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Proceedings Paper

Application of advanced cytometric and molecular technologies to minimal residual disease monitoring
Author(s): James F. Leary; Feng He; Lisa M. Reece
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Paper Abstract

Minimal residual disease monitoring presents a number of theoretical and practical challenges. Recently it has been possible to meet some of these challenges by combining a number of new advanced biotechnologies. To monitor the number of residual tumor cells requires complex cocktails of molecular probes that collectively provide sensitivities of detection on the order of one residual tumor cell per million total cells. Ultra-high-speed, multi parameter flow cytometry is capable of analyzing cells at rates in excess of 100,000 cells/sec. Residual tumor selection marker cocktails can be optimized by use of receiver operating characteristic analysis. New data minimizing techniques when combined with multi variate statistical or neural network classifications of tumor cells can more accurately predict residual tumor cell frequencies. The combination of these techniques can, under at least some circumstances, detect frequencies of tumor cells as low as one cell in a million with an accuracy of over 98 percent correct classification. Detection of mutations in tumor suppressor genes requires insolation of these rare tumor cells and single-cell DNA sequencing. Rare residual tumor cells can be isolated at single cell level by high-resolution single-cell cell sorting. Molecular characterization of tumor suppressor gene mutations can be accomplished using a combination of single- cell polymerase chain reaction amplification of specific gene sequences followed by TA cloning techniques and DNA sequencing. Mutations as small as a single base pair in a tumor suppressor gene of a single sorted tumor cell have been detected using these methods. Using new amplification procedures and DNA micro arrays it should be possible to extend the capabilities shown in this paper to screening of multiple DNA mutations in tumor suppressor and other genes on small numbers of sorted metastatic tumor cells.

Paper Details

Date Published: 11 April 2000
PDF: 9 pages
Proc. SPIE 3913, In-Vitro Diagnostic Instrumentation, (11 April 2000); doi: 10.1117/12.382038
Show Author Affiliations
James F. Leary, Univ. of Texas Medical Branch at Galveston (United States)
Feng He, Univ. of Texas Medical Branch at Galveston (United States)
Lisa M. Reece, Univ. of Texas Medical Branch at Galveston (United States)

Published in SPIE Proceedings Vol. 3913:
In-Vitro Diagnostic Instrumentation
Gerald E. Cohn, Editor(s)

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