MétaCan
Menu
← Back to cohort
Record W4389233418 · doi:10.1182/blood-2023-182447

Harnessing TCR/CAR Antagonism to Enhance Immunotherapeutic Precision

2023· article· en· W4389233418 on OpenAlexaff
Taisuke Kondo, François Bourassa, Sooraj Achar, Justyn DuSold, Pablo F. Céspedes, Madison Wahlsten, Audun Kvalvaag, Guillaume Gaud, Paul E. Love, Michael L. Dustin, Grégoire Altan‐Bonnet, Paul François, Naomi Taylor

Bibliographic record

VenueBlood · 2023
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsT-cell receptorChimeric antigen receptorAntigenAdoptive cell transferBiologyCD19ImmunologyImmunotherapyT cellCancer researchLeukemiaCell biologyImmune system

Abstract

fetched live from OpenAlex

Chimeric Antigen Receptor (CAR) T cell immunotherapy represents a conceptual breakthrough in the treatment of hematological malignancies. However, the rarity of cell surface protein targets specific to cancerous but not vital tissue has hindered its broad application to solid tumor treatment. While new logic-gated CAR designs have shown reduced toxicity against healthy tissues, the generalizability of such approaches across tumors remains unclear. Here, we harness a universal characteristic of endogenous T cell receptors (TCRs), their ability to discriminate between self and non-self ligands through inhibition of response against self (weak) antigens, to develop a broadly applicable method of enhancing immunotherapeutic precision. We hypothesized that this discriminatory mechanism, known as antagonism, would apply across receptors, allowing for a transfer of specificityfrom TCRs onto CARs. We therefore systematically mapped out the responses of CAR T cells to joint TCR and CAR stimulations. We transduced ovalbumin-specific TCR T cells with mouse CD19 CAR to produce T cells expressing both TCR and CAR and evaluated the response of TCR/CAR T cells using in vivo and in vitro leukemia models ( Figure 1A). We discovered that strong TCR antigen enhanced CAR T killing of CD19 + leukemia, while weak TCR antigen antagonized CAR T responses as assessed in vivo cytotoxicity and in vitro multiplexed dynamic profiling ( Figure 1B). We developed a mathematical model based on cross-receptor inhibitory coupling that accurately predicted the extent of TCR/CAR antagonism across a wide range of immunological settings. This model was validated in a CD19 + B16 mouse melanoma model showing that TCR/CAR antagonism decreased the infiltration of a tumor-reactive T cell cluster (cluster 1), while TCR/CAR agonism enhanced infiltration of this cluster1 ( Figure 1C). We then applied our quantitative knowledge of TCR/CAR crosstalk to design an Antagonism-Enforced Braking System (AEBS) for CAR T cell therapy. This was assessed in a model system using a CAR targeting the tyrosine-protein kinase erbB-2 (HER2), expressed on a subset of patients with both B-ALL and AML together with a hedgehog acyltransferase (HHAT) specific TCR, which responds strongly to mutated peptides presented on tumor cells and weakly to wild-type peptides presented on healthy tissue. Consistent with our discovery of the TCR/CAR antagonism, TCR signals against healthy cells expressing wild-type HHAT peptide antigen antagonized HER2 CAR T cell responses, minimizing on-target/off-tumor cytotoxicity against healthy cells. Notably though, AEBS-CAR T cells exhibited high anti-tumor cytotoxicity against tumor cells expressing HER2 and mutated HHAT peptides ( Figure 1D). AEBS CAR T cells sharpen the discriminatory power of synthetic anti-tumor lymphocytes, laying the groundwork for future studies to engineer complex logic into cells with minimal numbers of receptors ( Figure 1E). Our work highlights a novel mechanism by which TCRs can enforce CAR T cell specificity, with practical implications for the rational design of future anti-leukemia immunotherapies.

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)0.001
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 score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.034
GPT teacher head0.358
Teacher spread0.325 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueBlood→Same topicCAR-T cell therapy research→French-language works237,207→