A novel epidermal growth factor receptor-signaling platform and its targeted translation in pancreatic cancer
Bibliographic record
Abstract
Epidermal growth factor (EGF)-induced EGFR tyrosine kinase receptor activation in cancer cell survival responses has become a strategic molecular-targeting clinical therapeutic intent, but the failures of these targeted approaches in the clinical setting demand alternate strategies. Here, we uncover a novel neuraminidase-1 (Neu1) and matrix metalloproteinase-9 (MMP-9) cross-talk in alliance with GPCR neuromedin B, which is essential for EGF-induced receptor activation and cellular signaling. Neu1 and MMP-9 form a complex with EGFR on the cell surface. Tamiflu (oseltamivir phosphate), anti-Neu1 antibodies, broad range MMP inhibitor galardin (GM6001), neuromedin B GPCR specific antagonist BIM-23127, the selective inhibitor of whole heterotrimeric G-protein complex BIM-46174 and MMP-9 specific inhibitor dose-dependently inhibited Neu1 activity associated with EGF stimulated 3T3-hEGFR cells. Tamiflu, anti-Neu1 antibodies and MMP9i attenuated EGFR phosphorylation associated with EGF-stimulated cells. Preclinical data provide the proof-of-evidence for a therapeutic targeting of Neu1 with Tamiflu in impeding human pancreatic cancer growth and metastatic spread in heterotopic xenografts of eGFP-MiaPaCa-2 tumors growing in RAGxCγ double mutant mice. Tamiflu-treated cohort exhibited a reduction of phosphorylation of EGFR-Tyr1173, Stat1-Tyr701, Akt-Thr308, PDGFRα-Tyr754 and NFκBp65-Ser311 but an increase in phospho-Smad2-Ser465/467 and -VEGFR2-Tyr1175 in the tumor lysates from the xenografts of human eGFP-MiaPaCa-2 tumor-bearing mice. The findings identify a novel promising alternate therapeutic treatment of human pancreatic cancer.
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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) | 0.000 | 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 | 0.001 |
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.
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".