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

利用高通量基因组测序数据表征癌症基因组结构重排的新统计方法

• 批准号: MR/L001411/1
• 负责人: Christopher Yau
• 金额: $ 43.99万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FL001411%2F1
• 关键词: Novel; statistical; approaches; characterization; genomic

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.

结构改变是由于DNA片段的丢失、获得或重排而引起的一类重要的基因突变。在癌症中，这些结构变化可以改变或扰乱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启动、进展和后来对治疗的抵抗，并有助于疾病向其他系统传播。这些结构的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进行检测。各种国际合作努力使得能够为各种癌症建立广泛的结构变化目录，以便现在可以在疾病病因、预后和治疗效果的研究中与其他基因异常一起研究这些结构变化的影响。然而，有趣的是，尽管我们分析了许多癌症，但对新癌症样本的结构分析与最初研究的癌症没有什么不同。尽管许多结构变异在癌症中反复出现，但现有的知识并未得到利用。该项目的目的是在现有数据资源的基础上构建与癌症相关的结构变化的库，并开发先进的统计算法，利用这些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反复模式，以及这些模式是否与特定的遗传和临床特征有关，以及在从同一患者获得多个样本的情况下跟踪疾病进展。这些算法可能使患者能够被识别出可能适合新的靶向治疗或治疗方案的患者。

项目成果

A descriptive marker gene approach to single-cell pseudotime inference.

• DOI: 10.1093/bioinformatics/bty498
• 发表时间: 2019-01-01
• 期刊: Bioinformatics (Oxford, England)
• 作者: Campbell KR;Yau C
• 通讯作者: Yau C

Order under uncertainty: robust differential expression analysis using probabilistic models for pseudotime inference

不确定性下的顺序：使用伪时间推理的概率模型进行稳健的差异表达分析

• DOI: 10.1101/047365
• 发表时间: 2016
• 作者: Campbell K

Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference.

• DOI: 10.1371/journal.pcbi.1005212
• 发表时间: 2016-11
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Campbell KR;Yau C
• 通讯作者: Yau C

Probabilistic inference of bifurcations in single-cell data using a hierarchical mixture of factor analysers

使用层次混合因子分析器对单细胞数据中的分岔进行概率推断

• DOI: 10.1101/076547
• 发表时间: 2016
• 作者: Campbell K

switchde: inference of switch-like differential expression along single-cell trajectories.

• DOI: 10.1093/bioinformatics/btw798
• 发表时间: 2017-04-15
• 期刊: Bioinformatics (Oxford, England)
• 作者: Campbell KR;Yau C
• 通讯作者: Yau C