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Synergistic combination therapies involving regorafenib in experimental glioma based on genome-wide CRISPR-Cas9 functional screens

GSE272737 Homo sapiens Expression profiling by high throughput sequencing 24 samples 2024/08/21 GPL24676
Summary
Registered systemic treatment options for glioblastoma patients are limited, particularly for progressive glioblastoma, including bevacizumab in the US and some other countries as well as lomustine. The phase II REGOMA trial suggested an improvement of overall survival in progressive glioblastoma by the multi-tyrosine kinase inhibitor regorafenib. This has not been confirmed by GBM AGILE, however. Of note, regorafenib has been administered as monotherapy in progressive glioblastoma or as an addition to standard of care in newly diagnosed glioblastoma. Rational combination therapies involving regorafenib, instead of entirely discontinuing any consideration of this compound, might be therefore a reasonable strategy. We addressed this research gap and used an innovative discovery and validation approach to identify functionally-instructed combination therapies. We applied a genome-wide CRISPR-Cas9-based functional genomics experimental approach including activation and knockout screens for the discovery of potential molecular targets modifying the therapeutic efficacy of regorafenib in experimental glioma. Selected functionally instructed molecular hits were then validated regarding synergistic interaction in vitro, ex vivo and in two orthotopic mouse models in vivo.This functional genomics approach led to the discovery of several molecular targets. Based on subsequent validation experiments including a customized compound library, we identified novel combination therapies. Particularly, the combination of regorafenib with Bcl-2/Bcl-xL inhibition was among the most promising strategies.
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NCBI GEO page ↗ Paper (PMID 9756423) ↗ {# Names what the click gives you. "Open in finder" meant nothing to a visitor who arrived from a search engine and has never seen the tool. #} Find more human RNA-seq datasets →
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