Novel TREM-1 Inhibitors Attenuate Tumor Growth and Prolong Survival in Experimental Pancreatic Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pancreatic cancer (PC) is a highly lethal cancer with an urgent need to expand the limited treatment options for patients. Tumor-associated macrophages (TAMs) promote tumor aggressiveness and metastasis. High expression of triggering receptor expressed on myeloid cells 1 (TREM-1) on TAMs directly correlates with poor survival in patients with non-small cell lung cancer (NSCLC). We have previously hypothesized that blockade of TREM-1 could be a promising therapeutic strategy to treat cancer and shown that the novel, ligand-independent TREM-1 inhibitory peptides rationally designed using the signaling chain homooligomerization (SCHOOL) strategy suppress NSCLC growth in vivo. Here, we evaluated the therapeutic potential of these inhibitors in three human PC xenograft mouse models. Administration of SCHOOL peptides resulted in a strong antitumor effect achieving an optimal treatment/control (T/C) value of 19% depending on the xenograft and formulation used and persisting even after treatment was halted. The effect correlated significantly with increased survival and suppressed TAM infiltration. The peptides were well-tolerated when deployed either in free form or formulated into lipopeptide complexes for peptide half-life extension and targeted delivery. Finally, blockade of TREM-1 significantly reduced serum levels of interleukin (IL)-1α, IL-6, and macrophage colony-stimulating factor (M-CSF), but not vascular endothelial growth factor, suggesting M-CSF-dependent antitumor mechanisms. Collectively, these promising data suggest that SCHOOL TREM-1-specific peptide inhibitors have a cancer type independent, therapeutically beneficial antitumor activity and can be potentially used as a stand-alone therapy or as a component of combinational therapy for PC, NSCLC, and other solid tumors.
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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 | 0.000 | 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 | 0.000 |
| 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 it