Lung Cancer Detected by ‘Stiffness’ of Tiny Particles
New technology analyzes exosome mechanics for early diagnosis
Scientists have pioneered a groundbreaking method to identify lung cancer by measuring the physical properties of minuscule particles shed by tumors. This innovative approach, focusing on exosome stiffness, promises a less invasive and more precise diagnostic tool.
Unlocking Exosome Secrets
Researchers at DGIST, under President **Kunwoo Lee**, have developed a technique utilizing atomic force microscopy (AFM). This method can distinguish lung cancer gene mutations by assessing the “stiffness” of exosomes, which are tiny vesicles released into the bloodstream from cancer cells.
This advancement facilitates swift and accurate analysis of individual exosomes. It is anticipated to pave the way for a novel liquid biopsy-based diagnostic method for lung cancer.
Tackling a Deadly Disease
Non-small cell lung cancer (NSCLC) represents the vast majority of lung cancer cases. Its often asymptomatic early stages lead to diagnosis at advanced, harder-to-treat phases, contributing to its high mortality rate. Conventional tissue biopsies can be taxing for patients and limit repeat testing.
The push for non-invasive liquid biopsy technologies, using blood samples, has gained significant traction. This new exosome stiffness analysis aligns with that objective.
Linking Stiffness to Genetic Mutations
The DGIST team, led by Senior Researchers **Yoonhee Lee** and **Gyogwon Koo**, isolated exosomes from NSCLC cell lines with specific genetic mutations. They analyzed nanoscale physical characteristics, including stiffness and height-to-radius ratios, using AFM.
Exosomes from cells with a KRAS mutation showed markedly increased stiffness. This suggests that changes in cell membrane lipids due to the mutation are mirrored in the exosomes. Exosomes from cells with EGFR mutations, including a resistant form, displayed similar physical properties, correlating with their shared genetic makeup.
AI Boosts Accuracy
To precisely categorize these nanomechanical signatures, the research team integrated artificial intelligence. Data on exosome height and stiffness was fed into a deep learning model (DenseNet-121) for classification.
The AI model successfully identified exosomes from A549 cells with 96% accuracy, achieving an overall AUC of 0.92. This highlights the potential for a next-generation liquid biopsy platform that relies solely on exosome physical properties, bypassing the need for fluorescent labeling.
Future Clinical Application
Senior Researchers **Yoonhee Lee** and **Gyogwon Koo** expressed optimism, stating, This study presents a new diagnostic potential to distinguish lung cancer with specific genetic mutations using only a small amount of exosome samples.
They added, We plan to actively pursue the practical application of this technology by integrating a high-speed AFM platform in clinical sample validation.
This innovation aligns with broader trends in cancer diagnostics, where advancements like Guardant360 CDx offer comprehensive genomic profiling from blood samples, detecting over 500 cancer-related genes with high sensitivity (Guardant Health).
