Summary: | The development of predictive biomarkers of response to targeted therapies is an unmet
clinical need for many antitumoral agents. Recent genome-wide loss-of-function screens, such as
RNA interference (RNAi) and CRISPR-Cas9 libraries, are an unprecedented resource to identify
novel drug targets, reposition drugs and associate predictive biomarkers in the context of precision
oncology. In this work, we have developed and validated a large-scale bioinformatics tool named
DrugSniper, which exploits loss-of-function experiments to model the sensitivity of 6237 inhibitors
and predict their corresponding biomarkers of sensitivity in 30 tumor types. Applying DrugSniper to
small cell lung cancer (SCLC), we identified genes extensively explored in SCLC, such as Aurora
kinases or epigenetic agents. Interestingly, the analysis suggested a remarkable vulnerability to
polo-like kinase 1 (PLK1) inhibition in CREBBP-mutant SCLC cells. We validated this association
in vitro using four mutated and four wild-type SCLC cell lines and two PLK1 inhibitors (Volasertib
and BI2536), confirming that the effect of PLK1 inhibitors depended on the mutational status of
CREBBP. Besides, DrugSniper was validated in-silico with several known clinically-used treatments,
including the sensitivity of Tyrosine Kinase Inhibitors (TKIs) and Vemurafenib to FLT3 and BRAF
mutant cells, respectively. These findings show the potential of genome-wide loss-of-function screens
to identify new personalized therapeutic hypotheses in SCLC and potentially in other tumors, which
is a valuable starting point for further drug development and drug repositioning projects.
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