Genomic high-resolution profiling of single [CK.sup.pos]/[CD45.sup.neg] flow-sorting purified circulating tumor cells from patients with metastatic breast cancer.
Because of the extreme rarity of the CTCs (4), their further analysis is technically challenging, and consequently not much is known about their molecular and genetic characteristics (14). Comprehensive genetic characterization downstream of their immunological detection might help in the acquisition of a more complete picture of the biology of the CTC population and may facilitate a more personalized therapy, eventually targeting specific CTC subtypes. Therefore, it would be highly desirable to further analyze the captured cells. The release of these captured and stained cells from the diagnostic CellSearch cartridges is possible, although the kit was originally not designed for this. The very low concentration of CTCs and the unpredictable number of "contaminating" hematopoietic cells always cocaptured as background by the CellSearch system hamper direct downstream molecular analyses, and hence further purification and isolation of the rare CTCs is required. Initial data indicated that flow-sorting approaches might be suitable for CTC purification (15), but efficient protocols have been lacking. Here, we present a protocol to efficiently purify single CTCs captured with the CellSearch circulating tumor cell kit that enables their further comprehensive genetic analysis on a single-cell level. The proposed protocol can be used for affordable and systematic isolation of CTCs from clinical CellSearch samples. As a downstream readout system, we used single-cell array-based comparative genomic hybridization (aCGH) to screen the whole genome of single CTCs isolated from advanced metastatic breast cancer patients with >5 marker-positive CTCs for copy number alterations (CNAs) (16).
Detailed protocols, Supplemental Tables 1-6, and Supplemental Figs. 1-8 can be found in the Data Supplement that accompanies the online version of this report at http://www.clinchem.org/content/vol60/ issue10.
FLOW CYTOMETRY ANALYSIS OF CellSearch PREENRICHED CTC SAMPLES AND SORTING OF SINGLE PHYCOERYTHRIN-[CK.sup.pos]/ ALLOPHYCOCYANIN-[CD45.sup.neg]/[DAPI.sup.pos] CELLS
A total of 40 cartridges from 39 patients were available: 30 cartridges containing CTCs and 10 without CTCs. Samples were transferred from the CellSearch cartridges into 5-mL polypropylene round-bottom tubes for flow cytometry analyses. The protocol used for flow cytometry consisted of an initial discrimination of events based on phycoerythrin (PE)-CK, and allophycocyanin (APC)-CD45. PE-[CK.sup.pos]/APC-[CD45.sup.neg] events were subsequently analyzed for the presence of nucleic acid DAPI staining. Using the width of the side-scatter signals (SSC-width), single events were isolated from cellular aggregates. Single PE-[CK.sup.pos]/APC-[CD45.sup.neg]/ [DAPI.sub.pos] cells were sorted into individual 0.2-mL PCR tubes to be directly processed or to be frozen at -20[degrees]C until use.
MOLECULAR CHARACTERIZATION OF ISOLATED CTCs
The genetic material of single sorted cells was amplified using the adapter-linker PCR based on MseI digestion of the genome as previously described (17), which is now commercialized as the Ampli1 [TM] whole genome amplification (WGA) kit by Silicon Biosystems. Chromosomal CNAs were analyzed by high-resolution arraybased comparative genome hybridization (aCGH) (16). The same WGA products were used for a real-timebased assay to validate the amplification of the cyclin D1 (CCND1)  locus, and used to screen for mutations on exons 1, 9, and 20 of the phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA) gene (see online Supplemental Table 5) and exons 5, 7, and 8 of the tumor protein p53 (TP53) gene by sequencing.
