Multigene model for predicting metastatic prostate cancer using circulating tumor cells by microfluidic magnetophoresis

We aimed to isolate circulating tumor cells (CTCs) using a microfluidic technique with a novel lateral magnetophoretic microseparator. Prostate cancer-specific gene expressions were evaluated using mRNA from the isolated CTCs. A CTC-based multigene model was then developed for identifying advanced prostate cancer. Peripheral blood samples were obtained from five healthy donors and patients with localized prostate cancer (26 cases), metastatic hormone-sensitive prostate cancer (mHSPC, 10 cases), and metastatic castration-resistant prostate cancer (mCRPC, 28 cases). CTC recovery rate and purity (enriched CTCs/total cells) were evaluated according to cancer stage. The areas under the curves of the six gene expressions were used to evaluate whether multigene models could identify mHSPC or mCRPC. The number of CTCs and their purity increased at more advanced cancer stages. In mHSPC/mCRPC cases, the specimens had an average of 27.5 CTCs/mL blood, which was 4.2 x higher than the isolation rate for localized disease. The CTC purity increased from 2.1% for localized disease to 3.8% for mHSPC and 6.7% for mCRPC, with increased CTC expression of the genes encoding prostate-specific antigen (PSA), prostate-specific membrane antigen (PSMA), and cytokeratin 19 (KRT19). All disease stages exhibited expression of the genes encoding androgen receptor (AR) and epithelial cell adhesion molecule (EpCAM), although expression of the AR-V7 variant was relatively rare. Relative to each gene alone, the multigene model had better accuracy for predicting advanced prostate cancer. Our lateral magnetophoretic microseparator can be used for identifying prostate cancer biomarkers. In addition, CTC-based genetic signatures may guide the early diagnosis of advanced prostate cancer.

Automated summary

The study aimed to isolate circulating tumor cells using a novel microseparator technique, evaluate prostate cancer-specific gene expressions, and develop a multigene model for identifying advanced prostate cancer. Results showed a significant increase in CTC number and purity at different cancer stages, with the multigene model demonstrating higher accuracy in identifying advanced prostate cancer.