Researchers have developed a dynamic risk stratification tool for patients with smoldering multiple myeloma to predict their chance of progression to active multiple myeloma. The tool, called Precursor Asymptomatic Neoplasms by Group Effort Analysis–Smoldering Multiple Myeloma (PANGEA-SMM), outperformed established models for predicting disease progression, according to findings published in Nature Medicine.
The study authors enabled access to the PANGEA-SMM tool as a free, online calculator so other clinicians could monitor patients during follow-ups and use the tool as a comparator for other established models to identify areas for further improvement.
“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,” stated co-senior study author 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.”
Background and Study Methods
Current approaches to risk stratification for patients with smoldering multiple myeloma (including the 20/2/20 and International Myeloma Working Group [IMWG] models) do not take into account evolving biomarker trajectories, and so the researchers sought to develop a new risk stratification model that includes assessment of laboratory results and their changes over time.
They gathered a cohort of 2,344 patients with smoldering multiple myeloma from across seven international centers for training (n = 1,031) and validation (n = 1,313 in five cohorts) of the PANGEA-SMM model. They then looked at a set of candidate trend biomarkers to determine the optimal definition and threshold for the greatest impact on the model's predictive performance. They trained versions of the PANGEA-SMM model with and without bone marrow biopsy samples.
Key Findings
In the training cohort, 231 patients progressed to active multiple myeloma, along with 421 patients from the validation cohorts.
The researchers identified four dynamic biomarkers associated with a reduced time to progression: M-protein increase ≥ 0.2 g dl-1 (hazard ratio [HR] = 1.72, 95% confidence interval [CI] = 1.20–2.47), involved/uninvolved serum–free light chain ratio increase ≥ 20 (HR = 2.02; 95% CI = 1.23–3.31), creatinine increase > 25% (HR = 1.94; 95% CI = 1.13–3.32), and hemoglobin decrease ≥ 1.5 g dl-1 (HR = 3.21; 95% CI = 1.98–5.22).
PANGEA-SMM outperformed both the 20/2/20 and IMWG established models in terms of accurately prediction progression (C-statistic = 0.79), even without a history of biomarkers or recent bone marrow biopsy.
“Remarkably, PANGEA-SMM performs with similar accuracy whether or not recent bone marrow biopsy data are available,” said co-first study author Floris Chabrun, PhD, PharmD, of Dana-Farber Cancer Institute. “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.”
DISCLOSURE: Funding for this research was supportyed by the National Institutes of Health, the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation, Instituto de Salud Carlos III, ERA-NET TRANSCAN-2 iMMunocell, Cancer Research UK, the Scientific Foundation of the Spanish Association Against Cancer (FC AECC), the Italian Cancer Research Foundation (AIRC), the Multiple Myeloma Research Foundation, the CRIS Cancer Foundation, the Leukemia and Lymphoma Society, the European Commission Mission: Cancer, the Riney Family Multiple Myeloma Research Program Fund, and the Dietmar-Hopp Foundation. For full disclosures of the study authors, visit nature.com.