SYSTEMATIC FLOW CYTOMETRY ANALYSIS OF CLINICAL SAMPLES PROCESSED BY THE CellSearch SYSTEM
In a first step, we confirmed that under the used settings the MoFlo XDP device accurately isolated single cells from CellSearch cartridges and deposited them as single events for further analysis (see online Supplemental Fig. 1). We used 40 clinical samples analyzed by CellSearch. Of the 30 CTC-positive samples, 6 (20%) contained <5 CTCs, 8 contained 5-10 CTCs (26.7%), and 16 contained >10 CTCs (53.3%) (see online Supplemental Table 1). To isolate individual CTCs from the background of contaminating [CD45.sup.pos] cells, we first discriminated all events on the basis of the expression ofCK and CD45 (Fig. 1). We observed a very similar expression of these markers, resulting in a similar distribution of the events in the bivariate displays in 37/40 (92.5%) samples (see online Supplemental Fig. 2 and Supplemental Table 3). This enabled us to discriminate [CK.sup.pos]/[CD45.sup.neg] events using an identical gate for all investigated samples. In the remaining 3 samples (samples 10, 22, and 40), high autofluorescence interfered with an appropriate setup of the [CK.sup.pos]/[CD45.sup.neg] gate, and therefore these samples were excluded from further analyses (see online Supplemental Fig. 3A). After gating the [DAPI.sub.pos] events within the [CK.sup.pos]/ [CD45.sup.neg] population, we proceeded to discriminate single cells from cellular clusters on the basis of SSC-width (Fig. 1). One same SSC-width gate was appropriate for all samples, with the exception of sample 25, which was characterized by abnormally high values for SSCwidth, indicating a high proportion of cellular aggregates containing [CK.sup.pos]/[CD45.sup.neg]/[DAPI.sub.pos] events (see online Supplemental Fig. 3B). Interestingly, several cellular aggregates containing CTCs had been detected on the previous CellSearch analysis of this sample (see online Supplemental Fig. 3C). Because this interfered with the isolation of pure single CTCs, the sample was not considered for further analyses. Thus, the protocol and the chosen gates were suitable for the analysis and isolation of single cells from 36/40 (90.0%) CellSearch samples obtained from patients with metastasis (see online Supplemental Table 1).
EFFICIENT DETECTION AND ISOLATION OF SINGLE [CK.sup.pos]/[CD45.sup.neg]/[DAPI.sup.pos] CELLS FROM CLINICAL SAMPLES FOR DOWNSTREAM MOLECULAR CHARACTERIZATION
The flow cytometry analyses allowed us to assess the extent of the undesired background present in CellSearch samples. We observed that the number of total events or [DAPI.sub.pos] events was not proportional to the number of CellSearch CTCs in the cartridges and varied considerably among samples (Fig. 2A; also see online Supplemental Fig. 4A and Supplemental Table 3). To evaluate the efficiency of the established flow cytometry protocol to detect CTCs in these samples, we compared the numbers of CTCs detected by flow cytometry (FC-CTCs) with the original CTC counts by the CellSearch system (CS-CTCs). Despite the variable background in the 36 successfully analyzed samples (Fig. 2A; also see online Supplemental Fig. 4A) and the different storage times of the samples (see online Supplemental Table 1 and Supplemental Fig. 4B), we observed a positive linear correlation ([R.sup.2] = 0.9257) between the numbers of CTCs detected by the 2 techniques (Fig. 2B). But it is of note that samples with longer storage times showed a larger spread from the optimal situation (100%) (see online Supplemental Fig. 4B). The median percentage of CS-CTCs detected by flow cytometry was 83.3% (range, 0%-300%; mean, 102%) (see online Supplemental Table 1). For only 1 sample, flow cytometry failed to detect CTCs, although 7 CS-CTCs were counted. On the other hand, we detected FC-CTCs at low numbers in only 2 of the 10 samples in which no CS-CTCs were detected (see online Supplemental Table 1). In total, 1163 CTCs defined as PE-[CK.sup.pos]/APC-[CD45.sup.neg]/[DAPI.sub.pos] cells were detected in the set of clinical samples. From those detected CTCs, 883 were sorted from 27 CellSearch cartridges: 192 as single cells that were used for the present work and the rest as pools that were not further analyzed. We next aimed to evaluate if the single flowsorted FC-CTCs could be used for molecular analyses. For that we performed WGA and controlled the quality of the amplification product by a multiplex PCR (see online Supplemental Fig. 5, A and B): 140/192 (72.9%) single sorted CTCs were successfully amplified and 125/192 (65.1%) CTCs displayed 3 or more bands upon multiplex PCR, which qualified them for further analysis by high-resolution aCGH (16). Importantly, it was possible to amplify genetic material from at least 1 cell in each of the 25/27 (92.6%) analyzed clinical samples. Obvious effects on the quality of the WGA products associated with the time before CellSearch analysis (blood in CellSave tubes) or the time between CellSearch and flow cytometry (cap tured cells in the cartridges) were not noted (see online Supplemental Fig. 5, C-E).
