Using Evolving Biomarkers to ID High-Risk SMM-a Dynamic Model | Theresia Akhlaghi, MD| EHA25

My name is Therese Aklage and I'm a fellow in hematology oncology at Weill Cornell Medical Center. I wanted to talk a little bit about my research project on smoldering multiple myeloma. Smoldering myeloma is a precursor condition to multiple myeloma and it's a heterogeneous disease where some patients progress to active myeloma within just a couple of years while others can be monitored for a really long time and may never require treatment for multiple myeloma. To navigate this heterogeneity several risk models have been developed that classify patients into low intermediate or high risk of progression to myeloma. The problem with these models is that they're not concordant with each other meaning that some patients that are classified as high risk in one model may be classified as intermediate or low risk in another. And we currently have several clinical trials that are investigating treatment for high risk smoldering multiple myeloma so accurate risk stratification is crucial to ensure that patients that are indeed high risk receive treatment while those who would not progress within several years are instead being monitored. One of the issues with the models that we use today is that they all rely on risk factors at initial diagnosis of smoldering myeloma and they don't take into account how the disease may evolve over time. So we wanted to improve risk stratification for multiple myeloma by incorporating evolving biomarkers. We looked at 323 patients with smoldering myeloma who were followed at Memorial Sloan Kettering Cancer Center and we analyzed all the M protein and for lysine ratio measurements between the date of diagnosis of smoldering and progression to myeloma and we identified two evolving biomarkers. Evolving M protein as an increase of 0.4 or more grams per deciliter and evolving free lysine ratio as an increase of 40% or more during the first year of diagnosis with smoldering myeloma. And we found that these were significant to predict risk of progression even when we adjusted for other risk factors that we know increase the risk of progression such as the M protein and the free lysine ratio and the percentage of myeloma cells in the bone marrow measured at baseline. We then incorporated these evolving markers into the 2020 model or the Mayo Clinic model and we found that this dynamic model improved the predictive accuracy compared to the baseline model. So we hope that by using this dynamic model we can get a more comprehensive assessment of the disease and get a more accurate risk prediction in smoldering multiple myeloma.