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).

癌症是一种通过遗传和表观遗传改变而出现的疾病，这些改变扰乱了控制细胞生长、存活和分化的分子网络。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。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。它们用于我们的CellMiner“模式比较”、“交叉相关”、“遗传变异总和”、“遗传变异与药物可视化”和“细胞系特征”工具。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子网络进行比较。包括弹性网络回归算法（一种机器学习方法），以识别药物反应的稳健累积预测因子。该分析包括基因和microRNA转录本表达、基因拷贝数和基因序列变异。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。它既受到生物学问题的影响，也受到每种数学方法的优缺点的影响。这仍然是一个具有挑战性的奋进，特别是因为影响药物活性的因素在很大程度上是多因素的。在我们先前成功鉴定的分子药理学关联列表中，i）SLFN 11转录物表达为拓扑异构酶1和2抑制剂和烷化剂，ii）Ro 5 -3335作为核心结合因子白血病的先导化合物的鉴定TP 53相互作用抑制剂nutlin iv）ERBB 1和2表达和RAS-RAF-PTEN突变状态对厄洛替尼活性的多因素组合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vi）DNA复制和修复基因MUS 81的遗传变体与DNA合成抑制剂克拉屈滨，和vii）DNA损伤性异磷胺的DNA损伤修复基因RAD 52的遗传变体（Zoppoli等，PNAS，2012; Cunningham等，PNAS，2012; Abaan，Cancer Res，2013; Reinhold等，PLoS ONE，2014）。