Canine mammary gland tumor (CMT) is the most frequently diagnosed neoplasm in intact female dogs. As prognosis depends on the malignancy of tumors and metastasis levels, early and accurate diagnosis are crucial for prolongation of life expectancy. The genetic similarity of dogs with humans in addition to environmental and physiological similarities make them ideal models for the study of cancer. In this study, we analyzed differentially expressed microRNAs followed by RNA-Seq to investigate the alterations in mRNA levels based on the malignancy (benign, malignant) and the biopsy locations (tumors, surrounding normal tissues). We identified multiple breast cancer-related genes regardless of malignancy. We found cfa-miR-503 to be the only miRNA that showed altered expression in response to malignancy in CMTs. Although further validation is needed, cfa-miR-503 could be used as a potential diagnostic biomarker as well as a potential RNA-based anti-tumor drug in malignant CMTs.

The dominant paradigm in understanding drug action focuses on the intended therapeutic effects and frequent adverse reactions. However, this approach may limit opportunities to grasp unintended drug actions, which can open up channels to repurpose existing drugs and identify rare adverse drug reactions. Advances in systems biology can be exploited to comprehensively understand pharmacodynamic actions, although proper frameworks to represent drug actions are still lacking. We suggest a novel platform to construct a drug-specific pathway in which a molecular-level mechanism of action is formulated based on pharmacologic, pharmacogenomic, transcriptomic, and phenotypic data related to drug response ( http://databio.gachon.ac.kr/tools/ ). In this platform, an adoption of three conceptual levels imitating drug perturbation allows these pathways to be realistically rendered in comparison to those of other models. Furthermore, we propose a new method that exploits functional features of the drug-specific pathways to predict new indications as well as adverse reactions. For therapeutic uses, our predictions significantly overlapped with clinical trials and an up-to-date drug-disease association database. Also, our method outperforms existing methods with regard to classification of active compounds for cancers. For adverse reactions, our predictions were significantly enriched in an independent database derived from the Food and Drug Administration (FDA) Adverse Event Reporting System and meaningfully cover an Adverse Reaction Database provided by Health Canada. Lastly, we discuss several predictions for both therapeutic indications and side-effects through the published literature. Our study addresses how we can computationally represent drug-signaling pathways to understand unintended drug actions and to facilitate drug discovery and screening.

There have been many attempts to identify and develop new uses for existing drugs, which is known as drug repositioning. Among these efforts, text mining is an effective means of discovering novel knowledge from a large amount of literature data. We identify a gene regulation by a drug and a phenotype based on the biomedical literature. Drugs or phenotypes can activate or inhibit gene regulation. We calculate the therapeutic possibility that a drug acts on a phenotype by means of these two types of regulation. We assume that a drug treats a phenotype if the genes regulated by the phenotype are inversely correlated with the genes regulated by the drug. Based on this hypothesis, we identify drug–phenotype associations with therapeutic possibility. To validate the drug–phenotype associations predicted by our method, we make an enrichment comparison with known drug–phenotype associations. We also identify candidate drugs for drug repositioning from novel associations and thus reveal that our method is a novel approach to drug repositioning.

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