• Media type: E-Article
  • Title: Drug-Response Signature Predicts Outcome in Adult Acute Myeloid Leukemia and Associates Poor Response with Molecular Characteristics of Hematopoietic Stem Cells
  • Contributor: Heuser, Michael; Wingen, Luzie U.; Steinemann, Doris; Cario, Gunnar; von Neuhoff, Nils; Tauscher, Marcel; Bullinger, Lars; Dohner, Hartmut; Schlegelberger, Brigitte; Ganser, Arnold
  • Published: American Society of Hematology, 2004
  • Published in: Blood, 104 (2004) 11, Seite 2024-2024
  • Language: English
  • DOI: 10.1182/blood.v104.11.2024.2024
  • ISSN: 0006-4971; 1528-0020
  • Keywords: Cell Biology ; Hematology ; Immunology ; Biochemistry
  • Origination:
  • Footnote:
  • Description: Abstract Resistance to induction chemotherapy is of independent prognostic value in acute myeloid leukemia (AML). DNA microarrays were used to determine the gene-expression profile of AML blasts in 38 patients with good or poor response to induction chemotherapy. We selected an 11-sample training set, applying prediction analysis of microarrays (PAM) to devise a drug-response predictor which was tested on the remaining 27 samples and an independent set of samples recently published (Bullinger et al. 2004). Our drug-response predictor with 46 clones divided the 27 samples into two prognostic subgroups, the poor response group having a significantly shorter overall survival (P= .021). A subset of these 46 clones was sufficient to divide the published 62-sample test-set with intermediate risk cytogenetics into prognostically relevant subgroups (P= .028), adding prognostic information to that of known risk factors in multivariate analysis (hazard ratio, 2.8; 95 percent confidence interval, 1.4 to 5.8; P= .005). Thirteen of 39 drug resistance-enriched genes are known to be upregulated in hematopoietic stem/progenitor cells, and the expression pattern in normal CD34+ cells is highly correlated to the drug-resistance signature. This suggests that drug resistant AMLs show molecular features of hematopoietic stem/progenitor cells and can be identified by a characteristic gene-expression profile.
  • Access State: Open Access