JCO Early Release, published online ahead of print Jul 7 2008
Journal of Clinical Oncology, 10.1200/JCO.2007.12.0410
Received April 4, 2007
Accepted May 14, 2008
Five-Gene Model to Predict Survival in Mantle-Cell Lymphoma Using Frozen or Formalin-Fixed, Paraffin-Embedded Tissue
Elena Hartmann, Verònica Fernàndez, Victor Moreno, Joan Valls, Luis Hernández, Francesc Bosch, Pau Abrisqueta, Wolfram Klapper, Martin Dreyling, Eva Hoster, Hans Konrad Müller-Hermelink, German Ott, Andreas Rosenwald,* and Elías Campo
From the Institute of Pathology, University of Würzburg, Würzburg; Institute of Pathology, University of Kiel, Kiel; Department of Internal Medicine III, University of Munich, Munich, Germany; Hematopathology Section, Departments of Pathology and Hematology, Hospital Clinic, Institut d'Investigacions Biomediques August Pi i Sunyer, University of Barcelona; and Unit of Biostatistics and Bioinformatics, Cancer Epidemiology Service, Institut Català d'Oncologia-Institut d'Investigació de Bellvitge, Barcelona, Spain.
* To whom correspondence should be addressed. E-mail: rosenwald{at}mail.uni-wuerzburg.de
Purpose: Despite the common underlying translocation t(11;14) involving cyclin D1 that is present in nearly all cases of mantle-cell lymphoma (MCL), the clinical course of the disease is highly variable. The aim of the present study was to develop a quantitative gene expression–based model to predict survival in newly diagnosed patients with MCL that involves a minimum number of genes and is applicable to fresh-frozen and formalin-fixed, paraffin-embedded (FFPE) tumor samples.
Patients and Methods: The expression of 33 genes with potential prognostic and pathogenetic impact in MCL was analyzed using quantitative reverse-transcription polymerase chain reactions (qRT-PCR) in a low-density array format in frozen tumor samples from 73 patients with MCL. Multivariate Cox methods and stepwise algorithms were applied to build gene expression-based survival predictors. An optimized five-gene model was subsequently applied to FFPE tumor samples from 13 patients with MCL from the initial series and to 42 independent MCL samples.
Results: The optimized survival predictor was composed of the five genes RAN, MYC, TNFRSF10B, POLE2, and SLC29A2 and was validated for application in FFPE tissue samples. It allowed the survival prediction of patients with MCL with widely disparate clinical outcome and was superior to the immunohistochemical marker Ki-67, an established prognostic factor in MCL.
Conclusion: We here present a validated qRT-PCR–based test for survival prediction in patients with MCL that is applicable to fresh frozen as well as to FFPE tissue specimens. This test may prove useful to guide individualized treatment approaches for patients with MCL.
