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BioMed Research International
Volume 2014 (2014), Article ID 423174, 10 pages
Research Article

Identification of a 20-Gene Expression-Based Risk Score as a Predictor of Clinical Outcome in Chronic Lymphocytic Leukemia Patients

1INSERM, U1040, F-34197, Institute of Research in Biotherapy, CHU Montpellier, 80 Avenue Augustin Fliche, 34285 Montpellier Cedex, France
2CHU Montpellier, Institute of Research in Biotherapy, Montpellier, 80 Avenue Augustin Fliche, 34285 Montpellier, France
3Université Montpellier 1, UFR Médecine, Montpellier, 80 Avenue Augustin Fliche, 34285 Montpellier, France
4Institut de Biologie Computationnelle, Université Montpellier 2, 2 Place Eugène Bataillon, 34095 Montpellier Cedex 5, France

Received 26 September 2013; Revised 18 March 2014; Accepted 20 March 2014; Published 5 May 2014

Academic Editor: Carlo Visco

Copyright © 2014 Elias Bou Samra et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Despite the improvement in treatment options, chronic lymphocytic leukemia (CLL) remains an incurable disease and patients show a heterogeneous clinical course requiring therapy for many of them. In the current work, we have built a 20-gene expression (GE)-based risk score predictive for patients overall survival and improving risk classification using microarray gene expression data. GE-based risk score allowed identifying a high-risk group associated with a significant shorter overall survival (OS) and time to treatment (TTT) , comprising 19.6% and 13.6% of the patients in two independent cohorts. GE-based risk score, and NRIP1 and TCF7 gene expression remained independent prognostic factors using multivariate Cox analyses and combination of GE-based risk score together with NRIP1 and TCF7 gene expression enabled the identification of three clinically distinct groups of CLL patients. Therefore, this GE-based risk score represents a powerful tool for risk stratification and outcome prediction of CLL patients and could thus be used to guide clinical and therapeutic decisions prospectively.