Digital Decoding of Single Extracellular Vesicle Phenotype Differentiates Early Malignant and Benign Lung Lesions
Abstract Accurate identification of malignant lung lesions is a prerequisite for rational clinical management to reduce morbidity and mortality of lung cancer. However, classification of lung nodules into malignant and benign cases is difficult as they show similar features in computer tomography an...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202204207 |
| _version_ | 1797962175836323840 |
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| author | Junrong Li Abu A. I. Sina Fiach Antaw David Fielding Andreas Möller Richard Lobb Alain Wuethrich Matt Trau |
| author_facet | Junrong Li Abu A. I. Sina Fiach Antaw David Fielding Andreas Möller Richard Lobb Alain Wuethrich Matt Trau |
| author_sort | Junrong Li |
| collection | DOAJ |
| description | Abstract Accurate identification of malignant lung lesions is a prerequisite for rational clinical management to reduce morbidity and mortality of lung cancer. However, classification of lung nodules into malignant and benign cases is difficult as they show similar features in computer tomography and sometimes positron emission tomography imaging, making invasive tissue biopsies necessary. To address the challenges in evaluating indeterminate nodules, the authors investigate the molecular profiles of small extracellular vesicles (sEVs) in differentiating malignant and benign lung nodules via a liquid biopsy‐based approach. Aiming to characterize phenotypes between malignant and benign groups, they develop a single‐molecule‐resolution‐digital‐sEV‐counting‐detection (DECODE) chip that interrogates three lung‐cancer‐associated sEV biomarkers and a generic sEV biomarker to create sEV molecular profiles. DECODE capturessEVs on a nanostructured pillar chip, confines individual sEVs, and profiles sEV biomarker expression through surface‐enhanced Raman scattering barcodes. The author utilize DECODE to generate a digitally acquired sEV molecular profiles in a cohort of 33 people, including patients with malignant and benign lung nodules, and healthy individuals. Significantly, DECODE reveals sEV‐specific molecular profiles that allow the separation of malignant from benign (area under the curve, AUC = 0.85), which is promising for non‐invasive characterisation of lung nodules found in lung cancer screening and warrants further clinincal validaiton with larger cohorts. |
| first_indexed | 2024-04-11T01:09:15Z |
| format | Article |
| id | doaj.art-f81ecef562ca4861870794c81bc8d4c0 |
| institution | Directory Open Access Journal |
| issn | 2198-3844 |
| language | English |
| last_indexed | 2024-04-11T01:09:15Z |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj.art-f81ecef562ca4861870794c81bc8d4c02023-01-04T10:53:44ZengWileyAdvanced Science2198-38442023-01-01101n/an/a10.1002/advs.202204207Digital Decoding of Single Extracellular Vesicle Phenotype Differentiates Early Malignant and Benign Lung LesionsJunrong Li0Abu A. I. Sina1Fiach Antaw2David Fielding3Andreas Möller4Richard Lobb5Alain Wuethrich6Matt Trau7Centre for Personalised Nanomedicine Australian Institute for Bioengineering and Nanotechnology (AIBN) The University of Queensland Brisbane QLD 4072 AustraliaCentre for Personalised Nanomedicine Australian Institute for Bioengineering and Nanotechnology (AIBN) The University of Queensland Brisbane QLD 4072 AustraliaCentre for Personalised Nanomedicine Australian Institute for Bioengineering and Nanotechnology (AIBN) The University of Queensland Brisbane QLD 4072 AustraliaDepartment of Thoracic Medicine Royal Brisbane and Women's Hospital Herston QLD 4029 AustraliaTumour Microenvironment Laboratory QIMR Berghofer Medical Research Institute Herston Queensland 4006 AustraliaCentre for Personalised Nanomedicine Australian Institute for Bioengineering and Nanotechnology (AIBN) The University of Queensland Brisbane QLD 4072 AustraliaCentre for Personalised Nanomedicine Australian Institute for Bioengineering and Nanotechnology (AIBN) The University of Queensland Brisbane QLD 4072 AustraliaCentre for Personalised Nanomedicine Australian Institute for Bioengineering and Nanotechnology (AIBN) The University of Queensland Brisbane QLD 4072 AustraliaAbstract Accurate identification of malignant lung lesions is a prerequisite for rational clinical management to reduce morbidity and mortality of lung cancer. However, classification of lung nodules into malignant and benign cases is difficult as they show similar features in computer tomography and sometimes positron emission tomography imaging, making invasive tissue biopsies necessary. To address the challenges in evaluating indeterminate nodules, the authors investigate the molecular profiles of small extracellular vesicles (sEVs) in differentiating malignant and benign lung nodules via a liquid biopsy‐based approach. Aiming to characterize phenotypes between malignant and benign groups, they develop a single‐molecule‐resolution‐digital‐sEV‐counting‐detection (DECODE) chip that interrogates three lung‐cancer‐associated sEV biomarkers and a generic sEV biomarker to create sEV molecular profiles. DECODE capturessEVs on a nanostructured pillar chip, confines individual sEVs, and profiles sEV biomarker expression through surface‐enhanced Raman scattering barcodes. The author utilize DECODE to generate a digitally acquired sEV molecular profiles in a cohort of 33 people, including patients with malignant and benign lung nodules, and healthy individuals. Significantly, DECODE reveals sEV‐specific molecular profiles that allow the separation of malignant from benign (area under the curve, AUC = 0.85), which is promising for non‐invasive characterisation of lung nodules found in lung cancer screening and warrants further clinincal validaiton with larger cohorts.https://doi.org/10.1002/advs.202204207lung cancer screeningplasmonic nanostructuressmall extracellular vesiclessurface‐enhanced Raman scattering |
| spellingShingle | Junrong Li Abu A. I. Sina Fiach Antaw David Fielding Andreas Möller Richard Lobb Alain Wuethrich Matt Trau Digital Decoding of Single Extracellular Vesicle Phenotype Differentiates Early Malignant and Benign Lung Lesions Advanced Science lung cancer screening plasmonic nanostructures small extracellular vesicles surface‐enhanced Raman scattering |
| title | Digital Decoding of Single Extracellular Vesicle Phenotype Differentiates Early Malignant and Benign Lung Lesions |
| title_full | Digital Decoding of Single Extracellular Vesicle Phenotype Differentiates Early Malignant and Benign Lung Lesions |
| title_fullStr | Digital Decoding of Single Extracellular Vesicle Phenotype Differentiates Early Malignant and Benign Lung Lesions |
| title_full_unstemmed | Digital Decoding of Single Extracellular Vesicle Phenotype Differentiates Early Malignant and Benign Lung Lesions |
| title_short | Digital Decoding of Single Extracellular Vesicle Phenotype Differentiates Early Malignant and Benign Lung Lesions |
| title_sort | digital decoding of single extracellular vesicle phenotype differentiates early malignant and benign lung lesions |
| topic | lung cancer screening plasmonic nanostructures small extracellular vesicles surface‐enhanced Raman scattering |
| url | https://doi.org/10.1002/advs.202204207 |
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