Effect of ex vivo-expanded γꗏ-T cells combined with galectin-1 antibody on the growth of human cervical cancer xenografts in SCID mice
Bibliographic record
Abstract
OBJECTIVE: To investigate the antitumor activity of ex vivo-expanded γδ-T cells derived from tumor-infiltrating lymphocytes(γδTILs) from cervical cancer patients when combined with galectin-1 antibody and studied both in vitro and in vivo. METHODS: The presence of γδTILs in cervical cancer specimens was detected by immunohistochemistry and γδTILs were expanded using the solid-phase antibody method. The expression of galectin-1 by the human cervical cancer cell line, SiHa, was measured by Western blot and ELISA. In vitro cytotoxic activities of expanded γδTILs, with or without galectin-1 inhibitor, were determined using the LDH-release test. In vivo antitumor activity of γδTILs, combined with galectin-1 antibody, was evaluated using the SCID mouse model. RESULTS: γδTILs existed in the cervical cancer and the percentage of TCRγδ(+) cells in γδTILs after ex vivo expansion was 91.2±1.2% detected by flow cytometry. SiHa cell expressed and secreted galectin-1 as measured by Western blot and ELISA. Expanded γδTILs from human cervical cancer demonstrated marked cytotoxicity to SiHa or Hela cells. In comparison with non-treated group, the cytotoxicity of γδ TILs towards SiHa or Hela cell was significantly increased when effector and target cells were incubated with either lactose or galectin-1 antibody at E/T ratio of 1:1 (p < 0.05). γδTILs, in combination with galectin-1 antibody treatment, significantly suppressed the growth of xenografts in SCID mice, in comparison with all other groups (p < 0.05). γδTILs alone also showed the ability to inhibit tumour growth in vivo, but were more efficient when combined with specific antibody (p < 0.05). CONCLUSION: Taken together, our results suggest that γδ-T cells, combined with galectin-1 antibody treatment, could be a more effective adoptive immunotherapy for patients with cervical cancer than traditional adoptive immunotherapy methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.013 |
| 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 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".