Cross-species oncogenomics offers insight into human muscle-invasive bladder cancer
Bibliographic record
Abstract
In humans, muscle-invasive bladder cancer (MIBC) is highly aggressive and associated with a poor prognosis. With a high mutation load and large number of altered genes, strategies to delineate key driver events are necessary. Dogs and cats develop urothelial carcinoma (UC) with histological and clinical similarities to human MIBC. Cattle that graze on bracken fern also develop UC, associated with exposure to the carcinogen ptaquiloside. These species may represent relevant animal models of spontaneous and carcinogen-induced UC that can provide insight into human MIBC.Whole-exome sequencing of domestic canine (n = 87) and feline (n = 23) UC, and comparative analysis with human MIBC reveals a lower mutation rate in animal cases and the absence of APOBEC mutational signatures. A convergence of driver genes (ARID1A, KDM6A, TP53, FAT1, and NRAS) is discovered, along with common focally amplified and deleted genes involved in regulation of the cell cycle and chromatin remodelling. We identify mismatch repair deficiency in a subset of canine and feline UCs with biallelic inactivation of MSH2. Bovine UC (n = 8) is distinctly different; we identify novel mutational signatures which are recapitulated in vitro in human urinary bladder UC cells treated with bracken fern extracts or purified ptaquiloside.Canine and feline urinary bladder UC represent relevant models of MIBC in humans, and cross-species analysis can identify evolutionarily conserved driver genes. We characterize mutational signatures in bovine UC associated with bracken fern and ptaquiloside exposure, a human-linked cancer exposure. Our work demonstrates the relevance of cross-species comparative analysis in understanding both human and animal UC.
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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 | 0.000 |
| 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".