CTLA-4 Blockade Expands Infiltrating T Cells and Inhibits Cancer Cell Repopulation during the Intervals of Chemotherapy in Murine Mesothelioma
Bibliographic record
Abstract
Cancer immunotherapy has shown promising results when combined with chemotherapy. Blocking CTLA-4 signaling by monoclonal antibody between cycles of chemotherapy may inhibit cancer cell repopulation and enhance the antitumoral immune reaction, thus improve the efficacy of chemotherapy in mesothelioma. The impact of CTLA-4 blockade on the early stage of tumor development was evaluated in a subcutaneous murine mesothelioma model. CTLA-4 blocking antibody was administered following each cycle of chemotherapy, and monotherapy was included as controls. Antitumor effect was evaluated by tumor growth delay and survival of the animals. Tumor cell repopulation was quantified by bromodeoxyuridine incorporation and Ki67 by immunohistochemistry and/or flow cytometry. In vitro cell killing was determined by classic chromium-released assay, and reverse transcription PCR (RT-PCR) was carried out to determine the gene expression of associated cytokines. Anti-CTLA-4 monoclonal antibody was able to inhibit tumor growth at early stage of tumor development. Antitumor effect was achieved by administration of CTLA-4 blockade between cycles of chemotherapy. Tumor cell repopulation during the intervals of cisplatin was inhibited by CTLA-4 blockade. Anti-CTLA-4 therapy gave rise to an increased number of CD4 and CD8 T cells infiltrating the tumor. RT-PCR showed that the gene expression of interleukin IL-2, IFN-γ, granzyme B, and perforin increased in the tumor milieu. Blockade of CTLA-4 signaling showed effective anticancer effect, correlating with inhibiting cancer cell repopulation between cycles of chemotherapy and upregulating tumor-infiltrating T lymphocytes, cytokines, and cytolytic enzymes in a murine mesothelioma model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".