Modeling MRD Changes in Myeloma to Understand Treatment Effects, Predict Outcomes, and Investigate Curative Potential
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".