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Record W3215570798 · doi:10.3390/cancers13235967

Immune Checkpoints and Innate Lymphoid Cells—New Avenues for Cancer Immunotherapy

2021· review· en· W3215570798 on OpenAlexaff
Nicolas Jacquelot, Maryam Ghaedi, Kathrin Warner, Douglas C. Chung, Sarah Q. Crome, Pamela S. Ohashi

Bibliographic record

VenueCancers · 2021
Typereview
Languageen
FieldImmunology and Microbiology
TopicIL-33, ST2, and ILC Pathways
Canadian institutionsToronto General HospitalUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsImmune systemImmunotherapyCancer immunotherapyInnate lymphoid cellInnate immune systemBiologyCancerCytotoxic T cellImmunologyFunction (biology)Acquired immune systemMechanism (biology)ImmunityAutoimmunityCancer researchNeuroscienceCell biologyIn vitro

Abstract

fetched live from OpenAlex

Immune checkpoints (IC) are broadly characterized as inhibitory pathways that tightly regulate the activation of the immune system. These molecular "brakes" are centrally involved in the maintenance of immune self-tolerance and represent a key mechanism in avoiding autoimmunity and tissue destruction. Antibody-based therapies target these inhibitory molecules on T cells to improve their cytotoxic function, with unprecedented clinical efficacies for a number of malignancies. Many of these ICs are also expressed on innate lymphoid cells (ILC), drawing interest from the field to understand their function, impact for anti-tumor immunity and potential for immunotherapy. In this review, we highlight ILC specificities at different tissue sites and their migration potential upon inflammatory challenge. We further summarize the current understanding of IC molecules on ILC and discuss potential strategies for ILC modulation as part of a greater anti-cancer armamentarium.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.037
GPT teacher head0.310
Teacher spread0.273 · 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.

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

Citations19
Published2021
Admission routes1
Has abstractyes

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