Feasibility study: Phosphospecific flow cytometry enabling rapid functional analysis of bone marrow samples from patients with multiple myeloma
Bibliographic record
Abstract
Multiple myeloma (MM) is an incurable cancer accounting for about 2% of cancer deaths. Its diagnosis is based on a combination of criteria, which are not always easily measurable. Flow cytometry now allows multiplex analysis of intracellular signaling at the single cell level. We investigated the feasibility of using intracellular protein phosphorylation analysis by flow cytometry on primary plasma cells from bone marrow and its usefulness in MM diagnosis.Cells from frozen bone marrow of five MM patients and four normal donors were stimulated with LPS, IL-6, IL-21, IFNα and TNFα. Cells were stained by fluorescent cell barcoding to allow multiplex analysis. Staining with antibodies against phosphorylated NFkB-p65, Stat1, Stat3, and p38 were used to identify cellular responses following stimulation.Activation profiles of MM and normal plasma cells have been established. MM cells showed heterogeneous response profiles while normal cells responses were homogeneous between donors. We also noticed that many MM samples seemed to show elevated basal level of Stat3 phosphorylation. These results suggest that different response profiles in primary MM cells might correspond to different subtypes of the disease. Thus, we provide an example of how these results may be used as a criterion for MM subtypes classification.We demonstrate that flow cytometry can be used to study signaling pathways in primary MM cells. The heterogeneity observed in MM cells from different patients can prove valuable for MM characterization and represents an interesting avenue for future research in MM diagnosis.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".