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 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) | 0.001 | 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".