Carazo Fernando, Bértolo Cristina, Castilla Carlos, Cendoya Xabier, Campuzano Lucía, Serrano Diego, Gimeno Marian, Planes Francisco J, Pio Ruben, Montuenga Luis M, Rubio Angel
Department of Biomedical Engineering and Sciences, School of Engineering, University of Navarra, 20018 San Sebastián, Spain.
Program in Solid Tumors, Center for Applied Medical Research (CIMA), CIBERONC and Navarra's Health Research Institute (IDISNA), 31008 Pamplona, Spain.
Cancers (Basel). 2020 Jul 7;12(7):1824. doi: 10.3390/cancers12071824.
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 () inhibition in -mutant SCLC cells. We validated this association in vitro using four mutated and four wild-type SCLC cell lines and two inhibitors (Volasertib and BI2536), confirming that the effect of inhibitors depended on the mutational status of . Besides, DrugSniper was validated in-silico with several known clinically-used treatments, including the sensitivity of Tyrosine Kinase Inhibitors (TKIs) and Vemurafenib to and 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.
对于许多抗肿瘤药物而言，开发预测靶向治疗反应的生物标志物是一项尚未满足的临床需求。近期的全基因组功能丧失筛选，如RNA干扰（RNAi）和CRISPR-Cas9文库，是识别新的药物靶点、重新定位药物以及在精准肿瘤学背景下关联预测性生物标志物的前所未有的资源。在这项工作中，我们开发并验证了一种名为DrugSniper的大规模生物信息学工具，该工具利用功能丧失实验来模拟6237种抑制剂的敏感性，并预测它们在30种肿瘤类型中的相应敏感性生物标志物。将DrugSniper应用于小细胞肺癌（SCLC），我们鉴定出了在SCLC中广泛研究的基因，如极光激酶或表观遗传药物。有趣的是，分析表明在 -突变的SCLC细胞中对polo样激酶1（）抑制具有显著的脆弱性。我们使用四种突变和四种野生型SCLC细胞系以及两种 抑制剂（Volasertib和BI2536）在体外验证了这种关联，证实 抑制剂的作用取决于 的突变状态。此外，DrugSniper在计算机模拟中用几种已知的临床应用治疗方法进行了验证，包括酪氨酸激酶抑制剂（TKIs）和维莫非尼分别对 和 突变细胞的敏感性。这些发现表明全基因组功能丧失筛选在识别SCLC以及潜在其他肿瘤中的新的个性化治疗假设方面的潜力，这是进一步药物开发和药物重新定位项目的有价值的起点。
