Novel statistical approaches for the characterization of genomic structural rearrangements in cancers from high-throughput genome sequencing data

结构改变是一类重要的基因突变，由于DNA片段的丢失，获得或重排而产生。在癌症中，这些结构改变可以改变或破坏重要基因及其参与的生物系统的正常功能，这可能有助于疾病的发生、进展，随后赋予对治疗的抗性，并有助于疾病向其他系统的传播。这些结构改变可以使用高通量基因组测序在全基因组范围内以单个DNA核苷酸的理论分辨率检测。各种国际合作努力使广泛的目录的结构改变，以建立各种癌症，使这些结构变异的影响，现在可以调查一起研究其他遗传异常的疾病病因，预后和治疗效果。然而，值得注意的是，尽管我们已经分析了许多癌症，但新癌症标本的结构分析与研究的第一个癌症没有什么不同。尽管许多结构变异在癌症中是复发性的，但现有的知识没有得到利用。该项目的目的是基于现有数据资源构建癌症相关结构改变的文库，并开发先进的统计算法，利用这些文库从新患者的基因组测序数据中识别和分类结构变异。这些算法将使我们能够识别患者共有的结构异常的复发模式，以及这些模式是否与特定的遗传和临床特征有关，并跟踪从同一患者获得多个标本的疾病进展。这些算法可以使得能够识别可能适合于新型靶向疗法或治疗方案的患者。

Structural alterations are an important class of genetic mutations that arise due to the loss, gain or rearrangement of DNA segments. In cancer, these structural alterations can alter or disrupt the normal function of important genes and the biological systems in which they participate that may contribute to disease initiation, progression and later confer resistance to therapy and contribute to the spread of the disease to other systems. These structural alterations can be detected using high-throughput genome sequencing on a genome-wide scale at the theoretical resolution of a single DNA nucleotide. Various international collaborative efforts have enabled extensive catalogues of structural alterations to be built for various cancers and so that the effect of these structural variations can now investigated alongside other genetic abnormalities in studies of disease etiology, prognosis and therapeutic effectiveness. Yet, it is interesting to note though that despite the fact that we have analysed many cancers, the structural analysis of new cancer specimens is conducted no differently from the very first cancer that was studied. Existing knowledge is not utilised in spite of the fact that many structural variants are recurrent in cancer. The aim of this project is to construct libraries of cancer-related structural alterations based upon existing data resources and to develop advanced statistical algorithms that utilise these libraries to identify and classify structural variants from genome sequencing data of new patients. These algorithms will allow us to identify recurring patterns of structural abnormalities that are shared by patients and whether these relate to particular genetic and clinical features as well as track disease progression where multiple specimens are obtained from the same patient. These algorithms may enable patients to be identified who maybe suitable for novel targeted therapies or treatment regimes.