An EV-Guided Multi-Compartment Proof-of-Concept Framework for Biomarker Prioritization in Cholangiocarcinoma.

Area of Science:

• Biomarker Discovery
• Cancer Research
• Extracellular Vesicles

Background:

• Cholangiocarcinoma (CCA) is a complex cancer with many potential biomarkers that often fail in clinical use.
• A key challenge is identifying biomarkers that are detectable in accessible biofluids, as tumor signals can be lost across compartments.
• Lack of systematic criteria hinders biomarker prioritization during discovery.

Purpose of the Study:

• To develop and validate an extracellular vesicle (EV)-guided, multi-compartment framework for early-stage biomarker discovery.
• To assess biomarker robustness and translatability by evaluating signal behavior across different biological compartments.
• To provide a decision-support strategy for identifying promising biomarkers for heterogeneous cancers like CCA.

Main Methods:

• Analyzed EV proteomes from CCA cell lines and normal cholangiocytes using multivariate and machine learning approaches.
• Integrated EV proteomic data with transcriptomic, epigenetic, copy-number, promoter usage, and miRNA regulatory data.
• Assessed tissue relevance using TCGA/GTEx RNA-seq and examined signal behavior in serum- and urine-derived EVs from CCA patients and controls.

Main Results:

• Identified a small subset of conserved EV-associated proteins despite marked molecular heterogeneity across CCA models.
• Demonstrated consistent reduction of SERPINF2 EV-associated abundance across CCA models, with coordinated regulation across multiple molecular layers.
• Showed compartment-dependent signal behavior, with SERPINF2 depletion detectable in urine-derived EVs but not serum-derived EVs.

Conclusions:

• Presents an EV-guided, multi-compartment framework for prioritizing biomarker candidates in cancer research.
• This approach explicitly accounts for tumor heterogeneity and compartment-specific signal preservation.
• Offers a practical strategy for identifying biomarkers with greater translational potential in heterogeneous cancers like CCA.