The melanoma MEGA-study: Integrating proteogenomics, digital pathology, and AI-analytics for precision oncology

Area of Science:

• Oncology
• Genomics
• Proteomics
• Computational Pathology

Background:

• Melanoma is an aggressive skin cancer with high metastatic potential and treatment resistance.
• Current therapeutic strategies face challenges due to melanoma's genetic heterogeneity.
• The Melanoma MEGA-Study aims to address these challenges through integrated multi-omics and AI analysis.

Purpose of the Study:

• To integrate proteogenomic data, clinical metadata, and AI-driven digital pathology for melanoma.
• To enhance personalized treatment strategies and improve patient outcomes in melanoma.
• To identify novel biomarkers and metabolic signatures for improved patient stratification.

Main Methods:

• Analysis of 1653 primary and 361 metastatic melanoma tumor samples.
• High-resolution mass spectrometry and optimized workflows for formalin-fixed paraffin-embedded tissues.
• AI-driven digital pathology and machine learning for tumor microenvironment and immune evasion signature mapping.

Main Results:

• Precise mapping of the tumor microenvironment and immune evasion signatures.
• Identification of metabolic reprogramming and proteogenomic alterations.
• Discovery of novel protein biomarkers and metabolic signatures for melanoma.

Conclusions:

• The study provides unprecedented insights into melanoma's molecular landscape, progression, and therapeutic resistance.
• An integrative framework combining proteomics, AI pathology, and clinical data offers a scalable model for precision oncology.
• Findings have the potential to transform clinical practice through improved risk stratification and targeted therapies.