CytoPAN-Portable cellular analyses for rapid point-of-care cancer diagnosis
Rapid, automated, point-of-care cellular diagnosis of cancer remains difficult in remote settings due to lack of specialists and medical infrastructure. To address the need for same-day diagnosis, we developed an automated image cytometry system (CytoPAN) that allows rapid breast cancer diagnosis of...
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sg-ntu-dr.10356-1514852021-06-29T14:06:57Z CytoPAN-Portable cellular analyses for rapid point-of-care cancer diagnosis Min, Jouha Chin, Lip Ket Oh, Juhyun Landeros, Christian Vinegoni, Claudio Lee, Jeeyeon Lee, Soo Jung Park, Jee Young Liu, Ai Qun Castro, Cesar M. Lee, Hakho Im, Hyungsoon Weissleder, Ralph School of Electrical and Electronic Engineering Engineering CytoPAN FNA Rapid, automated, point-of-care cellular diagnosis of cancer remains difficult in remote settings due to lack of specialists and medical infrastructure. To address the need for same-day diagnosis, we developed an automated image cytometry system (CytoPAN) that allows rapid breast cancer diagnosis of scant cellular specimens obtained by fine needle aspiration (FNA) of palpable mass lesions. The system is devoid of moving parts for stable operations, harnesses optimized antibody kits for multiplexed analysis, and offers a user-friendly interface with automated analysis for rapid diagnoses. Through extensive optimization and validation using cell lines and mouse models, we established breast cancer diagnosis and receptor subtyping in 1 hour using as few as 50 harvested cells. In a prospective patient cohort study (n = 68), we showed that the diagnostic accuracy was 100% for cancer detection and the receptor subtyping accuracy was 96% for human epidermal growth factor receptor 2 and 93% for hormonal receptors (ER/PR), two key biomarkers associated with breast cancer. A combination of FNA and CytoPAN offers faster, less invasive cancer diagnoses than the current standard (core biopsy and histopathology). This approach should enable the ability to more rapidly diagnose breast cancer in global and remote settings. National Research Foundation (NRF) Accepted version We acknowledge the following grant support: NRFCRP13-2014-01 to A.-Q.L 2021-06-29T14:01:46Z 2021-06-29T14:01:46Z 2020 Journal Article Min, J., Chin, L. K., Oh, J., Landeros, C., Vinegoni, C., Lee, J., Lee, S. J., Park, J. Y., Liu, A. Q., Castro, C. M., Lee, H., Im, H. & Weissleder, R. (2020). CytoPAN-Portable cellular analyses for rapid point-of-care cancer diagnosis. Science Translational Medicine, 12(555), eaaz9746-. https://dx.doi.org/10.1126/scitranslmed.aaz9746 1946-6242 https://hdl.handle.net/10356/151485 10.1126/scitranslmed.aaz9746 32759277 555 12 eaaz9746 en NRFCRP13-2014-01 Science Translational Medicine This is the author’s version of the work. It is posted here by permission of the AAAS for personal use, not for redistribution. The definitive version was published in Science Translational Medicine on Vol 12 and 5 August 2020, DOI: 10.1126/scitranslmed.aaz9746 application/pdf |
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Engineering CytoPAN FNA |
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Engineering CytoPAN FNA Min, Jouha Chin, Lip Ket Oh, Juhyun Landeros, Christian Vinegoni, Claudio Lee, Jeeyeon Lee, Soo Jung Park, Jee Young Liu, Ai Qun Castro, Cesar M. Lee, Hakho Im, Hyungsoon Weissleder, Ralph CytoPAN-Portable cellular analyses for rapid point-of-care cancer diagnosis |
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Rapid, automated, point-of-care cellular diagnosis of cancer remains difficult in remote settings due to lack of specialists and medical infrastructure. To address the need for same-day diagnosis, we developed an automated image cytometry system (CytoPAN) that allows rapid breast cancer diagnosis of scant cellular specimens obtained by fine needle aspiration (FNA) of palpable mass lesions. The system is devoid of moving parts for stable operations, harnesses optimized antibody kits for multiplexed analysis, and offers a user-friendly interface with automated analysis for rapid diagnoses. Through extensive optimization and validation using cell lines and mouse models, we established breast cancer diagnosis and receptor subtyping in 1 hour using as few as 50 harvested cells. In a prospective patient cohort study (n = 68), we showed that the diagnostic accuracy was 100% for cancer detection and the receptor subtyping accuracy was 96% for human epidermal growth factor receptor 2 and 93% for hormonal receptors (ER/PR), two key biomarkers associated with breast cancer. A combination of FNA and CytoPAN offers faster, less invasive cancer diagnoses than the current standard (core biopsy and histopathology). This approach should enable the ability to more rapidly diagnose breast cancer in global and remote settings. |
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School of Electrical and Electronic Engineering |
author_facet |
School of Electrical and Electronic Engineering Min, Jouha Chin, Lip Ket Oh, Juhyun Landeros, Christian Vinegoni, Claudio Lee, Jeeyeon Lee, Soo Jung Park, Jee Young Liu, Ai Qun Castro, Cesar M. Lee, Hakho Im, Hyungsoon Weissleder, Ralph |
format |
Article |
author |
Min, Jouha Chin, Lip Ket Oh, Juhyun Landeros, Christian Vinegoni, Claudio Lee, Jeeyeon Lee, Soo Jung Park, Jee Young Liu, Ai Qun Castro, Cesar M. Lee, Hakho Im, Hyungsoon Weissleder, Ralph |
author_sort |
Min, Jouha |
title |
CytoPAN-Portable cellular analyses for rapid point-of-care cancer diagnosis |
title_short |
CytoPAN-Portable cellular analyses for rapid point-of-care cancer diagnosis |
title_full |
CytoPAN-Portable cellular analyses for rapid point-of-care cancer diagnosis |
title_fullStr |
CytoPAN-Portable cellular analyses for rapid point-of-care cancer diagnosis |
title_full_unstemmed |
CytoPAN-Portable cellular analyses for rapid point-of-care cancer diagnosis |
title_sort |
cytopan-portable cellular analyses for rapid point-of-care cancer diagnosis |
publishDate |
2021 |
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https://hdl.handle.net/10356/151485 |
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1705151285455486976 |