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Citations to this article

In vivo measurements document the dynamic cellular kinetics of chronic lymphocytic leukemia B cells
Bradley T. Messmer, … , Marc K. Hellerstein, Nicholas Chiorazzi
Bradley T. Messmer, … , Marc K. Hellerstein, Nicholas Chiorazzi
Published March 1, 2005
Citation Information: J Clin Invest. 2005;115(3):755-764. https://doi.org/10.1172/JCI23409.
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Categories: Article Hematology

In vivo measurements document the dynamic cellular kinetics of chronic lymphocytic leukemia B cells

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Abstract

Due to its relatively slow clinical progression, B cell chronic lymphocytic leukemia (B-CLL) is classically described as a disease of accumulation rather than proliferation. However, evidence for various forms of clonal evolution suggests that B-CLL clones may be more dynamic than previously assumed. We used a nonradioactive, stable isotopic labeling method to measure B-CLL cell kinetics in vivo. Nineteen patients drank an aliquot of deuterated water (2H2O) daily for 84 days, and 2H incorporation into the deoxyribose moiety of DNA of newly divided B-CLL cells was measured by gas chromatography/mass spectrometry, during and after the labeling period. Birth rates were calculated from the kinetic profiles. Death rates were defined as the difference between calculated birth and growth rates. These analyses demonstrated that the leukemic cells of each patient had definable and often substantial birth rates, varying from 0.1% to greater than 1.0% of the entire clone per day. Those patients with birth rates greater than 0.35% per day were much more likely to exhibit active or to develop progressive disease than those with lower birth rates Thus, B-CLL is not a static disease that results simply from accumulation of long-lived lymphocytes. Rather, it is a dynamic process composed also of cells that proliferate and die, often at appreciable levels. The extent to which this turnover occurs has not been previously appreciated. A correlation between birth rates and disease activity and progression appears to exist, which may help identify patients at risk for worsening disease in advance of clinical deterioration.

Authors

Bradley T. Messmer, Davorka Messmer, Steven L. Allen, Jonathan E. Kolitz, Prasad Kudalkar, Denise Cesar, Elizabeth J. Murphy, Prasad Koduru, Manlio Ferrarini, Simona Zupo, Giovanna Cutrona, Rajendra N. Damle, Tarun Wasil, Kanti R. Rai, Marc K. Hellerstein, Nicholas Chiorazzi

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