Protein analysis of extracellular vesicles to monitor and predict therapeutic response in metastatic breast cancer

• Published in: Nature communications Vol. 12; no. 1; p. 2536
• Main Authors: Tian, Fei, Zhang, Shaohua, Liu, Chao, Han, Ziwei, Liu, Yuan, Deng, Jinqi, Li, Yike, Wu, Xia, Cai, Lili, Qin, Lili, Chen, Qinghua, Yuan, Yang, Liu, Yi, Cong, Yulong, Ding, Baoquan, Jiang, Zefei, Sun, Jiashu
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 05.05.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 147/143; 631/1647/1888; 692/53/2421; 82/1; 9/10; Breast cancer; Extracellular vesicles; Humanities and Social Sciences; Markers; Metastases; Metastasis; multidisciplinary; Patients; Peripheral blood; Proteins; Science; Science (multidisciplinary); Survival; Vesicles
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-22913-7

Molecular profiling of circulating extracellular vesicles (EVs) provides a promising noninvasive means to diagnose, monitor, and predict the course of metastatic breast cancer (MBC). However, the analysis of EV protein markers has been confounded by the presence of soluble protein counterparts in peripheral blood. Here we use a rapid, sensitive, and low-cost thermophoretic aptasensor (TAS) to profile cancer-associated protein profiles of plasma EVs without the interference of soluble proteins. We show that the EV signature (a weighted sum of eight EV protein markers) has a high accuracy (91.1 %) for discrimination of MBC, non-metastatic breast cancer (NMBC), and healthy donors (HD). For MBC patients undergoing therapies, the EV signature can accurately monitor the treatment response across the training, validation, and prospective cohorts, and serve as an independent prognostic factor for progression free survival in MBC patients. Together, this work highlights the potential clinical utility of EVs in management of MBC. A thermophoretic aptasensor can be used to profile cancer-associated proteins of extracellular vesicles (EVs) in patients’ plasma. Here, the authors use this technique to develop an EV-signature able to discriminate metastatic breast cancer, monitor treatment response, and predict patients’ progression-free survival.