Discovery of Somatic Noncoding Variants that Serve as Drivers in Pediatric Cancers

项目总结/摘要 通过ENCODE和Roadmap表观基因组学对顺式调控元件的定位， 非编码调控区的重要性，作为下一个重大发现的重要体细胞 儿童癌症的变异。引起肿瘤的体细胞非编码突变的最新发现 黑色素瘤中的TERT和儿童T-ALL中的TAL 1的激活支持了这一观点， 癌症中的非编码体细胞突变的研究。然而，非编码基因组是巨大的， 它已被证明是难以区分的“致癌司机”从“乘客”之间的 许多非编码序列变体。通过将我们在基因组分析方面的计算专业知识与 我们的合作研究者托马斯Look和Suzanne Baker博士在分子肿瘤发生方面的实验专业知识 和Chunliang Li博士在新的检测开发中，我们的目标是发现新的体细胞非编码变体， 儿童癌症的驱动因素。这项工作充分利用了我们迄今为止在儿科癌症方面工作的优势 基因组景观，包括非编码区，以及我们独特的“组学”结果资源的丰富性 从现有的全基因组测序（WGS）和RNA-seq>2,000对肿瘤/正常儿童癌症 样品通过对33例T系急性淋巴细胞白血病的初步研究，我们证明， 综合的方法，我们能够成功地区分“司机突变”从“乘客”，并发现 儿童癌症非编码基因组的新变体。在目标1中，我们将发现躯体改变 通过分析WGS和RNA， 来自2，000个患者样品和来自已建立的癌细胞系的SEQ数据。我们将专注于序列 形成基因座特异性转录因子结合位点并采用大规模平行报告基因的改变 通过测定来测量候选非编码突变的增强子活性。在目标2中，我们将开发一个 基于患者统计分析预测非编码变体致病性的计算框架 实验室调查揭示的调控非编码变体的数据和机制研究。我们 将在儿科癌症患者来源的异种移植物（PDX）中发现异常的增强子-启动子相互作用 使用3-D染色质测定法如Capture-C的模型或细胞系。我们将使用ChIP-seq和RNA-seq来 分析PDX或癌细胞系中非编码变体的功能后果。同时，我们将使用 CRISPR-Cas9基因组编辑工具修饰或改变突变等位基因，以进一步探索调控 机制等在目标3中，我们将开发一个基于Web且用户友好的可视化工具，以加速发现 通过公开我们研究中发现的非编码变体， 与一个综合的基因组范围内的“组学”数据集的看法，供研究界使用。的发现 儿童癌症中的非编码体细胞“驱动”突变作为这种合作努力的结果将导致 我们对儿童癌症的分子发病机制的理解和我们的计算 预测模型将为个性化“精准医疗”的实施提供新的见解。

PROJECT SUMMARY / ABSTRACT Mapping of cis-regulatory elements by ENCODE and Roadmap Epigenomics has led to increased recognition of the importance of non-coding regulatory regions, as the target for next great discovery of important somatic variants in childhood cancers. Recent discoveries of somatic non-coding mutations that cause oncogenic activation of TERT in melanoma and TAL1 in pediatric T-ALL support this idea, and have inspired genome-wide investigations of non-coding somatic mutations in cancer. However, the noncoding genome is vast and largely uncharted and it has proven difficult to distinguish the “oncogenic drivers” from the “passengers” among the many non-coding sequence variants. By combining our computational expertise in genomic analysis with the experimental expertise of our co-investigators Drs. Thomas Look and Suzanne Baker in molecular oncogenesis and Dr. Chunliang Li in novel assay development, we aim to discover new somatic non-coding variants that serve as drivers of pediatric cancer. This effort capitalizes on the strength of our work to-date on the pediatric cancer genome landscape, including non-coding regions, and the richness of our unique resource of “omics” results from existing whole genome sequencing (WGS) and RNA-seq of >2,000 paired tumor/normal childhood cancer samples. Through a pilot study of 33 T-lineage acute lymphoblastic leukemia, we demonstrate that by an integrative approach we are able to successfully distinguish “driver-mutations” from “passengers” and discover novel variants in the non-coding genomes of childhood cancers. In Aim 1, we will discover somatic alterations in non-coding regions that are associated with aberrant, allele-specific expression by analyzing WGS and RNA- seq data from 2,000 patient samples and from established cancer cell lines. We will focus on sequence alterations that form locus-specific transcription factor binding sites and employ a massively parallel reporter assay to measure the enhancer activity of the candidate non-coding mutations. In Aim 2, we will develop a computational framework for predicting non-coding variant pathogenicity based on statistical analysis of patient data and mechanism studies underlying regulatory non-coding variants unveiled by laboratory investigation. We will discover abnormal enhancer-promoter interactions in pediatric cancer patient derived xenograft (PDX) models or cell lines using 3-D chromatin assays such as Capture-C. We will use ChIP-seq and RNA-seq to analyze the functional consequences of non-coding variants in PDXs or cancer cell lines. In parallel we will use CRISPR-Cas9 genome editing tools to modify or alter the mutant allele to further explore the regulation mechanisms. In Aim 3, we will develop a web-based and user-friendly visualization tool to accelerate discovery of non-coding driver mutations by making the non-coding variants discovered in our study publicly accessible with an integrated genome-wide view of “omics” datasets for use by the research community. The discovery of non-coding somatic “driver” mutations in childhood cancers as a result of this collaborative effort will lead to major advances in our understanding of the molecular pathogenesis of childhood cancers and our computational predictive model will provide new insight into implementation of individualized “precision medicine”.