Recently, a research team led by Dr. Jiazhu Sun from the National Center for Nanoscience and Technology, in collaboration with Professor Liqin Zhang from the School of Pharmacy of Peking University, Professor Shaohua Zhang from the Fifth Medical Center of the Chinese People's Liberation Army General Hospital, and Professor Yuguang Wang from the Peking University School of Stomatology, have developed a novel thermophoretic biosensor based on lectin-glycan molecular recognition, which enables rapid, highly sensitive, and selective analysis of the glycan profile of extracellular vesicles (EVs) in plasma. This method has been applied to precision diagnosis, treatment monitoring, and prognosis prediction of triple-negative breast cancer (TNBC). The related research findings have been published in Nature Communications.
EV Glycan Profiling by Immunoaffinity Capillary Electrophoresis for the Diagnosis and Monitoring of Triple-Negative Breast Cancer
[Image provided by the interviewee: EV glycan profiling by ICE for TNBC diagnosis.]
Triple-negative breast cancer (TNBC) is a highly aggressive subtype with poor prognosis. Due to the lack of specific biomarkers, clinical diagnosis and treatment of TNBC are limited.
"EV-associated glycans play a critical role in tumor progression and have great potential as novel cancer biomarkers," Jiazhu Sun told China Science Daily. "However, the measurement of EV glycan profiles in blood samples is challenging due to the complexity of the body fluid environment, high EV heterogeneity, and the complexity, tediousness, and low sensitivity of traditional glycan analysis methods."
In this study, the research team developed a novel EV glycan profiling method based on lectin-carbohydrate molecular recognition, termed EV lectin electrophoresis (EVLET). Fluorescently labeled lectins were used to specifically label surface glycans on EVs in plasma. Vibrating membrane filtration was employed to remove unbound lectins, soluble glycoproteins, and lipoproteins. Microfluidic electrophoresis was then used to enrich EVs and amplify the fluorescence signal of the conjugated lectins, enabling highly sensitive and quantitative detection of EV glycans with a sensitivity two orders of magnitude higher than conventional methods.
Combining bioinformatics and experimental approaches, the researchers screened a panel of specific lectin probes to obtain clinical plasma EV glycan profiles. Machine learning algorithms were used to construct EV glycan signatures, which were able to accurately diagnose TNBC with an accuracy of 91%. In terms of TNBC treatment monitoring, EV glycan signatures could predict disease progression or remission with an accuracy of 96% and serve as an independent prognostic factor for progression-free survival in TNBC patients.
The researchers believe that this study provides a novel approach for non-invasive cancer management based on EV glycan profiling.
Related paper information: https://doi.org/10.1038/s41467-024-46557-5
