Reinhold William C, Elloumi Fathi, Varma Sudhir, Robert Jacques, Mills Gordon B, Pommier Yves
Developmental Therapeutic Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States of America.
Developmental Therapeutic Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States of America; General Dynamics Information Technology, Falls Church, VA 22042, United States of America.
Transl Oncol. 2020 Oct;13(10):100830. doi: 10.1016/j.tranon.2020.100830. Epub 2020 Jul 8.
Using the information from our CellMiner (https://discover.nci.nih.gov/cellminer/) and CellMinerCDB (https://discover.nci.nih.gov/cellminercdb/) web-based applications, we identified 3978 molecular events with significant links to pharmacological response for genes that are either targets, biomarkers, or have established causal linkage to drugs. Molecular events included DNA copy number, methylation and mutation; and transcript; and whole or phospho-protein expression for the NCI-60 human cancer cell lines. While all forms of molecular data were informative in some (gene-drug) pairings, the type of significantly linked molecular events was found to vary widely by drug. Some forms of molecular data were found to have more frequent significant correlation than others. Leading were phosphoproteins as measured by antibody (31%), followed by transcript as measured by microarray (16%), and total protein levels as measured by mass spectrometry or antibody (14%). All other measurements ranged between 5 and 11%. Data reliability was underscored by concordant results when using differing drugs with the same targets, as well as different measurements of the same molecular parameter. The significance of correlations of the various molecular parameters to the pharmacological responses provides functional indication of those parameters that are biologically relevant for each gene-drug pairing, as well as comparisons between measurement types.
利用我们基于网络的CellMiner（https://discover.nci.nih.gov/cellminer/）和CellMinerCDB（https://discover.nci.nih.gov/cellminercdb/）应用程序中的信息，我们确定了3978个与药理学反应有显著关联的分子事件，这些事件涉及作为靶点、生物标志物或已确定与药物有因果联系的基因。分子事件包括DNA拷贝数、甲基化和突变；转录本；以及NCI - 60人类癌细胞系的全蛋白或磷酸化蛋白表达。虽然所有形式的分子数据在某些（基因 - 药物）配对中都有参考价值，但发现与药物显著相关的分子事件类型差异很大。发现某些形式的分子数据比其他数据具有更频繁的显著相关性。领先的是通过抗体检测的磷酸化蛋白（31%），其次是通过微阵列检测的转录本（16%），以及通过质谱或抗体检测的总蛋白水平（14%）。所有其他测量值在5%至11%之间。当使用针对相同靶点的不同药物以及对相同分子参数进行不同测量时，结果的一致性强调了数据的可靠性。各种分子参数与药理学反应相关性的显著性为那些与每个基因 - 药物配对具有生物学相关性的参数提供了功能指示，同时也对测量类型进行了比较。
