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Abstract
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.
Highlights
In recent years, genetic alterations, such as DNA mutations, translocations, or copy number variations, have been used as a source of therapeutic targets and therapy response biomarkers in cancer [1]
This is the case for the sensitivity to HER2-inhibitors for tumors with ERBB2 amplification in breast cancer, the vemurafenib sensitivity of BRAF-mutant cells in melanoma, or the relevance of EGFR mutation for EGFR-inhibitor treatments in non-small cell lung cancer (NSCLC)
By integrating protein-targeted drug information with the mutational landscape of SCLC cells, we identified genes extensively explored in SCLC as potential targets, such as Aurora kinases or epigenetic agents
Summary
Genetic alterations, such as DNA mutations, translocations, or copy number variations, have been used as a source of therapeutic targets and therapy response biomarkers in cancer [1]. Analyzing genetic essentiality in specific aberrant phenotypes has been demonstrated as an unprecedented approach to identify drug targets and develop personalized therapies in cancer. This is the case for the sensitivity to HER2-inhibitors for tumors with ERBB2 amplification in breast cancer, the vemurafenib sensitivity of BRAF-mutant cells in melanoma, or the relevance of EGFR mutation for EGFR-inhibitor treatments in non-small cell lung cancer (NSCLC). This approach is especially relevant in tumors that still lack a clear treatment guideline, such as small cell lung carcinoma (SCLC)
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