A survey of optimal strategy for signature-based drug repositioning and an application to liver cancer
Year: 2022
Type: article
Abstract: 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, currently there is no consensus on which methodologies and parameters are the most optimal to conduct such analysis. Aiming to fill this gap, we developed new benchmarking standards for quantitatively estimating drug retrieval performance. Investigations of potential factors influencing drug retrieval were conducted based on these standards. As a result, we determined an optimal strategy for LINCS data-based therapeutic discovery. With this approach, we further identified new therapeutics for liver cancer of which the current treatment modalities remain imperfect. Both computational and experimental results demonstrated homoharringtonine (HHT) could be a promising anti-liver cancer agent. In summary, our findings will not only impact the future applications of LINCS data but also offer new opportunities for therapeutic intervention for liver cancer.
Source: eLife
DOI: 10.7554/elife.71880
Authors: Chen Yang, Hailin Zhang, Mengnuo Chen, Siying Wang, Ruolan Qian, Linmeng Zhang, Xiaowen Huang, Jun Wang, Zhicheng Liu, Wenxin Qin, Cun Wang, Hualian Hang, Hui Wang
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,500
Awards: G2061857906, G2403295412, G3007787574
OpenAlex ID: W4220734682