Modeling MRD Changes in Myeloma to Understand Treatment Effects, Predict Outcomes, and Investigate Curative Potential.

PURPOSE: We designed mathematical models to describe and quantify the mechanisms and dynamics of minimal residual disease (MRD) in order to better understand these MRD dynamics; inform future treatment design, including when to stop or change treatment; and extrapolate from current progression-free survival (PFS) times to predict future PFS curves.

EXPERIMENTAL

DESIGN

This study aims to model individual sequential MRD data from phase III clinical trials (MAIA, CASTOR, and POLLUX) using previously developed mathematical models, which will be modified as needed to accurately reflect the actual MRD data. These models will then be used to project PFS curves into the future.

RESULTS

Patients with low MRD values either showed rapid disease regrowth, or the MRD values remained low for a prolonged period. Treatment seemed to be most effective in terms of cell kill within the first 6 to 12 months. Regrowth rates were correlated with estimated initial residual disease, particularly in MRD-negative patients. Three-year model extrapolations of PFS were closely comparable with clinical trial data.

CONCLUSIONS

This model could provide early prediction of PFS outcomes, which otherwise takes lengthy periods of time to observe in clinical trials. Patients showing rapid rebound from low MRD values may benefit from adding another treatment before reaching progressive disease. The MRD analyses and results presented, such as the results about efficacy occurring early in the first 6 to 12 months, may help guide the development and selection of optimal regimens. Longer follow-up periods and application to other trials and datasets are required to substantiate these findings.