A role for the transcription factor STAT5 in antagonizing CD8+ T cell exhaustion
Bibliographic record
Abstract
Abstract Exhaustion gradually establishes in chronically stimulated CD8s and this process is not reverted by current therapeutic approaches due to establishment of a stable epigenetic program. Recent advances have informed on the developmental process of exhaustion and highlighted TOX as a key lineage-defining TF in the process. Yet, little remains known on molecular pathways capable of antagonizing the TOX-dependent exhaustion program. By depicting transcriptional changes at key developmental steps of exhaustion, we demonstrate an antagonistic role for the TF STAT5 in the development of CD8 T cell exhaustion. STAT5 transcriptional network is heavily silenced upon chronic antigenic stimulation in a TOX-dependent manner which allows initiation of the exhaustion lineage. Increasing STAT5 activity abrogates establishment of the exhaustion lineage leading to the development of effector-like CD8s that acquire a unique transcriptional identity, distinct from exhausted cells, persist throughout chronicity and demonstrate higher protective capacity. Using temporal loss and gain of function approaches, we show that STAT5 triggers loss of progenitor identity by exhausted CD8s (Tex) and subsequent differentiation into the recently identified effector-like intermediate Tex subset. Temporal increase in STAT5 activity also robustly synergizes with PD-L1 blockade by further fostering intermediate Tex cells accumulation. Together, we show that modulating STAT5 activity may counteract the exhaustion process and favor instigation of effector-like characteristic in Tex cells suitable for optimal therapeutic efficacy. This work is supported by the Parker Institute for Cancer Immunotherapy (PICI). JC-Beltra is a PICI scholar awardee.
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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) | 0.002 | 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".