Abstract A054: Anti-CTLA-4 generates memory T-cell with greater expansion and functionality than anti-PD-1
Bibliographic record
Abstract
Abstract Blocking either cytotoxic T-lymphocyte antigen-4 (CTLA-4) or programmed cell death-1 (PD-1) pathway results in distinct T-cell differentiation. We investigated the effects of anti-CTLA-4 and anti-PD-1 on memory T-cell formation in mice. Anti-CTLA-4 generates a more potent memory response than anti-PD-1. Memory T-cells generated by anti-CTLA-4 expand at a greater frequency, have greater cytokine production, and exhibit higher antitumor activity than those generated by anti-PD-1. These memory cells also more frequently differentiate into KLRG1+ effector CD8 T-cells during re-challenge. Additionally, anti-CTLA-4 generates more TCF-1+ memory-like T-cells, while anti-PD-1 results in more TOX+ terminally differentiated T-cells. Together, we provide insights into the long-term effects of these immunotherapies on the immune system. Citation Format: Stephen Mok, Huey Liu, Nana-Ama A.S. Anang, James J Mancuso, Didem Ağaç Çobanoğlu, James P Allison. Anti-CTLA-4 generates memory T-cell with greater expansion and functionality than anti-PD-1 [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr A054.
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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) | 0.009 | 0.001 |
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".