RT Journal Article
T1 Prognostic Stratification of Diffuse Large B-cell Lymphoma Using Clinico-genomic Models: Validation and Improvement of the LymForest-25 Model
A1 Mosquera Orgueira, Adrián
A1 Bello López, José Luis
A1 Díaz Arías, Jose Ángel
A1 Cid López, Miguel
A1 Peleteiro Raíndo, Andrés
A1 López García, Alberto
A1 Abal García, Rosanna
A1 González Pérez, Marta Sonia
A1 Antelo Rodríguez, Beatriz
A1 Aliste Santos, Carlos
A1 Pérez Encinas, Manuel Mateo
A1 Fraga Rodríguez, Máximo
K1 Lymphoma
K1 Prognostic
AB Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma. Despite notable therapeutic advances in the last decades, 30%–40% of affected patients develop relapsed or refractory disease that frequently precludes an infamous outcome. With the advent of new therapeutic options, it becomes necessary to predict responses to the standard treatment based on rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). In a recent communication, we presented a new machine learning model (LymForest-25) that was based on 25 clinical, biochemical, and gene expression variables. LymForest-25 achieved high survival prediction accuracy in patients with DLBCL treated with upfront immunochemotherapy. In this study, we aimed to evaluate the performance of the different features that compose LymForest-25 in a new UK-based cohort, which contained 481 patients treated with upfront R-CHOP for whom clinical, biochemical and gene expression information for 17 out of 19 transcripts were available. Additionally, we explored potential improvements based on the integration of other gene expression signatures and mutational clusters. The validity of the LymForest-25 gene expression signature was confirmed, and indeed it achieved a substantially greater precision in the estimation of mortality at 6 months and 1, 2, and 5 years compared with the cell-of-origin (COO) plus molecular high-grade (MHG) classification. Indeed, this signature was predictive of survival within the MHG and all COO subgroups, with a particularly high accuracy in the “unclassified” group. Integration of this signature with the International Prognostic Index (IPI) score provided the best survival predictions. However, the increased performance of molecular models with the IPI score was almost exclusively restricted to younger patients (<70 y). Finally, we observed a tendency towards an improved performance by combining LymForest-25 with the LymphGen mutation-based classification. In summary, we have validated the predictive capacity of LymForest-25 and expanded the potential for improvement with mutation-based prognostic classifications.
PB Lippicott Williams & Wilkins
YR 2022
FD 2022-03-25
LK https://hdl.handle.net/10347/42540
UL https://hdl.handle.net/10347/42540
LA eng
NO Mosquera Orgueira A, Díaz Arías JÁ, Cid López M, Peleteiro Raíndo A, López García A, Abal García R, et al. Prognostic Stratification of Diffuse Large B-cell Lymphoma Using Clinico-genomic Models: Validation and Improvement of the LymForest-25 Model. HemaSphere. 2022 Mar 25;6(4):e706
DS Minerva
RD 29 abr 2026