Prognostic value of epigenetic markers for canine mast cell cancer
Bibliographic record
Abstract
Canine Mast cell tumors (MCTs) constitute approximately 21% of all canine skin tumors. Despite the use of comprehensive grading systems, biological aggressiveness is sometimes difficult to predict, therefore there is a need for better prognostic markers. Progression in various cancers involves DNA hypermethylation, hypomethylation and epigenetic enzyme dysregulation. Therefore, global levels of 5-methylcytosine, 5-hydroxymethylcytosine and associated enzymes DNMT1, and IDH1 expression may predict MCT aggressiveness. A tissue microarray (TMA) with cores from 244 different tumor samples from 189 dogs was immunolabelled and used to quantify the global DNA methylation and hydroxymethylation levels as well as the levels of the enzymes involved in DNA methylation and their relationship with canine MCT outcome. From the immunolabelled TMA, H-scores were generated using QuPath (v0.1.2) and analyzed with associated patient data. High 5MC and DNMT1, and low IDH1 levels were associated with poorer outcome when looking at all canine MCT cases. High 5MC levels showed significance for shorter disease-free interval (DFI) in subcutaneous cases and high 5MC levels showed poorer DFI and overall survival (OS) in cases with Kiupel's grading system high grade. Cases with grade II in Patnaik's grading system showed better DFI with low levels of DNMT1 and better OS with low levels of 5MC and 5HMC. High levels of DNMT1 staining were also associated with shorter DFI for dermal MCTs. For cases that received adjuvant therapy in addition to surgery, all parameters except IDH1 were significantly associated with OS. Therefore, there is potential for DNA methylation status and levels of enzymes associated with DNA methylation pathways to better predict outcome in canine MCT, and to possibly influence treatment decisions.
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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".