Literature DB >> 15820940

In chronic myeloid leukemia white cells from cytogenetic responders and non-responders to imatinib have very similar gene expression signatures.

Lucy C Crossman1, Motomi Mori, Yi-Ching Hsieh, Thoralf Lange, Peter Paschka, Christina A Harrington, Knut Krohn, Dietger W Niederwieser, Rüdiger Hehlmann, Andreas Hochhaus, Brian J Druker, Michael W N Deininger.   

Abstract

BACKGROUND AND OBJECTIVES: Imatinib induces complete cytogenetic responses (CCR) in the majority of patients with chronic myeloid leukemia (CML) in chronic phase (CP). However, a subgroup of patients is refractory at the cytogenetic level. Clinically, it would be advantageous to identify such patients a priori, since they may benefit from more aggressive therapy. DESIGN AND METHODS: To elucidate mechanisms underlying cytogenetic refractoriness, we used Affymetrix oligonucleotide arrays to determine the transcriptional signature associated with cytogenetic refractoriness in unselected white blood or bone marrow cells from 29 patients with CML in first CP prior to treatment with imatinib. Patients with CCR within 9 months were defined as responders (n = 16) and patients lacking a major cytogenetic response (> 35% Philadelphia-positive metaphases) after 1 year were defined as non-responders (n = 13).
RESULTS: Differences in gene expression between responders and non-responders were subtle. Stringent statistical analysis with multiple comparison adjustments revealed very few differentially expressed genes. Differentially expressed genes could not be confirmed in an independent test set. INTERPRETATION AND
CONCLUSIONS: We conclude that transcriptional profiling of unselected white cells is of limited value for identifying genes consistently associated with cytogenetic refractoriness to imatinib.

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Year:  2005        PMID: 15820940

Source DB:  PubMed          Journal:  Haematologica        ISSN: 0390-6078            Impact factor:   9.941


  16 in total

1.  Gene expression signature that predicts early molecular response failure in chronic-phase CML patients on frontline imatinib.

Authors:  Chung H Kok; David T Yeung; Liu Lu; Dale B Watkins; Tamara M Leclercq; Phuong Dang; Verity A Saunders; John Reynolds; Deborah L White; Timothy P Hughes
Journal:  Blood Adv       Date:  2019-05-28

2.  A method of gene expression data transfer from cell lines to cancer patients for machine-learning prediction of drug efficiency.

Authors:  Nicolas Borisov; Victor Tkachev; Maria Suntsova; Olga Kovalchuk; Alex Zhavoronkov; Ilya Muchnik; Anton Buzdin
Journal:  Cell Cycle       Date:  2018-01-17       Impact factor: 4.534

3.  Dysregulation of the mitogen granulin in human cancer through the miR-15/107 microRNA gene group.

Authors:  Wang-Xia Wang; Natasha Kyprianou; Xiaowei Wang; Peter T Nelson
Journal:  Cancer Res       Date:  2010-09-30       Impact factor: 12.701

4.  A gene expression signature of CD34+ cells to predict major cytogenetic response in chronic-phase chronic myeloid leukemia patients treated with imatinib.

Authors:  Shannon K McWeeney; Lucy C Pemberton; Marc M Loriaux; Kristina Vartanian; Stephanie G Willis; Gregory Yochum; Beth Wilmot; Yaron Turpaz; Raji Pillai; Brian J Druker; Jennifer L Snead; Mary MacPartlin; Stephen G O'Brien; Junia V Melo; Thoralf Lange; Christina A Harrington; Michael W N Deininger
Journal:  Blood       Date:  2009-10-16       Impact factor: 22.113

Review 5.  Exploring pathways from gene co-expression to network dynamics.

Authors:  Huai Li; Yu Sun; Ming Zhan
Journal:  Methods Mol Biol       Date:  2009

6.  Predictors of primary imatinib resistance in chronic myelogenous leukemia are distinct from those in secondary imatinib resistance.

Authors:  Wenyong W Zhang; Jorge E Cortes; Hui Yao; Li Zhang; Neelima G Reddy; Elias Jabbour; Hagop M Kantarjian; Dan Jones
Journal:  J Clin Oncol       Date:  2009-06-08       Impact factor: 44.544

7.  Rank-rank hypergeometric overlap: identification of statistically significant overlap between gene-expression signatures.

Authors:  Seema B Plaisier; Richard Taschereau; Justin A Wong; Thomas G Graeber
Journal:  Nucleic Acids Res       Date:  2010-07-21       Impact factor: 16.971

8.  Pathway and gene-set activation measurement from mRNA expression data: the tissue distribution of human pathways.

Authors:  David M Levine; David R Haynor; John C Castle; Sergey B Stepaniants; Matteo Pellegrini; Mao Mao; Jason M Johnson
Journal:  Genome Biol       Date:  2006-10-17       Impact factor: 13.583

9.  Integration of genome and chromatin structure with gene expression profiles to predict c-MYC recognition site binding and function.

Authors:  Yili Chen; Thomas W Blackwell; Ji Chen; Jing Gao; Angel W Lee; David J States
Journal:  PLoS Comput Biol       Date:  2007-04-06       Impact factor: 4.475

10.  Establishing a major cause of discrepancy in the calibration of Affymetrix GeneChips.

Authors:  Andrew P Harrison; Caroline E Johnston; Christine A Orengo
Journal:  BMC Bioinformatics       Date:  2007-06-11       Impact factor: 3.169

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