Clonal evolution is a key feature of cancer progression and relapse. Our recent study, which utilized a newly developed pipeline that estimates the fraction of cancer cells harboring each somatic mutation within a tumor through integration of whole-exome sequencing and local copy number data, linked the presence of subclones harboring putative driver mutations with adverse clinical outcome in chronic lymphocytic leukemia (CLL) and suggested that CLL therapy may accelerate the process of clonal evolution (Landau et al., Cell 2013). We propose that presence of subclonal mutations that are putative drivers are indicative of an active evolutionary process. We now seek to definitively establish the impact of subclonal mutations on CLL biology, the development of disease relapse and clinical outcome. This will be achieved by longitudinal analysis of clonal structure of serial samples collected from patients enrolled on phase II and phase III clinical trials (and hence uniformly treated) that address the treatment landscape of CLL. In particular, we will perform detailed genetic analysis of samples from patients receiving standard-of-care first line fludarabine-based chemotherapy (Aim 1). In parallel, we will examine patient samples exposed to ibrutinib, a highly promising irreversible inhibitor of Bruton's tyrosine kinase which i anticipated to be a cornerstone of future CLL therapy (Aim 2). Analysis of samples exposed to both these types of therapies will include characterization of subclonal structure as well as assessment of the dynamic phenotypic changes (detected by single cell RNA-sequencing) to validate mutation analysis and determine the transcriptional networks of drug resistant cells in order to reveal potential novel and effective treatment combinations. To causally link the impact of putative drivers and therapy on CLL clonal evolution, we will generate an in vivo model to study interclonal dynamics in the setting of therapy (Aim 3). We will use transformative genome-editing techniques to generate cell lines that model leukemic subpopulations bearing representative CLL driver mutations and thereby mechanistically dissect the contribution of individual genetic lesions to the evolutionary landscape. By creating an animal model of clonal evolution, we will have the potential to more effectively evaluate preclinically the impact of nove therapeutics on clonal selection. In total, these studies are designed to establish a framework for understanding the role of the dynamic evolutionary landscape of CLL on the diagnosis, prognosis and treatment of this currently incurable disease.
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 and how they change following exposure to chemotherapy or new CLL treatments. In doing so, we hope to better understand how drug resistance 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.
Showing the most recent 10 out of 17 publications
