<p>The Immunostimulative Effect and Mechanisms of a Novel Mouse Anti-Human PD-1 Monoclonal Antibody on Jurkat Lymphocytic Cells Cocultured with Hepatoma Cells</p>
Bibliographic record
Abstract
BACKGROUND: Monoclonal antibodies (mAbs) that target the programmed cell death-1 (PD-1)/programmed death-ligand 1 (PD-L1) immune checkpoint have demonstrated substantial clinical benefit for a variety of solid tumors. However, their applications in patients with hepatocellular carcinoma (HCC) are reported with unclear molecular mechanisms. Here, we report a novel mouse anti-human PD-1 mAb that can reverse the immunosuppressive effect of HePG2 cells on Jurkat cells. MATERIALS AND METHODS: HepG2 liver cancer cells, which were induced to overexpress PD-L1 by IFN-γ, were co-cultured with PHA-activated Jurkat lymphocytic cells to investigate the immunostimulative effect and mechanisms of the 14 newly generated PD-1 mAbs. Multiple cellular and molecular biology experiments were performed in this study, such as CCK-8, ELISA, flow cytometry, immunofluorescence and Western blot. RESULTS: We found that mAb B1C4 significantly enhanced the tumor-killing cytokine secretion level by Jurkat cells in the co-culture system and increased the killing ability of Jurkat cells on HepG2 cells. Co-culture with HePG2 cells led to Jurkat cell cycle delay in S phase, and B1C4 promoted cell cycle progression from S to G2/M. Co-culture with HePG2 cells also caused apoptosis in Jurkat cells, which was inhibited by B1C4. B1C4 reversed the immunosuppression of Jurkat cells resulted from co-cultured with HePG2 cells through inhibiting PTEN and activating PI3K/AKT/mTOR signaling pathways. CONCLUSION: Our study demonstrated that anti-PD-1 mAb B1C4 could inhibit the apoptosis of Jurkat cells induced by HePG2 hepatoma cells and reverse the immunosuppressive effect of HePG2 cells on Jurkat cells. The study provides a vital basis for applying PD-1 monoclonal antibodies in the treatment of HCC and provides antibody selection for the development of novel PD-1 mAb with blocking activity.
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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 | 0.000 |
| 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".