期刊
CLINICAL CANCER RESEARCH
卷 28, 期 12, 页码 2598-2609出版社
AMER ASSOC CANCER RESEARCH
DOI: 10.1158/1078-0432.CCR-21-3430
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资金
- Centro de Investigacion Biomedica en Red -Area de Oncologia -from Instituto de Salud Carlos III (CIBERONC) [CB16/12/00369, CB16/12/00400, CB16/12/00284]
- European Regional Development Fund-FEDER A way to make Europe (FIS) [PI15/01956, PI15/02049, FIS PI15/02062, PI18/01709, PI19/01451]
- Instituto de Salud Carlos III/Subdireccion General de Investigacion Sanitaria
- European Social Fund Plus (FSEthorn)
- European Union (PFIS) [FI21/00293]
- Cancer Research UK [C355/A26819]
- AIRC under the Accelerator Award Programme (EDITOR)
- FCAECC
- Black Swan Research Initiative of the International Myeloma Foundation
- European Research Council (ERC) [MYELOMANEXT 680200]
- CRIS Cancer Foundation [PR_ EX_2020-02]
- Riney Family Multiple Myeloma Research Program Fund
This study established a comprehensive weighted model using machine learning algorithms to accurately predict undetectable measurable residual disease (MRD) outcomes in multiple myeloma patients, providing a new concept for personalized treatment.
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.
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