Dissociation of Gemcitabine Sensitivity and Protein Kinase B Signaling in Pancreatic Ductal Adenocarcinoma Models
Bibliographic record
Abstract
OBJECTIVE: To understand the impact of protein kinase B (PKB; Akt) signaling on growth and protection from apoptosis in pancreatic ductal adenocarcinoma models demonstrating differences in PKB activity. METHODS: Gemcitabine sensitivity was investigated in a panel of cell lines, characterized by differences in levels of activated PKB. Suppression of PKB activity was achieved with an inhibitor of phosphatidylinositol 3-kinase (wortmannin) and silencing RNA. RESULTS: Enhanced gemcitabine (2',2'-difluoro-2'-deoxycytidine)-induced cytotoxicity in vitro was achieved with suppression of high PKB activity with wortmannin in BxPC-3, PK-1, and PK-8 cells and silencing RNA targeted to total PKB, rather than PKBbeta, in PANC-1 cells. Opposite to gemcitabine sensitivity levels in vitro, the growth of PANC-1 xenografts was inhibited with gemcitabine treatment, whereas BxPC-3 became drug resistant. Monolayer cell cultures reestablished from solid tumors behaved similarly to original cultures, suggesting that the tumor microenvironment has a critical role in determining drug sensitivity. A comparison of transcript profiles of the models indicated that PKB signaling might be modulated by a number of pathways responsive to the tumor hypoxia microenvironment. CONCLUSIONS: These results suggested that gemcitabine efficacy involving the PKB pathway depends on PKB activity, its mechanisms of enhanced activity, as well as its function in a signaling network.
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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 | 0.000 |
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".