Comparison of molecular factors to drug activities

分子因素与药物活性的比较

• 批准号: 9556847
• 负责人: William Reinhold
• 金额: $ 14.22万
• 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/9556847
• 关键词: 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 Methylation; 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; Mathematics; Methodology; MicroRNAs; Molecular; Molecular Profiling; Outcome; PTEN gene; Pathway interactions; Pattern; Pharmaceutical Preparations; Pharmacology; Pharmacology Study; Prediction of Response to Therapy; RAD52 gene; Ras/Raf; Resources; Structure; System; TP53 gene; Techniques; Thinness; Topoisomerase; Topoisomerase-I Inhibitor; Transcript; Variant; analytical method; base; cancer therapy; cell growth; disorder subtype; exome sequencing; genetic variant; genomic profiles; infancy; inhibitor/antagonist; leukemia; mathematical methods; mutational status; 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, gene sequence variation, and soon 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, and the strengths, weaknesses, and applicability of each mathematical approach. It remains a field in it's 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 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 vi) MUS81 with the DNA synthesis inhibitor cladribine, and vii) genetic variants for the DNA damage repair gene RAD52 for the DNA damaging ifosfomide.

癌症是一种通过基因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，至关重要的是要在这种网络的、系统级别的背景下定位和理解药物的作用。对于大多数抗癌药物，对其详细的作用机制只有部分了解。即使在已经确定靶点的地方，如FDA批准的和临床试验中的药物，更广泛的非靶点效应通常也很难理解。细胞系面板上的化合物活性和基因组图谱数据使人们能够尝试对分子药物反应决定因素进行计算预测。然而，这些计算技术存在于一个复杂的连续体中，每种技术都有其优点和缺点。我们已经并将使用从简单到复杂的各种方法的组合。我们使用皮尔逊或斯皮尔曼或马修的基于相关性的方法，可以通过与化合物活性谱显著相关的细胞系特征来识别基因组特征。这种方法已经证明了识别强相关参数的能力。它们被用于我们的CellMiner“模式比较”、“交叉相关”、“遗传变异求和”、“遗传变异与药物可视化”和“细胞系签名”工具。此外，我们使用最先进的数学技术，使用NCI-60癌细胞株将我们的大型药物化合物数据库与我们广泛的分子因素网络进行比较。其中包括弹性网络回归算法(一种机器学习方法)，以确定稳健的、累积的药物反应预测因素。这一分析包括基因和microRNA转录本的表达、基因拷贝数、基因序列变异，以及很快的DNA甲基化状态。对于那些与分子谱显著相关的已鉴定分子因子的路径富集化分析可能会被应用。选择哪种分析方法来识别与生物有关的事件并不是既定的或简单化的。它受所提出的生物学问题、可获得的生物学知识水平以及每种数学方法的优点、缺点和适用性的影响。它仍然是一个处于起步阶段的领域。在我们之前成功确定的分子-药理学关联列表中包括：i)拓扑异构酶1和2抑制剂、烷化剂和DNA合成抑制剂的SLFN11转录本表达；ii)Ro5-3335作为核心结合因子白血病的先导化合物的鉴定；iii)TP53突变状态和MDM2-TP53相互作用抑制因子的活性；iv)ErbB1和2的表达与ras-raf-PTEN突变状态的多因素组合对erlotinib的活性的影响v)DNA损伤药物博莱霉素、佐布霉素和培阳霉素的ATAD5突变状态vi)DNA复制和修复基因的遗传变体vi)DNA合成抑制剂clritine、zorbamcin和peplcin的MUS81和vii)DNA损伤修复基因RAD52的DNA损伤异福胺的遗传变异。