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Record W2885524635 · doi:10.1158/1538-7445.am2018-3563

Abstract 3563: Impact of the affinity of chimeric antigen receptor on immune activation profiles of T cells

2018· article· en· W2885524635 on OpenAlexaff
Chungyong Han, Seon-Hee Kim, Beom K. Choi, Byoung S. Kwon

Bibliographic record

VenueCancer Research · 2018
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsNexen (Canada)
Fundersnot available
KeywordsChimeric antigen receptorEpitopeAntigenChemistryHuman leukocyte antigenAntibodyMolecular biologyImmune systemT cellCell biologyBiologyImmunology

Abstract

fetched live from OpenAlex

Abstract Background: Chimeric antigen receptor (CAR) generally uses a single chain antibody fragment (scFv) of high affinity as an antigen-binding domain. Recent studies implied that T cells engineered with CAR (CAR T cells) of an extremely low affinity have the potential to reduce on-target off-tumor toxicity that is a serious side effect in CAR T cell therapy. Defining the relation of CAR affinity to immune activation profiles is important for understanding the nature of CAR T cells. Methods: We examined the functional variation of CAR T cells when stimulated with target antigens of various affinities. HLA-DR-specific MVR CAR T cells that were previously developed in our group were used in this study. Since MVR CAR recognizes HLA-DR, Epstein-Barr virus-transformed lymphoblastoid cell lines (EBV LCLs) which stably express HLA-DR, stimulated MVR CAR T cells. Importantly, owing to the broad spectrum of MVR CAR affinity against polymorphic epitope of HLA-DR, six different EBV LCLs each of which express unique HLA-DRs, stimulated MVR CAR T cells with different magnitude based on the binding affinities. Using the combination of MVR CAR T cells and six different EBV LCLs, we assessed the extent of effector functions and gene expression induced by different CAR affinity. Results: The cytolytic activity of MVR CAR T cells was correlated with the affinity between MVR CAR and HLA-DR. The induced polyfunctionality was highest in intermediate MVR CAR-target affinity and was decreased in weak and strong affinities. Gene expression analysis of the stimulated MVR CAR T cells identified a panel of genes of which the expression levels were correlated with CAR-target affinity. Gene ontology analysis revealed that the genes are mainly involved in a T cell activation pathway. Interestingly, we found that the genes involved in type I interferon signaling were upregulated following the stimulation with strong affinity antigens, while those expression levels were unchanged following the stimulation with weak and intermediate affinity antigens. Conclusions: Here we describe the affinity-associate functional variation of CAR T cells defined by the use of the combination of MVR CAR T cells and EBV LCLs of various affinities. Using those effector/target cell combinations, we investigated the relation between CAR-antigen affinity and the effector functions which include cytolytic activity and polyfunctionality. Furthermore, we identified that type I interferon signaling is a distinct characteristics of strong affinity-induced activation of CAR T cells. These effector/target cell combinations and the observations will help to understand the nature of CAR T cells. Citation Format: Chungyong Han, Seon-Hee Kim, Beom K. Choi, Byoung S. Kwon. Impact of the affinity of chimeric antigen receptor on immune activation profiles of T cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3563.

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 score0.008

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 head0.104
GPT teacher head0.453
Teacher spread0.349 · 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
Published2018
Admission routes1
Has abstractyes

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