Effective detection of BRAFV595E mutation in canine urothelial and prostate carcinomas using immunohistochemistry

Canine urothelial carcinoma (UC) and prostate carcinoma (PC) frequently exhibit the BRAFV595E mutation, akin to the BRAFV600E mutation common in various human cancers. Since the initial discovery of the BRAF mutation in canine cancers in 2015, PCR has been the standard method for its detection in both liquid and tissue biopsies. Considering the similarity between the canine BRAFV595E and human BRAFV600E mutations, we hypothesized that immunohistochemistry (IHC) using a BRAFV600E-specific antibody could effectively identify the canine mutant BRAFV595E protein. We tested 122 canine UC (bladder n = 108, urethra n = 14), 21 PC, and benign tissue using IHC and performed digital droplet PCR (ddPCR) on all 122 UC and on 14 IHC positive PC cases. The results from ddPCR and IHC were concordant in 99% (135/136) of the tumours. Using IHC, BRAFV595E was detected in 72/122 (59%) UC and 14/21 (65%) PC. Staining of all benign bladder and prostate tissues was negative. If present, mutant BRAF staining was homogenous, with rare intratumour heterogeneity in three (4%) cases of UC. Additionally, the BRAFV595E mutation was more prevalent in tumours with urothelial morphology, and less common in glandular PC or UC with divergent differentiation. This study establishes that BRAFV600-specific IHC is a reliable and accurate method for detecting the mutant BRAFV595E protein in canine UC and PC. Moreover, the use of IHC, especially with tissue microarrays, provides a cost-efficient test for large-scale screening of canine cancers for the presence of BRAF mutations. This advancement paves the way for further research to define the prognostic and predictive role of this tumour marker in dogs and use IHC to stratify dogs for the treatment with BRAF inhibitors.

Artificial Intelligence to Predict the BRAF V595E Mutation in Canine Urinary Bladder Urothelial Carcinomas

In dogs, the BRAF mutation (V595E) is common in bladder and prostate cancer and represents a specific diagnostic marker. Recent advantages in artificial intelligence (AI) offer new opportunities in the field of tumour marker detection. While AI histology studies have been conducted in humans to detect BRAF mutation in cancer, comparable studies in animals are lacking. In this study, we used commercially available AI histology software to predict BRAF mutation in whole slide images (WSI) of bladder urothelial carcinomas (UC) stained with haematoxylin and eosin (HE), based on a training (n = 81) and a validation set (n = 96). Among 96 WSI, 57 showed identical PCR and AI-based BRAF predictions, resulting in a sensitivity of 58% and a specificity of 63%. The sensitivity increased substantially to 89% when excluding small or poor-quality tissue sections. Test reliability depended on tumour differentiation (p < 0.01), presence of inflammation (p < 0.01), slide quality (p < 0.02) and sample size (p < 0.02). Based on a small subset of cases with available adjacent non-neoplastic urothelium, AI was able to distinguish malignant from benign epithelium. This is the first study to demonstrate the use of AI histology to predict BRAF mutation status in canine UC. Despite certain limitations, the results highlight the potential of AI in predicting molecular alterations in routine tissue sections.