CD57+ EMRA CD8+ T cells in cancer patients over 70: associations with prior chemotherapy and response to anti-PD-1/PD-L1 therapy
Bibliographic record
Abstract
Immune ageing complicates cancer treatment in older individuals. While immunotherapy targeting the PD-1/PD-L1 pathway can reinvigorate T cells, these cells tend to become senescent with age. This study investigates different CD8 + T cell subsets usually associated with senescence, in cancer patients over 70 years old who are undergoing anti-PD-1/PD-L1 immunotherapy, and examines the relationship between these senescent cells and prior chemotherapy exposure. We analyzed data from the Elderly Cancer Patient (ELCAPA) cohort, which included 35 patients enrolled between March 2018 and March 2021. Flow cytometry and unsupervised analysis were employed to characterize Effector Memory CD45RA + (EMRA) and CD8 + T cell senescence at baseline, before initiating PD-1/PD-L1 therapy. EMRA cells were found to overexpress CD57 and KLRG1 compared to overall CD8 + T cells. Chemotherapy prior to anti-PD-1/PD-L1 was associated with an increased proportion of CD57 + EMRA CD8 + T cells ( p = 0.009) and its granzyme B (GRZB) subset ( p = 0.007). Using a 10% cut-off to define positivity, the six-month non-response tends to be associated with the CD57 + GRZB + EMRA positivity ( p = 0.097). Other CD8 + T cell subsets (EMRA, CD57 + , or KLRG1 + ), usually associated with senescence, showed no significant association with previous chemotherapy or response to anti-PD-1/anti-PD-L1 therapy. These findings underscore the impact of prior chemotherapy on expanding the pool of senescent T cells, particularly CD57 + EMRA CD8 + T and CD57 + GRZB + EMRA CD8 + T cells, whose expansion could potentially affect the effectiveness of anti-PD-1/PD-L1 immunotherapy in elderly patients. This highlights the need for tailored approaches in this population.
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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 | 0.001 |
| 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) | 0.001 | 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".