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Accession of Tumor Heterogeneity by Multiplex Transcriptome Profiling of Single Circulating Tumor Cells.

Detection and molecular characterization of circulating tumor cells (CTCs) [7] could provide valuable information for the clinical management of cancer patients because CTCs represent a real-time snapshot of the current tumor burden (1). Mutational analysis of CTCs might help to investigate alterations in genes involved in the response to targeted therapies (2, 3), which supports the use of CTCs as a "liquid biopsy."

However, to identify signaling pathways and splice variants relevant to cancer biology and therapies, transcriptome analysis of viable cancer cells is essential (4). Quantitative real-time PCR (qPCR) targeting keratin19 mRNA (KRT19) [8] has been widely used to detect and monitor CTCs in cancer patients (5, 6). However, single marker analysis is not sufficient to understand the broad and complex nature of tumor biology. So far, multiplex qPCR targeting a few transcripts of interest has been applied (7, 8). However, most reports have relied on CTC-enriched fractions that still contain substantial numbers of contaminating leukocytes. Moreover, analyses of pooled CTC fractions miss the information on intrapatient heterogeneity of CTC populations.

Isolation of single CTCs and subsequent RNA analysis is challenging but the information obtained is much more extensive with regard to tumor cell heterogeneity in individual patients. A dual colorimetric RNA in situ hybridization assay was recently used to examine individual CTCs and tumor cell clusters for the expression of epithelial transcripts and mesenchymal markers (9). Heterogeneous RNA expression of single CTCs has also been demonstrated in patients with prostate cancer using the CTC-iChip for CTC isolation that is not commercially available (10).

In this study we performed single cell analysis after EpCAM-dependent (CellSearch[R], Janssen Diagnostics) or size-based (Parsortix[TM], ANGLE Plc) enrichment. We established a workflow to screen up to 84 genes specific for different tumor entities, genes involved in DNA repair, epithelial-mesenchymal transition (EMT), tumor suppressor genes, therapy-related targets, stem cell-related markers, transcripts of activated signaling pathways, or genes mediating cross talk between tumors and immune cells.

Materials and Methods


All cells [MDA-MB-468 (MDA-468), MDA-MB-231 (MDA-231), SKBR-3 and MCF7] were obtained from the American Type Culture Collection proposed by the vendor. Cells were harvested when they reached a confluence of approximately 80%.


Total RNA (500 ng) from MDA-468, MDA-231, SKBR3, and MCF7 cells was isolated using the RNeasy Micro Kit (QIAGEN) according to the protocol of the vendor. To generate first-strand cDNA, 0.5 [micro]g of total RNA was reverse transcribed using the First-Strand cDNA Synthesis Kit (Thermo Scientific). Qualitative PCR was performed in a peqStar Thermocycler (Peqlab) under the following conditions: after 10 min denaturation at 95[degrees]C, 35 cycles were carried out by denaturation at 94[degrees]C for 30 s, annealing at 60[degrees]C for 30 s, and elongation for 30 s at 72[degrees]C. After a final elongation step at 72[degrees]C for 10 min the samples were stored at 4[degrees]C. Ethidium bromide stained-agarose gel electrophoresis was used to confirm the formation of the expected PCR products. (See Fig. 1 in the Data Supplement that accompanies the online version of this article at http://www.

Primers were designed using the Primer3 software [epithelial cell adhesion molecule (EpCAM): forward 5'-GCTGGTGTGTGAACACTGCT-3' and reverse 5'-ACGCGTTGTGATCTCCTTCT-3'; epidermal growth factor receptor (EGFR): forward 5'-CAG CGCTACCTTGTCATTCA-3' and reverse 5'-TG CACTCAGAGAGCTCAGGA-3'; erb-b2 receptor tyrosine kinase 2 (ERBB2): forward 5'-TGCCTGT CCCTACAACTACC-3' and reverse 5'-CAGAC CATAGCACACTCGG; KRT19: forward 5'-CAG CGCTACCTTGTCATTCA-3' and reverse 5'-GAT CTGCATCTCCAGGTCGG-3'; and [beta]-Actin (ACTB): forward 5'-CCAACCGCGAGAAGATGA-3' and reverse 5'-CCAGAGGCGTACAGGGATAG-3'].


