Advancing Ultra Long-read Sequencing and Chromatin Interaction Analyses for Chromosomal and Extrachromosomal Structural Variation Characterization in Cancer

项目总结 癌症中的结构变异(SVs)，如缺失、插入、倒置、复制和易位 基因组可以通过干扰基因结构和表达来促进肿瘤的进展。另外， 染色体外DNA(EcDNA)--一种广泛存在于多种癌症类型中的极端形式的SV--是一个储存库 癌基因扩增，有助于肿瘤的遗传异质性和进化。因此，一个完整的 了解SVS和ecDNA在肿瘤中的结构和分布将有助于阐明它们在 肿瘤进展。然而，在分子水平上检测和表征SVS和ecDNA的能力 受到现有短读测序方法的限制：大而复杂的SVS阻碍了检测它们的努力 并正确定义其结构；而ecDNA的多拷贝、异质性破坏了 它们的一级结构的测定。而中期肿瘤的DAPI染色可以观察到ecDNA 细胞，确定它们的序列含量通常依赖于荧光原位杂交(FISH)来探测 候选致癌基因。为了支持一种公正和全面的分子方法来研究SVS， 该项目将开发和验证新兴的基因组技术，使检测和 将复杂的SVS和ecDNA表征为癌症基因组学中的标准实践。在目标1中， 通过改进基因组，纳米孔单分子测序平台的读取长度将进一步延长 DNA质量和优化文库制备反应，目标是达到75-100的N50阅读长度 KB。如此长的读数长度有望跨越许多SV，以更有效地揭示它们的分子结构 和阶段性信息。同时，最近的SV检测计算流水线Picky将进行优化，以 检测复杂SVS和ecDNA的分子特征，以便在长时间内准确和灵敏地检测它们 读取测序数据可达到0.8的准确率和召回率。EcDNA的活跃转录表明它们 与RNA聚合酶II转录复合体相关，使它们适合于无监督的 用染色质相互作用试验CHIA-PET检测。在目标2中，这种方法将被用来定位ecDNA 通过它们与RNA聚合酶II的关联，揭示了ecDNA之间转录相关的相互作用 和染色体。将开发专门检测ecDNA扩增的计算方法 Chia-PET数据中的序列及其相关致癌基因。此外，Chia-发现的ecDNA- 基于CRISPR/dCas9的靶向捕获方法将以pET为目标，以物理方式分离ecDNA 用于长读测序和结构表征的分子。目标3将建立在已开发方法的基础上 为胶质母细胞瘤中SVS和ecDNA的无偏向和无监督特征建立平台 神经球培养以及胶质母细胞瘤、乳腺癌和肺癌的异种移植瘤模型。加在一起， 该项目将开发方法和工具，使癌症研究社区能够自信和 综合检测肿瘤基因组中的SVS和ecDNA。

PROJECT SUMMARY Structural variants (SVs) such as deletions, insertions, inversions, duplications, and translocations in cancer genomes can promote tumor progression by perturbing gene structures and expression. Additionally, extrachromosomal DNA (ecDNA)—an extreme form of SV found in a wide range of cancer types—are a reservoir of oncogene amplification and contribute to the genetic heterogeneity and evolution of tumors. Thus, a complete understanding of the structure and distribution of SVs and ecDNAs in tumors would shed light on their roles in tumor progression. However, the ability to detect and characterize SVs and ecDNAs at the molecular level has been limited by existing short-read sequencing approaches: large and complex SVs thwart efforts to detect them and correctly define their structures; and the multi-copy, heterogenous nature of ecDNAs undermines determination of their primary structures. While ecDNAs can be observed by DAPI-staining of metaphase tumor cells, determining their sequence content has typically relied on fluorescence in situ hybridization (FISH) to probe for candidate oncogenes. To support an unbiased and comprehensive molecular approach to the study of SVs, this project will develop and validate emerging genomic technologies that will enable the detection and characterization of complex SVs and ecDNAs as standard practices in cancer genomics. In Aim 1, the read lengths of the nanopore single-molecule sequencing platform will be further extended by improving genomic DNA quality and optimizing library preparation reactions, with the goal of attaining N50 read lengths of 75-100 Kb. Such long read lengths are expected to span many SVs to more effectively reveal their molecular structures and phasing information. In parallel, the recent SV-detecting computational pipeline, Picky, will be optimized to detect molecular signatures of complex SVs and ecDNAs to allow their accurate and sensitive detection in long read sequencing data to >0.8 precision and recall rates. The active transcription of ecDNAs suggests that they are associated with RNA polymerase II transcription complexes, making them suitable for unsupervised detection by the chromatin interaction assay, ChIA-PET. In Aim 2, this method will be employed to map ecDNAs via their association with RNA polymerase II and reveal transcriptionally relevant interactions between ecDNAs and the chromosomes. Computational methods will be developed to specifically detect ecDNA-amplified sequences in ChIA-PET data and their associated oncogenic genes. Additionally, ecDNAs uncovered by ChIA- PET will be targeted by the CRISPR/dCas9-based targeted capture method to physically isolate ecDNA molecules for long-read sequencing and structural characterization. Aim 3 will build on the developed methods to generate a platform for unbiased and unsupervised characterization of SVs and ecDNAs in glioblastoma neurosphere cultures and in xenograft tumor models of glioblastoma, breast, and lung cancer. Taken together, this project will develop methods and tools that will empower the cancer research community to confidently and comprehensively detect SVs and ecDNAs in cancer genomes.