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.

Guerrero, C., Puig, N., Cedena, M., Goicoechea, I., Perez, C., Garces, J., et al. (2022). A machine learning model based on tumor and immune biomarkers to predict undetectable MRD and survival outcomes in multiple myeloma. CLINICAL CANCER RESEARCH [10.1158/1078-0432.CCR-21-3430].

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

Botta, Cirino;
2022-01-21

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.
21-gen-2022
Guerrero, C., Puig, N., Cedena, M., Goicoechea, I., Perez, C., Garces, J., et al. (2022). A machine learning model based on tumor and immune biomarkers to predict undetectable MRD and survival outcomes in multiple myeloma. CLINICAL CANCER RESEARCH [10.1158/1078-0432.CCR-21-3430].
File in questo prodotto:
File Dimensione Formato  
A machine learning model based on tumor and immune biomarkers to predict undetectable MRD and survival outcomes in multiple myeloma.pdf

Solo gestori archvio

Tipologia: Versione Editoriale
Dimensione 2.37 MB
Formato Adobe PDF
2.37 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/533544
Citazioni
  • ???jsp.display-item.citation.pmc??? 7
  • Scopus 14
  • ???jsp.display-item.citation.isi??? 13
social impact