CellSearch Epithelial Cell Profile Kit. The CellSearch Epithelial Cell Profile Kit (Janssen Diagnostics) contains a ferrofluid-based capture reagent coated to antibodies targeting the EpCAM antigen. EDTA blood samples were processed without fixation or permeabilization.

Parsortix System. The Parsortix (ANGLE Plc) is a marker-independent system for tumor cell enrichment capturing intact and viable tumor cells based on their size ([greater than or equal to] 10 [micro]m) (11).


To distinguish tumor cells from the leukocytes, an epithelial-cell specific staining protocol was established targeting EpCAM and/or pan-keratins (EpCAM and pan-keratins for CTCs isolated by Parsortix and pankeratins for tumor cells isolated by CellSearch). Briefly, enriched tumor cells were eluted into Eppendorf tubes, stained, and isolated by micromanipulation for single cell analysis.

The EpCAM antibody (Novocastra) was diluted in 10% AB-Serum/PBS (1:100) and incubated for 45 min on a rotor. Next, the sample was washed and a fluorescently labeled secondary antibody (Thermo Fisher) was added (1:150) for another 30 min in the dark. For subsequent pan-keratin staining, samples were washed and the CellSearch monoclonal pan-keratin antibody cocktail (included in the CellSearch Circulating Epithelial Cell Kit) was added (1:10; incubation time 20 min in the dark). Stained cells were washed and carefully transferred onto a glass slide for single cell isolation by micromanipulation (2).


We tested the NucleoSpin RNA II Kit[R] (MachereyNagel), the Picopure RNA Isolation Kit[R] (Life Technologies), the MessageBooster Kit[TM] (Epicentre), and the CelluLyser[TM] Micro Lysis and cDNA Synthesis Kit (TATAA Biocenter) for single cell analysis. Kits were applied according to the instructions of the vendors.


TATAA PreAMP and qPCR. To analyze a multimarker profile of single cells, cells were lysed using the CelluLyser Micro Lysis and cDNA Synthesis Kit. cDNA was produced using the GrandScript cDNA Synthesis Kit and was subsequently preamplified with the CTC Grand-Performance assays using the PreAmp GrandMaster[R] Mix (TATAA Biocenter). PCR was done according to the manufacturer's instructions using the Bio-Rad CFX96 cycler (Bio-Rad).

[RT.sup.2] PreAMP and [RT.sup.2] Profiler PCR Array. The Message Booster Kit[TM] was applied in combination with the [RT.sup.2] PreAMP Kit (QIAGEN) and different [RT.sup.2] Profiler PCR Arrays (Human Breast Cancer, Prostate Cancer, or EMT PCR Array or Cancer Inflammation & Immunity Cross talk PCR Array, QIAGEN). PCR was done according to the manufacturer's instructions using the Bio-Rad CFX96 cycler (Bio-Rad).


Single cell qPCR data were generated as illustrated in Fig. 1. Quantification cycle (Cq) values above 35 were treated as "off-scale data." All missing data points were also treated as "off-scale data" and replaced with a Cq value of 37. This was 2 cycles above the cutoff, which was considered giving a balanced weight to the negative observation of transcripts in individual cells (12). The Cq values were converted to relative quantities and transformed to log base 2 scale. The expression data were not normalized to any endogenous markers because of the stochasticity of single cell expression (13). The measured cDNA concentrations, which are proportional to mRNA concentrations (14), are thus expressed per cell. To display all data together, principal component analysis (PCA) and hierarchical clustering were used. Each method used a different mathematical approach to separate samples into groups. PCA reduced multidimensional data into 2-dimensional charts with small loss of information. Each point in the PCA graph contained information about the expression of all the genes. For the hierarchical clustering and PCA analyses, data were mean centered to reduce the influence of the variable overall expression level of the genes (15).


