A survey of optimal strategy for signature-based drug repositioning and an application to liver cancer

Pharmacologic perturbation projects, such as Connectivity Map (CMap) and Library of Integrated Network-based Cellular Signatures (LINCS), have produced many perturbed expression data, providing enormous opportunities for computational therapeutic discovery. However, there is no consensus on which me...

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Bibliographic Details
Published in:eLife 2022-02, Vol.11
Main Authors: Yang, Chen, Zhang, Hailin, Chen, Mengnuo, Wang, Siying, Qian, Ruolan, Zhang, Linmeng, Huang, Xiaowen, Wang, Jun, Liu, Zhicheng, Qin, Wenxin, Wang, Cun, Hang, Hualian, Wang, Hui
Format: Article
Language:English
Subjects:
Accuracy
Cancer Biology
Cancer therapies
Carbon tetrachloride
Computational Biology - methods
Computer applications
connectivity map
Disease
Drug dosages
drug prediction
Drug Repositioning - methods
Experiments
Fibrosis
Gene expression
Health aspects
homoharringtonine
Humans
Libraries
LINCS
Liver cancer
Liver Neoplasms - drug therapy
R&D
Reproducibility of Results
Research & development
Surveys
Tumors
Xenografts
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Summary:Pharmacologic perturbation projects, such as Connectivity Map (CMap) and Library of Integrated Network-based Cellular Signatures (LINCS), have produced many perturbed expression data, providing enormous opportunities for computational therapeutic discovery. However, there is no consensus on which methodologies and parameters are the most optimal to conduct such analysis. Aiming to fill this gap, new benchmarking standards were developed to quantitatively evaluate drug retrieval performance. Investigations of potential factors influencing drug retrieval were conducted based on these standards. As a result, we determined an optimal approach for LINCS data-based therapeutic discovery. With this approach, homoharringtonine (HHT) was identified to be a candidate agent with potential therapeutic and preventive effects on liver cancer. The antitumor and antifibrotic activity of HHT was validated experimentally using subcutaneous xenograft tumor model and carbon tetrachloride (CCL )-induced liver fibrosis model, demonstrating the reliability of the prediction results. In summary, our findings will not only impact the future applications of LINCS data but also offer new opportunities for therapeutic intervention of liver cancer.
ISSN:2050-084X
2050-084X
DOI:10.7554/eLife.71880