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Record W7132935331

Elucidating Cytotoxic Mechanisms of Allogeneic Double-Negative T cells Against Acute Myeloid Leukemia

2025· dissertation· W7132935331 on OpenAlexaff
Enoch Tin

Bibliographic record

VenueTSpace · 2025
Typedissertation
Language
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCytotoxic T cellMyeloid leukemiaLeukemiaHaematopoiesisMyeloidCytokineImmunotherapyStem cellEffector
DOInot available

Abstract

fetched live from OpenAlex

CD3+CD4-CD8- double-negative T cells (DNTs) are a rare, unconventional T lymphocyte subset that can be expanded ex vivo from healthy donors with potent anti-leukemic function against acute myeloid leukemia (AML). Allogeneic DNTs fulfill the requirements of an off-the-shelf cellular therapy, such as clinical scalability and absence of graft-versus-host disease induction. Furthermore, the feasibility, safety, and potential efficacy of allogeneic DNT therapy were demonstrated in a completed phase I clinical trial involving AML patients who relapsed after receiving allogeneic hematopoietic stem cell transplantation. However, about 30% of primary AML cells are resistant to DNT-mediated cytotoxic effects in preclinical models and 4 out of 10 patients from the clinical trial did not respond to DNT therapy. Additionally, the underlying mechanisms of AML resistance to DNTs are unclear. In this thesis, I performed three independent screening and analysis techniques to uncover the biology of DNTs and DNT-mediated cytotoxic pathways against AML. Using a flow cytometry-based high throughput screening assay, I identified a Tumor Necrosis Factor alpha (TNFα)-Intercellular Adhesion Molecule-1 (ICAM-1) cytotoxic axis used by DNTs. DNTs secrete TNFα, upon encountering susceptible AML, which can sensitize AML cells to further DNT killing, including previously DNT-resistant targets. Mechanistically, TNFα upregulates ICAM-1 on AML, through Janus Kinase 1 (JAK1) activation, which binds Lymphocyte Function-associated Antigen-1, an ICAM-1 receptor, expressed on DNTs. Next, I used the genome-wide clustered regularly interspaced short palindromic repeats (CRISPR) screen to uncover a Suppressor of Cytokine Signaling-1 (SOCS1)-regulated pathway that mediates AML resistance to T-cell killing. Inhibiting SOCS1 in AML cells sensitized them to DNT and conventional T cell-mediated anti-leukemic activities, while overexpressing SOCS1 increased their resistance to T cells. SOCS1 regulates AML sensitivity to T cells by modulating JAK1 activation and ICAM-1 expression. Clinically, AML patients with lower levels of SOCS1 detected in the bone marrow experienced significantly better survival than those with higher SOCS1 expression. Finally, using single-cell ribonucleic acid sequencing, I uncovered unique transcriptional patterns and cellular populations in DNTs that allow it to mount a more robust anti-leukemic response against AML, compared to γδ T cells expanded with zoledronic acid, partially through galectin-3. Altogether, in this thesis, I have identified novel targetable pathways and clinically relevant biomarkers to improve the efficacy of DNT therapy against AML.

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 score0.002

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 communication0.0000.000
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 head0.030
GPT teacher head0.356
Teacher spread0.326 · 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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