Analysis of human samples was carried out in accordance with the guidelines for experimentation with humans by the Chambers of Physicians of the State of Hamburg ("Hamburger Arztekammer"). Written informed consent was obtained from all patients and the experiments conformed to the principles set out in the World Medical Association (WMA) Declaration of Helsinki and the Department of Health and Human Services Belmont Report. Breast cancer samples were obtained from the Department of Gynecology, University Medical Center Hamburg-Eppendorf, Germany. Prostate cancer samples were collected at the Practice Hematology and Oncology, Hamburg, Germany. All blood samples were collected into EDTA tubes (7.5 mL) and processed within 4 h after blood withdrawal.



Four different kits were tested for single cell RNA analysis: the NucleoSpin RNA II Kit[R] (Macherey-Nagel); the PicoPure RNA Isolation Kit[R] (Life Technologies); the Message-Booster Kit (Epicentre); and the CelluLyser Micro Lysis and cDNA Synthesis Kit (TATAA Biocenter). To verify the kits, 1, 5,10, and 50 MDA-468 or the SKBR3 cells were directly spiked into lysis buffer followed by qualitative PCR amplifying EpCAM, EGFR, ERBB2, KRT19, and ACTB transcripts. Only the MessageBooster Kit and the CelluLyser Micro Lysis and cDNA Synthesis Kit produced consistent RNA profiles of the tested cell lines down to single cell level (see online Supplemental Fig. 1). Hence, these kits were used for further studies.

We had to establish a specific staining protocol that was compatible with downstream single cell profiling to distinguish CTCs from leukocytes in clinical samples. In our study, EpCAM and pan-keratins were used as target proteins for tumor cell identification (Fig. 2A and online Supplemental Fig. 2A).

Having established a reliable staining protocol, single breast (MDA-468 and SKBR3; n = 3) or prostate cancer cells (LnCap and PC3; n = 3) were spiked into 7.5 mL EDTA blood of healthy donors (n = 2) and processed by CellSearch or Parsortix. Next, tumor cells or remaining leukocytes were isolated and analyzed using either the [RT.sup.2] PreAmp and Profiler Human Prostate Cancer PCR Array (QIAGEN) or the TATAA PreAmp GrandMaster[R] Mix and TATAA Probe GrandMaster[R] Mix (TATAA Biocenter) (Fig. 1, blue arrow and green arrow).

Tumor cells could clearly be discriminated from leukocytes based on their expression profiles (Fig. 2, B and C). All details of the measured Cq values are listed in online Supplemental Tables 1 and 2.


For clinical samples, the criteria for a cell to be classified as CTC were: intact morphology (bright field microscopy), nucleus larger than 4 [micro]m in diameter, and positive staining for EpCAM and/or pan-keratins (see online Supplemental Fig. 2A). The KRT19 transcripts have been widely used to identify and study CTCs in cancer patients (5, 6). Hence, we decided to use this marker as an additional "CTC indicator" by qualitative PCR before in-depth multimarker qPCR analysis. In total, 55 single CTCs from 5 different donors were analyzed in our study. However, reliable multimarker analysis was only possible when intense signals for KRT19 were observed (n = 39). Thus, we would recommend performing additional "qualitative PCR" tests as quality control before single cell multimarker analyses in future studies (see online Supplemental Fig. 2B).


In-depth analysis of CTCs from a patient with breast cancer (enriched by CellSearch) revealed that RNA expression profiles of CTCs could clearly be discriminated from leukocytes (Fig. 3, A and B, and Fig. 1, green arrows). Genes such as EpCAM, cadherin 1_1 (CDH1_1), secretoglobin family 2A member 1 (SCGB2A1)/MAM, estrogen receptor 1 (ESR1), epithelial membrane protein 2 (EMP2), and KRT19 were exclusively expressed in CTCs, while protein tyrosine phosphatase, receptor type C (PTPRC; formerly called CD45) and vimentin (VIM) were predominant in the leukocytes. Although CTCs could clearly be distinguished from the leukocytes, 2 CTCs were positive for both, the epithelial transcripts (EpCAMand KRT19) and the leukocyte/mesenchymal markers (CD45 and VIM).

