Comparison of molecular factors to drug activities.

分子因素与药物活性的比较。

• 批准号: 8938487
• 负责人: William Reinhold
• 金额: $ 8.95万
• 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/8938487
• 关键词: Affect; Affective; Algorithms; Alkylating Agents; Antineoplastic Agents; Bioinformatics; Biological; Bleomycin; Cancer Patient; Cancer cell line; Cell Line; Cladribine; Clinical Trials; Complex; Computational Technique; Computer software; Core-Binding Factor; DNA Damage; DNA Repair; DNA Repair Gene; DNA Synthesis Inhibitors; DNA biosynthesis; Data; Databases; Disease; Drug Compounding; Drug effect disorder; EGFR gene; Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor; Epigenetic Process; Erlotinib; Event; FDA approved; Gene Dosage; Genes; Genetic; Genomics; Goals; Imagery; Knowledge; Lead; MDM2 gene; Machine Learning; Malignant Neoplasms; Methodology; MicroRNAs; Molecular; Molecular Profiling; Outcome; PTEN gene; Pathway interactions; Pattern; Pharmaceutical Preparations; Resources; Structure; System; TP53 gene; Techniques; Topoisomerase; Topoisomerase-I Inhibitor; Transcript; Variant; analytical method; base; cancer therapy; cell growth; disorder subtype; exome sequencing; genetic variant; inhibitor/antagonist; leukemia; mathematical methods; novel; response; tool

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. For most anti-cancer drugs, only partial knowledge exists 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 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. We employ Pearson's or Spearman's, or Matthew's correlation-based approaches that can identify genomic features with cell line profiles that are significantly correlated with a compounds activity profile. This methodology has demonstrated the ability to recognize robustly correlated parameters. They are employed in our CellMiner "Pattern comparison", "Cross correlation", "Genetic variant summation", "Genetic variant versus drug visualization", and "Cell line signature" tools. In addition, we use state-of-the-art mathematical techniques to compare our large drug compound database to our extensive network of molecular factors using the NCI-60 cancer cell lines. Included are the elastic net regression algorithm (a machine learning approach) to identify robust, cumulative predictors of drug response. Included in this analysis are gene and microRNA transcript expression, gene copy number, and gene sequence variation. 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 both the biological question being asked, and the strengths and weaknesses of each mathematical approach. It remains a challenging endeavor, especially because the factors that affect drug activity are largely multifactorial Among our previous successfully identified list of molecular-pharmacological associations are i) SLFN11 transcript expression for topoisomerase 1 and 2 inhibitors and alkylating agents, ii) the identification of Ro5-3335 as a lead compound for Core Binding Factor leukemias 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 v) ATAD5 mutational status for the DNA-damaging drugs bleomycin, zorbamycin, and peplomycin vi) genetic variants for the DNA replication and repair gene MUS81 with the DNA synthesis inhibitor cladribine, and vii) genetic variants for the DNA damage repair gene RAD52 for the DNA damaging ifosfomide (Zoppoli et al, PNAS, 2012; Cunningham et al, PNAS, 2012; Abaan, Cancer Res, 2013; Reinhold et al, PLoS ONE, 2014).

癌症是一种疾病，它会出现遗传和表观遗传学改变，该疾病会扰动分子网络控制细胞生长，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，只有有关详细作用机理的部分知识。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的组合。我们采用Pearson或Spearman的或Matthew基于相关的方法，这些方法可以识别具有与化合物活性概况显着相关的细胞系特征的基因组特征。该方法证明了能够识别牢固相关参数的能力。它们在我们的Cellminer“模式比较”，“跨相关”，“遗传变异总和”，“遗传变异与药物可视化”和“细胞系签名”工具中使用。此外，我们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使用NCI-60癌细胞系的广泛分子因素网络进行比较。其中包括弹性净回归算法（一种机器学习方法），以识别药物反应的稳健，累积预测指标。该分析中包括基因和microRNA转录本，基因拷贝数和基因序列变异。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。它都受到所询问的生物学问题以及每种数学方法的优势和缺点的影响。这仍然是一项具有挑战性的努力，尤其是因为影响药物活性的因素在我们以前的成功识别的分子 - 药理学关联列表中在很大程度上是多因素的，i）topoisomerase 1和2抑制剂的SLFN11转录物表达表达和烷基化因子和烷基化因子和烷基化药物的RO5-3335 AS COLINGIAN COMPININC II AS COMPININIAN COMPIND FERMING II AS COMPININIAN II CYMIAS COMING II）的鉴定，并鉴定出来。 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 v) ATAD5 mutational status for the DNA-damaging drugs bleomycin, zorbamycin, and peplomycin vi) genetic variants for the DNA replication and repair gene MUS81 with the DNA合成抑制剂cladribine和VII）DNA损伤修复基因RAD52的遗传变异（用于DNA损伤ifosfomide）（Zoppoli等，PNAS，PNAS，2012; Cunningham等，PNAS，PNAS，2012; Abaan，2013; Abaan，Cancer res，2013; Rein et allohl，Plos，Plos，Plos One，2014年）。