CD4 T Cells Require ICOS-Mediated PI3K Signaling to Increase T-Bet Expression in the Setting of Anti-CTLA-4 Therapy
Bibliographic record
Abstract
The transcription factor T-bet controls the Th1 genetic program in T cells for effective antitumor responses. Anti-CTLA-4 immunotherapy elicits dramatic antitumor responses in mice and in human patients; however, factors that regulate T-bet expression during an antitumor response mediated by anti-CTLA-4 remain to be elucidated. We were the first to report that treatment with anti-CTLA-4 led to an increase in the frequency of T cells expressing inducible costimulator (ICOS). In both treated patients and mice, our data revealed that CD4(+)ICOS(hi) T cells can act as effector T cells, which produce the Th1 cytokine IFN-γ. We also showed in a small retrospective analysis that an increased frequency of CD4(+)ICOS(hi) T cells correlated with better clinical outcome and the absence of ICOS or its ligand (ICOSL) in mouse models led to impaired tumor rejection. Here, we show that CD4(+)ICOS(hi) T cells from anti-CTLA-4-treated patients had an increase in signaling via the phospoinositide-3-kinase (PI3K) pathway and an increase in expression of T-bet. An ICOS-specific siRNA transfected into human T cells led to diminished PI3K signaling and T-bet expression. Therefore, we hypothesized that ICOS, and specifically ICOS-mediated PI3K signaling, was required for T-bet expression. We conducted studies in ICOS-deficient and ICOS-YF mice, which have a single amino acid change that abrogates PI3K signaling by ICOS. We found that ICOS-mediated PI3K signaling is required for T-bet expression during an antitumor response elicited by anti-CTLA-4 therapy. Our data provide new insight into the regulation of T-bet expression and suggest that ICOS can be targeted to improve Th1 antitumor responses.
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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".