Immune Phenotypes and Target Antigens of Clonally Expanded Bone Marrow T Cells in Treatment-Naïve Multiple Myeloma
Bibliographic record
Abstract
Multiple myeloma is a hematologic malignancy of monoclonal plasma cells that accumulate in the bone marrow. Despite their clinical and pathophysiologic relevance, the roles of bone marrow-infiltrating T cells in treatment-naïve patients are incompletely understood. We investigated whether clonally expanded T cells (i) were detectable in multiple myeloma bone marrow, (ii) showed characteristic immune phenotypes, and (iii) whether dominant clones recognized antigens selectively presented on multiple myeloma cells. Single-cell index sorting and T-cell receptor (TCR) αβ sequencing of bone marrow T cells from 13 treatment-naïve patients showed dominant clonal expansion within CD8+ cytolytic effector compartments, and only a minority of expanded T-cell clones expressed the classic immune-checkpoint molecules PD-1, CTLA-4, or TIM-3. To identify their molecular targets, TCRs of 68 dominant bone marrow clones from five selected patients were reexpressed and incubated with multiple myeloma and non-multiple myeloma cells from corresponding patients. Only 1 of 68 TCRs recognized antigen presented on multiple myeloma cells. This TCR was HLA-C-restricted, self-peptide-specific and could be activated by multiple myeloma cells of multiple patients. The remaining dominant T-cell clones did not recognize multiple myeloma cells and were, in part, specific for antigens associated with chronic viral infections. In conclusion, we showed that dominant bone marrow T-cell clones in treatment-naïve patients rarely recognize antigens presented on multiple myeloma cells and exhibit low expression of classic immune-checkpoint molecules. Our data provide experimental context for experiences from clinical immune-checkpoint inhibition trials and will inform future T cell-dependent therapeutic strategies.
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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 | 0.000 | 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 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".