Reliable and Sensitive Identification of Occult Tumor Cells Using the Improved Rare Event Imaging System
Overview
Authors
Affiliations
Purpose: The purpose of this study was to assess the feasibility of using rare event imaging system (REIS)-assisted analysis to detect occult tumor cells (OTCs) in peripheral blood (PB). The study also sought to determine whether REIS-assisted OTC detection presents a clinically viable alternative to manual microscopic detection to establish the true significance of OTC from solid epithelial tumors.
Experimental Design: We recently demonstrated proof of concept using a fluorescence-based automated microscope system, REIS, for OTC detection from the PB. For this study, the prototype of the system was adopted for high-throughput and high-content cellular analysis.
Results: The performance of the improved REIS was examined using normal blood (n = 10), normal blood added to cancer cells (n = 20), and blood samples obtained from cancer patients (n = 80). Data from the screening of 80 clinical slides from breast and lung cancer patients, by manual microscopy and by the REIS, revealed that as many as 14 of 35 positive slides (40%) were missed by manual screening but positively identified by REIS. In addition, REIS-assisted scanning reliably and reproducibly quantified the total number of cells analyzed in the assay and categorized positive cells based on their marker expression profile.
Conclusions: REIS-assisted analysis provides excellent sensitivity and reproducibility for OTC detection. This approach may enable an improved method for screening of PB samples and for obtaining novel information about disease staging and about risk evaluation in cancer patients.
Recent Advances in Methods for Circulating Tumor Cell Detection.
Vidlarova M, Rehulkova A, Stejskal P, Prokopova A, Slavik H, Hajduch M Int J Mol Sci. 2023; 24(4).
PMID: 36835311 PMC: 9959336. DOI: 10.3390/ijms24043902.
Circulating Tumor Cell Identification Based on Deep Learning.
Guo Z, Lin X, Hui Y, Wang J, Zhang Q, Kong F Front Oncol. 2022; 12:843879.
PMID: 35252012 PMC: 8889528. DOI: 10.3389/fonc.2022.843879.
Lee S, Chen C, Lim E, Shen L, Sathe A, Singh A J Pathol Inform. 2021; 12:18.
PMID: 34221634 PMC: 8240546. DOI: 10.4103/jpi.jpi_110_20.
A New Method for CTC Images Recognition Based on Machine Learning.
He B, Lu Q, Lang J, Yu H, Peng C, Bing P Front Bioeng Biotechnol. 2020; 8:897.
PMID: 32850745 PMC: 7423836. DOI: 10.3389/fbioe.2020.00897.
Gaikwad H, Li Y, Gifford G, Groman E, Banda N, Saba L Bioconjug Chem. 2020; 31(7):1844-1856.
PMID: 32598839 PMC: 7528420. DOI: 10.1021/acs.bioconjchem.0c00342.