COMPREHENSIVE GENOMIC ANALYSIS CONFIRMS THAT SINGLE [CK.sup.pos]/[CD45.sup.neg]/[DAPI.sub.pos] CELLS SORTED FROM CLINICAL SAMPLES SHOW CHARACTERISTIC CNAs OF ADVANCED BREAST CARCINOMA
After isolating single CTCs by their phenotype from CellSearch cartridges, we confirmed their malignant nature by aCGH. As proof of principle, we analyzed 2 cells randomly selected among those with [greater than or equal to] 3 bands in the control multiplex PCR, from each of 13 cartridges of 12 patients (samples 34 and 38 were independent samples obtained from 1 patient). Of these 26 CTCs, 25 (96.2%) displayed CNAs typical for advanced breast carcinoma (Fig. 3A; also see online Supplemental Fig. 7), e.g., gains at chromosomes 1q, 8q, 11q, 16p,17q, 19, and 20q and deletions at chromosomes 8p, 11q, 16q, and 18q. One CTC showed an almost balanced profile, similar to those obtained from [CD45.sup.pos] cells (Fig. 3A). No significant differences in terms of quality and quantity of aberrations were observed in the genomic profiles of cells isolated from samples with [greater than or equal to] 10 and >10 CS-CTCs (Fig. 3, B-D). Interestingly, in our CTC collective, aCGH analysis revealed a frequent amplification (12/26 CTCs, 46.2%) of the oncogenic CCND1 gene locus (Fig. 4A) (18, 19). Using the same 26 WGA products, we confirmed the gene amplification by performing a gene-specific genomic quantitative PCR (qPCR) assay in 11 of the 12 cases that displayed CCND1 amplification (Fig. 4B). Furthermore, we used the same 26 WGA products to screen for mutations in exons 1, 9, and 20 of the PIK3CA gene and exons 5,7, and 8 of the TP53 gene, frequent targets of somatic mutations in breast cancer (20). No mutations were found in the different exons of the TP53 gene in any of the cells. In exon 20 of the PIK3CA gene, we found mutations in the nucleotide position c.3140 in 2 of the 12 analyzed patients (16.7%): 1 homozygous mutation (c.3140A>G) in both CTCs of sample 33 and 1 heterozygous mutation (c.3140A>T) in 1 CTC of sample 23 with several CNAs (cell 23-1) (see online Supplemental Fig. 8). In the second cell of sample 23 (cell 23-2), which displayed an almost balanced aCGH profile, we did not detect any mutation in the exon 20 PIK3CA.
CTCs detected by CellSearch have a strong prognostic impact in metastatic breast cancer patients (12). Besides their enumeration, CTCs potentially provide access to the molecular characteristics of systemic cancer, and this information can be of value for diagnosis and therapy. In a very restricted way, further characterization of CTCs is already possible with the CellSearch system via the optional additional fluorescence channel, which allows assessment of 1 additional phenotypic marker, e.g., ERBB2 or EGFR expression (21, 22). However, a more comprehensive molecular-genetic characterization is hampered because CellSearch was designed as a closed system and, more importantly, it does not provide pure CTCs. Instead, the cartridges contain a high background of up to 8000 hematopoietic cells that is not proportional to the number of CTCs (Fig. 2A). Consequently, after the release of the cellular content from a CellSearch cartridge, the rare CTCs must be further purified, which is technically challenging. Here, we show that flow sorting provides a fast, automatable, and effective solution for isolation and subsequent analysis of individual CTCs compared with other techniques also described, such as micromanipulation (23). One other major advantage over microscope-based micromanipulation is that the standard fluorescence staining of the CellSearch system (PE-CK, APC-CD45, and DAPI) can be readily used for flow sorting. With one standardized flow-sorting protocol, we analyzed 90% of the clinical samples. Because we focused on the isolation and analysis of single CTCs, our protocol, which was designed for low SSCwidth values (Fig. 1), was not applicable to 1 sample with a particularly high number of aggregates (sample 25). Because CTC aggregates are otherwise very interesting for further molecular analyses, aggregates should be successfully sorted without this discrimination criterion (low SSC-width).
The settings of our flow-sorting protocol could even successfully detect an FS-CTC in a sample that contained only 1 CS-CTC. Notably, and despite the considerable technical differences between CellSearch and flow cytometry, the number of CTCs isolated with our protocol showed a very good correlation with the number of expected CTCs as originally determined by the CellSearch system. Only 2 of the 10 tested negative cartridges showed very low numbers of presumably false-positive FC-CTCs, which is surprising consider ing the lack of morphological control in flow cytometry. This low number of presumably false-positive FCCTCs is in concordance with the low number of nonaberrant CTCs (3.8%) observed upon aCGH.
