Chemical-induced gene expression ranking and its application to pancreatic cancer drug repurposing.
Chemical-induced gene expression ranking and its application to pancreatic cancer drug repurposing.
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DOI：
10.1016/j.patter.2022.100441
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发表时间：
2022-04-08
期刊：
影响因子：
6.5
通讯作者：
Zhang, Ping
中科院分区：
文献类型：
--
作者：
Pham, Thai-Hoang;Qiu, Yue;Liu, Jiahui;Zimmer, Steven;O'Neill, Eric;Xie, Lei;Zhang, Ping
化学诱导的基因表达谱提供了生物系统中化学物质的关键信息，从而为药物发现提供了新的机会。尽管取得了成功，但利用基因表达的大规模分析受到时间和成本的限制。尽管提出了几种预测基因表达的方法，但它们只专注于归因和分类设置，限制了对药物发现的现实世界场景的应用。因此，提出了一个化学诱导基因表达排名(CIGER)框架，以针对一个更现实但更具挑战性的环境，在其中预测从头化学物质诱导的基因表达谱的总体排名。实验结果表明，Ciger在排序和分类指标上都明显优于现有的方法。此外，还提出了一种基于Ciger的药物筛选流水线，以确定耐药胰腺癌的潜在治疗方法。我们的预测得到了实验的验证，从而表明了Ciger在精密医学表型化合物筛选中的有效性。一种新的用于化学诱导基因表达排序的深度学习方法(Ciger)Ciger可以从化学结构中预测从头开始的化学物质的基因表达我们发现了治疗耐药胰腺癌的药物。近年来，一种基于表型的药物发现方法被证明在药物发现和精密医学中是有效的。然而，在实验上确定所有感兴趣的化学物质的化学诱导基因表达是不可行的，从而阻碍了基于基因表达的化合物筛选的大规模应用。因此，设计一种计算方法来生成任何化学物质的基因表达信息是至关重要的。我们提出了一种新的深度学习框架，称为化学诱导基因表达排名(CIGER)，用于根据从头化学物质的化学结构预测从头化学物质诱导的标志性基因表达谱(即基因排名)。利用Ciger，我们预测并实验验证了几种现有的药物可以提高对耐药胰腺癌的治疗反应。我们的结果证明了Ciger在实践中对精确药物发现的有效性。由于基因表达数据的稀疏性和低吞吐量，利用化学诱导的基因表达的药物再利用方法的能力是有限的。我们提出了一个深度学习框架，从化学结构预测从头化学物质的基因表达谱(即基因排名)，以及一个基于表型的药物再利用管道，用于从现有药物中寻找潜在的疾病治疗方法。胰腺癌的一个案例研究证明了我们的方法在实践中用于精确药物发现的有效性。
Chemical-induced gene expression profiles provide critical information of chemicals in a biological system, thus offering new opportunities for drug discovery. Despite their success, large-scale analysis leveraging gene expressions is limited by time and cost. Although several methods for predicting gene expressions were proposed, they only focused on imputation and classification settings, which have limited applications to real-world scenarios of drug discovery. Therefore, a chemical-induced gene expression ranking (CIGER) framework is proposed to target a more realistic but more challenging setting in which overall rankings in gene expression profiles induced by de novo chemicals are predicted. The experimental results show that CIGER significantly outperforms existing methods in both ranking and classification metrics. Furthermore, a drug screening pipeline based on CIGER is proposed to identify potential treatments of drug-resistant pancreatic cancer. Our predictions have been validated by experiments, thereby showing the effectiveness of CIGER for phenotypic compound screening of precision medicine. A new deep-learning method (CIGER) for chemical-induced gene expression ranking CIGER can predict gene expression for de novo chemicals from chemical structures We discovered drugs for the treatment of drug-resistant pancreatic cancer In recent years, a phenotype-based drug discovery approach using chemical-induced gene expressions has shown to be effective in drug discovery and precision medicine. However, it is not feasible to experimentally determine chemical-induced gene expressions for all available chemicals of interest, thereby hindering the application of gene expression-based compound screening on a large scale. Thus, it is crucial to design a computational approach that can generate gene expression information for any chemicals. We proposed a new, deep-learning framework named chemical-induced gene expression ranking (CIGER) to predict a landmark gene expression profile (i.e., gene ranking) induced by de novo chemicals based on their chemical structures. Leveraging CIGER, we predicted and experimentally validated that several existing drugs can increase the therapeutic response on drug-resistant pancreatic cancer. Our results demonstrated the effectiveness of CIGER for precision drug discovery in practice. The power of drug-repurposing methods leveraging chemical-induced gene expression is limited due to the sparseness and low throughput of the gene expression data. We proposed a deep-learning framework to predict gene expression profiles (i.e., gene ranking) for de novo chemicals from their chemical structures as well as a phenotype-based drug-repurposing pipeline for finding potential treatments for diseases from existing drugs. A case study for pancreatic cancer demonstrates the effectiveness of our method for precision drug discovery in practice.
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影响因子：
5.7
作者：
Fujii A;Masuda T;Iwata M;Tobo T;Wakiyama H;Koike K;Kosai K;Nakano T;Kuramitsu S;Kitagawa A;Sato K;Kouyama Y;Shimizu D;Matsumoto Y;Utsunomiya T;Ohtsuka T;Yamanishi Y;Nakamura M;Mimori K
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影响因子：
20.3
作者：
Hassane, Duane C.;Guzman, Monica L.;Jordan, Craig T.
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影响因子：
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作者：
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