Rapid and Sensitive Detection of Breast Cancer Cells in Patient Blood with Nuclease-Activated Probe Technology

Abstract

A challenge for circulating tumor cell (CTC)-based diagnostics is the development of simple and inexpensive methods that reliably detect the diverse cells that make up CTCs. CTC-derived nucleases are one category of proteins that could be exploited to meet this challenge. Advantages of nucleases as CTC biomarkers include: (1) their elevated expression in many cancer cells, including cells implicated in metastasis that have undergone epithelial-to-mesenchymal transition; and (2) their enzymatic activity, which can be exploited for signal amplification in detection methods. Here, we describe a diagnostic assay based on quenched fluorescent nucleic acid probes that detect breast cancer CTCs via their nuclease activity. This assay exhibited robust performance in distinguishing breast cancer patients from healthy controls, and it is rapid, inexpensive, and easy to implement in most clinical labs. Given its broad applicability, this technology has the potential to have a substantive impact on the diagnosis and treatment of many cancers.

Keywords: breast cancer; cancer; circulating tumor cells; diagnostic markers; diagnostic nucleic acids; liquid biopsy; nucleases.

Figure 1

Nuclease-Activated Probe Assay for the Detection of Circulating Tumor Cells in Blood (A) Freshly drawn patient blood was filtered using a microfiltration column device to capture cancer cells. Cancer cells were then lysed in nuclease buffer, and lysates were incubated with nuclease-activated probes. Probe fluorescence indicates the presence of cancer cells in blood. The assay was typically carried out in less than 1 hr from the time of blood draw to the fluorescence readout, and thus provides a rapid turn-around for point-of-care diagnostics. (B) Schematic of the enzymatic nature of the nuclease-activated probe assay. The sequences of the three probes (dsDNA, ssDNA, and ssRNA) used in this study are shown in the Materials and Methods. All three probes are flanked by a FAM fluorophore (5′ terminus) and a pair of fluorescence quenchers (3′ terminus).

Figure 2

Nuclease Gene Expression in BCa Cell Lines and Patient Samples (A) BCa nuclease gene expression data were obtained from the EMBL-EBI database. Nuclease gene expression in BCa cells was normalized to that in the blood. Normalized fold expression is defined as the average mRNA expression of every nuclease gene across 60 BCa cell lines divided by the expression of the same genes in blood. Nucleases known to be upregulated in CTCs are highlighted (i.e., EXO1, NIEL3, FEN1, DNA2, and ERCC1). (B) Left panel: mRNA expression of EpCAM and nuclease genes from 60 different cell lines was normalized before plotting. Normalized EpCAM expression: EpCAM mRNA expression for each cell line was normalized to the average EpCAM mRNA expression level detected across these 60 cell lines (blue bars). Normalized nuclease gene expression: the sum of all 160 nucleases in each cell line was normalized to the average value across all 60 cell lines (orange line). Right panel: an analogous analysis was carried out with data from BCa patient tissues (n = 941) from The Cancer Genome Atlas (TCGA). (C) Nuclease expression in breast cancer cell lines during epithelial-to-mesenchymal transition (EMT). 60 breast cancer cell lines were ranked based on the expression of epithelial (EpCAM, cytokeratin 19, and E-cadherin) and mesenchymal (N-cadherin and vimentin) markers. Expression of EpCAM and nucleases (average expression of 161 nuclease genes) for each cell line was plotted. Yellow box: breast cancer cell lines with little-to-no EpCAM expression that are missed by EpCAM immune capture methods.

