期刊
BRIEFINGS IN BIOINFORMATICS
卷 22, 期 4, 页码 -出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbaa252
关键词
dog; cancer; machine learning; transcriptome; gene expression; pathology
资金
- National Canine Cancer Foundation [JHK15MN-004]
Angiosarcomas are rare soft-tissue sarcomas with poor clinical outcomes due to their aggressive behavior and high metastatic potential. Hemangiosarcomas in dogs share features with human angiosarcomas, characterized by irregular vascular channels and a mixture of endothelial cells. Machine learning models using transcriptomic data show promise for diagnosing hemangiosarcoma and identifying novel gene signatures for potential applications in treating this vascular malignancy.
Angiosarcomas are soft-tissue sarcomas that form malignant vascular tissues. Angiosarcomas are very rare, and due to their aggressive behavior and high metastatic propensity, they have poor clinical outcomes. Hemangiosarcomas commonly occur in domestic dogs, and share pathological and clinical features with human angiosarcomas. Typical pathognomonic features of this tumor are irregular vascular channels that are filled with blood and are lined by a mixture of malignant and nonmalignant endothelial cells. The current gold standard is the histological diagnosis of angiosarcoma; however, microscopic evaluation may be complicated, particularly when tumor cells are undetectable due to the presence of excessive amounts of nontumor cells or when tissue specimens have insufficient tumor content. In this study, we implemented machine learning applications from next-generation transcriptomic data of canine hemangiosarcoma tumor samples (n = 76) and nonmalignant tissues (n = 10) to evaluate their training performance for diagnostic utility. The 10-fold cross-validation test and multiple feature selection methods were applied. We found that extra trees and random forest learning models were the best classifiers for hemangiosarcoma in our testing datasets. We also identified novel gene signatures using the mutual information and Monte Carlo feature selection method. The extra trees model revealed high classification accuracy for hemangiosarcoma in validation sets. We demonstrate that high-throughput sequencing data of canine hemangiosarcoma are trainable for machine learning applications. Furthermore, our approach enables us to identify novel gene signatures as reliable determinants of hemangiosarcoma, providing significant insights into the development of potential applications for this vascular malignancy.
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