A prognostic model based on gene expression parameters predicts a better response to bortezomib-containing immunochemotherapy in diffuse large B-cell lymphoma - 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 (Frontiers in Oncology, 2023)
Diffuse Large B-cell Lymphoma (DLBCL) represents the most frequent type of aggressive mature lymphoid neoplasm. Roughly 60% of fit patients can be cured with upfront immunochemotherapy, whereas the remaining patients develop relapsed or refractory disease, which is associated with adverse survival. Risk stratification in DLBCL has been traditionally based on clinical risk scores Other strategies have been explored 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.