During prolonged storage times of analyzed samples on the CellSearch cartridges, dye fluorescence intensities (DAPI, PE, APC) would be expected to decrease, which could substantially impact efficient identification of CTCs by flow cytometry. Although we did not perform a systematic analysis of this issue, we recommend an immediate further processing to avoid prolonged storage times, because we observed a weaker correlation between CS-CTC and FC-CTC numbers in samples stored for longer time periods (see online Supplemental Fig. 4B).
A different flow-sorting protocol for isolation of CellSearch CTCs was very recently described by Swennenhuis et al (15). For 10 tested clinical CellSearch samples of patients with lung cancer, the authors reported a markedly lower CTC detection rate (mean efficiency, 43% vs 102%; median efficiency, 41% vs 83%). This divergence in efficiency can be explained by several important differences between the 2 studies: (a) the FACS (fluorescence-activated cell sorting) instruments, (b) the gate settings, (c) the protocols used for transferring the cells from the cartridge without cell loss, and (d) potential differences in CK and CD45 expression on CTCs of breast and lung cancer, respectively.
Because we tested our approach in patients with metastatic breast cancer mainly with [greater than or equal to] 5 CTCs in 7.5 mL peripheral blood, we anticipated all marker-positive cells to be genetically highly aberrant. We therefore checked the success rate of our work flow using aCGH (16) to interrogate CNAs in flow-sorted single CTCs. We successfully amplified 72.9% of the sorted cells via WGA, and 65.1% met the quality criteria (16) required for high-resolution aCGH analyses (see online Supplemental Fig. 5, A and B). Interestingly, Peeters et al. (24) reported a similar WGA success rate, also using an adapter-linker PCR for spiked cancer cells isolated with the DEParray after CellSearch analysis.
Because longer cell fixation times and/or prolonged storage of cells might cause DNA fragmentation, which could be critical for WGAs based on digestion with a restriction enzyme and ligation of adapters (25, 26), we checked whether we could detect quality differences in the WGA products. Our results obtained from the 192 cells analyzed did not reveal any significant decrease of WGA quality on cells fixed and/or stored for longer times (see online Supplemental Fig. 5, C-E), possibly because of the special characteristics of the Veridex proprietary fixative reagent. However, if longer storage times become necessary, a systematic analysis to determine the longest possible storage period should be done.
The subsequent genetic characterization performed here proved that 96.2% of the sorted individual CTCs were in fact cancer cells. The genomic profiles obtained from CTCs show several aberrations typically observed in metastatic breast cancers (27). Nevertheless, for 1 single marker-positive CTC, no significant chromosomal aberrations were observed and this cell also lacked the PIK3CA c.3140A>T mutation on exon 20 found in another genetically aberrant single cell from the same patient. A similar observation in terms of balanced CGH profiles was made when micromanipulation was used to isolate CTCs with a [CK.sup.pos]/ [CD45.sup.neg]/[DAPI.sup.pos] phenotype, indicating that it is not a failure rate exclusive of the flow-sorting approach. Also, it is not clear at this point whether such balanced cells with a CTC phenotype are cancer cells. But according to our data, such cells are very rare in metastatic breast cancer. This is in contrast to the nonmetastatic situation in breast cancer, in which balanced profiles were observed in around 50% of CTCs (4) or disseminated tumor cells (DTCs) isolated from bone marrow (28). Notably, both of the previous studies used low-resolution metaphase-based CGH and also a single CK staining. Whether this genotype pattern can be observed in [CK.sup.pos]/[CD45.sup.neg]/[DAPI.sup.pos] CTCs of nonmetastatic breast cancer patients when array CGH is used needs to be determined. However, our genomic CTC data are generally in concordance with the genomic profiles of DTCs from bone marrow in the metastatic situation. Interestingly, in the CTCs investigated here we observed a high degree of genomic similarities also observed among DTCs in patients with metastasized breast cancer (29). On the other hand, differences in CNAs between individual CTCs were clearly visible in all investigated cases. The power of our flow-sorting work flow for high-resolution profiling was exemplified by the direct identification of CCND1 as a frequently amplified gene in our study cohort. Cyclin D1, coded by the CCND1 gene, is a protein involved in cell cycle regulation, and amplification of its locus is also a long-recognized oncogenic factor in breast cancer (18, 19). Using a novel qPCR assay for CCND1, we validated the precision of our array CGH approach. The reliability of this novel qPCR assay will have to be confirmed in a larger collective of cells/samples, but the results obtained so far suggest that it might have great utility for routine molecular diagnostics.
