Literature DB >> 12800151

Molecular characterization of acute leukemias by use of microarray technology.

Alexander Kohlmann1, Claudia Schoch, Susanne Schnittger, Martin Dugas, Wolfgang Hiddemann, Wolfgang Kern, Torsten Haferlach.   

Abstract

Accurate subclassification of leukemia and the identification of prognostic determinants are essential to guide therapy and to improve patients' outcome. According to present standards, pre-therapeutic assessment depends on a combination of different methods. We aimed to expand the molecular characterization of different acute leukemia subtypes to identify new genome-wide diagnostic markers. Total RNA from 90 adult patients suffering from acute lymphoblastic leukemia (ALL, n = 25) and acute myeloid leukemia (AML, n = 65) was extracted at diagnosis and high density oligonucleotide microarrays were used to analyze the expression profiles of 12,000/22,000 genes in all specimens (Affymetrix U95Av2/U133A). All cases were thoroughly characterized by individual combinations of cytomorphology, cytogenetics, multiparameter immunophenotyping, and molecular genetics. The expression signature of a small set of differentially expressed genes was sufficient to accurately discriminate eight clinically relevant acute leukemia subgroups. Underlying chromosomal aberrations or immunophenotypical characteristics were strictly correlated with a distinct gene expression pattern for AML with t(8;21), t(15;17), t(11q23)/MLL, or inv(16) as well as for precursor B-ALL with t(9;22), t(8;14), or t(11q23)/MLL and precursor T-ALL. These data support a possible future application of microarray technology for classification of the acute leukemias. Copyright 2003 Wiley-Liss, Inc.

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Year:  2003        PMID: 12800151     DOI: 10.1002/gcc.10225

Source DB:  PubMed          Journal:  Genes Chromosomes Cancer        ISSN: 1045-2257            Impact factor:   5.006


  28 in total

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Authors:  Adolfo A Ferrando; A Thomas Look
Journal:  Int J Hematol       Date:  2004-12       Impact factor: 2.490

2.  Evaluation of gene expression signatures predictive of cytogenetic and molecular subtypes of pediatric acute myeloid leukemia.

Authors:  Brian V Balgobind; Marry M Van den Heuvel-Eibrink; Renee X De Menezes; Dirk Reinhardt; Iris H I M Hollink; Susan T J C M Arentsen-Peters; Elisabeth R van Wering; Gertjan J L Kaspers; Jacqueline Cloos; Evelien S J M de Bont; Jean-Michel Cayuela; Andre Baruchel; Claus Meyer; Rolf Marschalek; Jan Trka; Jan Stary; H Berna Beverloo; Rob Pieters; C Michel Zwaan; Monique L den Boer
Journal:  Haematologica       Date:  2010-10-22       Impact factor: 9.941

3.  Gene expression profiles in acute myeloid leukemia with common translocations using SAGE.

Authors:  Sanggyu Lee; Jianjun Chen; Guolin Zhou; Run Zhang Shi; Gerard G Bouffard; Masha Kocherginsky; Xijin Ge; Miao Sun; Nimanthi Jayathilaka; Yeong Cheol Kim; Neelmini Emmanuel; Stefan K Bohlander; Mark Minden; Justin Kline; Ozden Ozer; Richard A Larson; Michelle M LeBeau; Eric D Green; Jeffery Trent; Theodore Karrison; Piu Paul Liu; San Ming Wang; Janet D Rowley
Journal:  Proc Natl Acad Sci U S A       Date:  2006-01-17       Impact factor: 11.205

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

Authors:  Roel G W Verhaak; Bas J Wouters; Claudia A J Erpelinck; Saman Abbas; H Berna Beverloo; Sanne Lugthart; Bob Löwenberg; Ruud Delwel; Peter J M Valk
Journal:  Haematologica       Date:  2008-10-06       Impact factor: 9.941

5.  Heat shock protein 90 is critical for regulation of phenotype and functional activity of human T lymphocytes and NK cells.

Authors:  Jooeun Bae; Aditya Munshi; Cheng Li; Mehmet Samur; Rao Prabhala; Constantine Mitsiades; Kenneth C Anderson; Nikhil C Munshi
Journal:  J Immunol       Date:  2013-01-04       Impact factor: 5.422

6.  Expression profiling of murine acute promyelocytic leukemia cells reveals multiple model-dependent progression signatures.

Authors:  Matthew J Walter; John S Park; Steven K M Lau; Xia Li; Andrew A Lane; Rakesh Nagarajan; William D Shannon; Timothy J Ley
Journal:  Mol Cell Biol       Date:  2004-12       Impact factor: 4.272

7.  Integrative meta-analysis of differential gene expression in acute myeloid leukemia.

Authors:  Brady G Miller; John A Stamatoyannopoulos
Journal:  PLoS One       Date:  2010-03-01       Impact factor: 3.240

8.  Identification of genomic classifiers that distinguish induction failure in T-lineage acute lymphoblastic leukemia: a report from the Children's Oncology Group.

Authors:  Stuart S Winter; Zeyu Jiang; Hadya M Khawaja; Timothy Griffin; Meenakshi Devidas; Barbara L Asselin; Richard S Larson
Journal:  Blood       Date:  2007-05-10       Impact factor: 22.113

9.  Bioinformatics for medical diagnostics: assessment of microarray data in the context of clinical databases.

Authors:  M Dugas; S Merk; S Breit; C Schoch; T Haferlach; S Kääb
Journal:  AMIA Annu Symp Proc       Date:  2003

10.  Applications of microarray technology to Acute Myelogenous Leukemia.

Authors:  Rashmi S Goswami; Mahadeo A Sukhai; Mariam Thomas; Patricia P Reis; Suzanne Kamel-Reid
Journal:  Cancer Inform       Date:  2008-12-22
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