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Record W3016889239 · doi:10.3747/co.27.5285

Targeting T Cell Activation in Immuno-Oncology

2020· review· en· W3016889239 on OpenAlexaffvenue
Samuel D. Saibil, Pamela S. Ohashi

Bibliographic record

VenueCurrent Oncology · 2020
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersEMD SeronoSymphogen
KeywordsImmunotherapyMedicineT cellImmune checkpointBlockadeCancer immunotherapyImmune systemCancer researchImmunologyInternal medicineReceptor

Abstract

fetched live from OpenAlex

The years since 2009 have seen tremendous progress in unlocking the curative potential of the immune system for the treatment of cancer. Much of that revolution in immuno-oncology has been fueled by the clinical success of immune checkpoint inhibitors, particularly those targeting the PD-1 axis. Unfortunately, many patients still fail to benefit from checkpoint blockade or other immunotherapies. An inability to fully activate antitumour T cells contributes in part to the failure of those therapies. Here, we review the basic biology of T cell activation, with particular emphasis on the essential role of the dendritic cell and the innate immune system in T cell activation. The current understanding of the multiple factors that govern T cell activation and how they impinge on tumour immunotherapy are also discussed. Lastly, treatment strategies to potentially overcome barriers to T cell activation and to enhance the efficacy of immunotherapy are addressed.

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 categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0010.002

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 head0.101
GPT teacher head0.403
Teacher spread0.301 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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

Citations36
Published2020
Admission routes2
Has abstractyes

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