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Record W2055735604 · doi:10.1002/eji.201444826

Application of central immunologic concepts to cancer: Helping T cells and B cells become intolerant of tumors

2014· letter· en· W2055735604 on OpenAlexafffund
Colin C. Anderson

Bibliographic record

VenueEuropean Journal of Immunology · 2014
Typeletter
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsUniversity of Alberta
FundersAlberta Innovates
KeywordsBiologyImmunologyEpitopeAntigenImmune systemT cellCD8Immune toleranceCytotoxic T cellImmunityGenetics

Abstract

fetched live from OpenAlex

CD4-mediated T-cell help in the activation of CD8(+) T cells and B cells, through linked-recognition of antigenic determinants, is a long-standing concept foundational to our understanding of immunity (presence of help) versus tolerance (lack of help). Surprisingly, this function of CD4(+) T cells has not been extensively examined as a means to overcome immune tolerance of the self-antigens made by tumor cells. Hesitation to employ this powerful mechanism may be due to the potential to cause unwanted autoimmune pathology. In this issue of the European Journal of Immunology, Snook et al. [Eur. J. Immunol. 2014. 44: 1956-1966] identify a state of split tolerance, showing that CD4(+) T cells specific for a number of tumor-associated self-antigens are robustly tolerant, while their CD8(+) T-cell and B-cell counterparts are far less tolerant. Furthermore, the authors demonstrate that provision of linked foreign helper epitopes, such as influenza hemagglutinin, substantially enhances both CD8(+) T-cell and B-cell responses to tumor self-antigens without causing any overt autoimmune pathology. These findings provide a strong rationale to employ foreign helper epitopes in cancer vaccines and highlight the need to fully explore therapeutic strategies that are based on well-established immunologic concepts.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.013
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0130.012
Insufficient payload (model declined to judge)0.0020.003

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 head0.011
GPT teacher head0.238
Teacher spread0.227 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations6
Published2014
Admission routes2
Has abstractyes

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