Literature DB >> 20570445

Gene expression profiling for diagnosis and therapy in acute leukaemia and other haematologic malignancies.

Ulrike Bacher1, Alexander Kohlmann, Torsten Haferlach.   

Abstract

A decade ago, gene expression profiling (GEP) was successfully introduced in haematological research. Considering the heterogeneity of haematological malignancies, the growing arsenal of compounds, allowing targeted therapy, e.g. in myelodysplastic syndromes (MDS) or chronic myeloid leukaemia (CML), and the more differentiated indication to allogeneic stem cell transplantation, routine diagnostic procedures would highly benefit from an introduction of this novel methodology: by now, the majority of genetically defined leukaemia subtypes has been accurately reproduced on the basis of distinct gene expression patterns by various independent research groups. Moreover, classification of histomorphologically overlapping lymphoma subentities (e.g. Burkitt lymphoma and diffuse large B-cell lymphoma, DLBCL), was considerably improved by GEP. Beyond that, differential gene expression has provided the basis for assays being able to predict prognosis of individual patients as well as the response to specific treatment approaches, e.g. to lenalidomide in MDS. In a high proportion of Philadelphia positive acute lymphoblastic leukaemia (ALL) patients, prognostically adverse deletions of the IKZF1 gene coding for a specific transcription factor were identified with GEP analysis, which revealed new insights in the clinical variability of this disorder. Given these advantages of GEP, the introduction of this methodology in current diagnostic algorithms of haematological malignancies should further be validated in clinical studies.
Copyright © 2010 Elsevier Ltd. All rights reserved.

Entities:  

Mesh:

Substances:

Year:  2010        PMID: 20570445     DOI: 10.1016/j.ctrv.2010.05.002

Source DB:  PubMed          Journal:  Cancer Treat Rev        ISSN: 0305-7372            Impact factor:   12.111


  4 in total

1.  Expression and distribution of PPP2R5C gene in leukemia.

Authors:  Haitao Zheng; Yu Chen; Shaohua Chen; Yuzhe Niu; Lijian Yang; Bo Li; Yuhong Lu; Suxia Geng; Xin Du; Yangqiu Li
Journal:  J Hematol Oncol       Date:  2011-05-06       Impact factor: 17.388

2.  Characterization of Rare, Dormant, and Therapy-Resistant Cells in Acute Lymphoblastic Leukemia.

Authors:  Sarah Ebinger; Erbey Ziya Özdemir; Christoph Ziegenhain; Sebastian Tiedt; Catarina Castro Alves; Michaela Grunert; Michael Dworzak; Christoph Lutz; Virginia A Turati; Tariq Enver; Hans-Peter Horny; Karl Sotlar; Swati Parekh; Karsten Spiekermann; Wolfgang Hiddemann; Aloys Schepers; Bernhard Polzer; Stefan Kirsch; Martin Hoffmann; Bettina Knapp; Jan Hasenauer; Heike Pfeifer; Renate Panzer-Grümayer; Wolfgang Enard; Olivier Gires; Irmela Jeremias
Journal:  Cancer Cell       Date:  2016-12-01       Impact factor: 31.743

3.  Gene expression profiling of acute myeloid leukemia samples from adult patients with AML-M1 and -M2 through boutique microarrays, real-time PCR and droplet digital PCR.

Authors:  Luiza Handschuh; Maciej Kaźmierczak; Marek C Milewski; Michał Góralski; Magdalena Łuczak; Marzena Wojtaszewska; Barbara Uszczyńska-Ratajczak; Krzysztof Lewandowski; Mieczysław Komarnicki; Marek Figlerowicz
Journal:  Int J Oncol       Date:  2017-12-28       Impact factor: 5.650

4.  Identifying novel genetic alterations in pediatric acute lymphoblastic leukemia based on copy number analysis.

Authors:  Jéssica Almeida Batista-Gomes; Fernando Augusto Rodrigues Mello; Edivaldo Herculano Corrêa de Oliveira; Michel Platini Caldas de Souza; Alayde Vieira Wanderley; Laudreisa da Costa Pantoja; Ney Pereira Carneiro Dos Santos; Bruna Cláudia Meireles Khayat; André Salim Khayat
Journal:  Mol Cytogenet       Date:  2020-06-26       Impact factor: 2.009

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.