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

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.