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.
Our recent studies have demonstrated that blood cancers such as chronic lymphocytic leukemia (CLL) are composed of different subpopulations of cells, each of which have the potential to take over the entire leukemia population over time. We seek to better understand the characteristics of these subpopulations at the level of genes and mutations. In doing so, we hope to better understand how CLL develops, and to improve our ability to predict outcome to treatment as well as devise novel therapies for the treatment of this largely incurable leukemia.
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