Identifying the targets, off-target activities, and genetic dependencies of chemical agents, however, remains one of the principal obstacles in drug development ( Nijman, 2015). Critical to these efforts are therapeutic agents with well-defined targets and high specificity for these targets as well as a comprehensive understanding of how the efficacy of these agents is affected by different genetic backgrounds. The ready availability of genomic sequence information, combined with conceptual advances in our understanding of the molecular etiology of diseases, is enabling precision medicine efforts, which seek to develop rational therapies that specifically address the molecular and genetic basis of a disease ( Ashley, 2016). These results demonstrate the power of our chemical-genetic screening strategies for pinpointing the physiologically relevant targets of chemical agents. Finally, expression of tubulin with a structure-guided mutation in the rigosertib-binding pocket conferred resistance to rigosertib, establishing that rigosertib kills cancer cells by destabilizing microtubules. We showed that rigosertib indeed directly binds to and destabilizes microtubules using cell biological, in vitro, and structural approaches. Application of these strategies to rigosertib, a drug in phase 3 clinical trials for high-risk myelodysplastic syndrome whose molecular target had remained controversial, pointed singularly to microtubules as rigosertib’s target. Here, we present a two-tiered CRISPR-mediated chemical-genetic strategy for target identification: combined genome-wide knockdown and overexpression screening as well as focused, comparative chemical-genetic profiling. A central limitation to exploiting these compounds, however, has been in identifying their relevant cellular targets. H: Gene sensitivity and untreated growth phenotypes and p-values for sublibrary CRISPRa screen (related to Figure 3).Ĭhemical libraries paired with phenotypic screens can now readily identify compounds with therapeutic potential. G: sgRNA read counts and phenotypes for sublibrary CRISPRa screen (related to Figure 3). F: Gene sensitivity and untreated growth phenotypes and p-values for sublibrary CRISPRi screen (related to Figure 3). E: sgRNA read counts and phenotypes for sublibrary CRISPRi screen (related to Figure 3). D: Gene rigosertib sensitivity and untreated growth phenotypes and p-values for genome-wide CRISPRa screen (related to Figure 1). C: sgRNA read counts and phenotypes for genome-wide CRISPRa screen (related to Figure 1). B: Gene rigosertib sensitivity and untreated growth phenotypes and p-values for genome-wide CRISPRi screen (related to Figure 1). sgRNA Read Counts and Phenotypes as well as Gene-Level Phenotypes and p Values for CRISPRi and CRISPRa Screens, Related to Figures 1 and 3 A: sgRNA read counts and phenotypes for genome-wide CRISPRi screen (related to Figure 1).
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