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Spectral image cytometry for circulating tumor cell identification

University of British Columbia

Circulating tumor cells (CTCs) are exceedingly rare cancer cells shed from tumors into the bloodstream, where they have the potential to invade other tissues to seed metastases. CTCs are difficult to isolate but their critical role in tumor metastasis, as well as their proven prognostic value has attracted tremendous interest in recent years. While many methods have been developed to isolate CTCs, a major bottleneck to their clinical application has been the precise identification and characterization of these cells, owing to their tremendous phenotypic heterogeneity. To address these formidable challenges, a number of microscopy techniques have been applied to gather large amounts of information about captured cells. However, these studies are currently limited by two major concerns: First, due to the phenotypic plasticity of tumor cells, there may be significant variability in the properties of CTCs as observed using microscopy. Second, if the CTCs are subjected to multi-parameter analysis, the high-content data may be too expansive to analyze with a reasonable amount of time and effort. In this thesis, I developed an efficient and customizable spectral image cytometry platform to collect multi-spectral data from immunofluorescence micrographs of cell samples enriched for CTCs in order to quickly and easily analyze this information to facilitate CTC identification and characterization. This work includes the development of software tools to convert microscopy data for processing, to segment the images into single cell images, to rank potential CTCs, and to provide a user interface for rapid augmented review. The performance of this software platform has been evaluated by analyzing multi-spectral fluorescence imaging data previously collected by our group from ten patients with castrate resistant prostate cancer, and then comparing the result to unassisted manual reviews performed by blinded reviewers. The final CTC identification counts closely matched manual analysis with a slight increase in verified CTC counts, which is likely a result of the comprehensive nature of the automated screening process. The average computation time is 4.5 minutes per sample, which is faster than the time required to acquire the imaging data, and thus allows operators to quickly review results between acquisitions.

Text

2017-01-21

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/59954

Mechanical Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/