Published online 6 October 2008
Haematologica, Vol 94, Issue 1, 131-134 doi:10.3324/haematol.13299
Copyright © 2009 by Ferrata Storti Foundation

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Acute Myeloid Leukemia

Prediction of molecular subtypes in acute myeloid leukemia based on gene expression profiling

Roel G.W. Verhaak^1,2, Bas J. Wouters¹, Claudia A.J. Erpelinck¹, Saman Abbas¹, H. Berna Beverloo³, Sanne Lugthart¹, Bob Löwenberg¹, Ruud Delwel¹, Peter J.M. Valk¹

¹ Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands
² Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA; The Broad Institute of M.I.T. and Harvard, Cambridge, USA
³ Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands

Correspondence: Peter J.M. Valk, Erasmus University Medical Center Rotterdam, Department of Hematology, Ee1391a, Dr. Molewaterplein 50, 3015 GE Rotterdam Z-H, The Netherlands. E-mail:p.valk{at}erasmusmc.nl

We examined the gene expression profiles of two independent cohorts of patients with acute myeloid leukemia [n=247 and n=214 (younger than or equal to 60 years)] to study the applicability of gene expression profiling as a single assay in prediction of acute myeloid leukemia-specific molecular subtypes. The favorable cytogenetic acute myeloid leukemia subtypes, i.e., acute myeloid leukemia with t(8;21), t(15;17) or inv(16), were predicted with maximum accuracy (positive and negative predictive value: 100%). Mutations in NPM1 and CEBPA were predicted less accurately (positive predictive value: 66% and 100%, and negative predictive value: 99% and 97% respectively). Various other characteristic molecular acute myeloid leukemia subtypes, i.e., mutant FLT3 and RAS, abnormalities involving 11q23, –5/5q-, –7/7q-, abnormalities involving 3q (abn3q) and t(9;22), could not be correctly predicted using gene expression profiling. In conclusion, gene expression profiling allows accurate prediction of certain acute myeloid leukemia subtypes, e.g. those characterized by expression of chimeric transcription factors. However, detection of mutations affecting signaling molecules and numerical abnormalities still requires alternative molecular methods.

Key words: acute myeloid leukemia, gene expression profiling, prediction.

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