Selective Isolation of Heterogenic Circulating Tumor Cells(CTCs) using Magnetic Gradient based Microfluidic System

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Published Jul 14, 2017
DOI https://doi.org/10.36001/phmap.2017.v1i1.2063
Bongseop Kwak
Korea Institute of Machinery and Materials, Daegu Research center for Medical Devices and Rehab. Engineering, Department of Medical Device
Jaehun Lee
Korea Institute of Machinery and Materials, Daegu Research center for Medical Devices and Rehab. Engineering, Department of Medical Device
Jeonghun Lee
Korea Institute of Machinery and Materials, Daegu Research center for Medical Devices and Rehab. Engineering, Department of Medical Device

Abstract

Circulating tumor cells(CTCs) is one of key marker of cancer metastasis in human body. Despite of the importance to diagnose the cancer metastasis by CTCs, still it is formidable challenge to use in the clinical purpose because of the rarity and the heterogeneity of CTCs in the cancer
patient’s peripheral blood sample. To solve those addressed limitations, we have developed magnetic force gradient based microfluidic system for isolating the total number of CTCs in the sample and characterizing the state of CTCs simultaneously with respect to the epithelial cell adhesion
molecule (EpCAM) expression level. We have synthesized magnetic nanoparticles (MNPs) using hydrothermal method and functionalized anti-EpCAM on their surface for the specific binding with EpCAM on the tumor cell membrane. The microfluidic system designed to isolate and classify the CTCs by isolating at the different location in the chip using magnetic force differences depending on the EpCAM expression level. We observed 95.7% of EpCAM positive and 79.3% of EpCAM negative CTCs isolated in the microfluidic system. At the same time, the 71.3% of isolated EpCAM positive CTCs were isolated at the first half area whereas the 76.9% of EpCAM negative CTCs were collected at the latter half area.

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References
Hyun, K.A.,Jung,H.I.,2014.Lab.Chip14,45–56.
Mohamadi,R.M.,Besant,J.D.,Mepham,A.,Green,B.,Mahmoudian,L.,Gibbs,T.,Ivanov,I.,Malvea,A.,Stojcic,J.,Allan,A.L.,Lowes,L.E.,Sargent,E.H.,Nam,R.K.,Kelley,S.O.,2015.Angew.Chem.Int.Ed.Engl.54(1),139–143.
Ferrell, B. L. (1999), JSF Prognostics and Health Management. Proceedings of IEEE Aerospace Conference. March 6-13, Big Sky, MO. doi: 10.1109/AERO.1999.793190
Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, vol. 22, pp. 679-688. doi:10.1016/j.ijforecast.2006.03.001
International Standards Organization (ISO) (2004). Condition Monitoring and Diagnostics of Machines - Prognostics part 1: General Guidelines. In ISO, ISO13381-1:2004(e). vol. ISO/IEC Directives Part 2, I. O. f. S. (ISO), (p. 14). Genève, Switzerland: International Standards Organization.
Schwabacher, M., & Goebel, K. F. (2007). A survey of artificial intelligence for prognostics. Proceedings of AAAI Fall Symposium, November 9–11, Arlington, VA. www.aaai.org/Library/Symposia/Fall/2007/fs07-02-016.php
Vachtsevanos, G., Lewis, F. L., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering system. Hoboken, NJ: John Wiley & Sons, Inc
Issue
Vol. 1 No. 1 (2017): Proceedings of the Asia Pacific Conference of the PHM Society 2017
Section
Special Session Papers