Liu Jun, Bandyopadhyay Nirmalya, Ranka Sanjay, Baudis M, Kahveci Tamer
Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA.
Bioinformatics. 2009 Sep 1;25(17):2208-15. doi: 10.1093/bioinformatics/btp365. Epub 2009 Jun 15.
One of the mutational processes that has been monitored genome-wide is the occurrence of regional DNA copy number alterations (CNAs), which may lead to deletion or over-expression of tumor suppressors or oncogenes, respectively. Understanding the relationship between CNAs and different cancer types is a fundamental problem in cancer studies.
This article develops an efficient method that can accurately model the progression of the cancer markers and reconstruct evolutionary relationship between multiple types of cancers using comparative genomic hybridization (CGH) data. Such modeling can lead to better understanding of the commonalities and differences between multiple cancer types and potential therapies. We have developed an automatic method to infer a graph model for the markers of multiple cancers from a large population of CGH data. Our method identifies highly related markers across different cancer types. It then builds a directed acyclic graph that shows the evolutionary history of these markers based on how common each marker is in different cancer types. We demonstrated the use of this model in determining the importance of markers in cancer evolution. We have also developed a new method to measure the evolutionary distance between different cancers based on their markers. This method employs the graph model we developed for the individual markers to measure the distance between pairs of cancers. We used this measure to create an evolutionary tree for multiple cancers. Our experiments on Progenetix database show that our markers are largely consistent to the reported hot-spot imbalances and most frequent imbalances. The results show that our distance measure can accurately reconstruct the evolutionary relationship between multiple cancer types.
全基因组范围内监测到的突变过程之一是区域DNA拷贝数改变（CNA）的发生，这可能分别导致肿瘤抑制基因或癌基因的缺失或过表达。了解CNA与不同癌症类型之间的关系是癌症研究中的一个基本问题。
本文开发了一种高效方法，该方法可以使用比较基因组杂交（CGH）数据准确模拟癌症标志物的进展，并重建多种癌症类型之间的进化关系。这种建模可以更好地理解多种癌症类型之间的共性和差异以及潜在的治疗方法。我们开发了一种自动方法，从大量CGH数据中推断多种癌症标志物的图模型。我们的方法识别不同癌症类型之间高度相关的标志物。然后构建一个有向无环图，根据每个标志物在不同癌症类型中的常见程度显示这些标志物的进化历史。我们展示了该模型在确定癌症进化中标志物重要性方面的应用。我们还开发了一种基于不同癌症标志物来测量它们之间进化距离的新方法。该方法使用我们为单个标志物开发的图模型来测量癌症对之间的距离。我们使用这种测量方法为多种癌症创建了一棵进化树。我们在Progenetix数据库上的实验表明，我们的标志物在很大程度上与报道的热点失衡和最常见失衡一致。结果表明，我们的距离测量方法可以准确重建多种癌症类型之间的进化关系。
