Delineating Heterogeneous Structural Complexity in Cancer Genomes

描述（由申请人提供）：下一代测序（NGS）能够对癌症基因组中积累的体细胞畸变进行常规、系统分析。许多功能性突变是结构性的，涉及核苷酸序列的缺失、复制、易位、插入或倒位。检测这些结构变异从根本上是具有挑战性的，因为癌症基因组可以改变的方式非常多，并且存在广泛的重复序列，这些重复序列阻碍了短读段的准确比对。此外，结构的复杂性往往是由克隆异质性，即，肿瘤标本中含有异质性畸变的细胞群混合物，导致不同的结构和拷贝数特征。这些问题对开发实用的计算工具提出了前所未有的挑战，这些工具可用于识别结构变体的存在并阐明其功能和临床相关性。为了充分利用NGS的力量，并促进个性化医疗的进步，我们建议开发一套新的计算工具，用于检测异质性癌症基因组中的结构变异。具体来说，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体，以及3）通过大规模实验验证来检查我们的工具，并通过开源分发工具。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（International Cancer Genome Consortium）产生的惊人数量的多克隆NGS数据的转化，以提高我们对肿瘤演变的理解，并识别功能和临床相关性的变体。我们的长期目标是开发可用于临床环境的算法和原型，以进行个性化诊断和治疗。

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.