Defining the impact of clonal evolution on chronic lymphocytic leukemia

描述(申请人提供)：克隆进化是癌症进展和复发的关键特征。我们对这一现象进行了研究，开发了一种管道，通过整合完整外显子序列(WES)和局部拷贝数数据来估计肿瘤内含有每个体细胞突变的癌细胞的比例(Landau等人，Cell 2013，正在出版中)。通过对149例慢性淋巴细胞白血病(CLL)患者应用这种分析方法，我们发现了早期和晚期的癌症驱动因素，揭示了CLL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。我们建议通过使用一个基于微流体的平台，在单个细胞水平上集成突变检测和mRNA表达的定量(目标1)，直接表征亚群中单个进化细胞的特征。由于CLL的高度克隆性异质性可能是由早期B细胞系细胞中的克隆多样性所推动的，我们建议通过对患者白血病特异性突变的定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细胞前体细胞(目标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克隆和亚克隆突变，这些样本来自具有里程碑意义的基于氟达拉滨的化疗第三阶段临床试验，并表征单个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，我们将在亚克隆突变的数量、大小和组成与临床特征之间进行关联，以确定亚克隆人群中存在的突变对临床结果的影响(目标3)。通过这些研究，我们将建立一个了解B细胞逐步转化的框架，并将阐明CLL的动态演变格局在CLL的诊断、预后和治疗中的作用。

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.