Detalle Publicación

A machine learning model based on tumor and immune biomarkers to predict undetectable MRD and survival outcomes in multiple myeloma

Autores: Guerrero De Blois, Camila; Puig, N.; Cedena, M. T.; Goicoechea Oroz, Ibai; Pérez Ruiz, Cristina; Garcés Latre, Juan José; Botta, C.; Calasanz Abinzano, María José; Gutiérrez, N. C.; Martín-Ramos, M. L.; Oriol, A.; Ríos, R.; Hernández, M. T.; Martínez-Martínez, R.; Bargay, J.; de Arriba, F.; Palomera, L.; González-Rodríguez, A. P.; Mosquera-Orgueira, A.; González-Pérez, M. S.; Martínez-López, J.; Lahuerta, J. J.; Rosinol, L.; Blade, J.; Mateos, M. V.; San Miguel Izquierdo, Jesús; Paiva, Bruno (Autor de correspondencia)
Título de la revista: CLINICAL CANCER RESEARCH
ISSN: 1078-0432
Volumen: 28
Número: 12
Páginas: 2598 - 2609
Fecha de publicación: 2022
Purpose: Undetectable measurable residual disease (MRD) is a surrogate of prolonged survival in multiple myeloma. Thus, treat-ment individualization based on the probability of a patient achiev-ing undetectable MRD with a singular regimen could represent a new concept toward personalized treatment, with fast assessment of its success. This has never been investigated; therefore, we sought to define a machine learning model to predict undetectable MRD at the onset of multiple myeloma. Experimental Design: This study included 487 newly diagnosed patients with multiple myeloma. The training (n = 152) and internal validation cohorts (n = 149) consisted of 301 trans-plant-eligible patients with active multiple myeloma enrolled in the GEM2012MENOS65 trial. Two external validation cohorts were defined by 76 high-risk transplant-eligible patients with smoldering multiple myeloma enrolled in the Grupo Espanol de Mieloma (GEM)-CESAR trial, and 110 transplant-ineligible elderly patients enrolled in the GEM-CLARIDEX trial. Results: The most effective model to predict MRD status resulted from integrating cytogenetic [t(4;14) and/or del(17p13)], tumor burden (bone marrow plasma cell clonality and circulating tumor cells), and immune-related biomarkers. Accurate predic -ti ons of MRD outcomes were achieved in 71% of cases in the GEM2012MENOS65 trial (n = 214/301) and 72% in the external validation cohorts (n = 134/186). The model also predicted sustained MRD negativity from consolidation onto 2 years main-tenance (GEM2014MAIN). High-confidence prediction of unde-tectable MRD at diagnosis identified a subgroup of patients with active multiple myeloma with 80% and 93% progression-free and overall survival rates at 5 years. Conclusions: It is possible to accurately predict MRD outcomes using an integrative, weighted model defined by machine learning algorithms. This is a new concept toward individualized treatment in multiple myeloma.