Expression Profile of Twelve Transcripts as a Supporting Tool for the Molecular Characterization of Canine Cutaneous...

Mast cell tumors (MCTs) are the second most common malignant neoplasms in dogs. Histopathological grading and clinical staging are the main tools for estimating biological behavior and disease extent; thus, both are essential for therapeutic decision-making and prognostication. However, the biological behavior of MCTs in dogs is variable, and it sometimes deviates from expectations. In a previous study, we identified 12 transcripts whose expression profile allowed a clear distinction between Kiupel low-grade and high-grade cutaneous MCTs (cMCTs) and was associated with prognosis. Building on these findings, this study evaluated the predictive potential of these transcripts' expression profiles in classifying cMCTs into low-grade and high-grade.

A logistic regression classifier based on the expression profiles of the identified transcripts and able to classify cMCTs as low- or high-grade was developed and subsequently tested on a novel dataset of 50 cMCTs whose expression profiles have been determined in this study through qPCR.

The developed logistic regression classifier reaches an accuracy of 67% and an area under the receiver operating characteristic curve (AUC) of 0.76. Interestingly, the molecular classification clearly identifies stage-IV disease (90% true positive rate).

qPCR analysis of these biomarkers combined with the machine learning-based classifier might serve as a tool to support cMCT clinical management at diagnosis.