Targeting Cancer Stem Cells in Castration-Resistant Prostate Cancer
Bibliographic record
Abstract
PURPOSE: Clinical evidence suggests increased cancer stem cells (CSCs) in a tumor mass may contribute to the failure of conventional therapies because CSCs seem to be more resistant than differentiated tumor cells. Thus, unveiling the mechanism regulating CSCs and candidate target molecules will provide new strategy to cure the patients. EXPERIMENTAL DESIGN: The stem-like cell properties were determined by a prostasphere assay and dye exclusion assay. To find critical stem cell marker and reveal regulation mechanism, basic biochemical and molecular biologic methods, such as quantitative real-time PCR, Western blot, reporter gene assay, and chromatin immunoprecipitation assay, were used. In addition, to determine the effect of combination therapy targeting both CSCs and its progeny, in vitro MTT assay and in vivo xenograft model was used. RESULTS: We demonstrate immortalized normal human prostate epithelial cells, appeared nontumorigenic in vivo, become tumorigenic, and acquire stem cell phenotype after knocking down a tumor suppressor gene. Also, those stem-like cells increase chemoresistance to conventional anticancer reagent. Mechanistically, we unveil that Wnt signaling is a key pathway regulating well-known stem cell marker CD44 by directly interacting to the promoter. Thus, by targeting CSCs using Wnt inhibitors synergistically enhances the efficacy of conventional drugs. Furthermore, the in vivo mouse model bearing xenografts showed a robust inhibition of tumor growth after combination therapy. CONCLUSIONS: Overall, this study provides strong evidence of CSC in castration-resistant prostate cancer. This new combination therapy strategy targeting CSC could significantly enhance therapeutic efficacy of current chemotherapy regimen only targeting non-CSC cells.
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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 | 0.001 | 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".