The role of epigenetic heterogeneity in CLL evolution Admin supplement

描述：慢性淋巴细胞白血病(CLL)目前是不治之症。尽管进行了有效的治疗，但这种疾病总是会复发，部分原因是它的进化能力。我们已经证明，白血病内部的基因异质性预示着克隆进化会导致疾病复发。然而，细胞表型及其选择的适合性是遗传和表观遗传改变的结果。因此，整合遗传异质性和表观遗传异质性是癌症进化研究中的一大挑战。在初步研究中，我们发现CLL的样本内表观遗传异质性增加。为了了解这种异质性的基础，我们研究了从大规模平行的亚硫酸氢盐测序中获得的约100个初级CLL样本中包含的相邻CPGS甲基化状态的一致性。我们证明，大多数异质性源于无序甲基化，这是一种随机表观遗传漂移的形式。无序的甲基化影响到转录调控的重要区域，并与启动子甲基化和转录沉默之间的关系脱钩有关。最后，无序的甲基化受到选择的影响，并可能促进克隆进化。我假设无序的甲基化影响组蛋白的修饰和转录，从而促进CLL的进化。为了确定无序甲基化的影响，我们提出了以下独立但又相互关联的特定目标：(1)为了研究其与组蛋白修饰和转录的关系，我们将针对抑制性组蛋白标记制作全面的组蛋白CHIP-SEQ图谱和CHIP-亚硫酸氢盐-SEQ。我们将整合多维数据来推断表观遗传样本内的异质性，并用单细胞RNAseq来验证这一点，以评估细胞间的可变性是甲基化紊乱的一种功能。(2)我们将开发一个统计推断工具来检测癌症中假定的甲基化“驱动”事件，并考虑到背景随机变量。(3)为了研究甲基化紊乱对克隆进化和临床结局的影响，我们对350例接受统一治疗的患者和80例复发患者的治疗前样本进行了遗传和表观遗传学异质性分析。目前还没有可用的治疗策略来遏制癌症的演变。因此，这些研究解决了一个未得到满足的治疗需求。最后，在这份申请中，我勾勒了一个5年的职业发展计划，以实现我的目标，成为一名翻译癌症生物学的独立研究员，精通大数据科学方法论。我已经组建了一个由国际公认的专家组成的指导委员会，提供科学和职业指导。我将与顶尖的专家一起进行密集的教学课程和实践培训，以发展强大的计算和统计基础。最后，达纳-法伯癌症研究所是实现我的科学和职业目标的理想环境，因为它有出色的研究社区，对大数据科学的重视，以及在培训独立内科科学家方面的出色记录。

DESCRIPTION: Chronic lymphocytic leukemia (CLL) is currently incurable. Despite effective treatments, the disease invariably recurs due, in part, to its ability to evolve. We have shown that pretreatment intra-leukemic genetic heterogeneity foreshadows clonal evolution leading to disease relapse. Nevertheless, the cellular phenotype and its fitness for selection result from both genetic and epigenetic alterations. Therefore, a major challenge in the study of cancer evolution is to integrate genetic and epigenetic heterogeneity. In preliminary studies, we found increased intra-sample epigenetic heterogeneity in CLL. To understand the basis of this heterogeneity, we studied the uniformity of the methylation status of neighboring CpGs contained within individual reads from massively parallel bisulfite sequencing of ~100 primary CLL samples. We demonstrated that most of the heterogeneity stems from disordered methylation, a form of stochastic epigenetic drift. Disordered methylation affected regions important to transcriptional regulation and was associated with a decoupling of the relationship between promoter methylation and transcriptional silencing. Finally, disordered methylation was subjected to selection and may facilitate clonal evolution. I hypothesize that disordered methylation impacts histone modification and transcription, thereby enhancing CLL evolution. To define the impact of disordered methylation, we propose the following independent yet interrelated Specific Aims: (1) To examine its relationship to histone modification and transcription, we will produce comprehensive histone ChIP-seq mapping and ChIP-bisulfite-seq directed at repressive histone marks. We will integrate the multidimensional data to infer epigenetic intra-sample heterogeneity and validate this with single-cell RNAseq to assess cell-to-cell variability as a function of methylation disorder. (2) We will develop a statistical inferece tool to detect putative methylation "driver" events in cancer taking into account background stochastic variation. (3) To study the impact of disordered methylation on clonal evolution and clinical outcome, we will integrate genetic and epigenetic heterogeneity analysis in pretreatment samples from 350 patients who received uniform treatment, and 80 relapse samples. There are no therapeutic strategies currently available to curb cancer evolution. Thus, these studies address an unmet therapeutic need. Finally, in this application, I have outlined a 5-year career development plan to meet my goal of becoming an independent investigator in translational cancer biology, proficient in big data science methodology. I have assembled a Mentorship Committee of internationally recognized experts to provide scientific and career mentorship. I will pursue intensive didactic coursework and hands-on training with leading experts, to develop a strong computational and statistical foundation. Finally, Dana-Farber Cancer Institute is the ideal environment for attaining my scientific and career goals, given its outstanding research community, emphasis on big data science, and an excellent track record of training independent physician-scientists.