PANGEA-SMM: A Novel Prediction Model for Smoldering Multiple Myeloma Progression Across International Cohorts

New Predictive Tool Improves Monitoring of Smoldering Multiple Myeloma

A new risk stratification tool, called PANGEA-SMM, promises to more accurately predict which patients with smoldering multiple myeloma (SMM) will progress to active multiple myeloma, potentially allowing for earlier, more targeted treatment. Developed by a team at Dana-Farber Cancer Institute, and validated across multiple international centers, the tool incorporates evolving biomarker data to provide a more dynamic assessment of risk than existing models.

Currently, SMM, a precursor condition to multiple myeloma, is managed with a “watch and wait” approach for many patients. However, determining which patients will progress and benefit from early intervention remains a challenge. Existing risk stratification models, like the 20/2/20 criteria, rely on static biomarkers measured at diagnosis. PANGEA-SMM, detailed in a recent study published in Nature Medicine, addresses this limitation by analyzing how biomarkers change over time.

The research involved a cohort of 2,344 SMM patients from seven international centers, with longitudinal clinical and biological data collected between March 2021 and October 2024. Researchers identified four evolving biomarkers significantly associated with faster progression: increases in M-protein (greater than or equal to 0.2 g/dL), involved/uninvolved serum free light chain ratio (greater than or equal to 20%), creatinine (greater than 25%), and decreases in hemoglobin (greater than or equal to 1.5 g/dL). The tool then uses these changes, alongside age, to calculate a personalized risk score.

Validation studies, conducted on independent cohorts from Greece, the United Kingdom, Germany, Spain, and Italy, demonstrated that PANGEA-SMM outperforms established models, including the 20/2/20 and IMWG models. The tool achieved a C-statistic of 0.79 in predicting progression, even when biomarker history was incomplete or a recent bone marrow biopsy wasn’t available (C-statistic of 0.78 in both scenarios). A C-statistic measures the model’s ability to discriminate between patients who will and will not progress.

The development of PANGEA-SMM represents a significant step forward in personalized risk assessment for SMM. The study highlights the importance of tracking biomarker *trends* rather than relying solely on a snapshot in time. Here’s particularly relevant as SMM can remain stable for years, or progress rapidly, making accurate prediction crucial for avoiding both overtreatment and delayed intervention.

Researchers have made PANGEA-SMM an open-access tool, available through a dedicated website, along with validation tools allowing comparison to existing models. A clinical calculator is also available, enabling clinicians to input patient data and receive a personalized risk assessment. The tool is designed to be user-friendly, even for those without extensive statistical expertise.

While the study demonstrates the improved predictive power of PANGEA-SMM, further research is needed to determine the optimal clinical application of the tool. Specifically, future studies will need to assess whether using PANGEA-SMM to guide treatment decisions improves patient outcomes. The research team is also exploring the potential of incorporating additional biomarkers and genetic data to further refine the model’s accuracy. The availability of the tool and its open-access nature will facilitate ongoing research and collaboration in the field of multiple myeloma risk stratification.