Heterogeneous pathogenesis of melanoma: BRAF mutations and beyond.

Melanoma pathogenesis, conventionally perceived as a linear accumulation of molecular changes, discloses substantial heterogeneity driven by non-linear biological processes, including the direct transformation of melanocyte stem cells. This heterogeneity manifests in diverse biological phenotypes and developmental states, influencing variable responses to treatments. Unveiling the aberrant mechanisms steering melanoma initiation, progression, and metastasis is imperative. Beyond mutations in oncogenic and tumor suppressor genes, the involvement of distinct molecular pathways assumes a pivotal role in melanoma pathogenesis. Ultraviolet (UV) radiations, a principal factor in melanoma etiology, categorizes melanomas based on cumulative sun damage (CSD). The genomic landscape of lesions correlates with UV exposure, impacting mutational load and spectrum of mutations. The World Health Organization's 2018 classification underscores the interplay between sun exposure and genomic characteristics, distinguishing melanomas associated with CSD from those unrelated to CSD. The classification elucidates molecular features such as tumor mutational burden and copy number alterations associated with different melanoma subtypes. The significance of the mutated BRAF gene and its pathway, notably BRAFV600 variants, in melanoma is paramount. BRAF mutations, prevalent across diverse cancer types, present therapeutic avenues, with clinical trials validating the efficacy of targeted therapies and immunotherapy. Additional driver mutations in oncogenes further characterize specific melanoma pathways, impacting tumor behavior. While histopathological examination remains pivotal, challenges persist in molecularly classifying melanocytic tumors. In this review, we went through all molecular characterization that aid in discriminating common and ambiguous lesions. Integration of highly sensitive molecular diagnostic tests into the diagnostic workflow becomes indispensable, particularly in instances where histology alone fails to achieve a conclusive diagnosis. A diagnostic algorithm based on different molecular features inferred by the various studies is here proposed.