Novel Flowcytometry-Based Approach for Detection of Tumor Cells in Body Fluid Using Automated Hematology Analyzer

Introduction: Nucleated cells differential analysis of body fluid (BF) samples is important diagnostic tool for several diseases including cancer metastasis. Detection of tumor cells in BF requires the manual morphological scanning of slides by the cytopathologists, which is time-consuming, labor-intensive and not always reliable because of a relatively low overall sensitivity rates (ranging 40-90%) with the higher false-negative rates for lymphomas and mesotheliomas. This study aimed to develop the scattergram gating analysis for detection of tumor cells in BF using the automated hematology analyzer Sysmex XN-1000 (Sysmex, Kobe, Japan).

Methods: We used a total of 220 BF samples (53 cerebrospinal fluids, 73 pleural or ascitic fluids, and 94 peritoneal dialysis effluent) obtained from patients with cytological diagnoses (papanicolaou stain) including negative and positive of tumor cells, and chronic inflammation with an elevated lymphocyte and histiocyte fractions. As a reference method, morphological manual differential (200 cells counts) was performed by two experienced technologists using cytospin slides stained with the May-Grunwald Giemsa.

The BF mode of XN-1000 (XN-BF) determines the differential cell counts of BF samples using side scatter and fluorescence intensity in WDF channel after the nuclear DNA/RNA stain by nucleic acid dye. The polymorphonuclear cells, mononuclear cells and high fluorescence cells (HF-BF), corresponding with a high amount of nucleic acids, are differentiated. Mesothelial cells and macrophages are theoretically categorized as HF-BF cells and included in the total nucleated cell count but not in the WBC count.

We selected the tumor cells positive (n=18) and chronic inflammation positive samples (n=108) by morphological manual differential, and reviewed their scattergram patterns determined by XN-BF. Then the novel scattergram gating strategy targeting the tumor cells was evaluated. The gating criteria were based on the WDF scatter plots; #1: detect the cells with larger size and higher fluorescence signal in comparison with general leukocytes, which mainly derived from clustered tumor cells, #2: detect the middle sized mononuclear cells with less granules rather than neutrophils and similar fluorescence signal to monocytes, which targeting hematological malignant cells and solid tumor cells. BF samples that meet at least one criterion were interpreted as positive for tumor cells.

Results: The malignant BF samples containing tumor cells showed the different scattergram patterns from the benign ones with chronic inflammation; the malignant BF formed the isolated cellular clusters in our gating system, and the inflammatory BF showed the continuous expansion into the HF-BF area.

Our scattergram gating analysis achieved an overall sensitivity of 72.2% and specificity of 98.0% in detecting tumor cells positive samples when screening against all samples outcomes. The positive predictive value was 76.5% and the negative predictive value was 97.5%. For the samples with absence of tumor cells and inflammatory observations (n=94), no false positive was detected. Of notes, each of our gating criterion detected the different type of tumor cells. For example, the scattergram gating analysis #1 achieved an overall sensitivity of 72.7% and specificity of 99.0% in detecting adenocarcinoma with the positive predictive value of 80.0% and the negative predictive value of 98.6%.

Conclusions: A simple measurement of BF by automated hematology analyzer in which cells are minimally handled has a potential to reduce costs and allow routine cell screening in clinical applications. For the BF malignancy diagnostics, the scattergram gating analysis is promising to (i) augment diagnostic routines without requiring additional sample preparation procedure, (ii) limit operator bias, and (iii) provide a standardized measurement.