Integrating AI-based Digital Pathology and Proteome Profiling to Uncover Melanoma Progression and Heterogeneity

Melanoma, the deadliest form of skin cancer, is one of the fastest-growing cancers in the world. The marked molecular and morphological heterogeneity of this disease is directly connected to its aggressive behavior, progression, and treatment resistance. In this study, we integrate AI-based digital pathology and proteome profiling to deeply characterize histopathological features and melanoma subclones, and determine their molecular fingerprint in different melanoma cases to uncover the mechanism linked with progression.

Using artificial intelligence approaches and high-resolution HE images, tumor and stroma areas, histopathological components, and melanoma subclones were identified, further isolated by laser microdissection, and analyzed by a proteomics approach. Over 100 FFPE samples were investigated, corresponding to primary tumors (n=84) and paired metastases (n=20). The samples were selected from patients who experienced disease recurrence within 5 years, with metastases at the moment of diagnosis, and those without any progression even after 5 years of the initial diagnosis. By applying this approach, we investigated the whole progression of melanoma from early-stage to advanced metastatic cases, decoding the pathways involved in the disease progression.

The AI-based digital pathology results unveiled histopathological signatures in tumor cells and their surroundings, encompassing melanoma subclones and stromal components. Determining the proteomic signature of these areas revealed dysregulation of mitochondrial function and immune response-related pathways as the most significant drivers of progression. Proliferative pathways and histidine metabolism were up- and down-regulated, respectively, in line with the advancement of the disease. Additionally, suppression of immune system response in the tumor microenvironment of recurrent and metastatic melanomas underscores the importance of immunotherapies to improve patient outcomes in these groups of patients. The analyses of different melanoma subclones revealed molecular heterogeneity linked to metastatic melanoma and evidenced the critical role of mitochondria and histidine metabolism in melanoma progression, opening an opportunity to identify new druggable targets for therapy. Additionally, our study identified morphological markers associated with a high risk of melanoma recurrence and progression. Specifically, the presence of microerosion, budding formations, and melanocytic atypia, along with the absence of regression areas, were found to be significant risk factors for melanoma progression.

Altogether, our findings highlighted that integrating AI-based digital pathology and proteome profiling is a powerful strategy to map melanoma progression, identify new druggable targets for treatment, and determine markers of recurrence and progression, which may illuminate new early-stage melanoma prognostic factors and guide a new era of personalized therapy.