Asparaginase is used routinely in frontline clinical trials for the treatment of childhood acute lymphoblastic leukemia (ALL). The goals of this study were to assess the pharmacokinetics and pharmacodynamics of asparaginase and to mathematically model the dynamics between asparaginase and asparagine in relapsed ALL. Forty children were randomized to receive either native or PEGylated (PEG) Escherichia coli asparaginase during reinduction therapy. Serial plasma asparaginase and asparagine, cerebrospinal fluid (CSF) asparagine, and serum anti-asparaginase antibody samples were collected. The asparaginase clearance was higher (P=.001) for native vs. PEG asparaginase. Patients with antibodies to PEG asparaginase had faster PEG clearance (P=.004) than antibody-negative patients. Patients who were positive for antibodies had higher CSF asparagine than those who were negative (P=.04). The modeling suggests that by modifying dosages, comparable asparagine depletion should be achievable with both preparations. At relapse, there were significant pharmacokinetic and pharmacodynamic differences due to asparaginase preparation and antibody status.

Using a target gene approach, only a few host genetic risk factors for treatment-related myeloid leukemia (t-ML) have been defined. Gene expression microarrays allow for a more genome-wide approach to assess possible genetic risk factors for t-ML. We assessed gene expression profiles (n ¼ 12 625 probe sets) in diagnostic acute lymphoblastic leukemic cells from 228 children treated on protocols that included leukemogenic agents such as etoposide, 13 of whom developed t-ML. Expression of 68 probes, corresponding to 63 genes, was significantly related to risk of t-ML. Hierarchical clustering of these probe sets clustered patients into three groups with 94, 122 and 12 patients, respectively; 12 of the 13 patients who went on to develop t-ML were overrepresented in the latter group (Po0.0001). A permutation test indicated a low likelihood that these probe sets and clusters were obtained by chance (Po0.001). Distinguishing genes included transcription-related oncogenes (v-Myb, Pax-5), cyclins (CCNG1, CCNG2 and CCND1) and histone HIST1H4C. Common transcription factor recognition elements among similarly up-or downregulated genes included several involved in hematopoietic differentiation or leukemogenesis (Maz, PU.1, ARNT). This approach has identified several genes whose expression distinguishes patients at risk of t-ML, and suggests targets for assessing germline predisposition to leukemogenesis.