Principle component analysis (PCA) of the expression profiles--measured for the individual CTCs from this patient --indicated that there might be 2 clusters, suggesting 2 CTC subtypes (Fig. 3, C-E). One CTC, which had substantially lower overall transcript levels than the other cells, separated from the 2 CTC clusters and from the leukocytes, indicating it was a deviant possibly due to damage during processing (Fig. 3, A-D). Genes significantly differentially expressed between the 2 CTC subgroups are shown in Fig. 3E. CTCs in subgroup 1 displayed increased transcription of genes involved in cell growth and tumor proliferation [phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) and mechanistic target of rapamycin (MTOR)]. These cells furthermore showed low expression levels of the stem cell marker CD44 molecule (Indian blood group; CD44) and high expression levels for Wolf-Hirschhorn syndrome candidate 1 (Whsc1), a gene encoding for histone methyltransferase NSD2 known to play an important role in cancer proliferation, survival, and tumor growth (16). Detailed expression profiles (Cq values) of all CTCs from this patient are summarized in online Supplemental Table 3.

Thirteen additional CTCs from another breast cancer patient, enriched in parallel by Parsortix (n = 7 CTCs) and CellSearch (n = 6 CTCs), could be processed by targeting EMT-specific transcripts (Fig. 4, A-C, and Fig. 1, red arrows). Again, single cell CTC subpopulations could be identified depending on the expression profile but independently of the enrichment strategy (Fig. 4B). The most important genes clustering CTCs in the subgroups were cadherin 2 (CDH2), collagen type I alpha 2 chain (COL1A2), collagen type V alpha 2 chain (COL5A2), fibronectin 1 (FN1), microtubule associated protein 1B (MAP1B), matrix metallopeptidase 3 (MMP3), SRY-box 10 (S0X10), secreted phosphoprotein 1 (SPP1), zinc finger E-box binding homeobox 1 (ZEB1), and zinc finger E-box binding homeobox 2 (ZEB2; Fig. 4C).

CTCs from a patient with prostate cancer (n = 8) (enriched by Parsortix only) could also be clustered into different subgroups based on their expression profiles (Fig. 5, A-C, and Fig. 1, blue arrow). Only genes with transcripts detected in at least 3 of the CTCs were considered for PCA. The most important genes distinguishing CTCs into the subgroups were vascular endothelial growth factor A (VEGFA; a marker that stimulates vasculogenesis and angiogenesis also relevant as therapeutic target) (17), AR (a receptor that plays a key role in the development and progression of PC and is used as therapeutic target) (18), nuclear factor [kappa]B (NF-[kappa]B; a transcription factor known to regulate AR expression and PC growth) (19), NK3 homeobox 1 (NKX3-1; an androgen-regulated transcription factor believed to act as a suppressor of the pathogenic ERG, ETS transcription factor (TMPRSS2-ERG) rearrangement] (20), and transmembrane protease, serine 2 (TMPRSS2; an androgen-regulated protease known to be contributing to regulation of cancer growth) (20) (Fig. 5C). Cq values of all CTCs from this patient are summarized in online Supplemental Table 4.

CTC subgroups (enriched by Parsortix) were also present in an additional sample from another patient with prostate cancer (Fig. 6, A-C, and Fig. 1, gray arrow). CTCs in one of the subgroups showed increased expression levels of RAD51 recombinase (RAD51). Overexpression of this marker has been associated with aggressive prostate cancer (21). CTCs in the other subgroup displayed high expression of markers discussed to be relevant for supporting therapeutic decisions in prostate cancer [androgen receptor V7 (AR-V7) and signal transducer and activator of transcription 3 (STAT3)] (4, 22). Summarized Cq values of CTCs from this patient are listed in online Supplemental Table 5.


