Integrated analysis of next generation sequencing minimal residual disease (MRD) and PET scan in transplant eligible myeloma patients
Bibliographic record
Abstract
Abstract Minimal residual disease (MRD) assays allow response assessment in patients with multiple myeloma (MM), and negativity is associated with improved survival outcomes. The role of highly sensitive next generation sequencing (NGS) MRD in combination with functional imaging remains to be validated. We performed a retrospective analysis on MM patients who underwent frontline autologous stem cell transplant (ASCT). Patients were evaluated at day 100 post-ASCT with NGS-MRD and positron emission tomography (PET-CT). Patients with ≥ 2 MRD measurements were included in a secondary analysis for sequential measurements. 186 patients were included. At day 100, 45 (24.2%) patients achieved MRD negativity at a sensitivity threshold of 10 −6 . MRD negativity was the most predictive factor for longer time to next treatment (TTNT). Negativity rates did not differ according to MM subtype, R-ISS Stage nor cytogenetic risk. PET-CT and MRD had poor agreement, with high rates of PET-CT negativity in MRD-positive patients. Patients with sustained MRD negativity had longer TTNT, regardless of baseline risk characteristics. Our results show that the ability to measure deeper and sustainable responses distinguishes patients with better outcomes. Achieving MRD negativity was the strongest prognostic marker and could help guide therapy-related decisions and serve as a response marker for clinical trials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".