# Scaffold-Free Functional Deconvolution Identifies Clinically Relevant Metastatic Melanoma EV Biomarkers

## TL;DR

This study identifies specific proteins in melanoma cell-released particles that could help predict cancer aggression and guide new treatments.

## Contribution

The study introduces Scaffold-free Functional Deconvolution (SFD), a novel computational method to identify clinically relevant melanoma EV biomarkers.

## Key findings

- SFD identified 21 high-confidence proteins in melanoma EVs linked to cancer spread and immune evasion.
- Proteins like CD276, ICAM1, and MIC-1 were confirmed as enriched in metastatic melanoma EVs.
- Meta-analysis revealed these proteins are involved in metabolic, immune, and oncogenic pathways.

## Abstract

Melanoma is a serious skin cancer that can spread to other parts of the body. This research focused on tiny particles called extracellular vesicles (EVs) released by melanoma cells. By carefully analyzing these EVs, the study identified specific proteins that are much more common in melanoma EVs compared to EVs released by normal cells. These proteins, including ICAM-1, SPPL2A, CD276, and CD44, may help the cancer cells stick together, spread, and avoid the immune system. The findings suggest that these EV proteins could be used to help doctors predict how aggressive a melanoma is and potentially guide new treatment approaches, opening the door to more targeted therapies that could improve outcomes for patients with advanced melanoma.

Background: Melanoma metastasis, driven by tumor microenvironment (TME)-mediated crosstalk facilitated by extracellular vesicles (EVs), remains a major therapeutic challenge. A critical barrier to clinical translation is the overlap in protein cargo between tumor-derived and healthy cell EVs. Objective: To address this, we developed Scaffold-free Functional Deconvolution (SFD), a novel computational approach that leverages a comprehensive healthy cell EV protein database to deconvolute non-oncogenic background signals. Methods: Beginning with 1915 proteins (identified by MS/MS analysis on an Orbitrap Fusion Lumos Mass Spectrometer using the IonStar workflow) from melanoma EVs isolated using REIUS, SFD applies four sequential filters: exclusion of normal melanocyte EV proteins, prioritization of metastasis-linked entries (HCMDB), refinement via melanocyte-specific databases, and validation against TCGA survival data. Results: This workflow identified 21 high-confidence targets implicated in metabolic-associated acidification, immune modulation, and oncogenesis, and were analyzed for reduced disease-free and overall survival. SFD’s versatility was further demonstrated by surfaceome profiling, confirming enrichment of H7-B3 (CD276), ICAM1, and MIC-1 (GDF-15) in metastatic melanoma EV via Western blot and flow cytometry. Meta-analysis using Vesiclepedia and STRING categorized these targets into metabolic, immune, and oncogenic drivers, revealing a dense interaction network. Conclusions: Our results highlight SFD as a powerful tool for identifying clinically relevant biomarkers and therapeutic targets within melanoma EVs, with potential applications in drug development and personalized medicine.