Enhancement of Apoptotic and Autophagic Induction by a Novel Synthetic C-1 Analogue of 7-deoxypancratistatin in Human Breast Adenocarcinoma and Neuroblastoma Cells with Tamoxifen
Bibliographic record
Abstract
Breast cancer is one of the most common cancers amongst women in North America. Many current anti-cancer treatments, including ionizing radiation, induce apoptosis via DNA damage. Unfortunately, such treatments are non-selective to cancer cells and produce similar toxicity in normal cells. We have reported selective induction of apoptosis in cancer cells by the natural compound pancratistatin (PST). Recently, a novel PST analogue, a C-1 acetoxymethyl derivative of 7-deoxypancratistatin (JCTH-4), was produced by de novo synthesis and it exhibits comparable selective apoptosis inducing activity in several cancer cell lines. Recently, autophagy has been implicated in malignancies as both pro-survival and pro-death mechanisms in response to chemotherapy. Tamoxifen (TAM) has invariably demonstrated induction of pro-survival autophagy in numerous cancers. In this study, the efficacy of JCTH-4 alone and in combination with TAM to induce cell death in human breast cancer (MCF7) and neuroblastoma (SH-SY5Y) cells was evaluated. TAM alone induced autophagy, but insignificant cell death whereas JCTH-4 alone caused significant induction of apoptosis with some induction of autophagy. Interestingly, the combinatory treatment yielded a drastic increase in apoptotic and autophagic induction. We monitored time-dependent morphological changes in MCF7 cells undergoing TAM-induced autophagy, JCTH-4-induced apoptosis and autophagy, and accelerated cell death with combinatorial treatment using time-lapse microscopy. We have demonstrated these compounds to induce apoptosis/autophagy by mitochondrial targeting in these cancer cells. Importantly, these treatments did not affect the survival of noncancerous human fibroblasts. Thus, these results indicate that JCTH-4 in combination with TAM could be used as a safe and very potent anti-cancer therapy against breast cancer and neuroblastoma cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".