Artifactual Kappa Light Chain Restriction of Marrow Hematogones: A Potential Diagnostic Pitfall in Minimal Residual Disease Assessment of Plasma Cell Myeloma Patients on Daratumumab
Bibliographic record
Abstract
BACKGROUND: Daratumumab (DARA) is a humanized Immunoglobulin G(IgG)1-kappa monoclonal antibody against CD38 antigen that is shown to improve outcomes in relapsed/refractory plasma cell myeloma (PCM) patients. Since CD38 is expressed by different hematopoietic elements, DARA has the potential to interfere with flow cytometric assessment of bone marrow specimens. METHODS: Flow cytometric analysis of bone marrow samples from 10 PCM on DARA and 5 control samples was performed using two different antibody panels. RESULTS: Bone marrow samples from PCM patients on DARA exhibited a population of CD19+ CD10+ B-lymphoid cells with kappa light chain restriction. Further morphological and immunophenotypic studies suggested that this population represents marrow hematogones. Marrow hematogones from control samples showed normal immunophenotypic profiles. CONCLUSION: DARA on the surface of hematogones interferes with flow cytometric clonality study leading to artifactual kappa light chain restriction, which can result in false interpretation of a concurrent clonal B-cell proliferation. In the era of rapidly growing list of therapeutic monoclonal antibodies, flow cytometry pathologists should be aware of potential interferences to avoid misdiagnosis. © 2019 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 | 0.002 |
| 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 | 0.001 | 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".