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TGF-Beta Signaling Favors Central Memory Phenotype Expression By Ex-Vivo Stimulated Human T Cells

2014· article· en· W2980176712 on OpenAlexaff
Amina Dahmani, Cédric Carli, Julie Taillefer, Mathieu Goupil, Myriam Khalili, Jean‐Sébastien Delisle

Bibliographic record

VenueBlood · 2014
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsCD28Ex vivoT cellBiologyCell biologyCytotoxic T cellCD8Homing (biology)Adoptive cell transferTransforming growth factor betaIL-2 receptorCytokineImmunologyCancer researchTransforming growth factorIn vivoImmune systemIn vitro

Abstract

fetched live from OpenAlex

Abstract Adoptive immunotherapy using ex vivo differentiated and expanded T cell lines can be remarkably efficient to treat cancer and infections. Unfortunately, the process of ex vivo T cell stimulation can lead to terminal effector differentiation and functional exhaustion thereby limiting the persistence and therapeutic effects of these T cells after transfer. Accumulating evidence suggests that, owing to their proliferative capacity, self-renewal ability and long term persistence in vivo, T cells bearing a central memory (CD45RO+/CD62L+ - Tcm) instead of an effector memory (CD45RO+/CD62L- - Tem) phenotype before adoptive transfer can mediate more significant therapeutic activity. Transforming-growth factor-beta (TGF-β) is a pleiotropic cytokine that influences several aspects of T-cell biology and that is best known for its growth suppression and immunosuppressive activity. We show that TGF-β signaling can have a profound impact on the number of CD4 and CD8 T cells expressing the Tcm and Tem phenotype after anti-CD3e and anti-CD28 stimulation without altering the number of cells recovered at the end of the culture and without inducing regulatory T cells. By enhancing the percentage of the lymph-node homing receptors, L-selectin (CD62L) and CC-chemokine receptor 7 (CCR7) expressing cells, exogenous TGF-β, added to the culture medium to a concentration of 5 ng/ml, favors Tcm over Tem cell accumulation at 7 days (CD4+: 55.80 vs 35.51% (P= 0.0074); CD8+: 56.9 vs 40.3% (P= 0.0063)). Reciprocally, the inhibition of TGF-β signaling with a TGF-β receptor kinase inhibitor (GW788388) accentuated Tem phenotype acquisition. Importantly, these effects of TGF-β on Tcm marker expression were maintained in the presence of exogenous cytokines commonly used in ex-vivo cultures for adoptive immunotherapy (IL-2, IL-7 and IL-15). The manipulation of TGF-β signaling did not increase the expression of exhaustion markers (KRLG-1, CD57) but exogenous TGF-β decreased interferon-gamma (IFN-γ) expression. No effect was noted on TNF-α and IL-2 expression as well as on the percentage of polyfunctional cells generated. We also found that modulating TGF-β signaling during the course of clinically relevant cultures capable of expanding T-cells specific for Epstein-Barr virus LMP2 protein antigens, in the presence of IL-7 and IL-15, could increase the number of CD4 Tcm cells, even 2 weeks after the withdrawal of TGF-β from the culture (38.24 vs 27.82, N=3) without compromising antigen-specific IFN-γ release. In conclusion, the modulation of TGF-β signaling can significantly alter Tcm and Tem phenotype acquisition and may therefore be used to optimize Tcm phenotype expression by ex-vivo pathogen/antigen-specific T cells expanded for adoptive immunotherapy. Disclosures No relevant conflicts of interest to declare.

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 score0.002
Threshold uncertainty score0.007

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)0.0020.001

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 head0.019
GPT teacher head0.281
Teacher spread0.262 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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