Computational analysis of receptor tyrosine kinase inhibitors and cancer metabolism: implications for treatment and discovery of potential therapeutic signatures

BMC Cancer

Christian Saad¹  Bernhard Bauer¹  Yijiang Huang²  Kai Xu³  Kathrin Halfter⁴  Jian Li⁴  Ulrich R. Mansmann⁴  Mengying Zhang⁴  Lei Shi⁵ 

[1] Department of Computational Science, University of Augsburg;Department of Orthopaedics, Physical Medicine and Rehabilitation, University Hospital, LMU;Department of Orthopaedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology;Institute for Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-University München;Institute of Photomedicine, Shanghai Skin Disease Hospital, Tongji University School of Medicine;

关键词: Cancer metabolism;    Treatment prediction;    Computational modeling;    Systems biology;   

DOI  :  10.1186/s12885-019-5804-0

来源: DOAJ

【 摘 要 】

Abstract Background Receptor tyrosine kinase (RTK) inhibitors are frequently used to treat cancers and the results have been mixed, some of these small molecule drugs are highly successful while others show a more modest response. A high number of studies have been conducted to investigate the signaling mechanisms and corresponding therapeutic influence of RTK inhibitors in order to explore the therapeutic potential of RTK inhibitors. However, most of these studies neglected the potential metabolic impact of RTK inhibitors, which could be highly associated with drug efficacy and adverse effects during treatment. Methods In order to fill these knowledge gaps and improve the therapeutic utilization of RTK inhibitors a large-scale computational simulation/analysis over multiple types of cancers with the treatment responses of RTK inhibitors was performed. The pharmacological data of all eight RTK inhibitor and gene expression profiles of 479 cell lines from The Cancer Cell Line Encyclopedia were used. Results The potential metabolic impact of RTK inhibitors on different types of cancers were analyzed resulting in cancer-specific (breast, liver, pancreas, central nervous system) metabolic signatures. Many of these are in line with results from different independent studies, thereby providing indirect verification of the obtained results. Conclusions Our study demonstrates the potential of using a computational approach on signature-based-analysis over multiple cancer types. The results reveal the strength of multiple-cancer analysis over conventional signature-based analysis on a single cancer type.

【 授权许可】

Unknown