Optimization of a T cells education protocol to treat hematological cancers
Bibliographic record
Abstract
Abstract Allogeneic hematopoietic stem cell transplantation is the sole curative treatment for patients with high-risk hematological cancers. Most of its activity relies on the ability of engrafted T cells to recognize minor histocompatibility antigens (MiHAs) expressed on malignant cells resulting in their elimination. However, donor lymphocytes can also recognize MiHAs expressed on non-hematopoietic cells leading to graft-versus-host disease. Hence, the expansion of T lymphocytes displaying potent cytotoxic activity toward MiHA(s) preferentially expressed on leukemic cells would be greatly beneficial for engrafted patients. Using a proteo-genomic approach, novel MiHAs that are predominantly expressed on hematopoietic cells have been identified. We previously showed that these novel MiHAs can be exploited for ex vivo expansion of the antigen specific T cells. However, the frequencies of these MiHA specific T cells are extremely low. Thus it is challenging to generate an optimal ex vivo expansion of antigen specific T cells to a single MiHA. In this context, we aim to improve this complex manufacturing process by using simultaneously multiple MiHAs to enhance the probability of obtaining response to at least one MiHA. This approach allowed us to expand T cells populations with distinct MiHAs specificities. Further, upon restimulation, MiHA specific T cells express high levels of inflammatory cytokines Interferon-γ and Tumor Necrosis Factor-α thus suggesting their functionality. In conclusion, multipeptide T cell stimulation allows for simultaneous expansion of functional T cells specific for several MiHA. This could be translated in improved therapeutical approach reducing the probability of immune escape of malignant cells.
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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) | 0.003 | 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".