Enhancing Therapeutic Efficacy of Double Negative T Cells against Acute Myeloid Leukemia Using Idelalisib
Bibliographic record
Abstract
The double negative T cell (DNT) is a unique subset of T cells with potent anti-leukemic potential. Previously, DNT therapy has been shown to effectively target AML cells in patient-derived xenograft (PDX) models. Further, a recently completed phase I/IIa clinical study demonstrated the safety, feasibility, and potential efficacy in AML patients that relapsed after allogeneic hematopoietic stem cell transplantation. However, the persistence and durability of DNT-mediated anti-leukemic response is less well understood. In this study, we characterized the in vivo persistence of DNTs in PDX models. Further, we improved the efficacy and durability of DNT-mediated activity with phosphoinositide 3-kinase delta (PI3Kδ) inhibition. Mechanistically, DNTs treated with the PI3Kδ inhibitor, Idelalisib (Ide), exhibited early memory phenotype with superior viability and proliferative capacity but less cell exhaustion. Collectively, the findings from this study support the use of Ide-treated DNTs to improve its therapeutic outcome.
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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 | 0.000 |
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".