Mouse models of human AML accurately predict chemotherapy response

  1. Johannes Zuber1,
  2. Ina Radtke2,
  3. Timothy S. Pardee1,
  4. Zhen Zhao1,3,
  5. Amy R. Rappaport1,4,
  6. Weijun Luo1,
  7. Mila E. McCurrach1,
  8. Miao-Miao Yang1,
  9. M. Eileen Dolan5,
  10. Scott C. Kogan6,
  11. James R. Downing2 and
  12. Scott W. Lowe1,4,7,8
  1. 1Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA;
  2. 2Department of Pathology, St Jude Children's Research Hospital, Memphis, Tennessee 38105, USA;
  3. 3Genetics Program, Stony Brook University, Stony Brook, New York 11794, USA;
  4. 4Watson School of Biological Sciences, Cold Spring Harbor, New York 11724, USA;
  5. 5Section of Hematology–Oncology, Department of Medicine, University of Chicago, Chicago, Illinois 60637, USA;
  6. 6Department of Laboratory Medicine, University of California at San Francisco, San Francisco, California 94143, USA;
  7. 7Howard Hughes Medical Institute, Cold Spring Harbor, New York 11724, USA

    Abstract

    The genetic heterogeneity of cancer influences the trajectory of tumor progression and may underlie clinical variation in therapy response. To model such heterogeneity, we produced genetically and pathologically accurate mouse models of common forms of human acute myeloid leukemia (AML) and developed methods to mimic standard induction chemotherapy and efficiently monitor therapy response. We see that murine AMLs harboring two common human AML genotypes show remarkably diverse responses to conventional therapy that mirror clinical experience. Specifically, murine leukemias expressing the AML1/ETO fusion oncoprotein, associated with a favorable prognosis in patients, show a dramatic response to induction chemotherapy owing to robust activation of the p53 tumor suppressor network. Conversely, murine leukemias expressing MLL fusion proteins, associated with a dismal prognosis in patients, are drug-resistant due to an attenuated p53 response. Our studies highlight the importance of genetic information in guiding the treatment of human AML, functionally establish the p53 network as a central determinant of chemotherapy response in AML, and demonstrate that genetically engineered mouse models of human cancer can accurately predict therapy response in patients.

    Keywords:

    Keywords

    Footnotes

    | Table of Contents

    Life Science Alliance