Comparison of molecular factors to drug activities

分子因素与药物活性的比较

• 批准号: 10487249
• 负责人: William Reinhold
• 金额: $ 4.94万
• 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/10487249
• 关键词: Affective; Alkylating Agents; Antineoplastic Agents; Biological; Bleomycin; CDC2 gene; Cancer Patient; Cell Line; Characteristics; Cladribine; Clinical Trials; Complex; Computational Technique; Computer software; Core-Binding Factor; DNA Damage; DNA Methylation; DNA Repair; DNA Repair Gene; DNA Synthesis Inhibitors; DNA biosynthesis; Data; Databases; Disease; Drug Compounding; Drug Targeting; EGFR gene; Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor; Epigenetic Process; Erlotinib; Event; FDA approved; Gene Dosage; Genes; Genetic; Genomics; Goals; Knowledge; Lead; Linear Regressions; MDM2 gene; Machine Learning; Malignant Neoplasms; Manuscripts; Mathematics; Methodology; MicroRNAs; Modification; Molecular; Molecular Profiling; Outcome; Output; PTEN gene; Pathway interactions; Pattern; Pharmaceutical Preparations; Pharmacology; Pharmacology Study; Protein Isoforms; RAD52 gene; Ras/Raf; Resources; Structure; Subgroup; System; TP53 gene; Techniques; Thinness; Tissues; Topoisomerase; Topoisomerase-I Inhibitor; Transcript; Variant; Visualization; analytical method; base; cancer therapy; cell growth; data integration; drug action; genetic variant; infancy; inhibitor/antagonist; leukemia; mathematical methods; multiple datasets; mutational status; novel; response; web app

Cancer is a disease that emerges though genetic and epigenetic alterations that perturb molecular networks including 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. For most anti-cancer drugs, only partial knowledge exists about their detailed mechanism of action. Even where targets have been defined, as with FDA-approved and in-clinical-trial drugs, broader off-target effects are often poorly understood. Compound activity and genomic profiling data over well-characterized cell line panels allows one to attempt computational prediction of molecular drug response determinants. However, these computational techniques exist in a continuum of complexity, and each has its assets and shortcomings. We have and will use a combination of approaches ranging from the simple to the complex for these purposes. We employ Pearson's or Spearman's, or Matthew's correlation-based approaches that can identify genomic features within cell line profiles that are significantly correlated with a compound's activity profile. This methodology has demonstrated the ability to recognize robustly correlated parameters. Pearson's correlation is employed in our CellMiner "Pattern comparison", "Cross correlation", and "Genetic variant versus drug visualization", and utilize our "Cell line signature" and "Genetic variant summation" outputs. Our CellMinerCDB web-application uses Pearson's correlation in Compare Patterns and the scatter plot outputs. It also provides multi-variant analysis using either linear regression or the LASSO machine learning approach. In addition, we use state-of-the-art mathematical techniques in our manuscripts to compare our large drug compound database to our extensive network of molecular factors. Included in these forms of analysis may be gene and microRNA transcript expression, gene copy number, gene sequence variation, transcript isoform status, and DNA methylation status. Pathway enrichment analysis for those identified molecular factors with significantly correlated molecular profiles may be applied. The selection of which analytical method to use to identify biologically-related events is not settled or simplistic. It is influenced by the biological question being asked, the level of biological knowledge available, the data types available, and the strengths, weaknesses, and applicability of each mathematical approach. It remains a field in its infancy. Among our previous successfully identified list of molecular-pharmacological associations are i) SLFN11 transcript expression for topoisomerase 1 and 2 inhibitors, alkylating agents, and DNA synthesis inhibitors (PMID: 22927417), ii) the identification of Ro5-3335 as a lead compound for Core Binding Factor leukemias (PMID: 22912405), iii) TP53 mutational status and the activity of the MDM2-TP53 interaction inhibitor nutlin iv) a multifactorial combination of ERBB1 and 2 expression and RAS-RAF-PTEN mutational status for the activity of erlotinib (PMID: 23856246), v) ATAD5 mutational status for the DNA-damaging drugs bleomycin, zorbamycin, and peplomycin (PMID: 25758781) vi) genetic variants for the DNA replication and repair gene MUS81 with the DNA synthesis inhibitor cladribine (PMID: 26048278), vii) genetic variants for the DNA damage repair gene RAD52 for the DNA damaging ifosfomide (PMID: 25032700), CDK1, 20 transcript isoforms for the CDK inhibitor palbociclib (PMID: 31113817) and 46 diverse drug's activities for which the drug target is the same game gene whose molecular modification is correlated in a significant fashion (PMID: 32652468).

癌症是一种通过遗传和表观遗传改变而出现的疾病，这些改变扰乱了包括细胞生长、存活和分化在内的分子网络。为了开发更有针对性和更有效的癌症治疗方法，必须在这种网络化的系统级背景下了解和理解药物作用。对于大多数抗癌药物，只有部分知识存在关于其详细的作用机制。即使已经确定了目标，如FDA批准的和临床试验中的药物，更广泛的脱靶效应往往知之甚少。化合物活性和基因组分析数据在充分表征的细胞系面板允许一个尝试计算预测的分子药物反应决定因素。然而，这些计算技术存在于复杂的连续体中，并且每个都有其优点和缺点。我们已经并将使用从简单到复杂的各种方法来实现这些目的。我们采用Pearson或斯皮尔曼或Matthew的基于相关性的方法，这些方法可以鉴定与化合物活性谱显著相关的细胞系谱内的基因组特征。这种方法已经证明了识别鲁棒相关参数的能力。Pearson相关性用于我们的CellMiner "模式比较"、"交叉相关性"和"遗传变异与药物可视化"，并利用我们的"细胞系特征"和"遗传变异总和"输出。我们的CellMinerCDB网络应用程序在比较模式和散点图输出中使用Pearson相关性。它还使用线性回归或LASSO机器学习方法提供多变量分析。此外，我们在手稿中使用最先进的数学技术，将我们的大型药物化合物数据库与我们广泛的分子因子网络进行比较。这些形式的分析可以包括基因和microRNA转录本表达、基因拷贝数、基因序列变异、转录本同种型状态和DNA甲基化状态。可以应用对那些具有显著相关的分子谱的鉴定的分子因子的途径富集分析。选择哪种分析方法来识别生物相关事件尚未解决或过于简单。它受到生物学问题的影响，生物学知识的水平，可用的数据类型，以及每种数学方法的优点，缺点和适用性。它仍然是一个处于婴儿期的领域。在我们先前成功鉴定的分子药理学关联列表中，i）SLFN 11转录表达为拓扑异构酶1和2抑制剂、烷化剂和DNA合成抑制剂（PMID：22927417），ii）Ro5 - 3335作为核心结合因子白血病先导化合物的鉴定iii）TP53突变状态和MDM 2-TP53相互作用抑制剂nutlin的活性iv）ERBB 1和2表达以及RAS-RAF-PTEN突变状态对厄洛替尼活性的多因素组合（PMID：23856246），V）DNA损伤药物博来霉素、佐巴霉素和培洛霉素的ATAD 5突变状态（PMID：25758781）vi）DNA复制和修复基因MUS81的遗传变体与DNA合成抑制剂克拉屈滨（PMID：26048278），vii）DNA损伤修复基因RAD52的遗传变体，（PMID：25032700），CDK抑制剂palbociclib的CDK1，20转录物亚型（PMID：31113817）和46种不同的药物活性，其中药物靶标是相同的基因，其分子修饰以显著的方式相关（PMID：32652468）。