A novel curcumin-like dienone induces apoptosis in triple-negative breast cancer cells
Bibliographic record
Abstract
PURPOSE: According to the World Health Organization (WHO), breast cancer is the most common cancer affecting women worldwide. In the USA ~12.3 % of all women are expected to be diagnosed with various types of breast cancer, exhibiting varying degrees of therapeutic response rates. Therefore, the identification of novel anti-breast cancer drugs is of paramount importance. METHODS: The 1,5-diaryl-3-oxo-1,4-pentadienyl pharmacophore was incorporated into a number of cytotoxins. Three of the resulting dienones, 2a, 2b and 2c, were tested for their anti-neoplastic potencies in a variety of human breast cancer-derived cell lines, including the triple negative MDA-MB-231 cell line and its metastatic variant, using a live-cell bio-imaging method. Special emphasis was put on dienone 2c, since its anti-cancer activity and its mode of inflicting cell death have so far not been reported. RESULTS: We found that all three dienones exhibited potent cytotoxicities towards the breast cancer-derived cell lines tested, whereas significantly lower toxicities were observed towards the non-cancerous human breast cell line MCF-10A. The dienones 2b and 2c exhibited the greatest selective cytotoxicity at submicromolar concentration levels. We found that these two dienones induced phosphatidylserine externalization in MDA-MB-231 cells in a concentration-dependent manner, suggesting that their cytotoxic effect might be mediated by apoptosis. This possibility was confirmed by our observation that the dienone 2c can induce mitochondrial depolarization, caspase-3 activation, cell cycle disruption and DNA fragmentation in MDA-MB-231 cells. CONCLUSION: Our findings indicate that dienone 2c uses the mitochondrial/intrinsic pathway to inflict apoptosis in triple negative MDA-MB-231 breast cancer-derived cells. This observation warrants further assessment of dienone 2c as a potential anti-breast cancer drug.
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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".