Controllable Cascade Aggregation Ion Programmed Nanomachines and Simple Isolation Enable Tumor‐Derived Small Extracellular Vesicles Detection for Enhanced Breast Cancer Assessment

ABSTRACT Small extracellular vesicles (sEVs) carry biomolecules that reflect their cellular origin, making them attractive biomarkers for breast cancer assessment and human epidermal growth factor receptor 2 (HER2)‐status discrimination. However, existing methods involve lengthy isolation procedures and exhibit poor detection performance due to severe interference from excessive impurities. In this study, we constructed a dual‐target electrochemical (EC) sensing platform based on cascade aggregation effects. Combined with a facile filter‐based isolation strategy, this enables rapid and convenient dual‐marker (epithelial cell adhesion molecule (EpCAM) and HER2) analysis of breast cancer‐derived sEVs. This mechanism relies on the target‐triggered disassembly of DNA nanospheres to release Ag + /Hg 2+ for EC signaling. Subsequently, colistin specifically binds to G‐quadruplexes on unreacted nanospheres and induces their aggregation, sequestering background signal sources and preventing ion leakage. This noise‐silencing strategy improved matrix tolerance and reduced background leakage under a standardized diluted‐plasma workflow, enabling reliable detection of sEVs‐associated signals in filtration‐derived clinical samples. A proof‐of‐concept clinical study involving 63 breast cancer patients and 22 nonmalignant controls successfully demonstrated the platform's capability to distinguish cancer patients from controls. Furthermore, the platform achieved a 88.9% accuracy in differentiating HER2 status within this cohort. Overall, this streamlined “filtration‐to‐detection” platform offers a promising strategy for analyzing breast cancer‐specific sEVs signals and conducting exploratory HER2‐status assessments.

Authors

Institutions

Publication Details

Journal
Advanced Science
Published
2026-08-14
DOI
https://doi.org/10.1002/advs.77177
Primary Topic
Extracellular vesicles in disease
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Controllable Cascade Aggregation Ion Programmed Nanomachines and Simple Isolation Enable Tumor‐Derived Small Extracellular Vesicles Detection for Enhanced Breast Cancer Assessment

Piaopiao Chen, Yu Jiang, Yonggang Lv, Binxu Qiu et al.
Advanced Science
Extracellular vesicles in disease
article

Controllable Cascade Aggregation Ion Programmed Nanomachines and Simple Isolation Enable Tumor‐Derived Small Extracellular Vesicles Detection for Enhanced Breast Cancer Assessment

Piaopiao Chen, Yu Jiang, Yonggang Lv, Binxu Qiu, Jie Chen, Zijing Liu, Xiangyue Meng, Pengjun Jiang, Jie Qu
article en

Abstract

ABSTRACT Small extracellular vesicles (sEVs) carry biomolecules that reflect their cellular origin, making them attractive biomarkers for breast cancer assessment and human epidermal growth factor receptor 2 (HER2)‐status discrimination. However, existing methods involve lengthy isolation procedures and exhibit poor detection performance due to severe interference from excessive impurities. In this study, we constructed a dual‐target electrochemical (EC) sensing platform based on cascade aggregation effects. Combined with a facile filter‐based isolation strategy, this enables rapid and convenient dual‐marker (epithelial cell adhesion molecule (EpCAM) and HER2) analysis of breast cancer‐derived sEVs. This mechanism relies on the target‐triggered disassembly of DNA nanospheres to release Ag + /Hg 2+ for EC signaling. Subsequently, colistin specifically binds to G‐quadruplexes on unreacted nanospheres and induces their aggregation, sequestering background signal sources and preventing ion leakage. This noise‐silencing strategy improved matrix tolerance and reduced background leakage under a standardized diluted‐plasma workflow, enabling reliable detection of sEVs‐associated signals in filtration‐derived clinical samples. A proof‐of‐concept clinical study involving 63 breast cancer patients and 22 nonmalignant controls successfully demonstrated the platform's capability to distinguish cancer patients from controls. Furthermore, the platform achieved a 88.9% accuracy in differentiating HER2 status within this cohort. Overall, this streamlined “filtration‐to‐detection” platform offers a promising strategy for analyzing breast cancer‐specific sEVs signals and conducting exploratory HER2‐status assessments.

Advanced Science
Sichuan University (CN), West China Hospital of Sichuan University (CN)
Natural Science Foundation of Sichuan Province
Reduced inequalities
Openalex Percentile: Top 16%
Extracellular vesicles in disease
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.