Almost all currently approved systemic therapies for hepatocellular carcinoma (HCC) failed to achieve satisfactory therapeutic effect. Exploring tailored treatment strategies for different individuals provides an approach with the potential to maximize clinical benefit. Previously, multiple studies have reported that hepatoma cell lines belonging to different molecular subtypes respond differently to the same treatment. However, these studies only focused on a small number of typical chemotherapy or targeted drugs across limited cell lines due to time and cost constraints. To compensate for the deficiency of previous experimental researches as well as link molecular classification with therapeutic response, we conducted a comprehensive in silico screening, comprising nearly 2000 compounds, to identify compounds with subclass-specific efficacy. Here, we first identified two transcriptome-based HCC subclasses (AS1 and AS2) and then made comparison of drug response between two subclasses. As a result, we not only found that some agents previously considered to have low efficacy in HCC treatment might have promising therapeutic effects for certain subclass, but also identified novel therapeutic compounds that were not routinely used as anti-tumor drugs in clinic. Discovery of agents with subclass-specific efficacy has potential in changing the status quo of population-based therapies in HCC and providing new insights into precision oncology.
基金:
National Key Sci-Tech Special Projects of Infectious Diseases of China [2018ZX10732202-002-003]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [81874229, 81972208]; Shanghai Natural Science FoundationNatural Science Foundation of Shanghai [19ZR1452700]; Shanghai Municipal Education Commission-Gaofeng Clinical Medicine [20181703]; Foundation of National Facility for Translational Medicine [TMSK-2020-005]; development fund for Shanghai talents [2019081]
语种:
外文
被引次数:
WOS:
PubmedID:
中科院(CAS)分区:
出版当年[2020]版:
大类|1 区生物
小类|1 区生化研究方法1 区数学与计算生物学
最新[2025]版:
大类|2 区生物学
小类|1 区数学与计算生物学2 区生化研究方法
JCR分区:
出版当年[2019]版:
Q1BIOCHEMICAL RESEARCH METHODSQ1MATHEMATICAL & COMPUTATIONAL BIOLOGY
最新[2023]版:
Q1BIOCHEMICAL RESEARCH METHODSQ1MATHEMATICAL & COMPUTATIONAL BIOLOGY
Yang Chen,Chen Junfei,Li Yan,et al.Exploring subclass-specific therapeutic agents for hepatocellular carcinoma by informatics-guided drug screen[J].BRIEFINGS IN BIOINFORMATICS.2021,22(4):doi:10.1093/bib/bbaa295.
APA:
Yang, Chen,Chen, Junfei,Li, Yan,Huang, Xiaowen,Liu, Zhicheng...&Wang, Cun.(2021).Exploring subclass-specific therapeutic agents for hepatocellular carcinoma by informatics-guided drug screen.BRIEFINGS IN BIOINFORMATICS,22,(4)
MLA:
Yang, Chen,et al."Exploring subclass-specific therapeutic agents for hepatocellular carcinoma by informatics-guided drug screen".BRIEFINGS IN BIOINFORMATICS 22..4(2021)