Mass Cytometry Analysis Shows That a Novel Memory Phenotype B Cell Is Expanded in Multiple Myeloma
Bibliographic record
Abstract
It would be very beneficial if the status of cancers could be determined from a blood specimen. However, peripheral blood leukocytes are very heterogeneous between individuals, and thus high-resolution technologies are likely required. We used cytometry by time-of-flight and next-generation sequencing to ask whether a plasma cell cancer (multiple myeloma) and related precancerous states had any consistent effect on the peripheral blood mononuclear cell phenotypes of patients. Analysis of peripheral blood samples from 13 cancer patients, 9 precancer patients, and 9 healthy individuals revealed significant differences in the frequencies of the T-cell, B-cell, and natural killer-cell compartments. Most strikingly, we identified a novel B-cell population that normally accounts for 4.0% ± 0.7% (mean ± SD) of total B cells and is up to 13-fold expanded in multiple myeloma patients with active disease. This population expressed markers previously associated with both memory (CD27(+)) and naïve (CD24(lo)CD38(+)) phenotypes. Single-cell immunoglobulin gene sequencing showed polyclonality, indicating that these cells are not precursors to the myeloma, and somatic mutations, a characteristic of memory cells. SYK, ERK, and p38 phosphorylation responses, and the fact that most of these cells expressed isotypes other than IgM or IgD, confirmed the memory character of this population, defining it as a novel type of memory B cells.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".