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Blood. 2002 Jun 1;99(11):4100-8.

The nonobese diabetic/severe combined immunodeficient (NOD/SCID) mouse model of childhood acute lymphoblastic leukemia reveals intrinsic differences in biologic characteristics at diagnosis and relapse.

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  • 1Children's Cancer Institute Australia for Medical Research; School of Paediatrics, University of New South Wales, Australia. richard.lock@unsw.edu.au

Abstract

Acute lymphoblastic leukemia cells from 19 children, including 7 who remain in first complete remission (CR1), were engrafted into nonobese diabetic/severe combined immunodeficient (NOD/SCID) mice. High-level infiltration of bone marrow, spleen, and liver was observed, with variable infiltration of other organs. The immunophenotypes of xenografts were essentially unaltered compared with the original patient sample. In addition, sequencing of the entire p53 coding region revealed no mutations in 14 of 14 xenografts (10 from patients at diagnosis and 4 at relapse). Cells harvested from the spleens of engrafted mice readily transferred the leukemia to secondary and tertiary recipients. To correlate biologic characteristics of xenografts with clinical and prognostic features of the patients, the rates at which individual leukemia samples engrafted in NOD/SCID mice were analyzed. Differences in biologic correlates were encountered depending on stage of disease: a direct correlation was observed between the rate of engraftment and length of CR1 for samples harvested at relapse (r = 0.96; P =.002), but not diagnosis (r = 0.38; P =.40). In contrast, the in vivo responses of 6 xenografts to vincristine showed a direct correlation (r = 0.96; P =.002) between the length of CR1 and the rate at which the leukemia cell population recovered following vincristine treatment, regardless of whether the xenografts were derived from patients at diagnosis or relapse. This study supports previous findings that the NOD/SCID model of childhood ALL provides an accurate representation of the human disease and indicates that it may be of value to predict relapse and design alternative treatment strategies in a patient-specific manner.

PMID:
12010813
[PubMed - indexed for MEDLINE]
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