2-Triazenoazaindoles: A novel class of triazenes inducing transcriptional down-regulation of EGFR and HER-2 in human pancreatic cancer cells
Bibliographic record
Abstract
Pancreatic cancer is a complex malignancy arising from the accumulation of genetic and epigenetic defects in the affected cells. Standard chemotherapy for patients with advanced disease shows only modest effects and is associated with considerable toxicity. Overexpression or aberrant activation of members of the epidermal growth factor receptor tyrosine kinase family, which includes EGFR and HER-2, occurs frequently and is associated with multiple drug resistance and decreased patient survival. In this study, we have investigated the therapeutic potential of AS104, a novel compound of the triazene class, with potential inhibitory effects on EGFR. We found that treatment of cells with AS104 causes significant reduction of cell growth and metabolic activity in four human pancreatic cancer cell lines. Furthermore, we show that the AS104-mediated induction of apoptotic cell death is associated with stimulation of autophagy in a dose-dependent manner. Treatment of cells with AS104 results in significant down-regulation of EGFR and HER-2 expression and activity and subsequent inhibition of downstream signaling proteins. Quantitative RT-PCR analysis and assays with proteasome inhibitors revealed that AS104 regulates the expression of EGFR and HER-2 at the transcriptional level. These findings provide for the first time experimental evidence for efficacy of AS104 in the simultaneous transcriptional repression of EGFR and HER-2 genes and suggest that AS104 may have therapeutic potential in the treatment of pancreatic cancers that express high levels of the aforementioned receptor tyrosine kinases.
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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 | 0.000 |
| 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".