Toll-Like Receptors and the Response to Radiotherapy in Solid Tumors: Challenges and Opportunities
Bibliographic record
Abstract
Toll-like receptors (TLRs) are indispensable for the activation, maintenance and halting of immune responses. TLRs can mediate inflammation by recognizing molecular patterns in microbes (pathogen-associated molecular patterns: PAMPs) and endogenous ligands (danger-associated molecular patterns: DAMPs) released by injured or dead cells. For this reason, TLR ligands have attracted much attention in recent years in many cancer vaccines, alone or in combination with immunotherapy, chemotherapy and radiotherapy (RT). TLRs have been shown to play controversial roles in cancer, depending on various factors that can mediate tumor progression or apoptosis. Several TLR agonists have reached clinical trials and are being evaluated in combination with standard of care therapies, including RT. Despite their prolific and central role in mediating immune responses, the role of TLRs in cancer, particularly in response to radiation, remains poorly understood. Radiation is recognized as either a direct stimulant of TLR pathways, or indirectly through the damage it causes to target cells that subsequently activate TLRs. These effects can mediate pro-tumoral and anti-tumoral effects depending on various factors such as radiation dose and fractionation, as well as host genomic features. In this review, we examine how TLR signaling affects tumor response to RT, and we provide a framework for the design of TLR-based therapies with RT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".