Impact of immunological aging on T cell-mediated therapies in older adults with multiple myeloma and lymphoma
Bibliographic record
Abstract
The treatment landscape for lymphoma and multiple myeloma, which disproportionally affect older adults, has been transformed by the advent of T cell-mediated immunotherapies, including immune checkpoint inhibition, T cell-engaging bispecific antibodies, and chimeric antigen receptor (CAR) T cell therapy, during the last decade. These treatment modalities re-enable the patient's own immune system to combat malignant cells and offer the potential for sustained remissions and cure for various diseases.Age profoundly affects the physiological function of the immune system. The process of biological aging is largely driven by inflammatory signaling, which is reciprocally fueled by aging-related alterations of physiology and metabolism. In the T cell compartment, aging contributes to T cell senescence and exhaustion, increased abundance of terminally differentiated cells, a corresponding attrition in naïve T cell numbers, and a decrease in the breadth of the receptor repertoire. Furthermore, inflammatory signaling drives aging-related pathologies and contributes to frailty in older individuals. Thus, there is growing evidence of biological aging modulating the efficacy and toxicity of T cell-mediated immunotherapies.Here, we review the available evidence from biological and clinical studies focusing on the relationship between T cell-mediated treatment of hematologic malignancies and age. We discuss biological features potentially impacting clinical outcomes in various scenarios, and potential strategies to improve the safety and efficacy of immune checkpoint inhibitors, T cell-engaging bispecific antibodies, and CAR-T cell therapy in older patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".