Inhibition of IL2Rß-dependent STAT5 activity supports T-cell stemness and augments antitumor efficacy of CD8 <sup>+</sup> T cells by preventing T-cell exhaustion
Bibliographic record
Abstract
Abstract CD8 + T-cell exhaustion is a leading cause of adoptive cell therapy (ACT) failure. In contrast, maintaining a stem-like state correlates with better expansion, persistence, and anti-tumor activity of infused T-cell products. IL-2 is extensively used in ACT protocols given its ability to expand T-cell populations. Yet, IL-2 drives more differentiated and exhausted states, diminishing the quality of T-cell products. Understanding how cytokines of the IL2R family drive T-cell differentiation is essential to ultimately design optimal ACT protocols, safeguarding stem-like programs while ensuring sufficient T-cell expansion. Here, we show that cytokine signaling through IL2Rβ supports more differentiated exhausted T cells in chronic lymphocytic choriomeningitis infection. Similarly, high levels of IL-2 and IL-15 in vitro foster heightened differentiation and exhaustion of cells for adoptive cell therapy. In contrast, absence of IL2Rβ in vivo or transient inhibition of Janus kinase 3 (JAK3) or signal transducer and activator of transcription 5 (STAT5) in vitro favors features of T-cell stemness. Transcriptional analyses of in vitro expanded T cells further reveal that inhibition of STAT5 sustains a stemness program, which correlates with better antitumor activity in a mouse melanoma model. When applied to a human CAR T expansion model, inhibition of STAT5 supports memory progenitor differentiation and limit inhibitory receptor expression. These results demonstrate that continuous exposure to high levels of cytokines, such as IL-2 and IL-15, constrain CD8 + T cells towards more advanced states of exhaustion. In contrast, limiting cytokine signaling using specific kinase inhibitors preserves stem-like T-cell programs and enhance the quality of ACT products. One Sentence Summary Sustained IL-2/IL-15 signaling drives CD8 + T-cell exhaustion while JAK3/STAT5 inhibition preserves stemness, boosting adoptive cell therapy efficacy.
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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 | 0.001 |
| 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 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".