Conventional Treatment for Multiple Myeloma Drives Premature Aging Phenotypes and Metabolic Dysfunction in T Cells
Bibliographic record
Abstract
New diagnoses of multiple myeloma (MM) tend to occur after the age of 60, by which time thymic output is severely reduced. As a consequence, lymphocyte recovery after lymphopenia-inducing anti-MM therapies relies on homeostatic proliferation of peripheral T cells rather than replenishment by new thymic emigrants. To assess lymphocyte recovery and phenotype in patients with newly diagnosed (NDMM) and relapsed/refractory (RRMM) MM, we tracked CD4+ and CD8+ T cell populations at serial timepoints throughout treatment, and compared to age-matched healthy donors (HD). Anti-MM therapies and autologous stem cell transplant (ASCT) caused a permanent reduction in CD4:8 ratio, a decrease in naïve CD4+ T cells with an increase in effector memory T cells and PD1 expressing CD4+ T cells. Transcriptional profiling highlighted that genes associated with fatty acid β-oxidation were upregulated in T cells in RRMM, suggesting increased reliance on mitochondrial respiration. High mitochondrial mass was seen in all T cell subsets in RRMM but with relatively suppressed reactive oxygen species and mitochondrial membrane potential, indicating mitochondrial dysfunction. These findings highlight that anti-MM and ASCT therapies perturb the composition of the T cell compartment and drive substantial metabolic remodelling, which may affect the fitness of T cells for immunotherapies.
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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".