Fitness conferred by BCR-ABL kinase domain mutations determines the risk of pre-existing resistance in chronic myeloid leukemia

Abstract

Chronic myeloid leukemia (CML) is the first human malignancy to be successfully treated with a small molecule inhibitor, imatinib, targeting a mutant oncoprotein (BCR-ABL). Despite its successes, acquired resistance to imatinib leads to reduced drug efficacy and frequent progression of disease. Understanding the characteristics of pre-existing resistant cells is important for evaluating the benefits of first-line combination therapy with second generation inhibitors. However, due to limitations of assay sensitivity, determining the existence and characteristics of resistant cell clones at the start of therapy is difficult. Here we combined a mathematical modeling approach using branching processes with experimental data on the fitness changes (i.e., changes in net reproductive rate) conferred by BCR-ABL kinase domain mutations to investigate the likelihood, composition, and diversity of pre-existing resistance. Furthermore, we studied the impact of these factors on the response to tyrosine kinase inhibitors. Our approach predicts that in most patients, there is at most one resistant clone present at the time of diagnosis of their disease. Interestingly, patients are no more likely to harbor the most aggressive, pan-resistant T315I mutation than any other resistance mutation; however, T315I cells on average establish larger-sized clones at the time of diagnosis. We established that for patients diagnosed late, the relative benefit of combination therapy over monotherapy with imatinib is significant, while this benefit is modest for patients with a typically early diagnosis time. These findings, after pre-clinical validation, will have implications for the clinical management of CML: we recommend that patients with advanced-phase disease be treated with combination therapy with at least two tyrosine kinase inhibitors.

Figure 1. Existence and composition of pre-existing resistance.

(a) The panel shows the distribution of the number of distinct resistant clones at the time of diagnosis. There are (blue), (green), or (red) leukemic stem cells present at the time of diagnosis. The per base pair mutation rate is . (b) The panel displays the expected number of resistant cell types as a function of the detection size, , for varying mutation rates. (c) The expected number of resistant types at diagnosis is shown as a function of the mutation rate , for varying numbers of leukemic stem cells at diagnosis, . (d) The expected number of resistant cell types is displayed as a function of time in years, for varying mutation rates .

Figure 2. The frequency of pre-existing resistant cells.

(a) The panel displays the expected number of resistant cells of each type at diagnosis for leukemic stem cells. (b) The expected number of resistant cells of each type at diagnosis for leukemic stem cells is shown. (c) The panel displays the time evolution of the average number of cells of each type. (d) The ratio of the expected number of cells of each type to the expected total resistant cell number as a function of time. The mutation rate is per base per cell division for all panels.

Figure 3. The frequency of T315I and F317L mutations at diagnosis.

The figure shows the distribution of the number of T315I-positive (a) and F317L-positive (b) cells in the population at detection time. Parameters are and , and simulations are run for 100,000 samples.

Figure 4. Sensitivity to therapy.

(a) The panel displays the probability that there exists at least one cell with any specified mutation in the CML stem cell population at detection time. Parameters are and . (b) The probability that the leukemic stem cell population is free of mutants conferring resistance to imatinib, dasatinib, imatinib plus dasatinib, and all three drugs at detection time. Parameters are (red), (blue), and .