Development and preclinical validation of a therapeutic γδ TCR-engineered cell product for pan-cancer targeting 3442
Bibliographic record
Abstract
Abstract Description Adoptive cellular therapies have revolutionized cancer treatment, yet significant challenges remain, including antigen escape, toxicity and patient accessibility. γδ TCR-engineered cells offer the potential to overcome these barriers due to their ability to recognize upregulated stress ligands in an MHC-independent manner. Our lab has characterized T cells expressing a γδ TCR that was significantly expanded in a cancer patient who demonstrated a complete response to anti-PD-1 therapy. When cloned and expressed in Jurkats, this γδ TCR demonstrated wide reactivity to a variety of cancer cell lines including lung, melanoma, breast and myeloma, but not healthy cells. CRISPR editing of lines confirmed recognition of antigen independently of HLA and known γδ TCR ligands, thus positioning this TCR as a potentially novel, pan-cancer therapy. To advance this γδ TCR towards clinical application, we have optimized lentiviral transduction protocols to generate large quantities of γδ TCR-T cells capable of mediating potent and specific killing of target cells. Ongoing whole-genome screens aim to identify its ligand in an unbiased manner. Complementary efforts to expand its preclinical evaluation involve use of 3D organoid models, PDXs and NSG mice, to assess its safety and efficacy in physiologically relevant systems. This HLA-independent γδ TCR represents an innovative strategy for treating a more diverse patient population and overcoming current challenges of adoptive cell therapy. Funding Sources This work is supported by the Canadian Institutes of Health Research (CIHR) through the C-GSM program & UHN Innovation Accelerator (IA). Topic Categories Tumor Immunology: Checkpoints, Prevention, and Treatment (TIPT)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".