Potential role of Hsp90 inhibitors in overcoming cisplatin resistance of bladder cancer‐initiating cells
Bibliographic record
Abstract
For metastatic bladder cancer patients, systemic cisplatin (CDDP)-based combination chemotherapy is the first-line choice of treatment. Although up to 70% of advanced bladder cancer patients initially show good tumor response to this form of combination chemotherapy, over 90% of good responders relapse and eventually die of the disease. According to the cancer stem cell theory, this phenomenon is attributable to the re-growth of bladder cancer-initiating cells (BCICs) that have survived chemotherapy. In this study, the authors have isolated BCICs from cultured human bladder cancer cells to analyze their sensitivity to CDDP and to investigate whether heat-shock protein 90 (Hsp90) inhibitors potentiate the cytotoxicity of CDDP on BCICs. First, the authors have confirmed that a CD44+ subpopulation of 5637 cells met the requirements to be considered tumor-initiating cells. These BCICs were more resistant to CDDP and exhibited more activity in the Akt and ERK oncogenic signaling pathways when compared with their CD44- counterparts. The Hsp90 inhibitor 17-(dimethylaminoethylamino)-17-demethoxygeldanamycin (17-DMAG), which simultaneously inactivated both Akt and ERK signaling at noncytocidal concentrations, synergistically potentiated the cytotoxicity of CDDP against BCICs by enhancing CDDP-induced apoptosis in vitro. The potentiating effect of 17-DMAG was more effective than a combination of the two inhibitors specific for the Akt and ERK pathways. Finally, the authors have confirmed that, though human BCIC xenografts exhibited resistance to a single administration of CDDP and the Hsp90 inhibitor 17-(allylamino)-17-demethoxygeldanamycin (17-AAG), 17-AAG sensitized them to CDDP in a mouse model. These data encourage clinical trials of Hsp90 inhibitors as they may improve therapeutic outcomes of CDDP-based combination chemotherapy against advanced bladder cancer.
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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".