Lively debate is ongoing about communication between tumor cells and the cellular mediators of inflammation and immunity (23). Thus, we also decided to analyze the expression of genes involved in cancer inflammation and immune cross talk (Fig. 1, orange arrow). Here, both CellSearch and Parsortix enrichment strategies were applied to 2 blood samples from the same patient. The enrichment method did not bias expression of the reference genes included in the analysis (mean Cq after CellSearch enrichment: 25.31 vs mean Cq after Parsortix: 25.16), demonstrating consistent workflows of the 2 enrichment strategies. In this case, several genes involved in cross talks between immune cells and cancer cells, such as C-C motif chemokine ligand 4 (CCL4), C-X-C motif chemokine ligand 2 (CXCL2), CXCL9, interleukin 15 (IL15), IL1B, or IL8 showed different expression levels between the individual CTCs captured via EpCAM or by size (see online Supplemental Fig. 3 and online Supplemental Table 6). It is notable that tumor cell-derived CCL4 protein, which was one of the identified markers, can promote bone metastasis by interacting with CCR5-expressing nontumor cells (24). Chemokines have an important role in cancer metastasis and might mediate a signaling network that links metastasis formation and chemotherapy resistance. Chemokines, including CXCL2, CXCL8 (IL8), showed substantially higher expression in CTCs enriched with the Parsortix system. In relation to this observation, enhanced IL15 expression has previously been detected in colorectal cancer cells and shown to play an important role in cell proliferation, invasion, and metastasis formation (25). In our study, IL15 transcript levels showed large variations across CTCs independently of enrichment strategy.


We developed optimized workflows for robust enrichment and transcriptomic profiling of single CTCs. Enrichment of CTCs was performed with 2 different platforms: the EpCAM-dependent CellSearch system (approved by the US Food and Drug Administration) and a novel, marker-independent size-based approach (Parsortix) (11). Our workflows allow for the characterization of different CTC single cell subpopulations depending on the markers used for the identification.

Our single cell data showed high detection rates of the targeted transcripts. The GrandPerformance assays are probe based, thus avoiding false positive results due to formation of primer dimers and other aberrant products, while the [RT.sup.2] Profiler assays are SYBR based and generate signal also from aberrant PCR products. This is, however, minimized by careful assay optimization and by rejecting Cq values [greater than or equal to] 35. Expression of reference genes cannot be used for normalization of single cell data due to the underlying burst kinetics (26), but their overall expression level can serve as a quality indicator. CTCs with exceedingly low transcript levels of reference genes are likely to have been damaged during the processing.

We found a minor subpopulation of epithelial marker-positive (e.g., KRT19 and EpCAM) and CD45-positive cells in clinical samples (Fig. 3). These cells were also strongly positive for markers such as twist family bHLH transcription factor (Twist), CD44, ATP binding cassette subfamily C member 1 (ABCC1), or ADAM metallopeptidase domain 17 (ADAM17). We assume that these cells are tumor cells but the role of CD45-positive CTCs is not clear yet (27, 28). Thus, our assay might help to identify the relevance of this specific cell type in future studies.

Focusing on the transcript signatures of CTCs, different subgroups were identified in individual patients (independently of the enrichment strategy (see online Supplemental Fig. 4, A-C). These subgroups were characterized by divergent expression of, e.g., EMT-associated genes (CDH2, COL1A2, COL5A2, FN1, MAP1B, MMP3, SOX10, SPP1, ZEB1, and ZEB2), DNA repair genes (RAD51) (21) or targets relevant for cancer therapy. For example, EGFR or ERBB2-specific transcripts were frequently present in CTCs (Figs. 4-6). ERBB2 is an effective target in breast cancer and furthermore discussed to play also an important role in PC (29, 30). Here, ERBB2 transcripts were present in PC-derived CTCs (see online Supplemental Fig. 6). Controlling the ERBB2 pathway could be an attractive combinational therapeutic approach together with second-generation antiandrogen agents such as enzalutamide and abiraterone acetate to remove castration-resistant prostate cancer (30). We also show that ERBB2-positive CTCs might express AR-V7(3 out of 7 CTCs in the same patient) (Fig. 6). AR-V7, a constitutively active splice variant of the AR detected in CTC pools, was recently found to drive castration-resistant growth and resistance to enzalutamide in patients with prostate cancer (4). Thus, we demonstrate the differential expression of ERBB2 and/or AR-V7 between subgroups of CTCs in an individual patient with prostate cancer, indicating that unraveling the transcriptome of CTCs might help to monitor the emergence of driver molecules for resistance and thereby unveil patients who would profit from optional therapies, e.g., using galeterone instead of enzalutamide and abiraterone (plus ERBB2 inhibitors).

