Ochs Michael F, Rink Lori, Tarn Chi, Mburu Sarah, Taguchi Takahiro, Eisenberg Burton, Godwin Andrew K
Division of Oncology Biostatistics and Bioinformatics, Johns Hopkins University, Baltimore, Maryland 21205, USA.
Cancer Res. 2009 Dec 1;69(23):9125-32. doi: 10.1158/0008-5472.CAN-09-1709. Epub 2009 Nov 10.
Cell signaling plays a central role in the etiology of cancer. Numerous therapeutics in use or under development target signaling proteins; however, off-target effects often limit assignment of positive clinical response to the intended target. As direct measurements of signaling protein activity are not generally feasible during treatment, there is a need for more powerful methods to determine if therapeutics inhibit their targets and when off-target effects occur. We have used the Bayesian Decomposition algorithm and data on transcriptional regulation to create a novel methodology, Differential Expression for Signaling Determination (DESIDE), for inferring signaling activity from microarray measurements. We applied DESIDE to deduce signaling activity in gastrointestinal stromal tumor cell lines treated with the targeted therapeutic imatinib mesylate (Gleevec). We detected the expected reduced activity in the KIT pathway, as well as unexpected changes in the p53 pathway. Pursuing these findings, we have determined that imatinib-induced DNA damage is responsible for the increased activity of p53, identifying a novel off-target activity for this drug. We then used DESIDE on data from resected, post-imatinib treatment tumor samples and identified a pattern in these tumors similar to that at late time points in the cell lines, and this pattern correlated with initial clinical response. The pattern showed increased activity of ETS domain-containing protein Elk-1 and signal transducers and activators of transcription 3 transcription factors, which are associated with the growth of side population cells. DESIDE infers the global reprogramming of signaling networks during treatment, permitting treatment modification that leverages ongoing drug development efforts, which is crucial for personalized medicine.
细胞信号传导在癌症病因学中起着核心作用。许多正在使用或正在研发的治疗方法都以信号蛋白为靶点；然而，脱靶效应往往限制了将积极的临床反应归因于预期靶点。由于在治疗过程中直接测量信号蛋白活性通常不可行，因此需要更强大的方法来确定治疗方法是否抑制其靶点以及何时发生脱靶效应。我们使用贝叶斯分解算法和转录调控数据创建了一种新方法，即信号测定差异表达法（DESIDE），用于从微阵列测量中推断信号活性。我们应用DESIDE来推断用靶向治疗药物甲磺酸伊马替尼（格列卫）处理的胃肠道间质瘤细胞系中的信号活性。我们检测到KIT通路中预期的活性降低，以及p53通路中意外的变化。基于这些发现，我们确定伊马替尼诱导的DNA损伤是p53活性增加的原因，从而确定了该药物一种新的脱靶活性。然后，我们将DESIDE应用于伊马替尼治疗后切除的肿瘤样本数据，并在这些肿瘤中发现了一种与细胞系后期时间点相似的模式，这种模式与初始临床反应相关。该模式显示含ETS结构域的蛋白Elk-1以及信号转导和转录激活因子3转录因子的活性增加，这些与侧群细胞的生长有关。DESIDE可推断治疗期间信号网络的整体重编程，允许进行治疗调整，利用正在进行的药物研发成果，这对个性化医疗至关重要。
