AUTHOR=Mosquera Orgueira Adrián , Díaz Arías Jose Ángel , Serrano Martín Rocio , Portela Piñeiro Victor , Cid López Miguel , Peleteiro Raíndo Andrés , Bao Pérez Laura , González Pérez Marta Sonia , Pérez Encinas Manuel Mateo , Fraga Rodríguez Máximo Francisco , Vallejo Llamas Juan Carlos , Bello López José Luis TITLE=A prognostic model based on gene expression parameters predicts a better response to bortezomib-containing immunochemotherapy in diffuse large B-cell lymphoma JOURNAL=Frontiers in Oncology VOLUME=13 YEAR=2023 URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1157646 DOI=10.3389/fonc.2023.1157646 ISSN=2234-943X ABSTRACT=

Diffuse Large B-cell Lymphoma (DLBCL) is the most common type of aggressive lymphoma. Approximately 60% of fit patients achieve curation with immunochemotherapy, but the remaining patients relapse or have refractory disease, which predicts a short survival. Traditionally, risk stratification in DLBCL has been based on scores that combine clinical variables. Other methodologies have been developed based on the identification of novel molecular features, such as mutational profiles and gene expression signatures. Recently, we developed the LymForest-25 profile, which provides a personalized survival risk prediction based on the integration of transcriptomic and clinical features using an artificial intelligence system. In the present report, we studied the relationship between the molecular variables included in LymForest-25 in the context of the data released by the REMoDL-B trial, which evaluated the addition of bortezomib to the standard treatment (R-CHOP) in the upfront setting of DLBCL. For this, we retrained the machine learning model of survival on the group of patients treated with R-CHOP (N=469) and then made survival predictions for those patients treated with bortezomib plus R-CHOP (N=459). According to these results, the RB-CHOP scheme achieved a 30% reduction in the risk of progression or death for the 50% of DLBCL patients at higher molecular risk (p-value 0.03), potentially expanding the effectiveness of this treatment to a wider patient population as compared with other previously defined risk groups.