Dataset Information

A novel flow cytometry-based cell capture platform for the detection, capture and molecular characterization of rare tumor cells in blood.

ABSTRACT:

Background

Personalized cancer treatment relies on the accurate detection of actionable genomic aberrations in tumor cells. Circulating tumor cells (CTCs) could provide an alternative genetic resource for diagnosis; however, the technical difficulties in isolating and analyzing rare CTCs have limited progress to date. In this preclinical study, we aimed to develop an improved capture system for molecular characterization of CTCs based on a novel cell sorting technology.

Methods

We developed a cell capture platform using On-chip Sort (On-Chip Biotechnologies), a novel bench-top cell sorter equipped with a disposable microfluidic chip. Spike-in experiments comprising a series of lung cancer cell lines with varying epithelial cell adhesion molecule (EpCAM) expression levels were conducted to assess the capture and purification efficiency of the platform. Samples were negatively enriched using anti-CD45-coated magnetic beads to remove white blood cells, followed by sample fixation and labeling. The enriched and labeled samples were then sorted by On-chip Sort based on cytokeratin, vimentin, and CD45 expression. Captured cells were immediately subjected to whole genome amplification followed by mutation analysis using deep targeted sequencing, and copy number analysis using quantitative polymerase chain reaction (qPCR).

Results

Spike-in experiments revealed an excellent overall mean capture rate of 70.9%. A 100% success rate in the detection of EGFR, KRAS and BRAF mutations from captured cells was achieved using pyrosequencing and deep sequencing. The mutant variant detection rates were markedly higher than those obtained with the CellSearch profile kit. qPCR analysis of amplified DNA demonstrated reproducible detection of copy number changes of the EGFR in captured tumor cells.

Conclusions

Using a novel cell sorter, we established an efficient and convenient platform for the capture of CTCs. Results of a proof-of-principle preclinical study indicated that this platform has potential for the molecular characterization of captured CTCs from patients.

SUBMITTER: Watanabe M

PROVIDER: S-EPMC4053587 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature

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Publications

A novel flow cytometry-based cell capture platform for the detection, capture and molecular characterization of rare tumor cells in blood.

Watanabe Masaru M Serizawa Masakuni M Sawada Takeshi T Takeda Kazuo K Takahashi Toshiaki T Yamamoto Nobuyuki N Koizumi Fumiaki F Koh Yasuhiro Y

Journal of translational medicine 20140523

<h4>Background</h4>Personalized cancer treatment relies on the accurate detection of actionable genomic aberrations in tumor cells. Circulating tumor cells (CTCs) could provide an alternative genetic resource for diagnosis; however, the technical difficulties in isolating and analyzing rare CTCs have limited progress to date. In this preclinical study, we aimed to develop an improved capture system for molecular characterization of CTCs based on a novel cell sorting technology.<h4>Methods</h4>We ...[more]

PMID: 24886394

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Project description:Flow cytometry is the prototypical assay for multi-parameter single cell analysis, and is essential in vaccine and biomarker research for the enumeration of antigen-specific lymphocytes that are often found in extremely low frequencies (0.1% or less). Standard analysis of flow cytometry data relies on visual identification of cell subsets by experts, a process that is subjective and often difficult to reproduce. An alternative and more objective approach is the use of statistical models to identify cell subsets of interest in an automated fashion. Two specific challenges for automated analysis are to detect extremely low frequency event subsets without biasing the estimate by pre-processing enrichment, and the ability to align cell subsets across multiple data samples for comparative analysis. In this manuscript, we develop hierarchical modeling extensions to the Dirichlet Process Gaussian Mixture Model (DPGMM) approach we have previously described for cell subset identification, and show that the hierarchical DPGMM (HDPGMM) naturally generates an aligned data model that captures both commonalities and variations across multiple samples. HDPGMM also increases the sensitivity to extremely low frequency events by sharing information across multiple samples analyzed simultaneously. We validate the accuracy and reproducibility of HDPGMM estimates of antigen-specific T cells on clinically relevant reference peripheral blood mononuclear cell (PBMC) samples with known frequencies of antigen-specific T cells. These cell samples take advantage of retrovirally TCR-transduced T cells spiked into autologous PBMC samples to give a defined number of antigen-specific T cells detectable by HLA-peptide multimer binding. We provide open source software that can take advantage of both multiple processors and GPU-acceleration to perform the numerically-demanding computations. We show that hierarchical modeling is a useful probabilistic approach that can provide a consistent labeling of cell subsets and increase the sensitivity of rare event detection in the context of quantifying antigen-specific immune responses.

Dataset Information

A novel flow cytometry-based cell capture platform for the detection, capture and molecular characterization of rare tumor cells in blood.

Background

Methods

Results

Conclusions

Publications

A novel flow cytometry-based cell capture platform for the detection, capture and molecular characterization of rare tumor cells in blood.

Similar Datasets

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