Defining the impact of clonal evolution on chronic lymphocytic leukemia

描述（由申请人提供）：克隆演变是癌症进展和复发的关键特征。我们通过开发一种管道来研究这种现象，该管道通过整合全外显子组序列（WES）和局部拷贝数数据来估计肿瘤内携带每种体细胞突变的癌细胞的分数（朗道et al.，Cell 2013，in press）。通过将这种分析方法应用于149例慢性淋巴细胞白血病（CLL）病例，我们发现了早期和晚期的癌症驱动因素，揭示了CLL中的克隆进化模式，并将携带驱动突变的亚克隆的存在与不良临床结果联系起来。我们现在建议更深入地探讨亚克隆突变影响疾病生物学和临床结果的假设。我们建议通过使用基于微流体的平台来直接表征亚群内的个体进化细胞，该平台集成了在单细胞水平上的突变检测和mRNA表达的定量（目的1）。由于CLL中高水平的克隆异质性可能是由早期B细胞谱系细胞内的克隆多样性引起的，因此我们建议通过患者白血病特异性突变的靶向深度测序和突变谱与单细胞水平的mRNA表达的综合分析来表征CLL患者的骨髓B细胞前体（目的2）。最后，为了确定亚克隆突变是否能预测临床结局，我们将系统地识别前瞻性收集自300例受试者的系列样本中的克隆和亚克隆突变，这些受试者参加了一项具有里程碑意义的基于氟达拉滨的一线化疗的III期临床试验，并描述了个体病例中克隆景观的动态变化。这些分析将确定氟达拉滨对CLL亚克隆结构的影响。相反，我们将进行亚克隆突变的数量、大小和组成与临床特征之间的关联，以确定亚克隆群体中存在的突变对临床结局的影响（目的3）。通过这些研究，我们将建立一个框架，了解B细胞的逐步转化，并将阐明CLL的动态演变景观的诊断，预后和治疗CLL的作用。

DESCRIPTION (provided by applicant): Clonal evolution is a key feature of cancer progression and relapse. We investigated this phenomenon by developing a pipeline that estimates the fraction of cancer cells harboring each somatic mutation within a tumor through integration of whole-exome sequence (WES) and local copy number data (Landau et al., Cell 2013, in press). By applying this analysis approach to 149 chronic lymphocytic leukemia (CLL) cases, we discovered earlier and later cancer drivers, uncovered patterns of clonal evolution in CLL and linked the presence of subclones harboring driver mutations with adverse clinical outcome. We now propose to more deeply explore the hypothesis that subclonal mutations impact disease biology and clinical outcome. We propose to directly characterize individual evolving cells within subpopulations by using a microfluidics-based platform that integrates detection of mutations and quantitation of mRNA expression at the single cell level (Aim 1). Because the high level of clonal heterogeneity in CLL may be fueled by clonal diversity within earlier B cell lineage cells, we propose to characterize marrow B cell precursors of CLL patients through targeted deep sequencing of patient leukemia specific mutations and integrated analysis of mutation profiling with mRNA expression at the single cell level (Aim 2). Finally, to define if subclonal mutations are predictive of clinical outcome, we will systematically identify clonal and subclonal mutations in serial samples prospectively collected from 300 subjects enrolled on a landmark phase III clinical trial of frontline fludarabine-based chemotherapy and characterize the dynamic changes in the clonal landscape within individual cases. These analyses will define the impact of fludarabine on subclonal structure in CLL. Conversely, we will perform associations between number, size and composition of subclonal mutations and clinical characteristics to determine the impact of mutations present in subclonal populations on clinical outcome (Aim 3). Through these studies, we will establish a framework for understanding the stepwise transformation of B cells, and will elucidate the role of the dynamic evolutionary landscape of CLL on the diagnosis, prognosis and treatment of CLL.