Mass Spectrometry Provides a Highly Sensitive Noninvasive Means of Sequencing and Tracking M-Protein in the Blood of Multiple Myeloma Patients
Bibliographic record
Abstract
The amino acid sequence of the M-protein for multiple myeloma is unique compared to the polyclonal antibodies in patients' blood. This uniqueness is exploited to develop an ultrasensitive M-protein detection method utilizing mass spectrometry (MS). The method involves the de novo amino acid sequencing of the full-length M-protein, and a targeted MS/MS assay to detect and quantify the unique M-protein sequence in serum samples. Healthy control serum spiked with NISTmAb and serial samples from an MM patient were used to demonstrate the ability of the platform to sequence and monitor a target M-protein. The de novo NISTmAb protein sequence obtained matched the published sequence, confirming the ability of the platform to accurately sequence a target M-protein in serum. NISTmAb was quantified down to 0.0002 g/dL in serum, a level hundreds of times more sensitive than conventional blood-based tests such as SPEP and IFE. The M-protein in the patient sample could be quantified throughout complete remission, demonstrating the utility of the assay to track M-protein considerably beyond the sensitivities of current blood-based tests. Notably, the assay detected a 2-fold rise in M-protein levels 10 months before any changes were detected by conventional IFE. The MS-based assay is highly sensitive, noninvasive, and requires only a small amount of serum, less than 100 μL. Sequencing data is deposited into PRIDE with identifier PXD022784, and quantification data can be found in Panorama Public with identifier PXD022980.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".