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Targeting G Protein–Coupled Receptors in Immuno-Oncological Therapies

2024· review· en· W4402503401 on OpenAlexaff
John Stagg, Zhiyong Wang

Bibliographic record

VenueThe Annual Review of Pharmacology and Toxicology · 2024
Typereview
Languageen
FieldMedicine
TopicChemokine receptors and signaling
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersNational Institute of Dental and Craniofacial ResearchNational Cancer Institute
KeywordsG protein-coupled receptorImmunotherapyImmune systemTumor microenvironmentImmune checkpointReceptorCancer immunotherapyCancerCD8BlockadeCancer researchT cellComputational biologyBiologyMedicineBioinformaticsImmunologyInternal medicine

Abstract

fetched live from OpenAlex

The advent of cancer immunotherapy based on PD-1 and CTLA-4 immune checkpoint blockade (ICB) has revolutionized cancer treatment. However, many cancers do not respond to ICB, highlighting the urgent need for additional approaches to achieve durable cancer remission. The large family of G protein-coupled receptors (GPCRs) is the target of more than 30% of all approved drugs, but GPCRs have been underexploited in cancer immunotherapy. In this review, we discuss the central role of GPCRs in immune cell migration and function and describe how single-cell transcriptomic studies are illuminating the complexity of the human tumor immune GPCRome. These receptors include multiple GPCRs expressed in CD8 T cells that are activated by inflammatory mediators, protons, neurotransmitters, and metabolites that accumulate in the tumor microenvironment, thereby promoting T cell dysfunction. We also discuss new opportunities to target GPCRs as a multimodal approach to enhance the response to ICB for a myriad of human malignancies.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.916
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.392
Teacher spread0.360 · 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

Citations10
Published2024
Admission routes1
Has abstractyes

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