Combination Therapy with iRGD-antiCD3 and PD-1 Blockade Enhances Antitumor Potency of Cord Blood-Derived T Cells
Bibliographic record
Abstract
BACKGROUND: T cell-redirecting bispecific antibodies (BsAbs) are emerging as a potent cancer therapy that crosslinks tumor cells and T cells by simultaneously binding to tumor-associated antigen and CD3ε. However, immune inhibitory molecules can be remarkably upregulated after BsAbs treatment, leading to a suppressive tumor microenvironment and treatment resistance. This can be partially reversed by combination with immune checkpoint inhibitors. In our previous work, we successfully constructed the recombinant protein iRGD-antiCD3 and demonstrated that it promoted antitumor efficacy of transferred T cells by promoting T cell activation and infiltration. METHODS: We detected the levels of both PD-1 and PD-L1 as resistance to iRGD-antiCD3 treatment. Using cord blood-derived T cells, we assessed the activation and effects of iRGD-antiCD3 combined with PD-1 as evidenced by activation markers, Th1/Th2-cytokines, and killing capability against tumor cells in vitro. Moreover, to better mimic the physiological characteristics of in vivo solid tumors, we generated 3D spheroids from target cell lines. Spheroids were stained with a Viability/Cytotoxicity Assay Kit and examined by confocal microscopy to study the in vitro antitumor effect of T cells co-administered with combination iRGD-antiCD3 and PD-1 blockade. The mouse peritoneal metastatic gastric tumor model was employed. The synergistic antitumor effect and safety profiles in vivo were evaluated by tumor and body weight of tumor-bearing mice. RESULTS: We found that expression of both PD-1 and PD-L1 were increased as resistance to iRGD-antiCD3 treatment. We found that PD-1 blockade partially restored T cell activation as evidenced by elevated activation markers, Th1-cytokines, and killing capability against tumor cells in vitro. The combination of PD-1 blockade consistently and significantly increased cord blood-derived T cell cytotoxicity against 3D tumor spheroids. In vivo, we observed synergistic antitumor activity without obvious side effects. CONCLUSION: These results demonstrated that combining iRGD-antiCD3 with PD-1 blockade could further improve antitumor efficacy of T cells, and this strategy holds great potential for the treatment of solid malignancies.
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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".