In addition, transcripts that have been reported to be relevant for metastatic processes (EpCAM, CD44, CD24) or potential drug targets (mTOR, PI3K, VEGFA) (31--33) were also found to be significantly differently expressed among breast cancer-derived CTCs (Fig. 3). Previous reports suggested the existence of tumor stem cells that are characterized by upregulation of CD44 and the downregulation of CD24 (34). However, the role of CD24 remains controversial since both [CD44.sup.high]/[CD24.sup.low] and [CD44.sup.high]/[CD24.sup.high] cells have been shown to initiate tumor growth (35, 36). In the present study, CD44 emerged as an allocation factor between 2 subgroups of CTCs derived from the same breast cancer patient, whereas CD24 was marginally altered between the same subgroups. Interestingly, the [CD44.sup.low]/ [CD24.sup.high] subgroup showed high expression levels of PI3K and MTOR. The PI3K/AKT/mTOR signaling cascade is one of the most important intracellular pathways in cancer cells (37). Numerous efforts have been made to develop targeted therapies inhibiting this cascade in hormone receptor positive, ERBB2-negative breast cancer in combination with hormonal therapy (38).

Emerging data suggests that the heterogeneity of cancer thought to be causative for the functional plasticity necessary for tumor cells to evade therapeutic approaches, as well as prosperous adoption to foreign microenvironments within the metastatic niche, is driven by transcriptional changes (39). Understanding this heterogeneity with the presented workflow allows for a better understanding of the mechanisms involved in cancer progression. RNA analysis of CTCs offers the opportunity to stratify patients with metastatic disease into phenotypical subgroups according to their expression profiles, providing an important framework for therapeutic decisions based on a simple blood test ("liquid biopsy") (1, 28). Such information is expected to be most valuable in the selection of personalized therapies, since the most active pathways are critical for the particular CTCs present and may be their Achilles heel if suitable drugs targeting that pathway are available. Particularly when combined with mutational profiling, which reveals any critical mutations affecting the sensitivity for targeted therapy (2, 3), it might support decision-making for individualized treatments.

In summary, we established reliable workflows to study multimarker profiles of single CTCs by low cost qPCR approaches. In principle, these workflows can be combined with any CTC enrichment system that enables isolation of high quality RNA. Thus, these workflows could become a valuable new tool in biomedical research. Although the number of patients/CTCs studied in this proof-of-principle study was small, multivariate analysis of expression profiles indicated that subgroups of CTCs with different phenotypes are present within each CTC-positive patient. Intrapatient heterogeneity of CTCs might contribute to resistance to therapy. This hypothesis has to be tested in future clinical studies.

Author Contributions: AH 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: M. Kubista, TATAA Biocenter. Consultant or Advisory Role: None declared.

Stock Ownership: V. Novosadova, TATAA Biocenter; M. Kubista, TATAA Biocenter.

Honoraria: None declared.

Research Funding: Grant P303-16-10214S, Grant Agency of the Czech Republic, project BIOCEV CZ.1.05/1.1.00/02.0109 provided by ERDF and MEYS, and project BIOCEV-FAR LQ1604 NPU II provided by MEYS. T.M. Gorges, S. Riethdorf, and K. Pantel, City of Hamburg, Landesexzellenz initiative Hamburg (LEXI 2012; Tumor-targeting via cell surface molecules essential in cancer progression and dissemination), and ERC Advanced Investigator Grant DISSECT, TRANSCAN ERA-Network:Grant CTC-SCAN; T. M. Gorges and K. Pantel [+49-(0)40-7410-57893], Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115749, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies.

Expert Testimony: None declared.

Patents: None declared.

Role of Sponsor: The funding organizations played no role in the design of study, choice of enrolled patients, review and interpretation of data, and final approval of manuscript.

Acknowledgments: The authors thank all patients and their families.


