Background Extracellular vesicles (EV) play an important role in melanoma progression but their potential as clinical biomarkers has yet to be realized. EVs can be found in most liquid biopsies (e.g., blood, urine, CSF) and exosomes are the most prominent subcategory of EVs. Exosomes are 50–200 nm lipid-bilayer enclosed particles secreted by all body cells, including tumor cells, and serve as mediators of metastasis formation and typically contain several classes of bioactive molecules such as RNA, proteins, lipids, and metabolites. Blood and its liquid components plasma/serum are the most frequently used matrix for biomarker discovery due to the ease of collection. However, most proteomic platforms for plasma/serum profiling are unable to profile EV proteins due to the high dynamic range of protein concentrations in EV preparations. This is due to 1) EV isolation methods that vary in their potential to separate EVs from free proteins, and 2) the presence of a natural corona of high-abundant blood proteins attached to the EV surface.

Methods To tackle this challenge, we developed SEC-DIA-MS an integrated workflow combining size-exclusion chromatography, EV concentration, and optimized mass spectrometry to enable deep profiling of the proteome content of the enriched vesicles.

Results From 200 µl of plasma or serum from a test melanoma patient cohort (6 patients and 3 matched controls), we quantified 2,242 exosome-associated proteins, achieving a 2.5-fold increase in depth compared to previous melanoma studies. To gain a better understanding of the exosome enrichment efficiency, we extensively characterized the plasma/serum proteome by analyzing native, depleted, and EV-enriched blood from the same donors. We successfully validated well-known exosome markers such as CD9, CD63, CD81, PDCD6IP, and TSG101, and found that EV samples are significantly enriched in intact membrane proteins and those related to T cell biology, further underlining the uniqueness of the EV proteome composition. We further assessed the differences between plasma and serum EVs and suggest the use of plasma samples for future studies due to better separation of healthy and melanoma samples.

Furthermore, we deployed this workflow to identify predictive biomarkers of response in a clinical NSCLC cohort subjected to immune-checkpoint inhibitor treatment in combination with chemotherapy.

Conclusions Taken together, we demonstrated the workflow for biomarker discovery in plasma and serum. The ease of automating and scaling up such an approach enables a broader application to other indications and biological matrices.