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A novel detection strategy for living circulating tumor cells using 5-aminolevulinic acid.

Abstract
Most circulating tumor cell (CTC) detection methods have technical limitations, allowing the detection of only cells expressing epithelial antigens, and they cannot identify if the CTCs are alive or dead. Herein, we constructed a novel CTC detection system comprised of filter separation and 5-aminolevulinic acid (5-ALA)-based labeling, termed "Fs-ALA". Blood specimens (7.5 mL) were subjected to this method. Cells enriched on the filter were incubated with 5-ALA and Hoechst 33342 as positive markers for CTCs. Images of the whole filter surface were obtained using a fluorescence microscope. No 5-ALA positive cells were detected in healthy blood specimens. The Fs-ALA method was capable of detecting not only EpCAM-positive, but also EpCAM-negative tumor cells. In the Fs-ALA method, one or more CTCs were detected in samples from 13 of 18 (72.2%) colorectal cancer patients. The Fs-ALA method had a significantly higher CTC detection rate than CellSearch™ in colorectal cancer patients (P <0.05), and only the former was capable of identifying live cells. This method is highly efficient for detecting CTC populations having undergone phenotypic changes, such as epithelial-mesenchymal transition.
AuthorsSatoshi Matsusaka, Masahiro Kozuka, Hidenori Takagi, Hiroshi Ito, Sayuri Minowa, Mitsuharu Hirai, Kiyohiko Hatake
JournalCancer letters (Cancer Lett) Vol. 355 Issue 1 Pg. 113-20 (Dec 01 2014) ISSN: 1872-7980 [Electronic] Ireland
PMID25218346 (Publication Type: Comparative Study, Journal Article, Research Support, Non-U.S. Gov't)
CopyrightCopyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Chemical References
  • Photosensitizing Agents
  • Aminolevulinic Acid
Topics
  • Aminolevulinic Acid
  • Cell Line, Tumor
  • Cell Separation (methods)
  • Cell Survival
  • Filtration
  • Gastrointestinal Neoplasms (blood, pathology)
  • Humans
  • Microscopy, Fluorescence
  • Neoplastic Cells, Circulating (pathology)
  • Photosensitizing Agents
  • Predictive Value of Tests

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