Evolution of ibrutinib resistance in chronic lymphocytic leukemia (CLL)

Abstract

The Bruton tyrosine kinase inhibitor (BTKi) ibrutinib is a new targeted therapy for patients with chronic lymphocytic leukemia (CLL). Ibrutinib is given orally on a continuous schedule and induces durable remissions in the majority of CLL patients. However, a small proportion of patients initially responds to the BTKi and then develops resistance. Estimating the frequency, timing, and individual risk of developing resistance to ibrutinib, therefore, would be valuable for long-term management of patients. Computational evolutionary models, based on measured kinetic parameters of patients, allow us to approach these questions and to develop a roadmap for personalized prognosis and treatment management. Our kinetic models predict that BTKi-resistant mutants exist before initiation of ibrutinib therapy, although they only comprise a minority of the overall tumor burden. Furthermore, we can estimate the time required for resistant cells to grow to detectable levels. We predict that this can be highly variable, depending mostly on growth and death rates of the individual CLL cell clone. For a specific patient, this time can be predicted with a high degree of certainty. Our model can thus be used to predict for how long ibrutinib can suppress the disease in individual patients. Furthermore, the model can suggest whether prior debulking of the tumor with chemo-immunotherapy can prolong progression-free survival under ibrutinib. Finally, by applying the models to data that document progression during ibrutinib therapy, we estimated that resistant mutants might have a small (<2%) mean fitness advantage in the absence of treatment, compared with sensitive cells.evolutionary dynamics | drug resistance | personalized medicine | mathematical models | stochastic dynamics

Genomic and epigenomic heterogeneity in chronic lymphocytic leukemia

“…[85][86][87] In a separate example, recent mathematical modeling of the kinetics of ibrutinib resistance has suggested that such resistant clones are present at the time of treatment initiation. 88 Hence, high-resolution molecular characterization before and during therapy has the potential to detect disease relapse in advance of full hematologic evidence of resistance.…”

Preventing clonal evolutionary processes in cancer: Insights from mathematical models

“…A very important question is for how long ibrutinib therapy can maintain control of CLL and at what time disease relapse can be expected. Answering this question requires an understanding of the evolutionary dynamics of drug-resistant cells, and mathematical models have been crucial in this endeavor (67). To describe the exponential growth of CLL cells before treatment, and their decline during treatment, we consider a stochastic linear birth-death process.…”

“…When the model is parameterized, some important insights can be obtained (67), such as whether resistance is more likely to evolve before or after therapy. Within the measured parameter regions, the model strongly suggests that resistant mutants are almost certain to exist before treatment is initiated.…”

Highlights in the treatment of chronic lymphocytic leukemia from the 2014 meeting of the American Society of Hematology

“…As authors concluded, these results support the development of randomized Phase III studies with duvelisib in patients with relapsed/refractory CLL. For patients treated with ibrutinib who have disease progression, various mechanisms of ibrutinib resistance have been described [24]. Since the mechanism of action of duvelisib differs from ibrutinib, duvelisib was evaluated in a subset of 13 patients (6 with CLL, seven with non-Hodgkin's lymphoma) previously treated with ibrutinib who were enrolled in an ongoing Phase I study [25].…”