Literature DB >> 17485551

Gene-expression profiling identifies distinct subclasses of core binding factor acute myeloid leukemia.

Lars Bullinger1, Frank G Rücker, Stephan Kurz, Juan Du, Claudia Scholl, Sandrine Sander, Andrea Corbacioglu, Claudio Lottaz, Jürgen Krauter, Stefan Fröhling, Arnold Ganser, Richard F Schlenk, Konstanze Döhner, Jonathan R Pollack, Hartmut Döhner.   

Abstract

Core binding factor (CBF) leukemias, characterized by either inv(16)/t(16;16) or t(8;21), constitute acute myeloid leukemia (AML) subgroups with favorable prognosis. However, there exists substantial biologic and clinical heterogeneity within these cytogenetic groups that is not fully reflected by the current classification system. To improve the molecular characterization we profiled gene expression in a large series (n = 93) of AML patients with CBF leukemia [(inv (16), n = 55; t(8;21), n = 38)]. By unsupervised hierarchical clustering we were able to define a subgroup of CBF cases (n = 35) characterized by shorter overall survival times (P = .03). While there was no obvious correlation with fusion gene transcript levels, FLT3 tyrosine kinase domain, KIT, and NRAS mutations, the newly defined inv(16)/t(8;21) subgroup was associated with elevated white blood cell counts and FLT3 internal tandem duplications (P = .011 and P = .026, respectively). Supervised analyses of gene expression suggested alternative cooperating pathways leading to transformation. In the "favorable" CBF leukemias, antiapoptotic mechanisms and deregulated mTOR signaling and, in the newly defined "unfavorable" subgroup, aberrant MAPK signaling and chemotherapy-resistance mechanisms might play a role. While the leukemogenic relevance of these signatures remains to be validated, their existence nevertheless supports a prognostically relevant biologic basis for the heterogeneity observed in CBF leukemia.

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Year:  2007        PMID: 17485551     DOI: 10.1182/blood-2006-10-049783

Source DB:  PubMed          Journal:  Blood        ISSN: 0006-4971            Impact factor:   22.113


  32 in total

1.  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

2.  The genomic landscape of core-binding factor acute myeloid leukemias.

Authors:  Zachary J Faber; Xiang Chen; Amanda Larson Gedman; Kristy Boggs; Jinjun Cheng; Jing Ma; Ina Radtke; Jyh-Rong Chao; Michael P Walsh; Guangchun Song; Anna K Andersson; Jinjun Dang; Li Dong; Yu Liu; Robert Huether; Zhongling Cai; Heather Mulder; Gang Wu; Michael Edmonson; Michael Rusch; Chunxu Qu; Yongjin Li; Bhavin Vadodaria; Jianmin Wang; Erin Hedlund; Xueyuan Cao; Donald Yergeau; Joy Nakitandwe; Stanley B Pounds; Sheila Shurtleff; Robert S Fulton; Lucinda L Fulton; John Easton; Evan Parganas; Ching-Hon Pui; Jeffrey E Rubnitz; Li Ding; Elaine R Mardis; Richard K Wilson; Tanja A Gruber; Charles G Mullighan; Richard F Schlenk; Peter Paschka; Konstanze Döhner; Hartmut Döhner; Lars Bullinger; Jinghui Zhang; Jeffery M Klco; James R Downing
Journal:  Nat Genet       Date:  2016-10-31       Impact factor: 38.330

Review 3.  DNA damage accumulation and repair defects in acute myeloid leukemia: implications for pathogenesis, disease progression, and chemotherapy resistance.

Authors:  Maria Teresa Esposito; Chi Wai Eric So
Journal:  Chromosoma       Date:  2014-08-12       Impact factor: 4.316

4.  Identification of a 24-gene prognostic signature that improves the European LeukemiaNet risk classification of acute myeloid leukemia: an international collaborative study.

Authors:  Zejuan Li; Tobias Herold; Chunjiang He; Peter J M Valk; Ping Chen; Vindi Jurinovic; Ulrich Mansmann; Michael D Radmacher; Kati S Maharry; Miao Sun; Xinan Yang; Hao Huang; Xi Jiang; Maria-Cristina Sauerland; Thomas Büchner; Wolfgang Hiddemann; Abdel Elkahloun; Mary Beth Neilly; Yanming Zhang; Richard A Larson; Michelle M Le Beau; Michael A Caligiuri; Konstanze Döhner; Lars Bullinger; Paul P Liu; Ruud Delwel; Guido Marcucci; Bob Lowenberg; Clara D Bloomfield; Janet D Rowley; Stefan K Bohlander; Jianjun Chen
Journal:  J Clin Oncol       Date:  2013-02-04       Impact factor: 44.544

5.  Modeling interactions between leukemia-specific chromosomal changes, somatic mutations, and gene expression patterns during progression of core-binding factor leukemias.

Authors:  Dan Jones; Hui Yao; Angela Romans; Caroline Dando; Sherry Pierce; Gautam Borthakur; Amy Hamilton; Carlos Bueso-Ramos; Farhad Ravandi; Guillermo Garcia-Manero; Hagop Kantarjian
Journal:  Genes Chromosomes Cancer       Date:  2010-02       Impact factor: 5.006

6.  POU4F1 is associated with t(8;21) acute myeloid leukemia and contributes directly to its unique transcriptional signature.

Authors:  J M Fortier; J E Payton; P Cahan; T J Ley; M J Walter; T A Graubert
Journal:  Leukemia       Date:  2010-04-08       Impact factor: 11.528

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

Review 8.  A decade of genome-wide gene expression profiling in acute myeloid leukemia: flashback and prospects.

Authors:  Bas J Wouters; Bob Löwenberg; Ruud Delwel
Journal:  Blood       Date:  2008-08-14       Impact factor: 22.113

9.  p53 signaling in response to increased DNA damage sensitizes AML1-ETO cells to stress-induced death.

Authors:  Ondrej Krejci; Mark Wunderlich; Hartmut Geiger; Fu-Sheng Chou; David Schleimer; Michael Jansen; Paul R Andreassen; James C Mulloy
Journal:  Blood       Date:  2007-11-01       Impact factor: 22.113

Review 10.  MicroRNAs: new players in acute myeloid leukaemia.

Authors:  V Havelange; R Garzon; C M Croce
Journal:  Br J Cancer       Date:  2009-08-11       Impact factor: 7.640

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