Proposal and validation of a method to classify genetic subtypes of diffuse large B cell lymphoma

Abstract Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease whose prognosis is associated with clinical features, cell-of-origin and genetic aberrations. Recent integrative, multi-omic analyses had led to identifying overlapping genetic DLBCL subtypes. We used targeted massive sequenci...

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Autores principales: Lucía Pedrosa, Ismael Fernández-Miranda, David Pérez-Callejo, Cristina Quero, Marta Rodríguez, Paloma Martín-Acosta, Sagrario Gómez, Julia González-Rincón, Adrián Santos, Carlos Tarin, Juan F. García, Francisco R. García-Arroyo, Antonio Rueda, Francisca I. Camacho, Mónica García-Cosío, Ana Heredero, Marta Llanos, Manuela Mollejo, Miguel Piris-Villaespesa, José Gómez-Codina, Natalia Yanguas-Casás, Antonio Sánchez, Miguel A. Piris, Mariano Provencio, Margarita Sánchez-Beato
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/2d5795c274074d96834193cbf41542ef
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Sumario:Abstract Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease whose prognosis is associated with clinical features, cell-of-origin and genetic aberrations. Recent integrative, multi-omic analyses had led to identifying overlapping genetic DLBCL subtypes. We used targeted massive sequencing to analyze 84 diagnostic samples from a multicenter cohort of patients with DLBCL treated with rituximab-containing therapies and a median follow-up of 6 years. The most frequently mutated genes were IGLL5 (43%), KMT2D (33.3%), CREBBP (28.6%), PIM1 (26.2%), and CARD11 (22.6%). Mutations in CD79B were associated with a higher risk of relapse after treatment, whereas patients with mutations in CD79B, ETS1, and CD58 had a significantly shorter survival. Based on the new genetic DLBCL classifications, we tested and validated a simplified method to classify samples in five genetic subtypes analyzing the mutational status of 26 genes and BCL2 and BCL6 translocations. We propose a two-step genetic DLBCL classifier (2-S), integrating the most significant features from previous algorithms, to classify the samples as N12-S, EZB2-S, MCD2-S, BN22-S, and ST22-S groups. We determined its sensitivity and specificity, compared with the other established algorithms, and evaluated its clinical impact. The results showed that ST22-S is the group with the best clinical outcome and N12-S, the more aggressive one. EZB2-S identified a subgroup with a worse prognosis among GCB-DLBLC cases.