Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151485
Title: CytoPAN-Portable cellular analyses for rapid point-of-care cancer diagnosis
Authors: 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
Keywords: Engineering
Issue Date: 2020
Source: 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
Project: NRFCRP13-2014-01
Journal: Science Translational Medicine
Abstract: 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.
URI: https://hdl.handle.net/10356/151485
ISSN: 1946-6242
DOI: 10.1126/scitranslmed.aaz9746
Rights: 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
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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