Inhibition of JAK/STAT signaling sustain stem-like exhausted CD8+ T lymphocytes with enhanced antitumor effects
Bibliographic record
Abstract
Abstract T-cell exhaustion and reduced memory potential is an obstacle to successful adoptive cell therapy (ACT) of cancer. Ex-vivo expansion protocols require sustained TCR and cytokine stimulation that limit ACT efficacy and long-term persistence. We have shown that IL-2 and IL-15, JAK-STAT dependent-cytokines used in ACT expansion protocols, induce CD8+ T-cell exhaustion both in vitro and in vivo following chronic viral infection. We hypothesized that blocking JAK-STAT signaling during ex-vivo expansion protocols will limit T-cell exhaustion and sustain stemness of adoptively transferred cells. In the current study, we mimicked current ex-vivo expansion protocols using purified murine T-cells stimulated by tumor antigens in the presence of IL-2 or IL-15 and evaluated the impact of adding distinct inhibitors of the JAK-STAT pathway on expanded T cells. We observed that JAK-STAT inhibitors reduced CD8+ T-cell exhaustion and increased the number of Ly108+ TCF1+ stem-like progenitors. The reduction in T-cell exhaustion was observed by decreased expression of exhaustion markers, changes in specific transcription factors, and increased expression of effector cytokines using flow cytometry. Adoptive transfer of ex-vivo expanded T-cells to tumor-bearing mice indicated that CD8+ T-cells expanded in the presence of JAK-STAT inhibitor significantly reduced tumor size and increased mice survival after ACT. RNA-seq analysis identified a reduced exhaustion signature in JAK/STATi treated T-cells. These results suggest that targeting JAK-STAT signaling sustains potent antitumor reactivity of adoptive-transferred cells and may enhance the ex-vivo generation of stem-like progenitor CD8+ T cells for long-term survival in the host.
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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".