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A molecular risk score based on 4 functional pathways for advanced classical Hodgkin lymphoma

2010; Elsevier BV; Volume: 116; Issue: 8 Linguagem: Inglês

10.1182/blood-2010-02-270009

1528-0020

Beatriz Sánchez‐Espiridión, Carlos Montalbán, Angel Alfonso Velarde López, Javier Menárguez, P. Sabín, C Ruiz-Marcellán, Andrés López, Rafael Ramos, José Rodríguez, A. Alonso Cánovas, Carmen Camarero, Miguel Canales, Javier Alves, Reyes Arranz, Agustín Acevedo, Antonio Salar, Sérgio Serrano, Águeda Bas, José M. Moraleda, Pedro Sánchez‐Godoy, Fernando Burgos, C Rayón, Manuel Fresno, J García Laraña, Mónica García‐Cosío, Carlos Santonja, J. López, Marta Llanos, Manuela Mollejo, Joaquín González‐Carreró, Ana Isabel Marín, Jerónimo Forteza, Ramón García‐Sánz, José Francisco Tomás, Manuel M. Morente, Miguel Á. Piris, Juan F. Garcı́a,

Molecular Biology Techniques and Applications

Abstract Despite improvement in the treatment of advanced classical Hodgkin lymphoma, approximately 30% of patients relapse or die as result of the disease. Current predictive systems, determined by clinical and analytical parameters, fail to identify these high-risk patients accurately. We took a multistep approach to design a quantitative reverse-transcription polymerase chain reaction assay to be applied to routine formalin-fixed paraffin-embedded samples, integrating genes expressed by the tumor cells and their microenvironment. The significance of 30 genes chosen on the basis of previously published data was evaluated in 282 samples (divided into estimation and validation sets) to build a molecular risk score to predict failure. Adequate reverse-transcription polymerase chain reaction profiles were obtained from 262 of 282 cases (92.9%). Best predictor genes were integrated into an 11-gene model, including 4 functional pathways (cell cycle, apoptosis, macrophage activation, and interferon regulatory factor 4) able to identify low- and high-risk patients with different rates of 5-year failure-free survival: 74% versus 44.1% in the estimation set (P < .001) and 67.5% versus 45.0% in the validation set (P = .022). This model can be combined with stage IV into a final predictive model able to identify a group of patients with very bad outcome (5-year failure-free survival probability, 25.2%).