Anticlusterin treatment of breast cancer cells increases the sensitivities of chemotherapy and tamoxifen and counteracts the inhibitory action of dexamethasone on chemotherapy-induced cytotoxicity
Bibliographic record
Abstract
INTRODUCTION: Overexpression of the apoptosis-related protein clusterin is associated with breast cancer development and tumor progression. We describe the use of clusterin-specific antisense oligonucleotides and antibodies to sensitize breast carcinoma cells to anticancer drugs routinely used in breast cancer therapy. METHODS: MCF-7 and MDA-MB-231 cells were treated with the oligonucleotide or antibody, chemotherapeutic agents (doxorubicin or paclitaxel), tamoxifen, or with combinations of these. RESULTS: Treatments that include antisense clusterin oligonucleotide or antibody to clusterin have been shown to reduce the number of viable cells more effectively than treatment with the drugs alone. We also demonstrate that dexamethasone pretreatment of breast cancer cell lines inhibits chemotherapy-induced cytotoxicity and is associated with the transcriptional induction of clusterin. However, anticlusterin treatment increases chemotherapy-induced cytotoxicity, even in the presence of glucocorticoids, suggesting a possible role for these proteins in glucocorticoid-mediated survival. CONCLUSION: These data suggest that combined treatment with antibodies to clusterin or antisense clusterin oligodeoxynucleotides and paclitaxel, doxorubicin, or tamoxifen could be a novel and attractive strategy to inhibit the progression of breast carcinoma by regulation of the clusterin function. Moreover, glucocorticoid activation in breast cancer cells regulates survival signaling by the direct transactivation of genes like clusterin which encode proteins that decrease susceptibility to apoptosis. Given the widespread clinical administration of dexamethasone before chemotherapy, understanding glucocorticoid-induced survival mechanisms is essential for achieving optimal therapeutic responses.
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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) | 0.002 | 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".