The ability of cancer cells to evolve and adapt to therapy is a challenge that limits treatment success and durability of responses. This is certainly the case in chronic lymphocytic leukemia (CLL), a malignancy of mature B cells that remains incurable, despite the potent cytolytic effects of both existing standard-of-care fludarabine-based combination chemotherapy, and newly developed targeted inhibitors such as ibrutinib and ABT199. We focus on a series of informative well-characterized clinical cohorts of patients that have relapsed following CLL therapy, ranging from conventional chemotherapy to novel agents (ibrutinib, ABT199, anti-PD1 antibody). Through integrated whole-exome and RNA-sequencing of these cohorts, we will characterize the extent of clonal evolution following exposure to these agents, and identify if there are consistent genetic loci associated with therapeutic resistance or progression (Aim 1). Mathematical modeling together with frequent serial analysis of the clonal composition of leukemias in relationship to treatment response and relapse can inform us regarding the clone-specific decline/growth kinetics as they occur in individual patients, and thereby enable dissection of the mechanisms of relapse or progression. Through this process, we will further estimate the sizes of clones with rare resistance mutations at the start of treatment; understand whether distinct relapse- associated genetic lesions result in accelerated clonal growth, or rather, in insensitivity to therapy; and validate the size of the resistant population in the starting population using novel single cell droplet sequencing technology (Aim 2). Finally, we will use CRISPR/Cas technology to model the novel resistance mutations in B cell lines and introduce these lines in combination with other mutated cell lines both in vitro and in vivo into immunodeficient mice, in order to test their fitness both prior to and during therapy (Aim 3). Altogether, the proposed analyses serve to provide an analytic framework for gaining vital information regarding the fitness of different genetic lesions with and without therapy, which may be immensely beneficial to the design of the next generation of therapeutic approaches to overcome the evolutionary capacity of cancer.
Cancer therapies often fail as a consequence of cancer's resistance to treatment, which arises because cancer cells evolve to escape the effects of therapy. Using the knowledge of mutations in patient samples with chronic lymphocytic leukemia (gained from leukemia cell sequencing), we will use mathematical modeling together with new experimental data generated from unbiased bulk sequencing and novel single cell-based approaches together with animal studies to model clonal dynamics in order to uncover and quantify the evolution that leads to resistance to therapy.
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