TLR1/TLR2 Agonist Induces Tumor Regression by Reciprocal Modulation of Effector and Regulatory T Cells
Bibliographic record
Abstract
Using TLR agonists in cancer treatment can have either beneficial or detrimental effects. Therefore, it is important to determine their effect on the tumor growth and understand the underlying mechanisms in animal tumor models. In this study, we report a general immunotherapeutic activity of a synthetic bacterial lipoprotein (BLP), a TLR1/TLR2 agonist, on established lung carcinoma, leukemia, and melanoma in mice. Systemic treatment of 3LL tumor-bearing mice with BLP, but not LPS, led to a dose-dependent tumor regression and a long-lasting protective response against tumor rechallenge. The BLP-mediated tumor remission was neither mediated by a direct tumoricidal activity nor by innate immune cells, because it lacked therapeutic effect in immunodeficient SCID mice. Instead, BLP treatment reduced the suppressive function of Foxp3(+) regulatory T cells (Tregs) and enhanced the cytotoxicity of tumor-specific CTL in vitro and in vivo. Furthermore, adoptive cotransfer of BLP-pretreated but not untreated CTL and Tregs from wild-type but not from TLR2(-/-) mice was sufficient to restore antitumor immunity in SCID mice by reciprocally modulating Treg and CTL function. These results demonstrate that the TLR1/TLR2 agonist BLP may have a general tumor therapeutic property involving reciprocal downregulation of Treg and upregulation of CTL function. This property may play an important role in the development of novel antitumor strategies.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".