Rebuilding a repertoire; reconstitution of γδ T cells after allogeneic stem cell transplantation
Bibliographic record
Abstract
γδ T cells are among the first immune cells to recover after allogeneic stem cell transplantation (SCT). SCT still remains the only curative treatment for various hematological malignancies, but is often complicated by infections, relapses and graft versus host disease (GVHD). T cells play a crucial role in in both the desired graft versus leukemia effect and severe complications such as GVHD. Whereas αβ T cells are extensively studied after SCT, the role of γδ T cells is less clear. In this thesis the role of γδ T cells after SCT is explored and their potential for therapeutic interventions is investigated. We showed a favorable outcome with less GVHD after αβ T cell depleted SCT in a multicenter prospective phase 1/2 clinical study. In depth analyses of the immune system after αβ T cell depleted SCT in the context of viral reactivations such as CVM and EBV showed the differences in reconstitution of the αβ- and γδ T cells. In the abcense of sufficient numbers and diversity of αβ T cells, γδ T cells played a crucial role in the first months after SCT as was suggested by focussing of the γδ T cell receptor repertoire. In the second part of this thesis we identified novel tumor reactive γδ T cell receptors against a variety of cancer types. These receptors are tested in the TEG format (αβ T cells engineered to express a defined γδTCR) and will be leads for future γδ T cell based immune effector cell therapies.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".