Shroff Sanaya, Zhang Jie, Huang Kun
Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853 (USA).
Biomedical Informatics, The Ohio State University, Columbus, OH 43210 (USA).
AMIA Jt Summits Transl Sci Proc. 2016 Jul 20;2016:32-41. eCollection 2016.
Responsiveness to drugs is an important concern in designing personalized treatment for cancer patients. Currently genetic markers are often used to guide targeted therapy. However, deeper understanding of the molecular basis for drug responses and discovery of new predictive biomarkers for drug sensitivity are much needed. In this paper, we present a workflow for identifying condition-specific gene co-expression networks associated with responses to the tyrosine kinase inhibitor, Erlotinib, in lung adenocarcinoma cell lines using data from the Cancer Cell Line Encyclopedia by combining network mining and statistical analysis. Particularly, we have identified multiple gene modules specifically co-expressed in the drug responsive cell lines but not in the unresponsive group. Interestingly, most of these modules are enriched on specific cytobands, suggesting potential copy number variation events on these loci. Our results therefore imply that there are multiple genetic loci with copy number variations associated with the Erlotinib responses. The existence of CNVs in these loci is also confirmed in lung cancer tissue samples using the TCGA data. Since these structural variations are inferred from functional genomics data, these CNVs are functional variations. These results suggest the condition specific gene co- expression network mining approach is an effective approach in predicting candidate biomarkers for drug responses.
药物反应性是为癌症患者设计个性化治疗时的一个重要关注点。目前，基因标志物常被用于指导靶向治疗。然而，迫切需要更深入地了解药物反应的分子基础，并发现新的药物敏感性预测生物标志物。在本文中，我们通过结合网络挖掘和统计分析，利用癌症细胞系百科全书的数据，提出了一种工作流程，用于识别与肺腺癌细胞系中对酪氨酸激酶抑制剂厄洛替尼的反应相关的条件特异性基因共表达网络。特别地，我们鉴定出了多个在药物反应性细胞系中特异性共表达，但在无反应组中不共表达的基因模块。有趣的是，这些模块中的大多数在特定的细胞带中富集，这表明这些位点可能存在拷贝数变异事件。因此，我们的结果意味着存在多个与厄洛替尼反应相关的具有拷贝数变异的基因位点。使用TCGA数据在肺癌组织样本中也证实了这些位点中CNV的存在。由于这些结构变异是从功能基因组学数据推断出来的，所以这些CNV是功能变异。这些结果表明，条件特异性基因共表达网络挖掘方法是预测药物反应候选生物标志物的有效方法。