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Tobias M. Gorges, [1] * ([dagger]) Andra Kuske, (1[dagger]) Katharina Rock, [1] Oliver Mauermann, [1] Volkmar Muller, [2] Sven Peine, [3] Karl Verpoort, [4] Vendula Novosadova, [5] Mikael Kubista, [5,6] Sabine Riethdorf, [1] and Klaus Pantel [1] *

[1] Department of Tumor Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; [2] Department of Gynecology, University Hospital Hamburg-Eppendorf, Hamburg, Germany; [3] Department of Transfusion Medicine, University Hospital Hamburg-Eppendorf, Hamburg, Germany; [4] Practice for Haematology and Oncology, Hamburg, Germany; [5] Department of Biotechnology, Czech Academy of Sciences, Prague, Czech Republic; [6] TATAA Biocenter, Gothenburg, Sweden.

* Address correspondence to: T.M.G.and K.P. at Department of Tumor Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. Fax: +49-(0)40-7410-55379; e-mail or

([dagger]) T.M. Gorges and A. Kuske contributed equally to this work.

Received May 18, 2016; accepted August 15, 2016.

Previously published online at DOI: 10.1373/clinchem.2016.260299

[7] Nonstandard abbreviations: CTCs, circulating tumor cells; qPCR, quantitative real-time PCR; EMT, epithelial-mesenchymal transition; Cq, quantification cycle; PCA, principal component analysis.

[8] Human genes: KRT19, keratin19; EPCAM, epithelial cell adhesion molecule; EGFR, epidermal growth factor receptor; ERBB2, erb-b2 receptor tyrosine kinase 2; ACTB, [beta]-Actin; CDH1_1, cadherin 1_1; SCGB2A1, secretoglobin family 2A member 1; ESR1, estrogen receptor 1; EMP2, epithelial membrane protein 2; PTPRC, protein tyrosine phosphatase, receptor type C; VIM, vimentin; PIK3CA, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha; MTOR, mechanistic target of rapamycin; CD44, CD44 molecule (Indian blood group); WHSC1, Wolf-Hirschhorn syndrome candidate 1; CDH2, cadherin 2; COL1A2, collagen type I alpha 2 chain; COL5A2, collagen type V alpha 2 chain; FN1, fibronectin 1; MAP1B, microtubule associated protein 1B; MMP3, matrix metallopeptidase 3; SOX10, SRY-box 10; SPP1, secreted phosphoprotein 1; ZEB1, zinc finger E-box binding homeobox 1; ZEB2, zinc finger E-box binding homeobox 2; VEGFA, vascular endothelial growth factor A; NF-[kappa]B, nuclear factor [kappa]B; NKX3-1, NK3 homeobox 1; TMPRSS2-ERG, ERG, ETS transcription factor; TMPRSS2, transmembrane protease, serine 2; RAD51, RAD51 recombinase; AR-V7, androgen receptor V7; STAT3, signal transducer and activator of transcription 3; CCL4, C-C motif chemokine ligand 4; CXCL2, C-X-C motif chemokine ligand 2; IL15, interleukin 15; Twist, twist family bHLH transcription factor; ABCC1, ATP binding cassette subfamily C member 1; ADAM17, ADAM metallopeptidase domain 17.

Caption: Fig. 1. Methodological workflow of single tumor cell RNA profiling.

Caption: Fig. 2. Single cell RNA analysis of EpCAM and/or pan-keratin stained cell culture cells and leukocytes.

Caption: Fig. 3. Molecular signature of CTCs and leukocytes from a breast cancer patient.

Caption: Fig. 4. Molecular signature of CTCs from a breast cancer patient enriched by Parsortix and CellSearch.

Caption: Fig. 5. Molecular signature of CTCs from prostate cancer patient 1.

Caption: Fig. 6. Molecular signature of CTCs from prostate cancer patient 2.
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Title Annotation:Cancer Diagnostics
Author:Gorges, Tobias M.; Kuske, Andra; Rock, Katharina; Mauermann, Oliver; Muller, Volkmar; Peine, Sven; V
Publication:Clinical Chemistry
Date:Nov 1, 2016
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