Cancer genomic profiling identified dihydropyrimidine dehydrogenase deficiency in bladder cancer promotes sensitivity to gemcitabine
Bibliographic record
Abstract
Chemotherapy is a standard therapy for muscle-invasive bladder cancer (MIBC). However, genomic alterations associated with chemotherapy sensitivity in MIBC have not been fully explored. This study aimed to investigate the genomic landscape of MIBC in association with the response to chemotherapy and to explore the biological role of genomic alterations. Genomic alterations in MIBC were sequenced by targeted exome sequencing of 409 genes. Gene expression in MIBC tissues was analyzed by western blotting, immunohistochemistry, and RNA microarray. Cellular sensitivity to gemcitabine and gemcitabine metabolite was examined in bladder cancer cells after modulation of candidate gene. Targeted exome sequencing in 20 cases with MIBC revealed various genomic alterations including pathogenic missense mutation of DPYD gene encoding dihydropyrimidine dehydrogenase (DPD). Conversely, high DPYD and DPD expression were associated with poor response to gemcitabine-containing chemotherapy among patients with MIBC, as well as gemcitabine resistance in bladder cancer cells. DPD suppression rendered cells sensitive to gemcitabine, while DPD overexpression made cells gemcitabine-resistant through reduced activity of the cytotoxic gemcitabine metabolite difluorodeoxycytidine diphosphate. This study revealed the novel role of DPD in gemcitabine metabolism. It has been suggested that DPYD genomic alterations and DPD expression are potential predictive biomarkers in gemcitabine treatment.
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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 | 0.001 |
| 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".