Feline Oncogenomics: What Do We Know about the Genetics of Cancer in Domestic Cats?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cancer is a significant cause of morbidity and mortality in domestic cats. In humans, an understanding of the oncogenome of different cancer types has proven critical and is deeply interwoven into all aspects of patient care, including diagnostics, prognostics and treatments through the application of targeted therapies. Investigations into understanding the genetics of feline cancers started with cytogenetics and was then expanded to studies at a gene-specific level, looking for mutations and expression level changes of genes that are commonly mutated in human cancers. Methylation studies have also been performed and together with a recently generated high-quality reference genome for cats, next-generation sequencing studies are starting to deliver results. This review summarises what is currently known of the genetics of both common and rare cancer types in cats, including lymphomas, mammary tumours, squamous cell carcinomas, soft tissue tumours, mast cell tumours, haemangiosarcomas, pulmonary carcinomas, pancreatic carcinomas and osteosarcomas. Shining a spotlight on our current understanding of the feline oncogenome will hopefully serve as a springboard for more much-needed research into the genetics of cancer in domestic cats.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it