Enhanced Risk Stratification of Smoldering Multiple Myeloma with Dynamic Biomarkers: A Multinational, Multicenter Study including 2,270 Participants (PANGEA 2.0)

Abstract

Accurate prediction of risk of progression from smoldering (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 the largest cohort to date of 2,270 SMM patients from six international centers with longitudinal clinical and biological data to train and validate the PANGEA 2.0 risk models. Four evolving biomarkers were significantly associated with shorter time-to-progression: M-protein increase ≥0.2g/dL, involved:uninvolved serum free light chain ratio increase ≥20, creatinine increase >25%, and hemoglobin decrease ≥1.5g/dL. PANGEA 2.0 outperforms established models including the 20/2/20 and IMWG models by more accurately predicting progression (C-statistics=0.69-0.84), even without biomarker history (C-statistics=0.69-0.83) or recent bone marrow biopsy. PANGEA 2.0 is an easy-to-use, open-access tool (https://ghobrial.shinyapps.io/pangea_2_calculator) to improve and individualize SMM risk stratification. Validation tools are available to compare PANGEA 2.0 to established models (https://ghobrial.shinyapps.io/pangea_validation).

Keywords: Smoldering multiple myeloma; dynamic risk stratification; multiple myeloma; risk prediction.

Figure 1

Risk stratification of the PANGEA trajectory model (BM) compared to rolling 20/2/20 for the training cohort and validation cohorts 1-3. Plots show Kaplan-Meier estimates non-progression probability stratified by risk category (PANGEA or 20/2/20). Error bands show pointwise 95% confidence intervals for the probability of non-progression by each time point.

Figure 2

Point estimates and 95% confidence intervals of dynamic C-statistics for 20/2/20 and the PANGEA 2.0 BM model for risk prediction in the validation cohorts. The C-statistic at each time point (x-axis) is computed using only the most recent observation for each remaining patient who is still being followed up that many years after diagnosis.

Figure 3

A) Example biomarker trajectories and 2-year progression risk scores (PANGEA 2.0 with BM and rolling 20/2/20) for a patient. The line color for M-protein, FLC ratio, creatinine, and hemoglobin indicates whether a changing trajectory is detected at each timepoint. Rolling 20/2/20 risk increases at the latest time point, where the patient already shows signs of progression to myeloma (anemia and acute kidney failure). In contrast, a skewed M-protein concentration is observed at the penultimate lab report, which translates through PANGEA 2.0 as an increased predicted risk of progression. B) Predicted risk scores for (first row) three progressor patients with higher PANGEA 2.0 risk and lower 20/2/20 risk and for (second row) three non-progressor patients with lower PANGEA 2.0 risk and higher 20/2/20 risk.