Notch inhibition restores TRAIL-mediated apoptosis via AP1-dependent upregulation of DR4 and DR5 TRAIL receptors in MDA-MB-231 breast cancer cells
Bibliographic record
Abstract
Notch is a family of transmembrane receptors whose activation through proteolytic cleavage by γ-secretase targets genes which participate in cell development, differentiation and tumorigenesis. Notch signaling is constitutively activated in various cancers, including breast cancer and its upregulation is usually related with poor clinical outcomes. Therefore, targeting Notch signaling with γ-secretase inhibitors (GSIs) is considered a promising strategy for cancer treatment. We report that the γ-secretase inhibitor-I (GSI-I) sensitizes human breast cancer cells to apoptosis mediated by tumor necrosis factor-related apoptosis-inducing ligand (TRAIL). The antiproliferative GSI-I/TRAIL synergism was stronger in ER-negative MDA-MB-231 breast cancer cells compared with ER-positive MCF-7 cells. In MDA-MB-231 cells, GSI-I treatment induced upregulation of DR4 and DR5 TRAIL receptors. This effect seemed to be related to the activation of the transcription factor AP1 that was a consequence of Notch inhibition, as demonstrated by Notch-1 silencing experiments. Combined treatment induced loss of the mitochondrial transmembrane potential and activation of caspases. GSI-I alone and/or GSI-I/TRAIL combination also induced a significant decrease in the levels of some survival factors (survivin, c-IAP-2, Bcl-xL, BimEL and pAKT) and upregulation of pro-apoptotic factors BimL, BimS and Noxa, enhancing the cytotoxic potential of the two drugs. Taken together, these results indicate for the first time that GSI-I/TRAIL combination could represent a novel and potentially effective tool for breast cancer treatment.
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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".