Multiparametric flow cytometry profiling of neoplastic plasma cells in multiple myeloma
Bibliographic record
Abstract
BACKGROUND AND AIM: The clinical impact of multiparametric flow cytometry (MFC) in multiple myeloma (MM) is still unclear and under evaluation. Further progress relies on multiparametric profiling of the neoplastic plasma cell (PC) compartment to provide an accurate image of the stage of differentiation. The primary aim of this study was to perform global analysis of CD expression on the PC compartment and subsequently to evaluate the prognostic impact. Secondary aims were to study the diagnostic and predictive impact. DESIGN AND METHODS: The design included a retrospective analysis of MFC data generated from diagnostic bone marrow (BM) samples of 109 Nordic patients included in clinical trials within NMSG. Whole marrow were analyzed by MFC for identification of end-stage CD45(-) /CD38(++) neoplastic PC and registered the relative numbers of events and mean fluorescence intensity (MFI) staining for CD19, CD20, CD27, CD28, CD38, CD44, CD45, CD56, and isotypes for cluster analysis. RESULTS: The median MFC-PC number was 15%, and the median light microscopy (LM)-PC number was 35%. However, the numbers were significant correlated and the prognostic value with an increased relative risk (95% CI) of 3.1 (1.7-5.5) and 2.9 (1.4-6.2), P < 0.0003 and P < 0.004 of MFC-PC and LM-PC counts, respectively. Unsupervised clustering based on global MFI assessment on PC revealed two clusters based on CD expression profiling. Cluster I with high intensity for CD56, CD38, CD45, right-angle light-scatter signal (SSC), forward-angle light-scatter signal (FSC), and low for CD28, CD19, and a Cluster II, with low intensity of CD56, CD38, CD45, SSC, FSC, and high for CD28, CD19 with a median survival of 39 months and 19 months, respectively (P = 0.02). CONCLUSIONS: The MFC analysis of MM BM samples produces diagnostic, prognostic, and predictive information useful in clinical practice, which will be prospectively validated within the European Myeloma Network (EMN). © 2010 International Clinical Cytometry Society.
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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 | 0.000 |
| 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".