Literature DB >> 24559255

Risk stratification in myelodysplastic syndromes: is there a role for gene expression profiling?

Amer M Zeidan1, Thomas Prebet, Ehab Saad Aldin, Steven David Gore.   

Abstract

Evaluation of: Pellagatti A, Benner A, Mills KI et al. Identification of gene expression-based prognostic markers in the hematopoietic stem cells of patients with myelodysplastic syndromes. J. Clin. Oncol. 31(28), 3557-3564 (2013). Patients with myelodysplastic syndromes (MDS) exhibit wide heterogeneity in clinical outcomes making accurate risk-stratification an integral part of the risk-adaptive management paradigm. Current prognostic schemes for MDS rely on clinicopathological parameters. Despite the increasing knowledge of the genetic landscape of MDS and the prognostic impact of many newly discovered molecular aberrations, none to date has been incorporated formally into the major risk models. Efforts are ongoing to use data generated from genome-wide high-throughput techniques to improve the 'individualized' outcome prediction for patients. We here discuss an important paper in which gene expression profiling (GEP) technology was applied to marrow CD34(+) cells from 125 MDS patients to generate and validate a standardized GEP-based prognostic signature.

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Year:  2014        PMID: 24559255      PMCID: PMC4124601          DOI: 10.1586/17474086.2014.891437

Source DB:  PubMed          Journal:  Expert Rev Hematol        ISSN: 1747-4094            Impact factor:   2.929


  25 in total

Review 1.  Beyond hypomethylating agents failure in patients with myelodysplastic syndromes.

Authors:  Amer M Zeidan; Mohamed A Kharfan-Dabaja; Rami S Komrokji
Journal:  Curr Opin Hematol       Date:  2014-03       Impact factor: 3.284

2.  Independent validation of a prognostic genomic signature (ColoPrint) for patients with stage II colon cancer.

Authors:  Matthias Maak; Iris Simon; Ulrich Nitsche; Paul Roepman; Mireille Snel; Annuska M Glas; Tibor Schuster; Gisela Keller; Eliane Zeestraten; Inès Goossens; Klaus-Peter Janssen; Helmut Friess; Robert Rosenberg
Journal:  Ann Surg       Date:  2013-06       Impact factor: 12.969

3.  Clinical effect of point mutations in myelodysplastic syndromes.

Authors:  Rafael Bejar; Kristen Stevenson; Omar Abdel-Wahab; Naomi Galili; Björn Nilsson; Guillermo Garcia-Manero; Hagop Kantarjian; Azra Raza; Ross L Levine; Donna Neuberg; Benjamin L Ebert
Journal:  N Engl J Med       Date:  2011-06-30       Impact factor: 91.245

4.  High-resolution whole genome tiling path array CGH analysis of CD34+ cells from patients with low-risk myelodysplastic syndromes reveals cryptic copy number alterations and predicts overall and leukemia-free survival.

Authors:  Daniel T Starczynowski; Suzanne Vercauteren; Adele Telenius; Sandy Sung; Kaoru Tohyama; Angela Brooks-Wilson; John J Spinelli; Connie J Eaves; Allen C Eaves; Douglas E Horsman; Wan L Lam; Aly Karsan
Journal:  Blood       Date:  2008-07-28       Impact factor: 22.113

5.  Marked downregulation of the granulopoiesis regulator LEF1 is associated with disease progression in the myelodysplastic syndromes.

Authors:  Andrea Pellagatti; Teresa Marafioti; Jennifer C Paterson; Luca Malcovati; Matteo G Della Porta; Martin Jädersten; Beena Pushkaran; Tracy I George; Daniel A Arber; Sally Killick; Aristoteles Giagounidis; Eva Hellström-Lindberg; Mario Cazzola; James S Wainscoat; Jacqueline Boultwood
Journal:  Br J Haematol       Date:  2009-05-05       Impact factor: 6.998

6.  Towards the integration, annotation and association of historical microarray experiments with RNA-seq.

Authors:  Shweta S Chavan; Michael A Bauer; Erich A Peterson; Christoph J Heuck; Donald J Johann
Journal:  BMC Bioinformatics       Date:  2013-10-09       Impact factor: 3.169

Review 7.  The genetic basis of phenotypic heterogeneity in myelodysplastic syndromes.

Authors:  Azra Raza; Naomi Galili
Journal:  Nat Rev Cancer       Date:  2012-12       Impact factor: 60.716

Review 8.  Molecular profiling for breast cancer: a comprehensive review.

Authors:  Muaiad Kittaneh; Alberto J Montero; Stefan Glück
Journal:  Biomark Cancer       Date:  2013-10-29

9.  Clinical and biological implications of driver mutations in myelodysplastic syndromes.

Authors:  Elli Papaemmanuil; Moritz Gerstung; Luca Malcovati; Sudhir Tauro; Gunes Gundem; Peter Van Loo; Chris J Yoon; Peter Ellis; David C Wedge; Andrea Pellagatti; Adam Shlien; Michael John Groves; Simon A Forbes; Keiran Raine; Jon Hinton; Laura J Mudie; Stuart McLaren; Claire Hardy; Calli Latimer; Matteo G Della Porta; Sarah O'Meara; Ilaria Ambaglio; Anna Galli; Adam P Butler; Gunilla Walldin; Jon W Teague; Lynn Quek; Alex Sternberg; Carlo Gambacorti-Passerini; Nicholas C P Cross; Anthony R Green; Jacqueline Boultwood; Paresh Vyas; Eva Hellstrom-Lindberg; David Bowen; Mario Cazzola; Michael R Stratton; Peter J Campbell
Journal:  Blood       Date:  2013-09-12       Impact factor: 22.113

10.  An erythroid differentiation signature predicts response to lenalidomide in myelodysplastic syndrome.

Authors:  Benjamin L Ebert; Naomi Galili; Pablo Tamayo; Jocelyn Bosco; Raymond Mak; Jennifer Pretz; Shyam Tanguturi; Christine Ladd-Acosta; Richard Stone; Todd R Golub; Azra Raza
Journal:  PLoS Med       Date:  2008-02       Impact factor: 11.069

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  3 in total

Review 1.  Molecular Testing in Myelodysplastic Syndromes for the Practicing Oncologist: Will the Progress Fulfill the Promise?

Authors:  Aziz Nazha; Mikkael A Sekeres; Steven D Gore; Amer M Zeidan
Journal:  Oncologist       Date:  2015-07-20

2.  A two-stage approach for combining gene expression and mutation with clinical data improves survival prediction in myelodysplastic syndromes and ovarian cancer.

Authors:  Yan Li; Xinyan Zhang; Tomi Akinyemiju; Akinyemi I Ojesina; Jeff M Szychowski; Nianjun Liu; Bo Xu; Nengjun Yi
Journal:  J Bioinform Genom       Date:  2016-09-15

3.  DECO: decompose heterogeneous population cohorts for patient stratification and discovery of sample biomarkers using omic data profiling.

Authors:  F J Campos-Laborie; A Risueño; M Ortiz-Estévez; B Rosón-Burgo; C Droste; C Fontanillo; R Loos; J M Sánchez-Santos; M W Trotter; J De Las Rivas
Journal:  Bioinformatics       Date:  2019-10-01       Impact factor: 6.937

  3 in total

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