Interleukin-21 combined with PD-1 or CTLA-4 blockade enhances antitumor immunity in mouse tumor models
Bibliographic record
Abstract
Recent advances in cancer treatment with checkpoint blockade of receptors such as CTLA-4 and PD-1 have demonstrated that combinations of agents with complementary immunomodulatory effects have the potential to enhance antitumor activity as compared to single agents. We investigated the efficacy of immune-modulatory interleukin-21 (IL-21) combined with checkpoint blockade in several syngeneic mouse tumor models. After tumor establishment, mice were administered recombinant mouse IL-21 (mIL-21) alone or in combination with blocking monoclonal antibodies against mouse PD-1 or CTLA-4. In contrast to monotherapy, IL-21 enhanced antitumor activity of mCTLA-4 mAb in four models and anti-PD-1 mAb in two models, with evidence of synergy for one or both of the combination treatments in the EMT-6 and MC38 models. The enhanced efficacy was associated with increased intratumoral CD8+ T cell infiltrates, CD8+ T cell proliferation, and increased effector memory T cells, along with decreased frequency of central memory CD8+ T cells. In vivo depletion of CD8+ T cells abolished the antitumor activities observed for both combination and monotherapy treatments, further supporting a beneficial role for CD8+ T cells. In all studies, the combination therapies were well tolerated. These results support the hypothesis that the combination of recombinant human IL-21 with CTLA-4 or PD-1 monoclonal antibodies could lead to improved outcomes in cancer patients.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".