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Record W4381612093 · doi:10.3892/mco.2023.2656

Toll‑like receptor 3 ligands for breast cancer therapies (Review)

2023· review· en· W4381612093 on OpenAlexafffund
Carly Butkowsky, N. L. Aldor, Sarah J. Poynter

Bibliographic record

VenueMolecular and Clinical Oncology · 2023
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsWilfrid Laurier University
FundersWilfrid Laurier University
KeywordsBreast cancerCancerTLR3MedicineToll-like receptorImmunotherapyCancer researchImmune systemImmunologyInnate immune systemInternal medicine

Abstract

fetched live from OpenAlex

Breast cancer is the most common cause of cancer worldwide and is the leading cause of mortality for women across most of the world. Immunotherapy is a burgeoning area of cancer treatment, including for breast cancer; these are therapies that harness the power of the immune system to clear cancerous cells. Toll‑like receptor 3 (TLR3) is an RNA receptor found in the endosome, and ligands that bind to TLR3 are currently being tested for their efficacy as breast cancer immunotherapeutics. The current review introduces TLR3 and the role of this receptor in breast cancer, and summarizes data on the potential use of TLR3 ligands, mainly polyinosinic:polycytidylic acid and its derivatives, as breast cancer monotherapies or, more commonly, as combination therapies with chemotherapies, other immunotherapies and cancer vaccines. The current state of TLR3 ligand breast cancer therapy research is summarized by reporting on past and current clinical trials, and notable preliminary in vitro studies are discussed. In conclusion, TLR3 ligands have robust potential in anticancer applications as innate immune stimulants, and further studies combined with innovative technologies, such as nanoparticles, may contribute to their success.

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), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.079
GPT teacher head0.446
Teacher spread0.367 · 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

Citations11
Published2023
Admission routes2
Has abstractyes

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