BACKGROUND AND OBJECTIVES: From 20-50% of patients with acute myeloid leukemia (AML) are primarily resistant to induction chemotherapy. It has previously been shown that resistance to the first cycle of induction chemotherapy is an independent prognostic factor. We investigated whether resistance to chemotherapy be represented by gene-expression profiles, and which genes are associated with resistance. DESIGN AND METHODS: cDNA microarrays containing approximately 41,000 features were used to compare the gene-expression profile of AML blasts between 33 patients with good or poor response to induction chemotherapy. Data generated by cDNA-arrays were confirmed by quantitative reverse transcription polymerase chain reaction. RESULTS: Using significance analysis of microarrays, we identified a characteristic gene-expression profile which distinguished AML samples from patients with good or poor responses. In hierarchical clustering analysis poor responders clustered together with normal CD34+ cells. Moreover, 13/40 (32.5%) genes highly expressed in poor responders are also overexpressed in hematopoietic stem/progenitor cells. Prediction analysis using 10-fold cross-validation revealed an 80% overall accuracy. Using the treatment-response signature to predict the outcome in an independent test set of 104 AML patients, samples were separated into two subgroups with significantly inferior response rate (43.5% vs. 66.7%, p=0.04), significantly shorter event-free and overall survival (p=0.01 and p=0.03, respectively) in the poor-response compared to in the good-response signature group. In multivariate analysis, the treatment-response signature was an independent prognostic factor (hazard ratio, 2.1, 95% confidence interval 1.2 to 3.6, p=0.006). INTERPRETATION AND CONCLUSIONS: Resistance to chemotherapy in AML can be identified by gene-expression profiling before treatment and seems to be mediated by a transcriptional program active in hematopoietic stem/progenitor cells.
BACKGROUND AND OBJECTIVES: From 20-50% of patients with acute myeloid leukemia (AML) are primarily resistant to induction chemotherapy. It has previously been shown that resistance to the first cycle of induction chemotherapy is an independent prognostic factor. We investigated whether resistance to chemotherapy be represented by gene-expression profiles, and which genes are associated with resistance. DESIGN AND METHODS: cDNA microarrays containing approximately 41,000 features were used to compare the gene-expression profile of AML blasts between 33 patients with good or poor response to induction chemotherapy. Data generated by cDNA-arrays were confirmed by quantitative reverse transcription polymerase chain reaction. RESULTS: Using significance analysis of microarrays, we identified a characteristic gene-expression profile which distinguished AML samples from patients with good or poor responses. In hierarchical clustering analysis poor responders clustered together with normal CD34+ cells. Moreover, 13/40 (32.5%) genes highly expressed in poor responders are also overexpressed in hematopoietic stem/progenitor cells. Prediction analysis using 10-fold cross-validation revealed an 80% overall accuracy. Using the treatment-response signature to predict the outcome in an independent test set of 104 AMLpatients, samples were separated into two subgroups with significantly inferior response rate (43.5% vs. 66.7%, p=0.04), significantly shorter event-free and overall survival (p=0.01 and p=0.03, respectively) in the poor-response compared to in the good-response signature group. In multivariate analysis, the treatment-response signature was an independent prognostic factor (hazard ratio, 2.1, 95% confidence interval 1.2 to 3.6, p=0.006). INTERPRETATION AND CONCLUSIONS: Resistance to chemotherapy in AML can be identified by gene-expression profiling before treatment and seems to be mediated by a transcriptional program active in hematopoietic stem/progenitor cells.
Authors: A Sharma; H Yun; N Jyotsana; A Chaturvedi; A Schwarzer; E Yung; C K Lai; F Kuchenbauer; B Argiropoulos; K Görlich; A Ganser; R K Humphries; M Heuser Journal: Leukemia Date: 2014-05-20 Impact factor: 11.528
Authors: Sebastian Schwind; Guido Marcucci; Jessica Kohlschmidt; Michael D Radmacher; Krzysztof Mrózek; Kati Maharry; Heiko Becker; Klaus H Metzeler; Susan P Whitman; Yue-Zhong Wu; Bayard L Powell; Maria R Baer; Jonathan E Kolitz; Andrew J Carroll; Richard A Larson; Michael A Caligiuri; Clara D Bloomfield Journal: Blood Date: 2011-08-09 Impact factor: 22.113
Authors: Christian Langer; Michael D Radmacher; Amy S Ruppert; Susan P Whitman; Peter Paschka; Krzysztof Mrózek; Claudia D Baldus; Tamara Vukosavljevic; Chang-Gong Liu; Mary E Ross; Bayard L Powell; Albert de la Chapelle; Jonathan E Kolitz; Richard A Larson; Guido Marcucci; Clara D Bloomfield Journal: Blood Date: 2008-03-31 Impact factor: 22.113
Authors: Klaus H Metzeler; Manuela Hummel; Clara D Bloomfield; Karsten Spiekermann; Jan Braess; Maria-Cristina Sauerland; Achim Heinecke; Michael Radmacher; Guido Marcucci; Susan P Whitman; Kati Maharry; Peter Paschka; Richard A Larson; Wolfgang E Berdel; Thomas Büchner; Bernhard Wörmann; Ulrich Mansmann; Wolfgang Hiddemann; Stefan K Bohlander; Christian Buske Journal: Blood Date: 2008-08-20 Impact factor: 22.113
Authors: Nicolas Bonadies; Samuel D Foster; Wai-In Chan; Brynn T Kvinlaug; Dominik Spensberger; Mark A Dawson; Elaine Spooncer; Anthony D Whetton; Andrew J Bannister; Brian J Huntly; Berthold Göttgens Journal: PLoS One Date: 2011-01-28 Impact factor: 3.240