AHP-Weighted Exosomal Proteomics in TNBC Recovers an ECM Invasion Module and Nominates AGRN as an High-Priority Candidate
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Background
Triple-negative breast cancer (TNBC) is among the most clinically challenging breast cancer subtypes due to the absence of actionable molecular targets and the lack of non-invasive detection strategies. Tumor-derived exosomes are promising liquid biopsy analytes capable of reflecting tumor biology, yet the functional organization of their protein cargo and the identification of biologically meaningful candidates from high-dimensional proteomic data remain incompletely characterized.
Methods
We present a Composite Driver Score (CDS) framework that integrates differential expression magnitude with protein–protein interaction network topology and Analytic Hierarchy Process (AHP)-based multi-criteria weighting to prioritize exosomal protein candidates. The framework was applied to public label-free quantitative proteomic datasets comparing MDA-MB-231 (TNBC) and MCF-10A (non-tumorigenic) exosomal fractions, with cross-dataset validation performed on an independent proteomic dataset.
Results
CDS prioritization demonstrated robustness to variations in proteome depth and parameter weighting, consistently recovering extracellular matrix (ECM) and adhesion-associated proteins. Network and pathway analyses revealed coordinated co-enrichment of integrin receptors, cognate ECM ligands, and co-receptors, consistent with selective packaging of a functionally integrated invasion module. Agrin (AGRN), a heparan sulfate proteoglycan, exemplifies the class of candidate the framework is designed to surface: independently nominated through network integration alone, yet supported by emerging evidence of oncogenic and immunosuppressive roles across cancer types.
Conclusions
The CDS framework offers a transferable strategy for exosomal biomarker prioritization that treats statistical evidence as a continuous contributor to candidate priority rather than a binary filter. These findings establish proof of concept for systems-informed hypothesis generation in exosomal proteomics, with implications across cancer contexts.
Significance
Triple-negative breast cancer lacks targeted therapies and validated non-invasive detection strategies, with most patients diagnosed at advanced stages. Tumor-derived exosomes offer a promising liquid biopsy route, but identifying biologically meaningful protein candidates cannot rely on abundance ranking alone. We present a network-informed prioritization framework that recovers a coherent invasion module and nominates previously overlooked candidates for experimental follow-up.
