Early detection of canine hemangiosarcoma via cfDNA fragmentation and copy number alterations in liquid biopsies using machine learning.

Abstract

Hemangiosarcoma is a highly malignant tumor commonly affecting canines, originating from endothelial cells that line blood vessels, underscoring the importance of early detection. This canine cancer is analogous to human angiosarcoma, and the development of liquid biopsies leveraging cell-free DNA (cfDNA) represents a promising step forward in early cancer diagnosis. In this study, we utilized Whole Genome Sequencing (WGS) to analyze fragment sizes and copy number alterations (CNAs) in cfDNA from 21 hemangiosarcoma-affected and 36 healthy dogs, aiming to enhance early cancer detection accuracy through machine learning models. Our findings reveal that similar to trends in human oncology, hemangiosarcoma samples exhibited shorter DNA fragment sizes compared to healthy controls, with a notable leftward shift in the primary peak. Interestingly, canine hemangiosarcoma DNA fragment sizes demonstrated eight distinct periodic patterns diverging from those typically observed in human angiosarcoma. Additionally, we identified seven novel genomic gains and nine losses in the hemangiosarcoma samples. Applying machine learning to the cfDNA fragment size distribution, we achieved an impressive average Area Under the Curve (AUC) of 0.93 in 10-fold cross-validation, underscoring the potential of this approach for precise early-stage cancer classification. This study confirms distinctive cfDNA fragment size and CNA patterns in hemangiosarcoma-affected vs. healthy dogs and demonstrates the promise of these biomarkers in canine cancer screening, early detection, and monitoring via liquid biopsies. These findings establish a foundation for broader research on cfDNA analysis in various canine cancers, integrating methodologies from human oncology to enhance early detection and diagnostic precision in veterinary medicine.