Abstract LB258: Automated high-throughput isolation of extracellular vesicles (EVs) and small RNA sequencing profile in serum of breast cancer patients

Abstract Introduction: The molecular profiling of EVs is represents a promising approach for identifying disease-specific biomarkers and developing diagnostic tests for early detection. The clinical translation of EV-based diagnostics into routine clinical practice is hindered by the lack of efficient, reproducible and high-throughput EV isolation and downstream analysis method that can be easily integrated into clinical laboratory workflows. In this study, we used a high-throughput automated bead-based immunoaffinity system (EXO-NET®) to isolate EVs and identify differentially expressed miRNAs from serum samples obtained from women diagnosed with breast cancer to identify EV-associated biomarkers for earlier detection of breast to improve disease prognosis. Methods: EVs were isolated from 500 µl of serum obtained from women diagnosed with breast cancer (I, II, III, IV n=12 per stage) and age matched normal healthy women (n=48) using EXO-NET on high-throughput automated KingFisher Apex system. Small RNAs were isolated on the same system using Promega Maxwell® HT miRNA Plasma and Serum kit for small RNA sequencing and RT-PCR analysis. Results: High-throughput EVs isolation and their associated miRNAs from 96 serum samples was conducted in less than 2 hours. Small RNA seq analysis identified top 50 significantly differentially expressed miRNAs (log2 =2, p < 0.01) between normal healthy women and women diagnosed with breast cancer. Data modelling including GO and KEGG pathway analysis identified molecular pathways associated with breast cancer and EV compartments. Conclusion: We have established an efficient, reproducible and fit-for-purpose high-throughput automated system for the isolation of EVs and the analysis of associated RNAs and proteins. This system shows great potential for facilitating the translation of EV diagnostics into routine clinical practice. Citation Format: Ramin Khanabdali, Scott Zhu, Mathew Moore, Eric Vincent, Gregory Rice. Automated high-throughput isolation of extracellular vesicles (EVs) and small RNA sequencing profile in serum of breast cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB258.