Signatures of immune senescence predict outcomes and define checkpoint blockade-unresponsive microenvironments in acute myeloid leukemia
Bibliographic record
Abstract
Summary The function of senescent-like T cells, transcriptomic features of immune effector senescence (IES) and their influence on therapeutic response were investigated in independent AML clinical cohorts comprising 1,864 patients treated with chemotherapy and/or immune checkpoint blockade (ICB). We show that senescent-like bone marrow CD8 + T cells are impaired in killing autologous AML blasts, and that their proportion negatively correlates with overall survival (OS). We define new IES signatures using two gene expression platforms and report that IES scores correlate with adverse-risk molecular lesions, stemness, and poor outcomes as a potentially more powerful predictor of OS than 2017-ELN risk or LSC17 stemness score. IES expression signatures also identify an ICB- unresponsive tumor microenvironment and predict significantly worse OS in AML as well as in solid tumors. The newly described IES scores provide improved AML risk stratification and could facilitate the delivery of personalized immunotherapies to patients who are most likely to benefit.
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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 | 0.000 | 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".