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Record W2488182312 · doi:10.1038/icb.2016.45

Purity of transferred CD8<sup>+</sup> T cells is crucial for safety and efficacy of combinatorial tumor immunotherapy in the absence of SHP‐1

2016· article· en· W2488182312 on OpenAlexaff
H. Angharad Watson, Garry Dolton, Julia Ohme, Kristin Ladell, Miriam Ann Vigar, Sophie Wehenkel, James P. Hindley, Rebar N. Mohammed, Kelly L. Miners, Rhys A Luckwell, David A. Price, R. James Matthews, Ann Ager

Bibliographic record

VenueImmunology and Cell Biology · 2016
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsInstitute of Infection and Immunity
FundersMedical Research CouncilCardiff UniversityWellcome TrustWellcome
KeywordsAdoptive cell transferCytotoxic T cellCD8Cancer researchImmunologyImmunotherapyBiologyT cellCell biologyImmune systemBiochemistryIn vitro

Abstract

fetched live from OpenAlex

Adoptive transfer of tumor-specific cytotoxic T cells is a promising advance in cancer therapy. Similarly, checkpoint inhibition has shown striking clinical results in some patients. Here we combine adoptive cell transfer with ablation of the checkpoint protein Src homology 2-domain-containing phosphatase 1 (SHP-1, Ptpn6). Naturally occurring motheaten mice lack SHP-1 and do not survive weaning due to extensive immunopathology. To circumvent this limitation, we created a novel SHP-1(null) mouse that is viable up to 12 weeks of age by knocking out IL1r1. Using this model, we demonstrate that the absence of SHP-1 augments the ability of adoptively transferred CD8(+) T cells to control tumor growth. This therapeutic effect was only observed in situations where T-cell numbers were limited, analogous to clinical settings. However, adoptive transfer of non-CD8(+) SHP-1(null) hematopoietic cells resulted in lethal motheaten-like pathology, indicating that systemic inhibition of SHP-1 could have serious adverse effects. Despite this caveat, our findings support the development of SHP-1 inhibition strategies in human T cells to complement adoptive transfer therapies in the clinic.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.024
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.284
Teacher spread0.269 · 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 teacher head, 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

Citations19
Published2016
Admission routes1
Has abstractyes

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