TRAIL orchestrates ThINKK-induced NK cell cytotoxicity against childhood acute lymphoblastic leukemia
Bibliographic record
Abstract
Abstract Background Therapeutic Inducers of Natural Killer cell Killing (ThINKK) represent a novel class of immunotherapy designed to enhance the graft-versus-leukemia effect of hematopoietic stem cell transplantation in pediatric patients with high-risk or relapse leukemia. Our previous work identified high expression of TRAIL as a key signature of Natural Killer (NK) cell stimulation by ThINKK. In this study, we aim to elucidate the mechanisms underlying acute lymphoblastic leukemia (ALL) killing by ThINNK-stimulated NK cells and to identify predictive sensitivity markers of this innovative approach. Methods We performed NK cell cytotoxic assays using a panel of genetically diverse ALL cell lines and patients’ samples. Gene deletion and gene enforced expression in sensitive or resistant cell lines were performed to demonstrate the role of TRAIL-receptors expression and death receptor signaling pathway in ALL cell death induced by ThINKK-stimulated NK cells. These findings were further validated through the analysis of primary patients’ samples and transcriptomic profiling of a cohort of 320 ALL patients from the CHU Sainte-Justine. Results We found that ALL sensitivity to ThINKK-stimulated NK cell killing was independent of their genetic background or their HLA expression. In addition, our data revealed the dual role of TRAIL: first, a strong NK cell activating receptor that induced rapid killing of ALL expressing TRAIL-R2, and second, a death-receptor ligand inducing ALL apoptosis following sustained engagement with its receptors. The transcriptomic analysis of ALL patients’ samples indicated that TRAIL-R2 and TRAIL-R1 are widely expressed across ALL subtypes and are not downregulated at relapse. Conclusion These findings support the use of TRAIL receptor expression as a biomarker of sensitivity to ThINKK immunotherapy and establish a mechanistic framework to guide patient stratification and therapeutic optimization.
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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 | 0.000 |
| 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".