Oronasal mucosal melanoma is defined by two transcriptional subtypes in humans and dogs with implications for diagnosis and therapy
Bibliographic record
Abstract
Abstract Mucosal melanoma is a rare melanoma subtype associated with a poor prognosis and limited existing therapeutic interventions, in part due to a lack of actionable targets and translational animal models for pre-clinical trials. Comprehensive data on this tumour type is scarce, and existing data often overlooks the importance of the anatomical site of origin. We evaluated human and canine oronasal mucosal melanoma to determine whether the common canine disease could inform the rare human equivalent. Using a human and canine primary oronasal mucosal melanoma (OMM) cohort of treatment naive archival tissue, alongside clinicopathological data, we obtained transcriptomic immunohistochemical, and microbiome data from both species. We defined the transcriptomic landscape in both species, and linked our findings to immunohistochemical, microbiome and clinical data. Human and dog OMM stratified into two distinctive transcriptional groups which we defined using a species-independent 41-gene signature. These two subgroups are termed CTLA4-high and cMET-high, and indicate actionable targets for OMM patients. To guide clinical decision-making, we developed immunohistochemical diagnostic tools which distinguish between transcriptomic subgroups. For the first time, we find that OMM has conserved transcriptomic subtypes and biological similarity between the canine and human OMM, with significant implications for patient classification, treatment, and clinical trial design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".