In summary, we describe a protocol that can be used for the systematic purification and subsequent genomic analysis of individual CTCs captured by CellSearch in clinical samples. The data obtained with our new work flow gave a first glimpse on the genomic makeup and heterogeneity of individual CTCs at high resolution in metastasized breast cancer patients. We believe that our approach will enhance and facilitate further molecular characterization of the rare CTCs, thus providing deeper insight into their biology and enabling their use as liquid biopsy samples.
Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.
Authors' Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest:
Employment or Leadership: None declared.
Consultant or Advisory Role: None declared.
Stock Ownership: None declared.
Honoraria: None declared.
Research Funding: N.H. Stoecklein, Deutsche Krebshilfe e.v. (grant no. 109600) and Deutsche Forschungsgemeinschaft (grantno. STO464/2-2). Expert Testimony: None declared.
Patents: C. Klein, patent number PCT/EP99/06912.
Role of Sponsor: No sponsor was declared.
(1.) Devriese LA, Voest EE, Beijnen JH, Schellens JH. Circulating tumor cells as pharmacodynamic biomarker in early clinical oncological trials. Cancer Treat Rev 2011; 37:579-89.
(2.) Kaiser J. Medicine. Cancer's circulation problem. Science 2010; 327:1072-4.
(3.) van de Stolpe A, Pantel K, Sleijfer S, Terstappen LW, den Toonder JM. Circulating tumor cell isolation and diagnostics: toward routine clinical use. Cancer Res 2011; 71:5955-60.
(4.) Fischer JC, Niederacher D, Topp SA, Honisch E, Schumacher S, Schmitz N, et al. Diagnostic leukapheresis enables reliable detection of circulating tumor cells of nonmetastatic cancer patients. Proc Natl Acad SciUSA 2013; 110:16580-5.
(5.) Campbell PJ, Yachida S, Mudie LJ, Stephens PJ, Pleasance ED, Stebbings LA, et al. The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature 2010; 467:1109-13.
(6.) Goranova TE, Ohue M, Shimoharu Y, Kato K. Dynamics of cancer cell subpopulations in primary and metastatic colorectal tumors. Clin Exp Metastasis 2011; 28:427-35.
(7.) Klein CA. Selection and adaptation during metastatic cancer progression. Nature 2013; 501:36572.
(8.) Vermaat JS, Nijman IJ, Koudijs MJ, Gerritse FL, Scherer SJ, Mokry M, et al. Primary colorectal cancers and their subsequent hepatic metastases are genetically different: implications for selection of patients for targeted treatment. Clin Cancer Res 2012; 18:688-99.
(9.) Stoecklein NH, Klein CA. Genetic disparity between primary tumours, disseminated tumour cells, and manifest metastasis. Int J Cancer 2010; 126:589-98.
(10.) Racila E, Euhus D, Weiss AJ, Rao C, McConnell J, Terstappen LW, Uhr JW. Detection and characterization of carcinoma cells in the blood. Proc Natl Acad SciUSA 1998; 95:4589-94.
(11.) Allard WJ, Matera J, Miller MC, Repollet M, Connelly MC, Rao C, et al. Tumor cells circulate in the peripheral blood of all major carcinomas but not in healthy subjects or patients with nonmalignant diseases. Clin Cancer Res 2004; 10:6897-904.
(12.) Cristofanilli M, Budd GT, Ellis MJ, Stopeck A, Matera J, Miller MC, et al. Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med 2004; 351:781-91.
(13.) Baccelli I, Schneeweiss A, Riethdorf S, Stenzinger A, Schillert A, Vogel V, et al. Identification of a population of blood circulating tumor cells from breast cancer patients that initiates metastasis in a xenograft assay. Nat Biotechnol 2013; 31:53944.
(14.) Lianidou ES, Mavroudis D, Georgoulias V. Clinical challenges in the molecular characterization of circulating tumour cells in breast cancer. Br J Cancer 2013; 108:2426-32.
(15.) Swennenhuis JF, Reumers J, Thys K, Aerssens J, Terstappen LW. Efficiency of whole genome amplification of single circulating tumor cells enriched by CellSearch and sorted by FACs. Genome Med 2013; 5:106.
