Enhanced dynamic risk stratification of smoldering multiple myeloma
Summary
Accurate prediction of risk of progression from smoldering multiple myeloma (SMM) to active multiple myeloma (MM) is paramount to individualized early therapeutic strategies with minimum risk of overtreatment. Current risk stratification models do not account for evolving biomarker trajectories. We assembled a cohort of 2,344 patients with SMM from seven international centers with longitudinal clinical and biological data to train and validate the Precursor Asymptomatic Neoplasms by Group
Content
# Enhanced dynamic risk stratification of smoldering multiple myeloma
*Published: 2026 May*
Accurate prediction of risk of progression from smoldering multiple myeloma
(SMM) to active multiple myeloma (MM) is paramount to individualized early
therapeutic strategies with minimum risk of overtreatment. Current risk
stratification models do not account for evolving biomarker trajectories. We
assembled a cohort of 2,344 patients with SMM from seven international centers
with longitudinal clinical and biological data to train and validate the
Precursor Asymptomatic Neoplasms by Group Effort Analysis (PANGEA)-SMM risk
models. Four evolving biomarkers were significantly associated with shorter time
to progression: M-protein increase ≥0.2 g dl-1, involved/uninvolved serum free
light chain ratio increase ≥20, creatinine increase >25% and hemoglobin decrease
≥1.5 g dl-1. PANGEA-SMM outperforms established models, including the 20/2/20
and IMWG models, by more accurately predicting progression (C-statistic = 0.79),
even without biomarker history (C-statistic = 0.78) or recent bone marrow biopsy
(C-statistic = 0.78). We present PANGEA-SMM to the community as an easy-to-use,
open-access tool for risk stratification in SMM. Validation tools are available
to compare PANGEA-SMM to established models.
DOI: 10.1038/s41591-026-04304-x