MétaCan
Menu
Back to cohort
Record W2118775322 · doi:10.4049/jimmunol.0804089

Antigen Specificity Determines the Pro- or Antitumoral Nature of CD8+ T Cells

2009· article· en· W2118775322 on OpenAlexaff
Simone Cuff, Garry Dolton, R. James Matthews, Awen Gallimore

Bibliographic record

VenueThe Journal of Immunology · 2009
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsInstitute of Infection and Immunity
FundersBiotechnology and Biological Sciences Research CouncilWellcome Trust
KeywordsCytotoxic T cellCD8Cancer researchImmunologyTumor progressionBiologyMelanomaAntigenTumor microenvironmentInterleukin 21Tumor cellsIn vitroCancerBiochemistry

Abstract

fetched live from OpenAlex

Although CD8+ T cells are usually considered antitumoral, several recent studies report that the cells can also promote tumor progression. Using the melanoma cell line B16 as a murine model of pulmonary metastasis, we examined whether the pro- versus antitumoral effects of CD8+ T cells relate to their Ag specificity. Results of the study indicate that although CD8+ T cells specific for tumor Ags promote tumor rejection, CD8+ T cells specific for unrelated Ags promote tumor progression. We found the effect to be partly attributable to CD8+ T cells dampening effective antitumor NK cell responses. Notably, activation of CD8+ T cell responses by an unrelated stimulus, in this case infection with influenza virus, increased the number of pulmonary tumor nodules. These data provide a rationale for previously unexplained data identifying contrasting roles for CD8+ T cells in tumor progression.

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 score0.001
Threshold uncertainty score0.005

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)0.0010.001

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 head0.019
GPT teacher head0.289
Teacher spread0.270 · 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

Citations16
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of ImmunologySame topicCancer Immunotherapy and BiomarkersFrench-language works237,207