Comparison of molecular factors to drug activities

癌症是一种通过基因和表观遗传改变而出现的疾病，这些改变扰乱了包括细胞生长、生存和分化在内的分子网络。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，人们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。为了这些目的，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，可以在细胞系图谱中识别与化合物活性图谱显著相关的基因组特征。这种方法已经证明了识别强相关参数的能力。皮尔逊相关性被用于我们的CellMiner“模式比较”、“交叉相关”和“基因变异与药物可视化”，并利用我们的“细胞系签名”和“基因变异求和”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了皮尔逊相关性。它还使用线性回归或套索机器学习方法提供多变量分析。此外，我们在我们的手稿中使用最先进的数学技术来比较我们的大型药物化合物数据库和我们广泛的分子因子网络。这些形式的分析可能包括基因和microRNA转录本的表达、基因拷贝数、基因序列变异、转录本异构体状态和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。它受到所提出的生物学问题、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点、缺点和适用性的影响。它仍然是一个处于初级阶段的领域。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、烷化剂和脱氧核糖核酸合成抑制剂(pID：22927417)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(pID：22912405)的鉴定；iii)tp53突变状态和mdm2-tp53相互作用抑制物的活性；iv)erbB1和2表达和ras-raf-pten突变状态的多因素组合对erlotinib(pID：23856246)的活性；v)DNA损伤药物博莱霉素、佐布霉素、甲状旁腺素的ATAD5突变状态。其中包括：DNA复制和修复基因MUS81与DNA合成抑制剂cladriine(PMID：26048278)的基因变体；vii)DNA损伤修复基因RAD52的基因变体(PMID：25032700)；CDK 1，CDK抑制剂Palbociclib的20个转录本异构体(PMID：31113817)；以及46种不同药物的活性，药物靶标是分子修饰显著相关的同一个游戏基因(PMID：32652468)。

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).