mTOR inhibitors response and mTOR pathway in pancreatic neuroendocrine tumors
Bibliographic record
Abstract
Medical therapy of pancreatic neuroendocrine tumors (P-NET) may take advantage of Everolimus treatment. However, the extent of therapeutic response cannot be predicted. This study was aimed to identify the possible predictive markers of response to Everolimus in P-NET. We found that Everolimus reduced the cell viability and induced apoptosis in primary cultures of 6 P-NET (P-NET-R), where the proliferative and antiapoptotic effects of IGF1 were blocked by Everolimus. On the contrary, 14 P-NET primary cultures (P-NET-NR) were resistant to Everolimus and IGF1, suggesting an involvement of PI3K/AKT/mTOR pathway in the mechanism of resistance. The response to Everolimus in vitro was associated with an active AKT/mTOR pathway and seemed to be associated with a greater clinical aggressiveness. In addition, a patient sensitive to Everolimus in vitro was sensitive to this drug in vivo also and showed a positive p-AKT immunohistochemistry (IHC) at tissue level. Similarly, a patient resistant to Everolimus treatment after surgery was not sensitive to the drug in vitro and had a negative p-AKT IHC staining. Therefore, present data confirm that P-NET primary cultures may be considered a model for testing medical treatment efficacy and that IHC characterization of p-AKT might help in identifying human P-NET who can benefit from Everolimus treatment. These data encourage conducting a prospective multicenter study involving different groups of P-NET patients treated with Everolimus.
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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".