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Humanization and Pre-Clinical Validation of an Anti-HLA-A*02

2018· article· en· W2883138099 on OpenAlexaff
Nicholas A.J. Dawson, Caroline Lamarche, Peter Bergqvist, Qing Huang, Majid Mojibian, Jana Gillies, Paul C. Orban, Romy E. Hoeppli, Megan K. Levings

Bibliographic record

VenueTransplantation · 2018
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsCentre for Drug Research and DevelopmentBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsImmunogenicityPolyclonal antibodiesHuman leukocyte antigenAntigenImmunologyHumanized mouseChimeric antigen receptorAntibodyBiologyImmunotherapyComputational biologyImmune system

Abstract

fetched live from OpenAlex

Introduction Achieving transplant tolerance with regulatory T cell (Treg) adoptive immunotherapy is currently under investigation as a therapy to reduce graft rejection and improve long-term outcomes. Traditional approaches involve the of polyclonal Tregs, which are known to be less potent than antigen-specific cells, or antigen-expanded Tregs, which have several technical limitations. We and others have developed an alternate approach to generate antigen-specific Tregs by expressing a chimeric antigen receptor specific for HLA-A*02:01 (A2-CAR). In our initial studies the antigen-binding region (scFV) of the A2-CAR was derived from the mouse BB7.2 hybridoma, which, due to the high degree of homology between HLA molecules, has been reported to bind to HLA-A alleles in addition to *02:01. Here we sought to systematically define the antigen-specificity of the A2-CAR as well as humanize the sequence to minimize the risk of immunogenicity. Methods We designed 20 humanized versions of the A2-CAR and systematically tested them to determine which were highly expressed on the surface of human Tregs and capable of mediating A2-stimulated activation, expansion, and suppression. We also developed a novel method to systematically, and comprehensively test HLA-allele specificity. Results Of the 20 humanized A2-CARs, 10 were expressed on Tregs and retained A*02:01 binding capacity. We used a series of functional screens to define which of these 10 A2-CARs most effectively stimulated Treg activation, proliferation and proliferation. We then took advantage of the Panel Reactive Antibody (PRA) assay (One Lambda) and created a new method to test CAR-expressing Tregs to bind to specific HLA-alleles. We found that the majority of the humanized A2-CARs had a significantly reduced reactivity to binding to alleles other than A*02:01. We also tested the biological relevance of HLA cross reactivity by stimulating A2-CAR expressing Tregs with cell lines expressing HLA alleles that were or were not found to be cross reactive using the PRA assay. Ultimately, six humanized anti-A2 CARs showed the desired properties, with an ability to activate Tregs, bind to HLA-A2 but not to a comprehensive panel of other common A or B alleles. The potent ability of one of these variants to suppress rejection was confirmed in a humanized model of xenogeneic graft-versus-host disease. Conclusion We successfully developed a series of humanized A2-CARs which were comprehensively screened for desirable properties to generate antigen-specific Tregs. This body of pre-clinical data will support the development of a first-in-human clinical trial of A2-CAR-engineered Tregs to prevent organ allograft rejection.

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)0.003
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 score0.004
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.060
GPT teacher head0.408
Teacher spread0.348 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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