CDK-Inhibitors as New Therapeutic Treatment for Human Bronchial Carcinoids
Bibliographic record
Abstract
Introduction: mTOR inhibitor Everolimus is approved for the treatment of well-differentiated PNET.The heterogeneity of the response rate and the potential toxicity warrant predictive biomarkers.Aim(s): Define through a dual high throughput proteomic and transcriptomic approach predictive biomarkers of Everolimus sensitivity.Materials and Methods: Fifteen well-differentiated PNET tumors were minced in 300 uM slices and cultured for 48 h with no drug, Everolimus or BEZ235.Caspase 3 levels assessed by immunohistochemistry at 48 h were used to define sensitive and resistant tumors.Transcriptomic profiles were determined and key oncogenic proteins were quantified by reverse phase protein array (RPPA).Results: Nine tumors were defined as resistant to mTOR inhibitors.RPPA showed that resistant tumors had a higher level of the activated phosphorylated forms of mTOR, p70S6K and S6 but not 4EBP1.Phosphorylated form of p38, b-catenin and NF-kB p65 were also increased in resistant tumors.Transcriptomic signatures of hypoxia and glycolysis were enriched in resistant tumors together with signatures linked to chromatin remodeling.Conclusion: In this model, resistant tumors harbor non-canonical activation of the mTOR pathway together with hypoxia and specific metabolic (glycolytic) gene signatures.Chromatin remodeling signatures suggest that DAXX/ATRX status may impact mTOR inhibitors sensitivity.This warrants further metabolomic investigations and validation in a cohort of patient treated with mTOR inhibitors.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".