Therapeutic Exploration of γδT cells: the Quest to Utilize these Iconic Lymphocytes for Anti-cancer therapies
Bibliographic record
Abstract
Despite significant efforts to improve treatments, many types of cancer remain incurable. For this reason, researchers are not only focused on improving existing therapies but are also exploring novel approaches. One promising new direction lies within our own bodies: the immune system, which protects us daily from a wide range of diseases. Increasingly, research is directed towards harnessing the immune system to fight cancer, particularly using T cells. A fraction of T cells can effectively recognize tumor cells and subsequently kill these tumor cells via their receptors. These receptors can be used to transform non-tumor recognizing T cells into tumor recognizing cells, for instance, using so-called bispecific molecules or by genetically modifying cells. In this thesis, we have investigated a specific type of T cell, the γδT cell, and its potential as an immunotherapy against cancer. We aimed to improve current γδT cell-based therapies and demonstrate the unique therapeutic potential of γδT cells in two types of solid tumors. First, we explored ways to enhance the efficacy of our γδT cell-based bispecific molecules, named GABs, and we observed that when we increase the binding of the GABs to the tumor, we found a more beneficial therapeutic success. The improved therapeutic success could also be reproduced in a different γδT cell-based therapy, genetically modified cells called TEGs. Furthermore, we examined the potential of distinct γδT cell subsets in glioblastoma and proposed strategies to overcome potential glioblastoma escape mechanisms. Lastly, we studied the role of γδT cells in colorectal cancer, which yielded novel insights for future treatment approaches. Altogether, our findings contribute to the broader understanding of γδT cells and their receptors and offer new opportunities for improving existing therapies and developing innovative immunotherapeutic strategies against cancer.
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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 | 0.001 | 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".