Largest Study to Date Advances Smoldering Multiple Myeloma Risk Prediction
A groundbreaking study, the PANGEA project, has identified new dynamic biomarkers to improve the prediction of progression from smoldering multiple myeloma (SMM) to active multiple myeloma (MM). The research, conducted across multiple international centers including the Dana-Farber Cancer Institute (DFCI) in Boston, MA, represents the largest cohort study to date focused on characterizing this transition. The findings offer the potential for more personalized risk assessment and treatment strategies for individuals with SMM.
Study Design and Cohort Details
The PANGEA project, approved by the Dana-Farber/Harvard Cancer Center institutional review board (no. 21-127), retrospectively analyzed data from 1,031 patients diagnosed with SMM at DFCI between March 25, 2021 and October 21, 2024. The primary goal was to enhance prediction accuracy compared to existing SMM stratification models by incorporating dynamic biomarkers – those that change over time – into individual clinical profiles.
To validate the findings, researchers utilized five independent cohorts from six international centers:
- National and Kapodistrian University of Athens (Athens, Greece) – 380 cases
- University College London (London, UK) – 105 cases
- Heidelberg University Hospital (Heidelberg, Germany) – 447 cases
- University of Navarra (Pamplona, Spain) – 240 cases
- University of Milan (Milan, Italy) – 67 cases
- University Hospital of Würzburg (Würzburg, Germany) – 74 cases
Ethical guidelines were followed, with a waiver of informed consent granted by the institutional review board for retrospective data collection where minimal risk to patients was involved. Informed consent was obtained for validation cohorts where required, with approvals from respective ethics committees.
Identifying Dynamic Biomarkers
The study focused on identifying biomarkers that, when changes in their values were considered, significantly improved the prediction of progression to active MM. Researchers evaluated several definitions of “dynamic” changes, looking at increases or decreases in biomarkers over time. These included percentage changes, absolute changes, and trends based on regression analysis. The optimal definitions were determined by assessing which improved the model’s predictive accuracy the most.
PANGEA-SMM Models and Validation
The PANGEA-SMM models are two multivariate Cox regression models – a ‘BM’ model including bone marrow plasma cell (BMPC) data and a ‘no-BM’ model for cases where BMPC data is unavailable. Both models incorporate biomarkers like M-protein, involved/uninvolved sFLC ratio, creatinine, and dynamic trends in these biomarkers. The models output a 2-year risk score representing the probability of progression to MM.
Validation across the international cohorts demonstrated improved ranking accuracy and predictive value compared to the existing 20/2/20 risk stratification model. The study likewise developed open-access web applications – a validation application for researchers and a clinical calculator for clinicians – to facilitate the use of PANGEA-SMM in both research and clinical settings.
Clinical Implications and Future Directions
The PANGEA project’s findings represent a significant step forward in personalized risk assessment for SMM. By incorporating dynamic biomarkers, the PANGEA-SMM models offer a more nuanced and accurate prediction of progression to active MM, potentially enabling more tailored treatment decisions. The availability of open-access tools further promotes the adoption and utilization of these advancements in clinical practice.
Further research will focus on refining the models, exploring the impact of cytogenetic markers, and expanding the application of these predictive tools to broader patient populations.
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