Whole Slide Images (WSIs) have significantly advanced the field of pathology by providing highly detailed views of tissue samples. Integrating Deep Learning (DL) into this area of research, particularly through transformer-based foundational models, has marked a new era in automated image analysis. These foundational models are adept at extracting features from WSIs, an essential step in their analysis process. The subsequent application of weakly supervised learning techniques combines these features to predict critical biomarkers, such as BRAF mutations and sentinel lymph node (SLN) biopsy positivity, which are vital in guiding patient treatment strategies. However, the limited availability of labelled datasets in pathology hinders the usefulness of DL models. Domain adaptation strategies adeptly overcome this hurdle, enabling model knowledge transfer between different tissue types, thus addressing data scarcity. Our study employs a form of domain adaptation by fine-tuning two DINOv2 models, one pre-trained on natural images and the other on WSI of colorectal cancer from the TCGA dataset, adapting them for melanoma analysis. We also incorporate a comparison with features extracted by a third DINOv1 model trained solely on WSIs of breast cancer. With this approach, we find some notable success in detecting BRAF mutations. Nonetheless, predicting SLN positivity presents a more intricate challenge, largely due to the indirect correlation between local histopathological features in WSIs of primary tumours and lymph node metastasis manifestation. This dual-faceted approach not only combats the issue of limited data but also showcases the potential for enhanced accuracy in the field of digital pathology.