Digital profiling of tumor-derived extracellular vesicle-RNA: A sensitive approach for early detection of EGFR mutations in osimertinib-resistant non-small cell lung cancer.

e20076 Background: Osimertinib resistance in EGFR-mutant non-small cell lung cancer (NSCLC) patients presents a significant challenge due to limited post-osimertinib treatment options. The C797S mutation is a common tertiary EGFR mutation conferring osimertinib resistance. Current diagnostic methods lack sensitivity for early detection of C797S and T790M mutations, necessitating novel approaches. This study explores the use of extracellular vesicle RNA (EV-RNA) from plasma as a diagnostic tool, utilizing a new assay that facilitates membrane fusion between EVs and liposomes. Methods: The study introduces a digital EV-RNA profiling assay (LiquiDyne) that employs charge-mediated fusion between extracellular vesicles and charged liposomes (CLIPs) carrying molecular beacons. This process occurs on a droplet microfluidic chip, where the surface charge of CLIPs is optimized for fusion efficiency. The assay was validated analytically using EVs from NSCLC cell lines (H1975, SL777) with known EGFR mutations (L858R, T790M, C797S). Clinical validation involved testing retrospective patient samples with a history of osimertinib treatment. Results: The assay requires only 20 µL of plasma, eliminating the need for prior EV isolation or RNA preparation, thus minimizing sample loss. It successfully detected EGFR mutations using a digital EV-RNA profiling approach, enhanced by an artificial intelligence algorithm, in both cell lines and patient samples with high accuracy, demonstrating its potential as a reliable diagnostic tool. Conclusions: This innovative digital assay presents a groundbreaking method for early detection of acquired EGFR mutations, enabling precise quantification of rare EV subpopulations. This approach demonstrates significant potential beyond initial diagnosis, offering a powerful tool for monitoring minimal residual disease and treatment response. By addressing critical limitations in current NSCLC diagnostics, this digital assay could substantially enhance patient care through more timely and accurate molecular profiling, potentially leading to improved treatment strategies and outcomes in EGFR-mutant NSCLC.

Authors

Institutions

Publication Details

Journal
Journal of Clinical Oncology
Published
2025-05-28
DOI
https://doi.org/10.1200/jco.2025.43.16_suppl.e20076
Primary Topic
RNA modifications and cancer
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Digital profiling of tumor-derived extracellular vesicle-RNA: A sensitive approach for early detection of EGFR mutations in osimertinib-resistant non-small cell lung cancer.

Beomseok Lee, Kyusang Lee, Beomhee Ahn, Sekyoo Jeong et al.
1 links
Journal of Clinical Oncology
RNA modifications and cancer
article

Digital profiling of tumor-derived extracellular vesicle-RNA: A sensitive approach for early detection of EGFR mutations in osimertinib-resistant non-small cell lung cancer.

Beomseok Lee, Kyusang Lee, Beomhee Ahn, Sekyoo Jeong, Hyojin Um, Yoonkyoung Cho, Yeochan Kim, Hanna Kim, Hyunji Kim, Sangtae Choi, Minchan Kim, Ji‐Hye Ahn, J. U. Kwon
article en

Abstract

e20076 Background: Osimertinib resistance in EGFR-mutant non-small cell lung cancer (NSCLC) patients presents a significant challenge due to limited post-osimertinib treatment options. The C797S mutation is a common tertiary EGFR mutation conferring osimertinib resistance. Current diagnostic methods lack sensitivity for early detection of C797S and T790M mutations, necessitating novel approaches. This study explores the use of extracellular vesicle RNA (EV-RNA) from plasma as a diagnostic tool, utilizing a new assay that facilitates membrane fusion between EVs and liposomes. Methods: The study introduces a digital EV-RNA profiling assay (LiquiDyne) that employs charge-mediated fusion between extracellular vesicles and charged liposomes (CLIPs) carrying molecular beacons. This process occurs on a droplet microfluidic chip, where the surface charge of CLIPs is optimized for fusion efficiency. The assay was validated analytically using EVs from NSCLC cell lines (H1975, SL777) with known EGFR mutations (L858R, T790M, C797S). Clinical validation involved testing retrospective patient samples with a history of osimertinib treatment. Results: The assay requires only 20 µL of plasma, eliminating the need for prior EV isolation or RNA preparation, thus minimizing sample loss. It successfully detected EGFR mutations using a digital EV-RNA profiling approach, enhanced by an artificial intelligence algorithm, in both cell lines and patient samples with high accuracy, demonstrating its potential as a reliable diagnostic tool. Conclusions: This innovative digital assay presents a groundbreaking method for early detection of acquired EGFR mutations, enabling precise quantification of rare EV subpopulations. This approach demonstrates significant potential beyond initial diagnosis, offering a powerful tool for monitoring minimal residual disease and treatment response. By addressing critical limitations in current NSCLC diagnostics, this digital assay could substantially enhance patient care through more timely and accurate molecular profiling, potentially leading to improved treatment strategies and outcomes in EGFR-mutant NSCLC.

Journal of Clinical OncologyVol. 43(16_suppl)
Northwestern University (US), Ulsan National Institute of Science and Technology (KR)
Openalex Percentile: Top 89%
RNA modifications and cancer
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.