Delineating Heterogeneous Structural Complexity in Cancer Genomes

描述(由申请人提供)：下一代测序(NGS)使积累在癌症基因组中的体细胞异常能够进行常规的、系统的分析。许多功能突变是结构性的，涉及核苷酸序列的缺失、复制、易位、插入或倒置。检测这些结构变异从根本上是具有挑战性的，因为癌症基因组可能有大量的改变方式，并且存在广泛的重复，阻碍了短读取的准确比对。此外，克隆的异质性常常使结构的复杂性变得复杂，即在肿瘤标本中含有异质像差的混合细胞群体，这导致了不同的结构和拷贝数分布。这些问题对开发实用的计算工具提出了前所未有的挑战，这些工具可以用来识别结构变体的存在并阐明其功能和临床意义。为了充分利用NGS的力量并促进个性化医学的发展，我们建议开发一套新的计算工具来检测异质癌症基因组中的结构变异。具体地说，我们计划追求以下目标：1)开发用于敏感断点检测和组装的新型计算工具，2)开发一个统计框架来表征异质性肿瘤中的结构变异，3)通过大规模的实验验证来检验我们的工具，并通过开放源代码分发这些工具。我们的短期目标是促进癌症基因组测序项目(如癌症基因组图谱和国际癌症基因组联盟)产生的数量惊人的多克隆NGS数据的转化，以提高我们对肿瘤进化的理解，并确定具有功能和临床意义的变异。我们的长期目标是开发可用于临床环境的个性化诊断和治疗的算法和原型。

DESCRIPTION (provided by applicant): Next-generation sequencing (NGS) is enabling the routine, systematic analysis of somatic aberrations that accumulate in cancer genomes. Many of the functional mutations are structural, involving the deletion, duplication, translocation, insertion, or inversion of nucleotide sequences. Detecting these structural variations is fundamentally challenging due to the enormous number of ways a cancer genome can be altered and the presence of widespread repeats that obstruct the accurate alignment of short reads. Moreover, structural complexities are often compounded by clonal heterogeneity, i.e., mixtures of cell populations that contain heterogeneous aberrations in a tumor specimen, which result in diverse structural and copy number profiles. These issues pose an unprecedented challenge to developing practically useful computational tools that can be used to identify the presence of a structural variant and elucidate its functional and clinical relevance. To fully harness the power of NGS and to facilitate advances toward personalized medicine, we propose to develop a set of novel computational tools for detecting structural variants in heterogeneous cancer genomes. Specifically, we plan to pursue the following aims: 1) Develop novel computational tools for sensitive breakpoint detection and assembly, 2) Develop a statistical framework to characterize structural variants in heterogeneous tumors, and 3) Examine our tools through large-scale experimental validation and distribute the tools through an open source. Our short-term goal is to boost the transformation of the staggering amount of polyclonal NGS data produced by cancer genome sequencing projects such as The Cancer Genome Atlas and by the International Cancer Genome Consortium, to improve our understanding of tumor evolution and identify variants of functional and clinical relevance. Our long-term goal is to develop algorithms and prototypes that are usable in clinical settings for personalized diagnosis and treatment.