(16.) Mohlendick B, Bartenhagen C, Behrens B, Honisch E, Raba K, Knoefel WT, Stoecklein NH. A robust method to analyze copy number alterations of less than 100 kb in single cells using oligonucleotide array CGH. PLoS One 2013; 8: e67031.
(17.) Klein CA, Schmidt-Kittler O, Schardt JA, Pantel K, Speicher MR, Riethmuller G. Comparative genomic hybridization, loss of heterozygosity, and DNA sequence analysis of single cells. Proc Natl Acad SciUSA 1999; 96:4494-9.
(18.) Gillett C, Fantl V, Smith R, Fisher C, Bartek J, Dickson C, et al. Amplification and overexpression of cyclin D1 in breast cancer detected by immunohistochemical staining. Cancer Res 1994; 54:1812-7.
(19.) Buckley MF, Sweeney KJ, Hamilton JA, Sini RL, Manning DL, Nicholson RI, et al. Expression and amplification of cyclin genes in human breast cancer. Oncogene 1993; 8:2127-33.
(20.) Bamford S, Dawson E, Forbes S, Clements J, Pettett R, Dogan A, et al. The COSMIC (Catalogue of Somatic Mutations in Cancer) database and website. Br J Cancer 2004; 91:355-8.
(21.) Gasch C, Bauernhofer T, Pichler M, LangerFreitag S, Reeh M, Seifert AM, et al. Heterogeneity of epidermal growth factor receptor status and mutations of KRAS/PIK3CA in circulating tu mor cells of patients with colorectal cancer. Clin Chem 2013; 59:252-60.
(22.) Riethdorf S, Muller V, Zhang L, Rau T, Loibl S, Komor M, et al. Detection and HE[R.sup.2] expression of circulating tumor cells: prospective monitoring in breast cancer patients treated in the neoadjuvant GeparQuattro trial. Clin Cancer Res 2010; 16:2634-45.
(23.) Heitzer E, Auer M, Gasch C, Pichler M, Ulz P, Hoffmann EM, et al. Complex tumor genomes inferred from single circulating tumor cells by array-CGH and next-generation sequencing. Cancer Res 2013; 73:2965-75.
(24.) Peeters DJ, De Laere B, Van den Eynden GG, Van Laere SJ, Rothe F, Ignatiadis M, et al. Semiautomated isolation and molecular characterisation of single or highly purified tumour cells from CellSearch enriched blood samples using dielectrophoretic cell sorting. Br J Cancer 2013; 108:135867.
(25.) Stoecklein NH, Erbersdobler A, Schmidt-Kittler O, Diebold J, Schardt JA, Izbicki JR, Klein CA. SCOMP is superior to degenerated oligonucleotide primedpolymerase chain reaction for global amplification of minute amounts of DNA from microdissected archival tissue samples. Am J Pathol 2002; 161: 43-51.
(26.) van Beers EH, Joosse SA, Ligtenberg MJ, Fles R, Hogervorst FB, Verhoef S, Nederlof PM. A multiplex PCR predictor for aCGH success of FFPE samples. Br J Cancer 2006; 94:333-7.
(27.) Baudis M, Cleary ML. Progenetix.net: an online repository for molecular cytogenetic aberration data. Bioinformatics 2001; 17:1228-9.
(28.) Schmidt-Kittler O, Ragg T, Daskalakis A, Granzow M, Ahr A, Blankenstein TJ, et al. From latent disseminated cells to overt metastasis: genetic analysis of systemic breast cancer progression. Proc Natl Acad SciUSA 2003; 100:7737-42.
(29.) Klein CA, Blankenstein TJ, Schmidt-Kittler O, Petronio M, Polzer B, Stoecklein NH, Riethmuller G. Genetic heterogeneity of single disseminated tumour cells in minimal residual cancer. Lancet 2002; 360:683-9.
Received February 21, 2014; accepted July 16, 2014.
Previously published online at DOI: 10.1373/clinchem.2014.222331
* Address correspondence to this author at: Department of General, Visceral, and Pediatric Surgery, University Hospital of the Heinrich-Heine University Dusseldorf, Moorenstrasse 5, 40225 Dusseldorf, Germany. Fax +49-211-8119205; e-mail firstname.lastname@example.org.
