Defining the impact of clonal evolution on chronic lymphocytic leukemia

描述（由申请人提供）：克隆进化是癌症进展和复发的关键特征。我们通过开发一个管道来研究这一现象，该管道通过整合全外显子组序列（WES）和本地拷贝数数据来估计肿瘤中携带每种体细胞突变的癌细胞的比例（Landau等人，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中克隆进化的模式，并将含有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索亚克隆突变影响疾病生物学和临床结果的假设。我们建议使用基于微流体的平台来直接表征亚群中的个体进化细胞，该平台集成了单细胞水平的突变检测和mRNA表达的定量（目的1）。由于CLL的高水平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推动的，我们建议通过对患者白血病特异性突变的靶向深度测序和单细胞水平mRNA表达的突变谱综合分析来表征CLL患者的骨髓B细胞前体（Aim 2）。最后，为了确定亚克隆突变是否可以预测临床结果，我们将系统地识别从300名受试者中前瞻性收集的一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临床试验，并描述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，我们将执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果的影响（目标3）。通过这些研究，我们将建立一个理解B细胞逐步转化的框架，并将阐明CLL的动态进化景观对CLL的诊断、预后和治疗的作用。

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.