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Record W4405049156 · doi:10.1182/blood-2024-208762

Harnessing Favorable Dendritic Cells Features to Improve Antigen-Specific T Cell Expansion

2024· article· en· W4405049156 on OpenAlexaff
Annabelle Minguy, Jessica Trottier, Jaime Leonel Sanchez-Dardon, Jean-Philippe Bastien, Vibhuti P. Davé, Denis‐Claude Roy

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsPriming (agriculture)AntigenImmunologyImmune systemAntigen-presenting cellDendritic cellCancer researchT cellBiologyMedicine

Abstract

fetched live from OpenAlex

Introduction: Allogeneic hematopoietic stem cell transplantation (aHSCT) relies mostly on its graft-versus-leukemia (GvL) activity, which is mediated by donor T cells, to eliminate cancer cells escaping the conditioning regimen. The GvL effect represents a superb illustration of the capacity of the immune system to recognize tumor antigens and to eliminate the cancer cells harboring these antigens. Dendritic cells (DCs) are the actors with the most important capacity to present tumor antigens to T cells so that they can exert their cytotoxicity toward specific cancer targets. They play a key role in vivo in patients, and can be harnessed ex vivo for the generation of adoptive T cell therapies as well as tumor vaccine strategies. Nevertheless, the long period of time required for DC generation from monocytes represents a most important factor limiting their clinical usage. While DCs are usually produced over 9 days (DC9), they can also be generated over shorter periods of time and within as few as 3 days (DC3). However, DC3 present different biologic features from DC9 and usually demonstrate limited maturation and priming activity. Goal: To enhance the clinical potential of DCs, we investigated a TLR-based strategy aiming to increase the ability of DC3 to mature and generate antigen-specific T cells. Methods: Dendritic cells were generated from monocytes, isolated from peripheral blood, and matured using the gold standard maturation cocktail (GM-CSF, IL-4, IL-6, IL-1β, TNFα and PGE2). We determined the effect of TLR-3 and TLR-7/8 agonists on DC3 maturation status, cytokine production, and T cell reactivity (DC3-TLR). DCs were pulsed with Wilm's Tumor 1 (WT1) antigen, a well-known tumor-associated antigen, and added to peripheral blood mononuclear cell (PBMC) cultures at 7-day intervals for a total of 21 days. Anti-WT1 T cell reactivity was measured through IFNγ, TNFα, IL-2, and CD107a expression upon WT1 peptide restimulation, after co-culture with WT1-pulsed DC9, DC3 or DC3-TLR. Results: Both DC3 and DC3-TLR were of smaller size than DC9 (p<0.001), and harbored reduced expression of CD80 (p<0.0001). DC3 and DC3-TLR also demonstrated superior antigen uptake and processing capacity compared to DC9 (p=0.004). In addition, we found that DC3-TLR showed increased expression of the co-stimulatory molecule CD80 (p=0.007), and enhanced production of pro-inflammatory cytokines (IL-1β, IL-6, IL-12, and TNFα) in comparison to untreated DC3 (all p<0.005). We then sought to investigate the antigen-specific priming activity of DC9, DC3 and DC3-TLR. Interestingly, addition of TLR agonists to DC3 significantly increased CD8+ T cell expansion. Those DC3-TLR-primed CD8+ T cells harbored an effector memory phenotype, with significantly lower expression of the exhaustion marker LAG3 compared to DC3-primed T cells (p=0.008). Importantly, upon restimulation with WT1 peptide, the frequency and absolute number of DC3-TLR-primed WT1-specific T cells expressing functional markers were significantly increased compared to DC9-primed cultures (all p<0.05). Further, DC3-TLR-primed T cells displayed strong cytotoxic activity toward WT1-expressing autologous blast cells. To further define the DC features favoring WT1-specific T cells expansion, DC9, DC3 and DC3-TLR underwent RNA sequencing. These different cell types harbored distinct gene signatures. Notably, several chemokine transcripts, and most importantly CCL3 and CCL4 transcripts were upregulated in DC3-TLR compared to both DC3 and DC9. CCL3 and CCL4 chemokine production was also increased in DC3-TLR over DC3. Importantly, blocking the CCL3- and CCL4-receptor CCR5 reduced WT1-specific T cells in DC3-TLR-primed cultures. The addition of CCL3 and CCL4 to DC3-primed cultures did not recapitulate the WT1-specific T cell expansion observed in DC3-TLR, suggesting that chemokine gradients are important for antigen-specific T cell expansion. Conclusions: We have identified distinct phenotypic and molecular DC features leading to improved antigen-specific T cell expansion. This TLR-based strategy resulted in enhanced CD80 expression, IL-12 production, and other proinflammatory cytokines and chemokines. Thus, DC3-TLR offer interesting characteristics for adoptive T cell therapies and vaccination strategies, enabling greater clinical applicability and potentially superior anti-cancer activity.

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 score0.006

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 integrity0.0000.001
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 head0.008
GPT teacher head0.225
Teacher spread0.216 · 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
Published2024
Admission routes1
Has abstractyes

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