Rui P.L. Neves,  Katharina Raba,  Oliver Schmidt,  Ellen Honisch,  Franziska Meier-Stiegen,  Bianca Behrens,  Birte Mohlendick,  Tanja Fehm,  Hans Neubauer,  Christoph A. Klein, [5,6] Bernhard Polzer,  Christoph Sproll,  Johannes C. Fischer,  Dieter Niederacher,  and Nikolas H. Stoecklein  *
 Department of General, Visceral and Pediatric Surgery,  Institute for Trans plantation Diagnostics and Cell Therapeutics,  Department of Obstetrics and Gynecology, and  Department of Maxillo- and Facial Plastic Surgery, University Hospital and Medical Faculty of the Heinrich-Heine University Dusseldorf, Dijssel dorf, Germany;  Chair of Experimental Medicine and Therapy Research, University of Regensburg, Regensburg, Germany;  Fraunhofer Institute for Toxicology and Experimental Medicine, Regensburg, Germany.
 Nonstandard abbreviations: CTC, circulating tumor cell; CK, cytokeratin; DAPI, 4',6-diamidino-2-phenylindole; CNA, copy number alteration; PE, phycoerythrin; APC, allophycocyanin; SSC-width, width of the side-scatter signals; WGA, whole genome amplification; aCGH, array-based comparative genome hybridization; FC-CTC, flow cytometry-detected CTC; CS-CTC, CellSearch-detected CTC; qPCR, quantitative PCR; DTC, disseminated tumor cell.
 Human genes: CCND1, cyclin D1; PK3CA, phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha; TP53, tumor protein p53.
Fig. 4. Validation of the aCGH results obtained in CTCs and CD45pos cells for the CCND1 gene locus by an independent real-time PCR (RT-PCR)-based assay. (A), Scatter plots (left panels) and histograms showing aCGH profiles of CTCs with different degrees of gains in the CCND1 gene locus on 11q13.3. The position of the CCND1 gene locus is indicated by the blue horizontal solid line. Alterations are represented in each plot by vertical solid lines. The upper panel shows profiles from cells displaying log2 ratio values of >1 for CCND1. The lower left panel shows profiles of cells with log2 ratio values of <1 and the lower right panel shows profiles of cells without detectable alteration in the CCND1 gene copy number by aCGH. (B), Table comparing the log2 ratio values obtained by aCGH for the CCND1 genomic region with the amplification probability score of CCND1 determined by RT-PCR. Values of the log2 ratio of >1, corresponding to gains of the CCND1 locus superior to 2-fold, are highlighted in red; and probability scores above 0.95, defined as the threshold to be considered amplified, are highlighted in orange. ND, not done. Cell ID Log2 ratio (by aCGH) Amplification probability score (by RT-PCR) 17-01 2.231545 0.994197 17-02 2.590163 0.998556 18-01 -0.177027 0.632182 18-02 -0.186239 0.589516 19-01 0.237995 ND 19-02 -0.023603 0.783672 21-01 1.631925 ND 21-02 1.999966 0.991581 23-01 0.561137 0.477211 23-02 -0.012169 0.290288 24-01 -0.297823 0.578751 24-02 -0.196478 0.437893 30-01 1.501794 0.97268 30-02 1.860950 0.972751 33-01 1.289223 0.937081 33-02 1.564706 0.957632 34-01 1.744334 0.961007 34-02 1.312342 0.974063 36-01 0.554560 0.832779 36-02 0.478241 0.500749 37-01 0.263901 0.451994 37-02 0.129880 0.32846 38-01 1.877326 0.979064 38-02 1.319337 0.965135 39-01 -0.752864 0.719935 39-02 -0.513475 0.874703 CD45-1 -2.518728 ND CD45-2 -0.323278 0.161337 CD45-5 ND 0.234243 CD45-6 ND 0.269557 CD45-8 ND 0.289184 CD45-9 ND 0.227410
|Printer friendly Cite/link Email Feedback|
|Title Annotation:||Molecular Diagnostics and Genetics|
|Author:||Neves, Rui P.L.; Raba, Katharina; Schmidt, Oliver; Honisch, Ellen; Meier-Stiegen, Franziska; Behrens|
|Date:||Oct 1, 2014|
|Previous Article:||ERCC1-positive circulating tumor cells in the blood of ovarian cancer patients as a predictive biomarker for platinum resistance.|
|Next Article:||Noninvasive detection of a balanced fetal translocation from maternal plasma.|