Mouse models of human AML accurately predict chemotherapy response.

TitleMouse models of human AML accurately predict chemotherapy response.
Publication TypeJournal Article
Year of Publication2009
AuthorsZuber J, Radtke I, Pardee TS, Zhao Z, Rappaport AR, Luo W, McCurrach ME, Yang M-M, M Dolan E, Kogan SC, Downing JR, Lowe SW
JournalGenes Dev
Volume23
Issue7
Pagination877-89
Date Published2009 Apr 01
ISSN1549-5477
KeywordsAnimals, Antineoplastic Agents, Core Binding Factor Alpha 2 Subunit, Disease Models, Animal, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Genes, ras, Genotype, Humans, Kaplan-Meier Estimate, Leukemia, Myeloid, Acute, Mice, Mice, Inbred C57BL, Myeloid-Lymphoid Leukemia Protein, Oncogene Proteins, Fusion, Prognosis, RUNX1 Translocation Partner 1 Protein, Tumor Suppressor Protein p53
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.

DOI10.1101/gad.1771409
Alternate JournalGenes Dev
PubMed ID19339691
PubMed Central IDPMC2666344
Grant ListP30 CA008748 / CA / NCI NIH HHS / United States
/ HHMI / Howard Hughes Medical Institute / United States
Related Faculty: 
Zhen Zhao, Ph.D.

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