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CIRINO BOTTA

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

  • Authors: Guerrero, Camila; Puig, Noemi; Cedena, Maria-Teresa; Goicoechea, Ibai; Perez, Cristina; Garces, Juan-Jose; Botta, Cirino; Calasanz, Maria-Jose; Gutierrez, Norma C; Martin-Ramos, Maria-Luisa; Oriol, Albert; Rios, Rafael; Hernandez, Miguel-Teodoro; Martinez-Martinez, Rafael; Bargay, Joan; de Arriba, Felipe; Palomera, Luis; Gonzalez-Rodriguez, Ana Pilar; Mosquera-Orgueira, Adrian; Gonzalez-Perez, Marta-Sonia; Martinez-Lopez, Joaquin; Lahuerta, Juan-Jose; Rosiñol, Laura; Blade, Joan; Mateos, Maria-Victoria; San Miguel, Jesus F; Paiva, Bruno
  • Publication year: 2022
  • Type: Articolo in rivista
  • OA Link: http://hdl.handle.net/10447/533544

Abstract

Purpose: Undetectable measurable residual disease (MRD) is a surrogate of prolonged survival in multiple myeloma (MM). Thus, treatment individualization based on the probability of a patient to achieve undetectable MRD with a singular regimen, could represent a new concept towards 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 MM. Experimental design: This study included 487 newly-diagnosed MM patients. The training (n=152) and internal validation cohort (n=149) consisted of 301 transplant-eligible active MM patients enrolled in the GEM2012MENOS65 trial. Two external validation cohorts were defined by 76 high-risk transplant-eligible smoldering MM patients enrolled in the 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 predictions 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 maintenance (GEM2014MAIN). High-confidence prediction of undetectable MRD at diagnosis identified a subgroup of active MM patients with 80% and 93% progression-free and overall survival rates at five years. Conclusion: It is possible to accurately predict MRD outcomes using an integrative, weighted model defined by machine learning algorithms. This is a new concept towards individualized treatment in MM.