Direct Detection of Monoclonal Free Light Chains in Serum by Use of Immunoenrichment-Coupled MALDI-TOF Mass Spectrometry
Bibliographic record
Abstract
BACKGROUND: Free light chain (FLC) quantification is the most analytically sensitive blood-based method commercially available to diagnose and monitor patients with plasma cell disorders (PCDs). However, instead of directly detecting monoclonal FLCs (mFLCs), FLC assays indirectly assess clonality based on quantifying κ and λ FLCs and determination of the к/λ FLC ratio. Often an abnormal FLC ratio is the only indication of a PCD, and confirmation by a direct method increases diagnostic confidence. The aim of this study was to develop an analytically sensitive method for direct detection of mFLCs. METHODS: Patient sera (n = 167) previously assessed by nephelometric FLC quantification and immunofixation electrophoresis (IFE) were affinity enriched for IgG, IgA, and total and free κ and λ light chains and subjected to MALDI-TOF MS. Relative analytical sensitivity of these methods was determined using serially diluted sera containing mFLCs. RESULTS: In sera with abnormal FLC ratios (n = 127), 43% of monoclonal proteins were confirmed by IFE, 57% by MALDI-TOF MS without FLC enrichment, and 87% with FLC enrichment MALDI-TOF MS. In sera with normal FLC ratios (n = 40), the FLC MALDI-TOF MS method identified 1 patient with an mFLC. Serial dilution and analysis of mFLC containing sera by IFE, nephelometry, and FLC MALDI-TOF MS demonstrated that FLC MALDI-TOF MS analysis had the highest analytical sensitivity. CONCLUSIONS: FLC immunoenrichment coupled to MALDI-TOF MS enables direct detection of mFLCs and significantly increases the confirmation of abnormal serum FLC ratios over IFE and MALDI-TOF MS without FLC enrichment, thereby providing added confidence for diagnosing FLC PCDs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".