The Eighth Medical Center of PLA General Hospital, Beijing, China.
Institute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Shenzhen, Guangdong Province, China.
Comb Chem High Throughput Screen. 2021;24(9):1340-1350. doi: 10.2174/1386207323666201027120149.
Conventional high-throughput chemical screens in conjunction with genome-wide gene expression profiling proves to be successful in novel anti-cancer agent discovery and provides comprehensive insights into the mechanisms of action and off-target effects of single small-molecule compound. However, systematic evaluation on heterogeneous transcriptional responses of different cancer cell types to thousands of independent perturbations in a bioinformatics way is still limited.
Here, we introduce cancer transcriptome modifying potential (CTMP) which uses "Connectivity Score" to quantify and compare the effects of approved antineoplastic drugs on transcriptionally restoring dysregulated (both up- and down-) gene expressions at cancer state towards normal state. As a proof-of-concept, we applied this CTMP computational evaluation on > 10,000 small-molecule compounds using >200,000 Library of Integrated Network-based Cellular Signatures (LINCS) expression profiles generated upon 4 different cancer cell lines. We screened and proposed a candidate list of cancer transcriptome modifying therapeutics (CTMTs), among which the approved on-market drugs are further validated using GDSC drug sensitivity data, highlighting their potential to facilitate direct antineoplastic repositioning.
In total, we calculated CTMPs of 85 on-market antineoplastic drugs and 15,000 smallmolecule compounds using 253,813 transcriptomes across four cancer cell lines of lung, melanoma, prostate, and colon. Our results reveal that regardless of the chemical structure and targeted proteins majority of approved antineoplastic drugs present significant bilateral CTMPs across all 4 cancer cell lines. Bilateral CTMP-based systematic screen further indicates that candidate CTMTs are limited and most notably cancer-type specific. In particular, for each cancer cell type we proposed 35 CTMTs that are approved drugs with potent sensitivity data to support development in antineoplastic indications.
Our work establishes CTMP to evaluate the antineoplastic property of small-molecule compounds and suggests CTMP-based systematic screen of cancer type-specific CTMTs as a feasible strategy in drug repositioning for precise anti-cancer purposes.
传统的高通量化学筛选结合全基因组基因表达谱分析已被证明在新型抗癌药物的发现中取得了成功，并为单一小分子化合物的作用机制和脱靶效应提供了全面的见解。然而，在生物信息学方面，对不同癌症细胞类型对数千个独立扰动的异质转录反应进行系统评估仍然有限。
在这里，我们引入了癌症转录组修饰潜力（CTMP），它使用“连接分数”来量化和比较已批准的抗肿瘤药物对转录恢复癌症状态下失调（上调和下调）基因表达向正常状态的影响。作为概念验证，我们使用 >200,000 个基于整合网络的细胞特征（LINCS）表达谱，对 >10,000 种小分子化合物进行了 CTMP 计算评估，这些表达谱是在 4 种不同的癌细胞系中生成的。我们筛选并提出了一个癌症转录组修饰治疗剂（CTMT）的候选列表，其中已批准的市售药物使用 GDSC 药物敏感性数据进一步验证，突出了它们促进直接抗肿瘤再定位的潜力。
总共，我们使用 4 种癌细胞系的 253,813 个转录组计算了 85 种市售抗肿瘤药物和15,000 种小分子化合物的 CTMP。我们的结果表明，无论化学结构和靶向蛋白如何，大多数已批准的抗肿瘤药物在所有 4 种癌细胞系中均表现出显著的双向 CTMP。基于双向 CTMP 的系统筛选进一步表明，候选 CTMT 数量有限，最显著的是癌症类型特异性。特别是，对于每种癌细胞类型，我们提出了 35 种 CTMT，它们是具有强大敏感性数据的批准药物，以支持在抗肿瘤适应症中的开发。
我们的工作建立了 CTMP 来评估小分子化合物的抗肿瘤特性，并提出了基于 CTMP 的癌症类型特异性 CTMT 的系统筛选，作为精确抗癌目的药物重定位的可行策略。
