Comparison of molecular factors to drug activities

分子因素与药物活性的比较

• 批准号: 10703059
• 负责人: William Reinhold
• 金额: $ 11.83万
• 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/10703059
• 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; leukemia; mathematical methods; multiple datasets; mutational status; novel; response; translational applications; 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).

癌症是一种疾病，它会出现遗传和表观遗传学改变，这些疾病扰乱了分子网络，包括细胞生长，生存和分化。为了开发更有针对性和有效的癌症治疗，在这种网络，系统级别的环境中进行局部和了解药物作用至关重要。对于大多数抗癌药物，只有部分知识就其详细的作用机理存在。即使定义了目标，就像FDA批准和临床上的审判药物一样，更广泛的脱靶效应通常也很熟悉。在特征良好的细胞系板上的复合活性和基因组分析数据允许人们尝试对分子药物反应决定因素的计算预测。但是，这些计算技术存在于复杂性的连续性中，并且每个计算技术都有其资产和缺点。我们已经并且将使用从简单到复杂的方法结合使用这些目的的方法。我们采用Pearson或Spearman的或Matthew基于相关的方法，这些方法可以识别与化合物的活性概况显着相关的细胞系概要组中的基因组特征。该方法证明了能够识别牢固相关参数的能力。 Pearson的相关性用于我们的CellMiner“模式比较”，“跨相关”和“遗传变异与药物可视化”，并利用我们的“细胞系签名”和“遗传变体求和”输出。我们的CellMinerCDB Web应用程序在比较模式和散点图输出中使用Pearson的相关性。它还使用线性回归或套索机器学习方法提供了多变量分析。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广泛的分子因素网络进行比较。这些分析形式包括基因和microRNA转录本表达，基因拷贝数，基因序列变异，转录本同工型状态和DNA甲基化状态。可以应用具有显着相关分子谱的分子因子的途径富集分析。选择用于识别生物学相关事件的分析方法的选择不是解决或简单的。它受到所提出的生物学问题的影响，可用的生物学知识水平，可用的数据类型以及每种数学方法的优势，劣势和适用性。它仍然是其起步阶段的领域。 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 MDM2-TP53相互作用抑制剂NUTLIN IV的突变状态和活性）ERBB1和2表达和RAS-RAF-RAF-PTEN突变状态的多因素组合（PMID：PMID：23856246），v）atad5 atad5 atad5突变状态的DNA突变状态 (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抑制剂PALBOCICLIB（PMID：31113817）和46种药物的活性是同一游戏基因，其分子修饰以很大的方式相关（PMID：32652468）。