Expression profile of twelve transcripts as a supporting tool for the molecular characterization of canine cutaneous mast cell tumors at diagnosis: association with histological grading and clinical staging

Abstract

Background/Objectives: 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. Methods: 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. Results: 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). Conclusions: qPCR analysis of these biomarkers combined with the machine learning-based classifier might serve as a tool to support cMCT clinical management at diagnosis