“High-content quantum dot-based subtype diagnosis and classification of breast cancer patients using hypermulticolor quantitative single cell imaging cytometry”

Abstract

Summary Successful therapeutic modalities against breast cancer (BC) are highly dependent on the accurate prognosis and quantitative simultaneous classification of BC subtypes. Current methods based on semi-quantitative analysis or subjective interpretations are not efficient for BC prognosis and classification considering tumor heterogeneity. In this work, simultaneous monitoring and quantitative estimation of four targeted receptors (EGFR1, HER2, ER and PR) was carried out using quantum dot (QD) based hypermulticolor high-content single cell imaging cytometry. Four different QD-antibody conjugates (QD525-EGFR1, QD565-HER2, QD605-ER, and QD655-PR) were used as QD immunoassay probes for the subtype classification of biopsied samples from BC patients. The molecular profiling obtained by QD imaging cytometry was in line with that of conventional immunohistochemistry and Western blot analysis with regard to the expression profiles of known predominant target receptors in in vitro model. Multi-target-single cell imaging cytometry facilitated the quantitative classification of BC subtypes with deeper insights into the existence of heterogeneity and homogeneity in BC cell lines (MCF-7, SK-BR-3, JIMT-1, and HCC-70) and primary cells from biopsied tissues. Quantitative estimates showed 36.96%, 41.45%, 23.14%, 57.45% and 63.21% heterogeneity in MCF-7, SK-BR-3, JIMT-1, HCC-70 and MCF-10A cell lines, respectively, in addition to the remarkable heterogeneity of primary cells. In consideration of BC tumor heterogeneity, highly sensitive QD immunohistochemistry was executed on different regions of BC tumors. Significant heterogeneous subtypes that have to be considered for proper drug treatment were determined depending on the tumor regions of BC patients. Herein, we presented a quantitative hypermulticolor high-content QD single cell imaging cytometry as a new model for BC prognosis and classification based on accurate quantification of tumor heterogeneity.