Capture of circulating tumour cells with epithelial and mesenchymal features for prostate cancer prognosis


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Lei Xu1,Xueying Mao1,Tianyu Guo1,Puiying Chan2,Greg Shaw2,John Hines2,Tim Oliver2,Daniel Berney1,Jonathan Shamash2,Yong-jie Lu1
1Barts Cancer Institute,2Barts Health NHS



Epithelial to mesenchymal transition (EMT) is a critical step for tumour metastasis. The aim of this study is to show that, in prostate cancer, circulating cells expressing the mesenchymal marker Vimentin (VIM) are cancer cells, and to correlate different CTC subtypes to prostate cancer progression.


We optimised the Parsortix size and deformability-based platform for the isolation of CTCs with both epithelial and mesenchymal properties and developed a multiple FISH rehybridisation method to analyse multiple genomic changes on the CTCs after immunofluorescence signals were completely stripped. 81 prostate cancer patients comprising 38 untreated and 43 progressive diseases were recruited. Enumerations of CK+/VIM-/CD45- (epithelial type), CK+/VIM+/CD45- (EMTing type), and CK-/VIM+/CD45- cells (mesenchymal type) were recorded.


Analysing several genomic regions, we detected genomic alterations in a similar proportion of CK+ and VIM+ groups of CD45- circulating cells.  These genomic aberration results indicate that majority of VIM+/CD45- cells are circulating prostate cancer cells with EMT. Among the CTC types, the number of EMTing CTCs correlated the best with the presence of metastases (p = 0.0001) and high risk localised disease (p = 0.0004) and had a closest area under the ROC curve (AUC) to PSA level (AUC of 0.7552 vs 0.8232) for distinguishing patients with detectable metastases. Mesenchymal CTCs performed the best amongst other subgroups in correlation with serum PSA level (Kendall’s τ = 0.26, p = 0.0010) and high primary Gleason-sum score (p = 0.0010) and were an independent risk factor by multivariate Cox regression model for overall survival in patients with progressive disease (HR: 10.975, p = 0.007). 


Therefore, we developed a novel CTC detection and genomic analysis approach, which can efficiently analyse CTCs undergoing/undergone EMT. This greatly enhances our ability to investigate cancer metastasis process and to predict/monitor cancer progression using CTCs.

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