Clustering of the drug activities of the NCI-60 cancerous cell lines

NCI-60 癌细胞系药物活性的聚类

• 批准号: 8763783
• 负责人: William Reinhold
• 金额: $ 6.02万
• 依托单位: DIVISION OF BASIC SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8763783
• 关键词: Algorithms; Alkylating Agents; Antineoplastic Agents; Cancer cell line; Cancerous; Cell Line; Clinical Trials; Collaborations; DNA Damage; Data; Databases; Disease; Drug Compounding; Drug Targeting; Drug effect disorder; Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor; Epigenetic Process; FDA approved; Foundations; Gene Dosage; Genes; Genetic; Goals; Grouping; Learning; Machine Learning; Malignant Neoplasms; Methods; Mining; Modeling; Molecular; Molecular Profiling; Pathway interactions; Pharmaceutical Preparations; Publications; Resources; Specificity; Structure; System; Techniques; Time; Topoisomerase-I Inhibitor; Transcript; Variant; base; cancer therapy; cell growth; drug structure; drug synthesis; inhibitor/antagonist; kinase inhibitor; novel; response; tool; web site

For most anti-cancer drugs, relatively little is known about the detailed mechanism of action. Even where targets have been defined, as with FDA-approved and in-clinical-trial drugs, broader off-target effects remain poorly understood. Cancer is a disease that emerges though genetic and epigenetic alterations that perturb molecular networks controlling cell growth, survival, and differentiation. To develop more targeted and efficacious cancer treatments, it is essential to situate and understand drug actions in this networked, systems-level context. We have combined state-of-the-art techniques to organize our large drug compound database (20,602 compounds) into a network of coherent clusters of compounds sharing similar response profiles over the NCI-60 cancer cell lines. The resulting network is highly concordant with the existing understanding of compound class relationships, grouping known mechanism of action drugs into coherent clusters, together with novel compounds sharing similar response profiles. At the same time, the drug cluster network reveals numerous clusters of response profile-related drugs with little or no relation to clusters enriched for known mechanism of action drugs. These drug compound clusters may represent agents that are active against novel targets and pathways. To characterize these potential target pathways, we will applied two complementary analysis approaches to representative 'hub' compounds from each cluster. First, the activity profile of each hub compound will be correlated with molecular profiling data associated with the NCI-60 cell lines (transcript expression, gene copy number, and gene sequence variants), followed by a pathway enrichment analysis for genes with significantly correlated molecular profiles. Second, the elastic net regression algorithm (a machine learning approach) will be applied to learn robust, multifactorial predictors of drug response using the aforementioned molecular profiling data. We will establish the suitability of these approaches by presenting several 'positive control' results based on clusters enriched for known kinase inhibitors and DNA damaging drugs. From this foundation, we will present predicted target pathways and response-related gene sets for several entirely uncharacterized compound clusters. These results will be additionally focused using compound structure-based methods to characterize drug cluster target specificity. Integrating drug structure, activity and molecular profiling data over the widely studied NCI-60 cancer cell lines, we will be able to organize a large drug compound database into functionally related groups, providing a foundational resource for further, focused studies.

对于大多数抗癌药物，人们对其详细的作用机制知之甚少。即使目标已经确定，例如 FDA 批准的和临床试验药物，更广泛的脱靶效应仍然知之甚少。癌症是一种通过遗传和表观遗传改变扰乱控制细胞生长、存活和分化的分子网络而出现的疾病。为了开发更有针对性和更有效的癌症治疗方法，必须在这个网络化、系统级的背景下定位和理解药物的作用。我们结合了最先进的技术，将我们的大型药物化合物数据库（20,602 种化合物）组织成一个连贯的化合物簇网络，这些化合物对 NCI-60 癌细胞系具有相似的反应特征。由此产生的网络与现有对化合物类别关系的理解高度一致，将已知的作用药物机制分组为连贯的簇，以及具有相似反应特征的新化合物。同时，药物簇网络揭示了许多与反应谱相关的药物簇，与富含已知作用机制药物的簇很少或没有关系。这些药物化合物簇可能代表对新靶点和途径具有活性的药物。为了表征这些潜在的目标途径，我们将对每个簇中的代表性“中心”化合物应用两种互补的分析方法。首先，每个中心化合物的活性谱将与 NCI-60 细胞系相关的分子谱数据（转录本表达、基因拷贝数和基因序列变异）相关联，然后对具有显着相关分子谱的基因进行通路富集分析。其次，弹性网络回归算法（一种机器学习方法）将用于使用上述分子分析数据来学习药物反应的稳健、多因素预测因子。我们将通过展示一些基于已知激酶抑制剂和 DNA 损伤药物富集的簇的“阳性对照”结果来确定这些方法的适用性。在此基础上，我们将为几个完全未表征的化合物簇提供预测的靶标途径和反应相关基因集。这些结果将进一步使用基于化合物结构的方法来表征药物簇靶点特异性。通过整合广泛研究的 NCI-60 癌细胞系的药物结构、活性和分子谱数据，我们将能够将大型药物化合物数据库组织成功能相关的组，为进一步的重点研究提供基础资源。