From Primary Melanoma to Metastatic Evolution: AI-Powered Pathology Integrated with Functional Analysis and Clinical...

Area of Science:

• Oncology
• Bioinformatics
• Medical Imaging

Background:

• Current melanoma treatment lacks a comprehensive understanding of tumor evolution.
• Clinical complexity necessitates advanced, biologically informed decision-support tools.
• Integrating diverse data is crucial for personalized melanoma management.

Purpose of the Study:

• To implement a multimodal disease profiling framework for melanoma patient treatment optimization.
• To enhance clinical decision-making across the entire disease course.
• To develop patient-specific models for tracking tumor evolution and treatment response.

Main Methods:

• Integrating proteogenomics to analyze tumor signaling and protein dynamics.
• Utilizing AI-driven digital image analysis for pathology and immune context assessment.
• Combining proteogenomic, imaging, and longitudinal clinical data for a holistic view.

Main Results:

• Proteogenomics identifies vulnerabilities driving melanoma progression and resistance.
• AI pathology captures spatial and functional changes related to metastasis.
• Integrated data enables patient-specific models of tumor evolution and treatment exposure.

Conclusions:

• Multimodal disease profiling provides a dynamic, evolving view of melanoma.
• This framework supports precision melanoma care through biologically grounded decision support.
• It facilitates prediction of treatment response, resistance, and disease trajectory for adaptive therapy.