Tyrosine kinase inhibitor therapy-induced changes in humoral immunity in patients with chronic myeloid leukemia

Purpose Tyrosine kinase inhibitors (TKIs) have well-characterized immunomodulatory effects on T and NK cells, but the effects on the humoral immunity are less well known. In this project, we studied TKI-induced changes in B cell-mediated immunity. Methods We collected peripheral blood (PB) and bone marrow (BM) samples from chronic myeloid leukemia (CML) patients before and during first-line imatinib (n = 20), dasatinib (n = 16), nilotinib (n = 8), and bosutinib (n = 12) treatment. Plasma immunoglobulin levels were measured, and different B cell populations in PB and BM were analyzed with flow cytometry. Results Imatinib treatment decreased plasma IgA and IgG levels, while dasatinib reduced IgM levels. At diagnosis, the proportion of patients with IgA, IgG, and IgM levels below the lower limit of normal (LLN) was 0, 11, and 6% of all CML patients, respectively, whereas at 12 months timepoint the proportions were 6% (p = 0.13), 31% (p = 0.042) and 28% (p = 0.0078). Lower initial Ig levels predisposed to the development of hypogammaglobulinemia during TKI therapy. Decreased Ig levels in imatinibtreated patients were associated with higher percentages of immature BM B cells. The patients, who had low Ig levels during the TKI therapy, had significantly more frequent minor infections during the follow-up compared with the patients with normal Ig values (33% vs. 3%, p = 0.0016). No severe infections were reported, except recurrent upper respiratory tract infections in one imatinib-treated patient, who developed severe hypogammaglobulinemia. Conclusions TKI treatment decreases plasma Ig levels, which should be measured in patients with recurrent infections.Peer reviewe

How accurate are estimates of glacier ice thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment

© Author(s) 2017. Knowledge of the ice thickness distribution of glaciers and ice caps is an important prerequisite for many glaciological and hydrological investigations. A wealth of approaches has recently been presented for inferring ice thickness from characteristics of the surface. With the Ice Thickness Models Intercomparison eXperiment (ITMIX) we performed the first coordinated assessment quantifying individual model performance. A set of 17 different models showed that individual ice thickness estimates can differ considerably - locally by a spread comparable to the observed thickness. Averaging the results of multiple models, however, significantly improved the results: on average over the 21 considered test cases, comparison against direct ice thickness measurements revealed deviations on the order of 10 ± 24% of the mean ice thickness (1σ estimate). Models relying on multiple data sets - such as surface ice velocity fields, surface mass balance, or rates of ice thickness change - showed high sensitivity to input data quality. Together with the requirement of being able to handle large regions in an automated fashion, the capacity of better accounting for uncertainties in the input data will be a key for an improved next generation of ice thickness estimation approaches