Marrow-Infiltrating Regulatory T Cells Correlate with the Presence of Dysfunctional CD4+PD-1+ Cells and Inferior Survival in Patients with Newly Diagnosed Multiple Myeloma
Bibliographic record
Abstract
Abstract Purpose: Immune dysregulation is described in multiple myeloma. While preclinical models suggest a role for altered T-cell immunity in disease progression, the contribution of immune dysfunction to clinical outcomes remains unclear. We aimed to characterize marrow-infiltrating T cells in newly diagnosed patients and explore associations with outcomes of first-line therapy. Experimental Design: We undertook detailed characterization of T cells from bone marrow (BM) samples, focusing on immune checkpoints and features of immune dysfunction, correlating with clinical features and progression-free survival. Results: We found that patients with multiple myeloma had greater abundance of BM regulatory T cells (Tregs) which, in turn, expressed higher levels of the activation marker CD25 compared with healthy donors. Patients with higher frequencies of Tregs had shorter PFS and a distinct Treg immune checkpoint profile (increased PD-1, LAG-3) compared with patients with lower frequencies of Tregs. Analysis of CD4 and CD8 effectors revealed that low CD4effector (CD4eff):Treg ratio and increased frequency of PD-1–expressing CD4eff cells were independent predictors of early relapse over and above conventional risk factors, such as genetic risk and depth of response. Ex vivo functional analysis and RNA sequencing revealed that CD4 and CD8 cells from patients with greater abundance of CD4effPD-1+ cells displayed transcriptional and secretory features of dysfunction. Conclusions: BM-infiltrating T-cell subsets, specifically Tregs and PD-1–expressing CD4 effectors, negatively influence clinical outcomes in newly diagnosed patients. Pending confirmation in larger cohorts and further mechanistic work, these immune parameters may inform new risk models, and present potential targets for immunotherapeutic strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".