Development of human ex vivo expanded Vδ1 γδ T cells armed with CAR as a metabolically fit, off-the-shelf cell therapy for solid tumors 2995
Bibliographic record
Abstract
Abstract Description γδ T cells are unconventional T cells which kill tumors independently of antigen presentation by MHC class I, making them a promising candidate for allogeneic cell therapy. While Vδ2 γδ T cells are the most prominent in blood, the less prevalent Vδ1 subset has shown superior tumor killing. However, difficulties in expanding Vδ1 T cells has limited their clinical use. Here, we evaluated the expansion and activation of Vδ1 T cells using K562 feeder cells expressing membrane-bound IL-21. We first expanded γδ T cells from PBMCs long-term, and assessed their anti-tumor functions against breast and ovarian cancer cell lines and a xenograft model of human ovarian cancer. We also tested their metabolic function within the suppressive ovarian cancer ascites tumor microenvironment (TME) to assess metabolic fitness. Lastly, we generated Vδ1 cells with stable anti-HER2 chimeric antigen receptor (CAR) expression and assessed their cytotoxicity against HER2+ tumor cells. We found that expanded γδ T cells were primarily Vδ1 and displayed higher cytotoxicity than unexpanded γδ T cells. Expanded Vδ1 cells significantly reduced tumor burden in vivo and retained their cytotoxicity and metabolism in the ascites TME. Anti-HER2 CAR-Vδ1 T cells displayed enhanced cytotoxicity and degranulation against HER2+ breast cancer cells. Overall, we demonstrate the anti-tumor potential of HER2 CAR-expressing expanded Vδ1 cells as a metabolically fit, off-the-shelf cell therapy for hard-to-treat solid tumors. Funding Sources Supported by the Canadian Institutes of Health (CIHR) Research Doctoral Scholarship Award; CIHR Project Grant. Topic Categories Tumor Immunology: Checkpoints, Prevention, and Treatment (TIPT)
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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".