Integrating AI and Spatial Proteomics: Unlocking Metastasis Mechanisms for Melanoma Progression Prediction

This research program aims to revolutionize melanoma management by
integrating artificial intelligence (AI)-driven digital pathology, spatial
proteomics, and clinical metadata to enhance risk assessment and
personalized treatment strategies. Leveraging one of the largest cohorts of
primary melanoma samples, the study focuses on improving early-stage
recurrence prediction and optimizing therapeutic decisions for advanced
melanoma.
Key objectives include developing AI-driven predictive models, identifying
molecular biomarkers using spatial proteomics, improving early detection,
and personalizing treatment strategies. Machine learning algorithms will
analyze multi-omics and clinical datasets to enhance melanoma subtyping,
risk stratification, and therapeutic targeting.
Building on previous research at Lund University, which classified
melanoma into four subtypes, this study will utilize AI-powered imaging
tools and high-resolution proteomics to refine melanoma classification.
Preliminary findings have already identified over 10,000 proteins
influencing melanoma progression and highlighted mitochondrial
reprogramming as a key factor in tumor growth.
Expected outcomes include improved early detection methods, enhanced
prognostic accuracy, and the development of AI-driven decision-support
tools for clinicians. By integrating these innovations into clinical workflows,
this study will advance precision oncology, facilitating more effective and
personalized interventions for melanoma patients.