- [Resource Hub](/) - [Publication](/?resource-type=publication#library) - High-recovery visual identification and single-cell retrieval of circulating tumor cells for genomic analysis using a dual-technology platform integrated with automated immunofluorescence staining # High-recovery visual identification and single-cell retrieval of circulating tumor cells for genomic analysis using a dual-technology platform integrated with automated immunofluorescence staining Campton DE, Ramirez AB, Nordberg JJ, Drovetto N, Clein AC, Varshavskaya P, et al. BMC Cancer . 2015;15:360. DOI [10.1186/s12885-015-1383-x](https://doi.org/10.1186/s12885-015-1383-x). PMID 25944336. PMCID PMC4430903. How to cite ### AMA Campton DE, Ramirez AB, Nordberg JJ, Drovetto N, Clein AC, Varshavskaya P, et al. High-recovery visual identification and single-cell retrieval of circulating tumor cells for genomic analysis using a dual-technology platform integrated with automated immunofluorescence staining. BMC Cancer . 2015;15:360. doi:10.1186/s12885-015-1383-x ### APA Campton, D. E., Ramirez, A. B., Nordberg, J. J., Drovetto, N., Clein, A. C., Varshavskaya, P., et al. (2015). High-recovery visual identification and single-cell retrieval of circulating tumor cells for genomic analysis using a dual-technology platform integrated with automated immunofluorescence staining. BMC Cancer , 15, 360. https://doi.org/10.1186/s12885-015-1383-x ### BibTeX @article{campton2015highrecovery, title = {High-recovery visual identification and single-cell retrieval of circulating tumor cells for genomic analysis using a dual-technology platform integrated with automated immunofluorescence staining}, author = {Campton, Daniel E and Ramirez, Arturo B and Nordberg, Joshua J and Drovetto, Nick and Clein, Alisa C and Varshavskaya, Paulina and others}, journal = {BMC Cancer}, volume = {15}, pages = {360}, year = {2015}, doi = {10.1186/s12885-015-1383-x} } Circulating tumor cells escape a solid tumor and travel in the blood, where they carry a readable record of how a cancer is changing and resisting treatment. They are extraordinarily rare, and many detection methods first capture cells by a single surface protein, which quietly discards any tumor cell that does not carry enough of it. This study characterized a platform that skips that up-front capture. It separates every nucleated cell from a blood sample by density, spreads them onto slides, stains and images them to pick out tumor cells by their cytokeratin, and then lifts chosen cells off the slide one at a time for genetic study. Across four cell lines it recovered roughly nine in ten spiked tumor cells, found a single cell in 7.5 mL of blood, and let the team confirm a tumor mutation in individually retrieved cells. [Read publication at BMC Cancer](https://pmc.ncbi.nlm.nih.gov/articles/PMC4430903/) ## Key findings - Spiked tumor cells were recovered at about 90% regardless of surface-marker level. Model cells from four cancer lines (A549, LNCaP, PC3, MCF7) were recovered at 90 to 91% (mean 90.5%, SD 4.5) across five replicates each, with equal yield for high- and low-EpCAM lines. - The platform detected a single tumor cell in 7.5 mL of blood, with a low false-positive rate. Among single-digit spike-ins, 22 of 27 cells (81%) were identified and a lone cell was found in 7.5 mL; across 20 recovery samples the count never exceeded the number spiked in, and the few extra objects were morphologically distinguishable contaminants. - Single retrieved cells carried readable genomes. The TP53 R175H mutation was confirmed in individually picked SKBR3 cells by nested PCR with Sanger sequencing and in all nine whole-exome-sequenced samples, and array comparative genomic hybridization reproduced the published SKBR3 karyotype. ## The AccuCyte–CyteFinder workflow in the methods “Blood from 10 patients with advanced breast, prostate or colorectal cancer was evaluated in a clinical feasibility study. Two 7.5 mL specimens of blood were drawn from cancer patients at the same time; one was given to the University of Washington (UW) Medical Center clinical laboratory for CTC evaluation by CellSearch and the other to RareCyte for CTC evaluation by AccuCyte – CyteFinder. CTCs were counted by CellSearch according to manufacturer’s instructions (Janssen Diagnostics, Raritan, NJ) and by AccuCyte – CyteFinder as described above. CTCs identified by AccuCyte – CyteFinder met CellSearch criteria: positive staining for cytokeratin and nucleus and negative staining for CD45. Investigators at RareCyte were blinded to the CellSearch counts until after the results from both assays were documented and delivered to investigators at UW.” — Campton et al., BMC Cancer (2015), Methods, “CTC enumeration comparison” Disclosure: RareCyte is listed as an author affiliation on the publication cited above. Disclosure: RareCyte is named in the competing-interests statement of the publication cited above. ## Why it matters for The AccuCyte–CyteFinder workflow users If you are choosing how to find and study rare tumor cells in blood, this paper is where the AccuCyte–CyteFinder workflow was first characterized end to end, and it sets the numbers later studies build on. Blood goes into a density separation tube, and instead of selecting cells by one surface marker first, the sample-preparation step collects nearly the whole buffy coat and spreads every nucleated cell onto slides. Automated staining and the CyteFinder scanner then identify tumor cells by cytokeratin, so a cell carrying little surface antigen is still seen rather than lost before anyone reviews it. From there, single identified cells are lifted off the slide with an integrated ceramic-tipped needle and carried into whole-genome amplification, sequencing, and copy-number analysis. For your own work, the point is that one workflow both counts rare cells and returns intact single cells you can genotype, without an antigen-capture step deciding in advance which cells you get to keep. 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