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J Clin Oncol. 2013 Oct 1;31(28):3557-64. doi: 10.1200/JCO.2012.45.5626. Epub 2013 Sep 3.

Identification of gene expression-based prognostic markers in the hematopoietic stem cells of patients with myelodysplastic syndromes.

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Andrea Pellagatti, Janet Perry, James S. Wainscoat, and Jacqueline Boultwood, University of Oxford, Oxford; Ken I. Mills, Queen's University Belfast, Belfast; Emma-Jane McDonald and Sally Killick, Royal Bournemouth Hospital, Bournemouth, United Kingdom; Axel Benner, German Cancer Research Center, Heidelberg; Aristoteles Giagounidis, St Johannes Hospital, Duisburg; Lars Bullinger, University Hospital of Ulm, Ulm, Germany; Mario Cazzola, Luca Malcovati, and Matteo G. Della Porta, Fondazione Istituto di Ricovera e Cura a Carattere Scientifico Policlinico San Matteo, Pavia, Italy; Martin Jädersten and Eva Hellström-Lindberg, Karolinska Institutet, Stockholm, Sweden; and Amit Verma, Albert Einstein College of Medicine, New York, NY.



The diagnosis of patients with myelodysplastic syndromes (MDS) is largely dependent on morphologic examination of bone marrow aspirates. Several criteria that form the basis of the classifications and scoring systems most commonly used in clinical practice are affected by operator-dependent variation. To identify standardized molecular markers that would allow prediction of prognosis, we have used gene expression profiling (GEP) data on CD34+ cells from patients with MDS to determine the relationship between gene expression levels and prognosis.


GEP data on CD34+ cells from 125 patients with MDS with a minimum 12-month follow-up since date of bone marrow sample collection were included in this study. Supervised principal components and lasso penalized Cox proportional hazards regression (Coxnet) were used for the analysis.


We identified several genes, the expression of which was significantly associated with survival of patients with MDS, including LEF1, CDH1, WT1, and MN1. The Coxnet predictor, based on expression data on 20 genes, outperformed other predictors, including one that additionally used clinical information. Our Coxnet gene signature based on CD34+ cells significantly identified a separation of patients with good or bad prognosis in an independent GEP data set based on unsorted bone marrow mononuclear cells, demonstrating that our signature is robust and may be applicable to bone marrow cells without the need to isolate CD34+ cells.


We present a new, valuable GEP-based signature for assessing prognosis in MDS. GEP-based signatures correlating with clinical outcome may significantly contribute to a refined risk classification of MDS.

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