Abstract 2259: Predictive modeling of smoldering multiple myeloma progression to multiple myeloma by continuous variable analysis
Authors: Cowan Annie , El-Khoury Habib , Ferrari Federico , Freeman Samuel S. , Redd Robert , Perry Jacqueline , Patel Vidhi , Kaur Priya , Barr Hadley , Downey Katelyn , Argyelan David , Justis Anna V. , Lee David J. , Lightbody Elizabeth D. , Theodorakakou Foteini , Fotiou Despina , Kanellias Nikolaos , Liacos Christine , Getz Gad , Trippa Lorenzo , Marinac Catherine , Kastritis Efstathios , Meletios Dimopoulos , Ghobrial Irene 
Published: June 2022

Introduction: Multiple myeloma (MM) is consistently preceded by two precursor conditions, monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). The distinctions between MGUS, SMM, and MM rely on clinical values with inherent variation relating to tumor burden quantified from bone marrow biopsies, a measure subject to inconsistencies in location, timing, and pathologist interpretation. These challenges limit the potential of current standardized risk criteria and advocate for models that examine precursor disease kinetics. We thus developed a risk model that leverages dynamic changes in markers of precursor disease to improve clinical ability to predict time to disease progression.

Methods: To model the evolution of progression risk, we built PANGEA, an international retrospective cohort of precursor patients with baseline and serial time points of clinical and biological variables. This cohort comprises 1095 SMM patients, 254 (23%) of which progressed to MM. Using this cohort, we modeled progression to MM with Cox regression using time-dependent and continuous clinical variables. The model was trained on a subset of data restricted to patients of the Dana-Farber Cancer Institute (DFCI) and validated its performance by computing the c-statistic in a sub-cohort independent from the DFCI training cohort.

Results: The PANGEA cohort was first used to validate current models of SMM disease progression. We validated the 20/2/20 International Myeloma Working Group criteria for SMM patients using binary cutoffs of initial measurements (baseline model), and then extended this model, allowing for re-stratification by the 20/2/20 criteria over time (dynamic model). We then assessed whether rates of change in a set of myeloma-specific clinical variables unrestricted to those of the 20/2/20 criteria improved the predictive ability of the model. This improved our progression prediction as indicated by a c-statistic increase of more than 10% with respect to both 20/2/20 models (baseline and dynamic). Specifically, changes in disease indicators such as age and creatinine are highly predictive of imminent disease progression (p-value &lt; 0.01). Finally, we clustered patients based on latent trajectories of these time-varying clinical variables and included the trajectory classes in the Cox regression. The resulting multivariable, dynamic algorithm is a dramatic improvement over current clinical standards in predicting progression from SMM to MM disease.

Conclusion: The PANGEA multivariable algorithm’s use of continuous clinical variables enhances progression risk predictions in SMM. These findings demonstrate that disease progression from SMM to MM, which likely occurs by the acquisition of sequential changes to the plasma cell clone, can be tracked by trends in clinical values, thus improving prognostication for precursor patients.

Citation Format: Annie Cowan, Habib El-Khoury, Federico Ferrari, Samuel S. Freeman, Robert Redd, Jacqueline Perry, Vidhi Patel, Priya Kaur, Hadley Barr, Katelyn Downey, David Argyelan, Anna V. Justis, David J. Lee, Elizabeth D. Lightbody, Foteini Theodorakakou, Despina Fotiou, Nikolaos Kanellias, Christine Liacos, Gad Getz, Lorenzo Trippa, Catherine Marinac, Efstathios Kastritis, Dimopoulos Meletios, Irene Ghobrial. Predictive modeling of smoldering multiple myeloma progression to multiple myeloma by continuous variable analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2259.