as a tractable combination partner. These findings provide a framework for resistance-aware deployment of WRN inhibitors to improve therapeutic durability.
- organism: human
- assay: CRISPR screen
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
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