RT Journal Article
SR Electronic
T1 CytoPAN—Portable cellular analyses for rapid point-of-care cancer diagnosis
JF Science Translational Medicine
FD American Association for the Advancement of Science
SP eaaz9746
DO 10.1126/scitranslmed.aaz9746
VO 12
IS 555
A1 Min, Jouha
A1 Chin, Lip Ket
A1 Oh, Juhyun
A1 Landeros, Christian
A1 Vinegoni, Claudio
A1 Lee, Jeeyeon
A1 Lee, Soo Jung
A1 Park, Jee Young
A1 Liu, Ai-Qun
A1 Castro, Cesar M.
A1 Lee, Hakho
A1 Im, Hyungsoon
A1 Weissleder, Ralph
YR 2020
UL http://stm.sciencemag.org/content/12/555/eaaz9746.abstract
AB Accurate and timely diagnosis and categorization of cancer are not always simple even under optimal conditions, and it can be near impossible in the developing world, where the necessary specialists and equipment may not be available and biopsy results can take months to return. To address this diagnostic bottleneck, Min et al. devised an automated image cytometry system named CytoPAN, which can correctly detect breast cancer and identify its subtype in 1 hour using samples obtained by fine needle aspiration, a less invasive technique than core biopsy. The system is relatively affordable and requires minimal training, which should decrease the barriers to access in low-resource areas.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.