Enhanced Risk Stratification of Smoldering Multiple Myeloma with Dynamic Biomarkers: A Multinational, Multicenter...

Area of Science:

• Hematology
• Oncology
• Biostatistics

Background:

• Accurate prediction of smoldering multiple myeloma (SMM) progression to active multiple myeloma (MM) is essential for personalized treatment strategies.
• Current risk stratification models lack the ability to incorporate evolving biomarker data, limiting their predictive accuracy.

Purpose of the Study:

• To develop and validate a novel risk prediction model, PANGEA 2.0, for SMM progression.
• To incorporate longitudinal biomarker data for improved accuracy in predicting time-to-progression.

Main Methods:

• Assembled the largest cohort to date (2,270 SMM patients) from six international centers with longitudinal clinical and biological data.
• Trained and validated the PANGEA 2.0 risk models using identified evolving biomarkers.
• Compared PANGEA 2.0 performance against established models like 20/2/20 and IMWG using C-statistics.

Main Results:

• Four evolving biomarkers significantly predicted shorter time-to-progression: M-protein increase, involved:uninvolved serum free light chain ratio increase, creatinine increase, and hemoglobin decrease.
• PANGEA 2.0 demonstrated superior predictive accuracy (C-statistics=0.69-0.84) compared to existing models.
• The model maintained high accuracy even without historical biomarker data or recent bone marrow biopsy results.

Conclusions:

• PANGEA 2.0 offers improved and individualized risk stratification for SMM patients.
• The model is an easy-to-use, open-access tool to aid clinicians in treatment decisions.
• PANGEA 2.0 outperforms current models, facilitating better management of SMM progression risk.