Comparison of molecular factors to drug activities

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，在这种网络化的系统级背景下定位和理解药物作用是至关重要的。对于大多数抗癌药物，对其详细的作用机制只有部分的了解。即使已经确定了靶标，如fda批准的和临床试验药物，更广泛的脱靶效应往往也知之甚少。经过充分表征的细胞系面板上的化合物活性和基因组分析数据允许人们尝试计算预测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，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方法组合。我们采用Pearson’s或Spearman’s或Matthew’s基于相关性的方法，可以识别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。它们被用于我们的CellMiner“模式比较”、“相互关联”、“遗传变异汇总”、“遗传变异与药物可视化”和“细胞系签名”工具中。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。其中包括弹性网络回归算法（一种机器学习方法），用于识别药物反应的鲁棒累积预测因子。该分析包括基因和microRNA转录表达、基因拷贝数、基因序列变异和DNA甲基化状态。对于鉴定出的具有显著相关分子谱的分子因子，可以进行途径富集分析。选择哪一种分析方法来识别生物相关事件是不确定的或简单的。它受到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。在我们之前成功鉴定的分子药理学关联列表中，i)拓扑异构酶1和2抑制剂、烷基化剂的SLFN11转录物表达；和DNA合成抑制剂ii)鉴定Ro5-3335作为核心结合因子白血病的先导化合物iii) TP53突变状态和MDM2-TP53相互作用抑制剂nutlin的活性iv) ERBB1和2表达的多因子组合和RAS-RAF-PTEN的突变状态erlotinib的活性v) ATAD5的突变状态DNA损伤药物博来霉素，佐巴霉素，vi) DNA复制和修复基因的遗传变异vi)具有DNA合成抑制剂cladribine的MUS81，以及vii) DNA损伤修复基因RAD52的遗传变异。

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.