Machine learning determines stemness associated with simple and basal-like canine mammary carcinomas

Abstract

Simple and complex carcinomas are the most common type of malignant Canine Mammary Tumors (CMTs), with simple carcinomas exhibiting aggressive behavior and poorer prognostic. Stemness is an ability associated with cancer initiation, malignancy, and therapeutic resistance, but is still few elucidated in canine mammary tumor subtypes. Here, we first validated, using CMT samples, a previously published canine one-class logistic regression machine learning algorithm (OCLR) to predict stemness (mRNAsi) in canine cancer cells. Then, using the canine mRNAsi, we observed that simple carcinomas exhibit higher stemness than complex carcinomas and other histological subtypes. Also, we confirmed that stemness is higher and associated with basal-like CMTs and with NMF2 metagene signature, a tumor-specific DNA-repair metagene signature. Using correlation analysis, we selected the top 50 genes correlated with higher stemness, and the top 50 genes correlated with lower stemness and further performed a gene set enrichment analysis to observe the biological processes enriched for these genes. Finally, we suggested two promise stemness-associated targets in CMTs, POLA2 and APEX1, especially in simple carcinomas. Thus, our work elucidates stemness as a potential mechanism behind the aggressiveness and development of canine mammary tumors, especially in simple carcinomas, describing evidence of a promising strategy to target this disease.

Keywords

• Canine mammary tumors
• Simple carcinomas
• Basal-like tumors
• Stemness
• Machine learning