New Tool Tracks Biomarker Changes to Predict Myeloma Progression

Smoldering multiple myeloma (SMM) precedes multiple myeloma and poses a monitoring challenge because progression risk varies widely among patients. Static, one-time laboratory assessments can miss clinically meaningful shifts that signal advancing disease. Treatment for high‑risk SMM has recently been approved in the United States, reinforcing the need for precise, timely risk stratification. Researchers have now introduced a dynamic, open-access tool that improves prediction of progression from SMM to active disease.

Dana-Farber Cancer Institute (Boston, MA, USA) investigators developed PANGEA‑SMM, an online model that integrates serial laboratory results to refine risk assessment in SMM. Unlike snapshot approaches, PANGEA‑SMM updates predictions as new data are added and tracks the direction and speed of biomarker change over time. The aim is to identify patients who are likely to benefit from early treatment while avoiding unnecessary intervention in those with stable disease.

Image: The open-access tool that improves prediction of progression from smoldering multiple myeloma to active disease (photo courtesy of Shutterstock)terstock)

The model leverages four routinely collected biomarkers in SMM follow-up: M‑protein, light chains, kidney function, and blood counts. Specific temporal shifts in these parameters act as red flags for imminent progression; for example, an increase in M‑protein of 0.2 g/dL or more over 18 months. Because the inputs are part of standard surveillance, the tool is positioned for broad use in diverse care settings.

In a study published in Nature Medicine, the team assembled a cohort of 2,344 patients with SMM from seven international centers to develop and validate PANGEA‑SMM. Using the four dynamic biomarkers, the model more accurately identified both high- and low-risk states than existing tools such as the 20/2/20 and IMWG models, which rely on static measurements. Notably, predictions remained highly accurate even without a complete longitudinal history or access to recent bone marrow biopsy data.

An online calculator is available for routine follow-up to generate risk estimates and to compare performance against established models to identify areas for improvement. The investigators note that future work will continue to refine predictive accuracy and determine optimal monitoring frequency.

“By watching the speed and direction of the disease's trajectory, the tool can more accurately identify patients at high risk who need early treatment, while sparing those with stable disease from unnecessary interventions,” said Irene Ghobrial, MD, Director of the Center for Early Detection and Interception of Blood Cancers at Dana-Farber Cancer Institute. "A unified, straightforward, and precise risk stratification model incorporating dynamic biomarkers is essential to facilitate the implementation of therapeutic strategies and improve patient outcomes in smoldering multiple myeloma."

“Remarkably, PANGEA-SMM performs with similar accuracy whether or not recent bone marrow biopsy data is available. This allows for continuous risk assessment throughout routine follow-up without the need for frequent invasive sampling, which typically requires specialized expertise and can be burdensome for patients,” said Floris Chabrun, PhD, PharmD, co-first author at Dana-Farber Cancer Institute.

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Dana-Farber Cancer Institute