A Personalized Mass Spectrometry–Based Assay to Monitor M-Protein in Patients with Multiple Myeloma (EasyM)
Bibliographic record
Abstract
Abstract Purpose: M-protein is a well-established biomarker used for multiple myeloma monitoring. Current improvements in multiple myeloma treatment created the need to monitor minimal residual disease (MRD) with high sensitivity. Measuring residual levels of M-protein in serum by MS was established as a sensitive assay for disease monitoring. In this study we evaluated the performance of EasyM—a noninvasive, sensitive, MS-based assay for M-protein monitoring. Experimental Design: Twenty-six patients enrolled in MCRN-001 clinical trial of two high-dose alkylating agents as conditioning followed by lenalidomide maintenance were selected for the study. All selected patients achieved complete responses (CR) during treatment, whereas five experienced progressive disease on study. The M-protein of each patient was first sequenced from the diagnostic serum using our de novo protein sequencing platform. The patient-specific M-protein peptides were then measured by targeted MS assay to monitor the response to treatment. Results: The M-protein doubling over 6 months measured by EasyM could predict the relapse in 4 of 5 relapsed patients 2 to 11 months earlier than conventional testing. In 21 disease-free patients, the M-protein was still detectable by EasyM despite normal FLC and MRD negativity. Importantly, of 72 MRD negative samples with CR status, 62 were positive by EasyM. The best sensitivity achieved by EasyM, detecting 0.58 mg/L of M-protein, was 1,000- and 200-fold higher compared with serum protein electrophoresis and immunofixation electrophoresis, respectively. Conclusions: EasyM was demonstrated to be a noninvasive, sensitive assay with superior performance compared with other assays, making it ideal for multiple myeloma monitoring and relapse prediction.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".