An Epigenomic Approach to Improving Response to Neoadjuvant Cisplatin Chemotherapy in Bladder Cancer
Bibliographic record
Abstract
Bladder cancer is among the five most common cancers diagnosed in the Western world and causes significant mortality and morbidity rates in affected patients. Therapeutic options to treat the disease in advanced muscle-invasive bladder cancer (MIBC) include cystectomy and chemotherapy. Neoadjuvant cisplatin-based combination chemotherapy is effective in MIBC; however, it has not been widely adopted by the community. One reason is that many patients do not respond to neoadjuvant chemotherapy, and no biomarker currently exists to identify these patients. It is also not clear whether a strategy to sensitize chemoresistant patients may exist. We sought to identify cisplatin-resistance patterns in preclinical models of bladder cancer, and test whether treatment with the epigenetic modifier decitabine is able to sensitize cisplatin-resistant bladder cancer cell lines. Using a screening approach in cisplatin-resistant bladder cancer cell lines, we identified dysregulated genes by RNA sequencing (RNAseq) and DNA methylation assays. DNA methylation analysis of tumors from 18 patients receiving cisplatin-based chemotherapy was used to confirm in vitro results. Cisplatin-resistant bladder cancer cells were treated with decitabine to investigate epigenetic sensitization of resistant cell lines. Our results show that HOXA9 promoter methylation status is associated with response to cisplatin-based chemotherapy in bladder cancer cell lines and in metastatic bladder cancer. Bladder cancer cells resistant to cisplatin chemotherapy can be sensitized to cisplatin by the DNA methylation inhibitor decitabine. Our data suggest that HOXA9 promoter methylation could serve as potential predictive biomarker and decitabine might sensitize resistant tumors in patients receiving cisplatin-based chemotherapy.
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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".