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Autologous Gamma-Delta T (GD-T) Cells in Acute Myeloid Leukemia (AML): Potential Immune Effector Cells for Minimal Disease?.

2004· article· en· W2562371662 on OpenAlexaff
J. Aswald, Xinghua Wang, S. Aswald, Loralyn A. Benoît, Mark D. Minden, Hans A. Messner, Armand Keating

Bibliographic record

VenueBlood · 2004
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsHospital for Sick ChildrenPrincess Margaret Cancer CentreUniversity of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsImmune systemImmunologyMyeloidT cellMedicinePopulationCancer researchFlow cytometry

Abstract

fetched live from OpenAlex

Abstract Prolonging event-free survival of AML with autologous activated immune cells is a promising concept. GD-T cells are a rare circulating lymphocyte population (1%) and a component of the innate immune system capable of exerting anti-neoplastic activity. Their role as potential anti-cancer immune effector cells deserves further exploration. It is noteworthy that GD-T cells are over-represented in reactive regions surrounding melanoma lesions. While patients with an accumulation of GD-T cells showed a survival benefit over those who did not, such increases were not present in patients with metastatic disease and high tumor cell burden (Bachelez, J. Invest. Dermatol.98:369,1992). Little is known about the role of GD-T cells as immuno-effectors, their absolute numbers in peripheral blood or the feasibility of purifying functional GD-T cells from patients with AML. We are interested in testing the clinical feasibility of using GD-T cells freshly purified from PB against minimal residual disease in AML. As a first step towards achieving this goal, we compared circulating GD-T cell levels sequentially in 33 AML patients with 20 healthy adult volunteers. We used ultra-low volume multi-color flow-cytometry and microbeads to measure absolute numbers of GD-T cells in PB. Functional studies were done by the chromium release assay and single-cell intra-cellular interferon-gamma detection. We observed that AML patients with a high leukemic blast cell burden (e.g. prior to chemotherapy) had marginally decreased GD-T cell levels compared with healthy controls: median 38/μl, Q1-Q3, 27–86/μl, versus median 83/μl, Q1-Q3, 45–122/μl, respectively, p= 0.051. We re-examined the AML patients at several time points after induction therapy and observed significantly increased numbers of GD-T cells in patients with lower but detectable residual disease (either molecular maker positive or borderline bone marrow blast infiltration by morphology) compared to patients with persistently high blast cell burden: median 105/μl, Q1-Q3, 105–133/μl versus median, 7/μl, Q1-Q3, 6–15/μl; p=0.008. Patients with residual disease also showed significantly higher numbers of absolute GD-T cells per microliter blood compared to those retested after they had achieved complete remission (CR); p=0.0025. In CR, GD-T cell counts remained lower than those of healthy individuals: median 33/μl, Q1-Q3, 22–35/μl versus median 83/μl, Q1-Q3, 45–122/μl; p=0.030. Interestingly, we found a sharp increase (on average, 4.9-fold higher than values obtained in CR) in GD-T levels at the time of very early morphologic (n=3) or molecular relapse (n=2). Hence, we were interested in studying the functional properties of the GD-T cells from AML patients. We were able to isolate functional GD-T cells from the PB of patients with AML in CR-1 in sufficient numbers and purity to assay for interferon-gamma and found that similar numbers of GD-T cells expressed the Th1 cytokine compared with healthy controls: 84% versus 93% of all GD-T cells, respectively. We also showed that GD-T cells were able to kill leukemic target cells (AML-OCI2) in vitro more efficiently than CD3+ T cells. Our data suggest that further studies to investigate the potential therapeutic role of autologous GD-T cells in patients with AML in CR are warranted.

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 communication0.0000.000
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 head0.010
GPT teacher head0.267
Teacher spread0.258 · 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
Published2004
Admission routes1
Has abstractyes

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