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Blood, 15 June 2008, Vol. 111, No. 12, pp. 5654-5662.
Prepublished online as a Blood First Edition Paper on February 27, 2008; DOI 10.1182/blood-2007-11-126003.


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Submitted November 26, 2007
Accepted February 24, 2008

Discovery of agents that eradicate leukemia stem cells using an in silico screen of public gene expression data

Duane C. Hassane, Monica L. Guzman, Cheryl Corbett, Xiaojie Li, Ramzi Abboud, Fay Young, Jane L. Liesveld, Martin Carroll, and Craig T. Jordan*

James P. Wilmot Cancer Center, University of Rochester School of Medicine and Dentistry, Rochester, NY, United States
Division of Hematology/Oncology, University of Pennsylvania, Philadelphia, PA, United States

* Corresponding author; email: craig_jordan{at}urmc.rochester.edu.

Increasing evidence indicates that malignant stem cells are important for the pathogenesis of acute myelogenous leukemia (AML), and represent a reservoir of cells that drive the development of AML and relapse. Therefore, new treatment regimens are necessary to prevent relapse and improve therapeutic outcomes. Previous studies have shown that the sesquiterpene lactone, parthenolide (PTL), ablates bulk, progenitor, and stem AML cells while causing no appreciable toxicity to normal hematopoietic cells. Thus, PTL must evoke cellular responses capable of mediating AML selective cell death. Given recent advances in chemical genomics such as gene expression-based high-throughput screening (GE-HTS) and the Connectivity Map, we hypothesized that the gene expression signature resulting from treatment of primary AML with PTL could be used to search for similar signatures in publicly available gene expression profiles deposited into the Gene Expression Omnibus (GEO). We therefore devised a broad in silico screen of the GEO database using the PTL gene expression signature as a template and discovered two new agents, celastrol and 4-hydroxy-2-nonenal, that effectively eradicate AML at the bulk, progenitor, and stem cell level. These findings suggest the utility of multi-center collections of high-throughput data to facilitate discovery of leukemia drugs and drug targets.


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