Linking faecal microbiota changes to clinical and prognostic features in dogs with mast cell tumours through 16S rRNA gene sequencing

Mast cell tumours (MCT) represent one of the most relevant oncologic diseases in veterinary medicine, due to their high incidence, significant impact on clinical decision-making and patient outcomes. Therapeutic options remain limited, thereby reinforcing the critical need to identify additional oncogenic drivers, and a growing body of evidence has underscored the pivotal role of the microbiota in the pathogenesis and progression of neoplastic diseases. This study aimed to evaluate whether taxonomic groups identified in the faecal microbiota of dogs diagnosed with MCT are associated with clinicopathological features and patient's outcomes. Using 16S rRNA gene sequencing, the faecal microbiota was characterised in 47 dogs. Statistical and bioinformatic analyses were conducted in Python using a DADA2-based pipeline to process amplicon sequence variants (ASV). Statistical models were applied to identify taxonomic groups associated with clinicopathological and prognostic features. Increased abundance of Pseudomonadota and Clostridium sensu stricto 1 was detected in patients with lower disease-free survival (DFS) ( p < 0.001; p = 0.01) and lower overall survival (OS) ( p < 0.01; p < 0.01). Moreover, the class Clostridia was also more prevalent in patients with metastasis ( p = 0.02). Patients with grade III MCT and mitotic index ≥5 HPF had a higher predominance of Phascolarctobacterium ( p = 0.03; p = 0.01). The genus Escherichia-Shigella was also significantly associated with a lower DFS ( p < 0.01) and OS ( p < 0.01). This study was the first to provide insight into faecal microbial profiles associated with MCT clinicopathological features and patients' prognosis in dogs and it was found that the relative abundance of certain bacterial taxa was significantly increased in individuals with more aggressive disease, particularly those taxa considered potentially pathogenic or detrimental to health. • First study connecting microbiota, clinical features and oncology in dogs. • Linking faecal microbiota and clinicopathological features in mast cell tumours. • Pathogenic bacterial taxa associated with histological features and metastasis. • Microbiota diversity correlates with patients' outcomes (DFS and OS). • Innovative Python-based tools applied for advanced microbiome data analysis.