Bash Biotech Inc, 600 est Broadway, Suite 700, San Diego, CA 92101, USA; Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden.
Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm SE-17165, Sweden.
EBioMedicine. 2022 Apr;78:103963. doi: 10.1016/j.ebiom.2022.103963. Epub 2022 Mar 25.
The response rates of the clinical chemotherapies are still low in clear cell renal cell carcinoma (ccRCC). Computational drug repositioning is a promising strategy to discover new uses for existing drugs to treat patients who cannot get benefits from clinical drugs.
We proposed a systematic approach which included the target prediction based on the co-expression network analysis of transcriptomics profiles of ccRCC patients and drug repositioning for cancer treatment based on the analysis of shRNA- and drug-perturbed signature profiles of human kidney cell line.
First, based on the gene co-expression network analysis, we identified two types of gene modules in ccRCC, which significantly enriched with unfavorable and favorable signatures indicating poor and good survival outcomes of patients, respectively. Then, we selected four genes, BUB1B, RRM2, ASF1B and CCNB2, as the potential drug targets based on the topology analysis of modules. Further, we repurposed three most effective drugs for each target by applying the proposed drug repositioning approach. Finally, we evaluated the effects of repurposed drugs using an in vitro model and observed that these drugs inhibited the protein levels of their corresponding target genes and cell viability.
These findings proved the usefulness and efficiency of our approach to improve the drug repositioning researches for cancer treatment and precision medicine.
This study was funded by Knut and Alice Wallenberg Foundation and Bash Biotech Inc., San Diego, CA, USA.
透明细胞肾细胞癌 (ccRCC) 的临床化疗反应率仍然较低。计算药物再定位是一种很有前途的策略，可以发现现有药物的新用途，以治疗无法从临床药物中获益的患者。
我们提出了一种系统的方法，包括基于 ccRCC 患者转录组谱的共表达网络分析的靶标预测，以及基于人肾细胞系 shRNA 和药物扰动特征谱的癌症治疗药物再定位。
首先，基于基因共表达网络分析，我们在 ccRCC 中鉴定出两种类型的基因模块，它们显著富集了不良和有利的特征，分别表明患者的生存结果较差和较好。然后，我们基于模块的拓扑分析，选择了 BUB1B、RRM2、ASF1B 和 CCNB2 这四个基因作为潜在的药物靶点。进一步，我们通过应用我们提出的药物重定位方法，为每个靶点重新定位了三种最有效的药物。最后，我们通过体外模型评估了这些重新定位药物的效果，观察到这些药物抑制了相应靶基因的蛋白水平和细胞活力。
这些发现证明了我们的方法在提高癌症治疗和精准医学药物再定位研究方面的有效性和效率。
本研究由 Knut 和 Alice Wallenberg 基金会和 Bash Biotech Inc. 资助，后者位于美国加利福尼亚州圣地亚哥。
