Profiling protein post-translational modifications in plasma-derived extracellular vesicles as fingerprints for breast cancer subtypes.

Authors

Marco Hadisurya, Hillary Andaluz Aguilar, I-Hsuan Chen, Mengting Xu, Juan Sebastian Paez, Rachit Bisht, Jyoti Singh, Zheng-Chi Lee, Amirhesam Mashaollahi, Anton Iliuk, Weizhou Zhang, W Andy Tao

Published in

Genome biology. Volume 27. Issue 1. Jul 24, 2026. Epub Jul 24, 2026.

Abstract

Addressing tumor heterogeneity in breast cancer research is crucial, given the distinct subtypes like triple-negative, luminal A/B, and HER2, requiring precise differentiation for effective treatment. This study introduces a non-invasive method by analyzing post-translationally modified proteins in plasma extracellular vesicles, which play a role in immune regulation and intercellular communication. Examining modifications like phosphorylation, acetylation and glycosylation in extracellular vesicles provides insights into breast cancer dynamics. One hundred and one plasma samples from luminal A/B breast cancer, triple-negative breast cancer and healthy individuals underwent discovery and validation experiments.
The study identified over 28,000 unique non-modified peptides, 5,000 phosphopeptides, 680 acetyl peptides and 1,300 glycopeptides that are successfully characterized. Bioinformatics analyses revealed significant overexpression of 815 non-modified proteins, 3,958 phosphopeptides, 352 acetyl peptides and 895 glycopeptides in luminal A/B breast cancer or triple-negative breast cancer subtypes. Phosphorylated and glycosylated PD-L1 peptides emerge as potential markers for breast cancer, regardless of subtype. Aligning the findings with literature and PAM50 gene signatures highlights markers correlated with lower survival rates. The study also reports 123 scheduled parallel reaction monitoring analyses, leveraging machine learning to pinpoint a panel of specific modification sites with high accuracy for subtype differentiation.
This research reveals diagnostic markers and enhances understanding of the molecular landscape, contributing to more effective and personalized breast cancer diagnostics and treatments.

PMID:
42902395
Bibliographic data and abstract were imported from PubMed on 21 Oct 2026.

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