A multicellular basis for the origination of blast crisis in chronic myeloid leukemia

Predictive nonlinear modeling of malignant myelopoiesis and tyrosine kinase inhibitor therapy

Chronic myeloid leukemia (CML) is a blood cancer characterized by dysregulated production of maturing myeloid cells driven by the product of the Philadelphia chromosome, the BCR-ABL1 tyrosine kinase. Tyrosine kinase inhibitors (TKIs) have proved effective in treating CML, but there is still a cohort of patients who do not respond to TKI therapy even in the absence of mutations in the BCR-ABL1 kinase domain that mediate drug resistance. To discover novel strategies to improve TKI therapy in CML, we developed a nonlinear mathematical model of CML hematopoiesis that incorporates feedback control and lineage branching. Cell-cell interactions were constrained using an automated model selection method together with previous observations and new in vivo data from a chimeric BCR-ABL1 transgenic mouse model of CML. The resulting quantitative model captures the dynamics of normal and CML cells at various stages of the disease and exhibits variable responses to TKI treatment, consistent with those of CML patients. The model predicts that an increase in the proportion of CML stem cells in the bone marrow would decrease the tendency of the disease to respond to TKI therapy, in concordance with clinical data and confirmed experimentally in mice. The model further suggests that, under our assumed similarities between normal and leukemic cells, a key predictor of refractory response to TKI treatment is an increased maximum probability of self-renewal of normal hematopoietic stem cells. We use these insights to develop a clinical prognostic criterion to predict the efficacy of TKI treatment and design strategies to improve treatment response. The model predicts that stimulating the differentiation of leukemic stem cells while applying TKI therapy can significantly improve treatment outcomes.

Aberrant activation of CaMKIIγ accelerates chronic myeloid leukemia blast crisis

Blast crisis (BC) is the final deadly phase of chronic myeloid leukemia (CML), but its molecular basis remains poorly understood. Here, we show that CML BC is regulated by calcium-calmodulin-dependent kinase IIγ (CaMKIIγ). Genetic deletion of CaMKIIγ greatly inhibits disease progression via selectively impairing the self-renewal of leukemia stem cells (LSCs) in mouse models, whereas overexpression of CaMKIIγ has the opposite effects. In human CML, phosphorylated CaMKIIγ abundance is significantly associated with BC. Moreover, CaMKIIγ phosphorylates and reduces the nuclear cyclin-dependent kinase inhibitor p27Kip1, a critical brake that maintains LSC quiescence. These findings suggest that CaMKIIγ might be an important switch for the transition of CML BC and identify a unique mechanism by which CaMKIIγ promotes the self-renewal of LSCs by deceasing nuclear p27Kip1 to wake up dormant LSCs. Therefore, CaMKIIγ may provide a new therapeutic target to treat CML BC.

The effect of nilotinib plus arsenic trioxide on the proliferation and differentiation of primary leukemic cells from patients with chronic myoloid leukemia in blast crisis

Aim

To determine the effects of arsenic trioxide (ATO) and nilotinib (AMN107, Tasigna) alone or in combination on the proliferation and differentiation of primary leukemic cells from patients with chronic myeloid leukemia in the blast crisis phase (CML-BC).

Methods

Cells were isolated from the bone marrow of CML-BC patients and were treated with 1 μM ATO and 5 nM nilotinib, either alone or in combination. Cell proliferation was evaluated using a MTT assay. Cell morphology and the content of hemoglobin were examined with Wright-Giemsa staining and benzidine staining, respectively. The expression of cell surface markers was determined using flow cytometric analysis. The levels of mRNA and protein were analyzed using RT-PCR and Western blotting, respectively.

Results

ATO and nilotinib alone or in combination suppressed cell proliferation in a dose- and time-dependent pattern (P < 0.01 vs. control). Drug treatments promoted erythroid differentiation of CML-BC cells, with a decreased nuclei/cytoplasm ratio but increased hemoglobin content and glycophorin A (GPA) expression (P < 0.01 compared with control). In addition, macrophage and granulocyte lineage differentiation was also induced after drug treatment. The mRNA and protein levels of basic helix-loop-helix (bHLH) transcription factor T-cell acute lymphocytic leukemia protein 1 (TAL1) and B cell translocation gene 1 (BTG1) were both upregulated after 3 days of ATO and Nilotinib treatment.

Conclusions

Our findings indicated that ATO and nilotinib treatment alone or in combination greatly suppressed cell proliferation but promoted the differentiation of CML-BC cells towards multiple-lineages. Nilotinib alone preferentially induced erythroid differentiation while combined treatment with ATO preferentially induced macrophage and granulocyte lineage differentiation.

Etiology and treatment of hematological neoplasms: stochastic mathematical models

Leukemias are driven by stemlike cancer cells (SLCC), whose initiation, growth, response to treatment, and posttreatment behavior are often "stochastic", i.e., differ substantially even among very similar patients for reasons not observable with present techniques. We review the probabilistic mathematical methods used to analyze stochastics and give two specific examples. The first example concerns a treatment protocol, e.g., for acute myeloid leukemia (AML), where intermittent cytotoxic drug dosing (e.g., once each weekday) is used with intent to cure. We argue mathematically that, if independent SLCC are growing stochastically during prolonged treatment, then, other things being equal, front-loading doses are more effective for tumor eradication than back loading. We also argue that the interacting SLCC dynamics during treatment is often best modeled by considering SLCC in microenvironmental niches, with SLCC-SLCC interactions occurring only among SLCC within the same niche, and we present a stochastic dynamics formalism, involving "Poissonization," applicable in such situations. Interactions at a distance due to partial control of total cell numbers are also considered. The second half of this chapter concerns chromosomal aberrations, lesions known to cause some leukemias. A specific example is the induction of a Philadelphia chromosome by ionizing radiation, subsequent development of chronic myeloid leukemia (CML), CML treatment, and treatment outcome. This time evolution involves a coordinated sequence of > 10 steps, each stochastic in its own way, at the subatomic, molecular, macromolecular, cellular, tissue, and population scales, with corresponding time scales ranging from picoseconds to decades. We discuss models of these steps and progress in integrating models across scales.

Quantitative modeling of chronic myeloid leukemia: insights from radiobiology

Mathematical models of chronic myeloid leukemia (CML) cell population dynamics are being developed to improve CML understanding and treatment. We review such models in light of relevant findings from radiobiology, emphasizing 3 points. First, the CML models almost all assert that the latency time, from CML initiation to diagnosis, is at most ∼10 years. Meanwhile, current radiobiologic estimates, based on Japanese atomic bomb survivor data, indicate a substantially higher maximum, suggesting longer-term relapses and extra resistance mutations. Second, different CML models assume different numbers, between 400 and 10(6), of normal HSCs. Radiobiologic estimates favor values>10(6) for the number of normal cells (often assumed to be the HSCs) that are at risk for a CML-initiating BCR-ABL translocation. Moreover, there is some evidence for an HSC dead-band hypothesis, consistent with HSC numbers being very different across different healthy adults. Third, radiobiologists have found that sporadic (background, age-driven) chromosome translocation incidence increases with age during adulthood. BCR-ABL translocation incidence increasing with age would provide a hitherto underanalyzed contribution to observed background adult-onset CML incidence acceleration with age, and would cast some doubt on stage-number inferences from multistage carcinogenesis models in general.