Toll‑like receptor 3 ligands for breast cancer therapies (Review)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".