A drug repurposing approach for individualized cancer therapy based on transcriptome sequencing and virtual drug screening. (May 2023)

• Record Type: Journal Article
• Authors: Kadioglu, Onat; Bahramimehr, Faranak; Dawood, Mona; Mahmoud, Nuha; Elbadawi, Mohamed; Lu, Xiaohua; Bülbül, Yagmur; Schulz, Jana Agnieszka; Krämer, Lisa; Urschel, Marie-Kathrin; Künzli, Zoe; Abdulrahman, Leila; Aboumaachar, Fadwa; Kadalo, Lajien; Nguyen, Le Van; Shaidaei, Sara; Thaher, Nawal; Walter, Kathrin; Besler, Karolin Christiane; Spuller, Andreas; Munder, Markus; Greten, Henry Johannes; Efferth, Thomas
• Is Part Of: Computers in biology and medicine. Volume 157(2023)
• Publication Date: 2023-05
• Subjects: Cancer -- Drug discovery -- Drug repurposing -- Mutation analysis -- Personalized medicine -- Precision medicine -- Targeted chemotherapy; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106781
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: RNA-sequencing has been proposed as a valuable technique to develop individualized therapy concepts for cancer patients based on their tumor-specific mutational profiles. Here, we aimed to identify drugs and inhibitors in an individualized therapy-based drug repurposing approach focusing on missense mutations for 35 biopsies of cancer patients. The missense mutations belonged to 9 categories (ABC transporter, apoptosis, angiogenesis, cell cycle, DNA damage, kinase, protease, transcription factor, tumor suppressor). The highest percentages of missense mutations were observed in transcription factor genes. The mutational profiles of all 35 tumors were subjected to hierarchical heatmap clustering. All 7 leukemia biopsies clustered together and were separated from solid tumors. Based on these individual mutation profiles, two strategies for the identification of possible drug candidates were applied: Firstly, virtual screening of FDA-approved drugs based on the protein structures carrying particular missense mutations. Secondly, we mined the Drug Gene Interaction (DGI) database (https://www.dgidb.org/ ) to identify approved or experimental inhibitors for missense mutated proteins in our dataset of 35 tumors. In conclusion, our approach based on virtual drug screening of FDA-approved drugs and DGI-based inhibitor selection may provide new, individual treatment options for patients with otherwise refractory tumors that do not respond anymore to standard chemotherapy. Graphical abstract: Image 1 Highlights: We applied a personalized therapy based-strategy for a panel of cancer patients. Some approved drugs and inhibitors were commonly observed in multiple cancer types. This study could be a reference considering the patient-specific missense mutations.