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Record W4416181852 · doi:10.1101/2025.11.12.687655

TRAIL orchestrates ThINKK-induced NK cell cytotoxicity against childhood acute lymphoblastic leukemia

2025· preprint· W4416181852 on OpenAlexaff
Emilie Ollame-Omvane, Leila Ben Khemis, Paulo Cordeiro, Claire Fuchs, Kathie Béland, Élie Haddad, Daniel Sinnett, Sabine Herblot, Michel Duval

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Language
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsCytotoxic T cellTranscriptomeNatural killer cellGene expression profilingProgrammed cell deathCellLymphokine-activated killer cellImmunotherapyCytotoxicityApoptosis

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.207
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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