Figure 3

Nuclease-Activated Probe Assay Sensitivity (A) The sensitivity of the nuclease-activated probe assay was measured using SkBr3 BCa cells. Nuclease-activated probes were incubated at 37°C for 6 hr with lysates derived from varying amounts of SKBr3 BCa cells (0–30 cancer cells). Fluorescence was measured every 20 min over the course of 6 hr using a microplate reader. (B) Signals derived from the digestion of the dsDNA nuclease-activated probe showed a linear correlation in the range of 1–30 cells (data based on 3–100 cells/mL fluid evaluated in the assay). Trend lines are given for all single-measurement time points in the standard assay procedure (0–360 min with 20 min increments). Coefficient of determination (R²) was >0.99 for time points >60 min. A linear correlation was found for several BCa cell lines tested. Shown is a representative plot from an experiment with the SKBr3 BCa cell line. By investigating lysates from six different cell numbers (1, 2, 4, 8, 15, and 30), a linear regression plot was used to assess signal-to-cell number correlation. (C) Additional BCa cell lines were examined for the sensitivity and correlation of assay signal to cell number as shown in (A) and (B) for SKBr3 cells. The table sums up the sensitivity levels and R² values at the different time points of the 6 hr assay.

Figure 4

Nuclease-Activated Probe Assay Specificity (A) Nuclease-activated probes (dsDNA, ssDNA, and 2′F-RNA) were incubated with lysates of 100 white blood cells isolated from the blood of a healthy donor or various BCa cell lines (BT20, MDA-MB-468, HCC1937, MDA-MB-231, MDA-MB-453, Iowa1T, MCF7, SKBr-3, and BT474). Fold WBC is defined as the fluorescence signal generated from each individual BCa cell line divided by that generated by the white blood cells. Fluorescence was measured in a microplate reader every 20 min for a total of 6 hr. The 4-hr time point is plotted. (B) Sensitivity of detection of cancer cells in blood without prior cancer cell capture and/or enrichment step. A defined amount of healthy donor blood (about 1 mL) with the addition of increasing amounts of MDA-MB-453 cells (1–100,000 cells per 1 mL of blood) was subjected to the nuclease-activated probe assay using the three nuclease-activated probes (dsDNA, ssDNA, and 2′F-RNA). (C) Nuclease-activated probe assay sensitivity. Blood samples from a healthy donor were spiked with either 100 or 25 HCC1937 cancer cells per milliliter of blood, and samples were evaluated using the nuclease-activated probe assay. Blood, not spiked with cancer cells, served as a control (blood). The signal intensity was normalized to the background fluorescence of the corresponding probe. (D) Blood samples from a healthy donor with and without the addition of 200 HCC1937 cancer cells per milliliter of blood. Cancer cells in blood were immunostained with mouse anti-human PanCytokeratin (green) and rat anti-EpCAM (red). Scale bars: 25 μm.

Figure 5

Cancer Cell Retention and/or Capture Efficiency Using the SCREENCELL Filters Signal intensity (%) of lysates from cells filtered with SCREENCELL filters compared with lysates of the same number of cells without filtration. Lysates were incubated with the nuclease-activated probes, and fluorescence measurements were obtained as described above. Varying number of HCC1937 cells (0–100 cells) were either directly lysed in nuclease lysis buffer or subjected to the filtration capture and/or enrichment step.

Figure 6

Detection of Cancer Cells in Blood of Patients with Stage IV BCa Samples from patients with stage IV BCa were examined using the nuclease-activated probe assay. Fluorescence intensity from the stage IV BCa samples was compared with that of healthy donor samples. (A) Nuclease activity detected after 6 hr from blood of stage IV BCa patients (blue) or healthy donors (orange). The black line indicates the median of the two groups. (B) Plotted are mean values and corresponding SEM for patients (closed circles, n = 29) and healthy donors (open circles, n = 15). Asterisks indicate statistical significance (p values of a two-way ANOVA are described in the depicted table: **p < 0.01; ***p < 0.001; ****p < 0.0001). Statistical analysis of the time points that are the best predictors of the nuclease-activated probe assay. Area under the curve (AUC) plot based on receiver operating characteristic (ROC) analysis of the data. Additionally, Table 1 gives the cutoff values for sensitivity and specificity at these best predictor time points.