Detection of M-Protein in Transplant-Ineligible Multiple Myeloma Patients on Daratumumab Using Liquid Chromatography Quadruple Time of Flight Mass Spectrometry
Bibliographic record
Abstract
Background Given the recent transition to novel front-line combinations capable of achieving a deeper response, the incorporation of measurable residual disease (MRD) into the response criteria for multiple myeloma (MM) could allow for better prognostication and potentially introduces MRD-adaptive treatment approaches. Liquid chromatography high-resolution mass spectrometry (LC-HRMS) is a powerful analytical tool that combines physical separation with high-resolution mass spectrometric detection. Mass spectrometry (MS) has emerged as a new diagnostic platform for monitoring M-proteins in MM, offering enhanced sensitivity for detecting M-proteins from peripheral blood compared to conventional serum protein electrophoresis/immunofixation analysis. Its utility has been endorsed in the 2021 IMWG guidelines. Methods Peripheral blood samples from MM patients were collected and used for validation of the MS. Samples were prepared in a multistep analytical process involving sample preparation by affinity purification targeting the conserved regions of immunoglobulins G, A, M, kappa, and lambda to enrich immunoglobulins and wash away serum proteins. Samples were analyzed on an Agilent 6545XT AdvanceBio LC/Q-TOF. M-proteins were identified by intact light chain masses above the polyclonal background. Samples were analyzed and compared to immunofixation electrophoresis (IFE) and flow cytometry (FC) (preliminary 10-4, forthcoming 10-5). Results were grouped into 4 categories: IFE+/QTOF+, IFE+/QTOF-, IFE-/QTOF+, IFE-/QTOF-. Limit of detection (LOD) studies were conducted by spiking IgG-kappa biologics into serum for both LC/Q-TOF and IFE. Active and ongoing results The LC/Q-TOF was able to isotype M-proteins similarly to IFE but with a mass accuracy approximately 1 Da. The LC/Q-TOF LOD was ~35 mg/L. 183 patient serum samples were analyzed. 134 were QTOF+/IFE+, 23 were QTOF-/IFE-, 23 were QTOF+/IFE-, and 3 were QTOF-/IFE+. When QTOF+/IFE- results were excluded due to the improved sensitivity of the LC/Q-TOF method, the concordance of LC/Q-TOF with IFE was ~98%. Conclusions Accurate mass determination of the M-protein intact light chain can readily differentiate daratumumab from IgG-kappa M-proteins. Excluding QTOF+/IFE-, the concordance rate was 98% between IFE and QTOF. QTOF demonstrated a LOD for IgG-kappa ~ 35 mg/L which is ~ 5-10x lower than most IFE estimated LODs. Future samples will compare QTOF to MRD assessments using next generation flow cytometry at a detection level of 10-5.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".