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Sci Rep. 2016 Oct 14;6:35474. doi: 10.1038/srep35474.

Label-free enumeration, collection and downstream cytological and cytogenetic analysis of circulating tumor cells.

Author information

Department of Bioengineering, University of California, 420 Westwood Plaza, 5121 Engineering V, P.O. Box 951600, Los Angeles, CA 90095, USA.
Vortex Biosciences Inc., 1490 O'Brien Drive, Suite E, Menlo Park, CA 94025, USA.
Department of Surgery, Stanford University School of Medicine, MSLS Bldg, 1201 Welch Road, Stanford, CA 94305, USA.
Department of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Jonsson Comprehensive Cancer Center, Los Angeles, CA 90095, USA.
UCLA Santa Monica Hematology Oncology, 2020 Santa Monica Blvd, Suite 600, Santa Monica, CA 90404, USA.
California NanoSystems Institute, 570 Westwood Plaza, Building 114, Los Angeles, CA 90095, USA.
Division of Dermatology, UCLA Medical Center, 52-121 CHS, Los Angeles, CA 90095, USA.


Circulating tumor cells (CTCs) have a great potential as indicators of metastatic disease that may help physicians improve cancer prognostication, treatment and patient outcomes. Heterogeneous marker expression as well as the complexity of current antibody-based isolation and analysis systems highlights the need for alternative methods. In this work, we use a microfluidic Vortex device that can selectively isolate potential tumor cells from blood independent of cell surface expression. This system was adapted to interface with three protein-marker-free analysis techniques: (i) an in-flow automated image processing system to enumerate cells released, (ii) cytological analysis using Papanicolaou (Pap) staining and (iii) fluorescence in situ hybridization (FISH) targeting the ALK rearrangement. In-flow counting enables a rapid assessment of the cancer-associated large circulating cells in a sample within minutes to determine whether standard downstream assays such as cytological and cytogenetic analyses that are more time consuming and costly are warranted. Using our platform integrated with these workflows, we analyzed 32 non-small cell lung cancer (NSCLC) and 22 breast cancer patient samples, yielding 60 to 100% of the cancer patients with a cell count over the healthy threshold, depending on the detection method used: respectively 77.8% for automated, 60-100% for cytology, and 80% for immunostaining based enumeration.

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