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Chaperonin containing TCP1 as a marker for identification of circulating tumor cells in blood [1]

['Amanda Cox', 'Burnett School Of Biomedical Science', 'College Of Medicine', 'University Of Central Florida', 'Orlando', 'Florida', 'United States Of America', 'Ana Martini', 'Heba Ghozlan', 'Rebecca Moroose']

Date: 2022-08 

Herein we report the use of Chaperonin-Containing TCP-1 (CCT or TRiC) as a marker to detect circulating tumor cells (CTCs) that are shed from tumors during oncogenesis. Most detection methods used in liquid biopsy approaches for enumeration of CTCs from blood, employ epithelial markers like cytokeratin (CK). However, such markers provide little information on the potential of these shed tumor cells, which are normally short-lived, to seed metastatic sites. To identify a marker that could go beyond enumeration and provide actionable data on CTCs, we evaluated CCT. CCT is a protein-folding complex composed of eight subunits. Previously, we found that expression of the second subunit (CCT2 or CCTβ) inversely correlated with cancer patient survival and was essential for tumorigenesis in mice, driving tumor-promoting processes like proliferation and anchorage-independent growth. In this study, we examined CCT2 expression in cancer compared to normal tissues and found statistically significant increases in tumors. Because not all blood samples from cancer patients contain detectable CTCs, we used the approach of spiking a known number of cancer cells into blood from healthy donors to test a liquid biopsy approach using CCT2 to distinguish rare cancer cells from the large number of non-cancer cells in blood. Using a clinically validated method for capturing CTCs, we evaluated detection of intracellular CCT2 staining for visualization of breast cancer and small cell lung (SCLC) cancer cells. We demonstrated that CCT2 staining could be incorporated into a CTC capture and staining protocol, providing biologically relevant information to improve detection of cancer cells shed in blood. These results were confirmed with a pilot study of blood from SCLC patients. Our studies demonstrate that detection of CCT2 could identify rare cancer cells in blood and has application in liquid biopsy approaches to enhance the use of minimally invasive methods for cancer diagnosis.

Competing interests: We have read the journal’s policy and one of the authors of this manuscript (Dr. Annette Khaled) has the following competing interests: [shareholder in Seva Therapeutics, Inc.] This commercial entity holds a license to use intellectual property developed by the inventor (Dr. Khaled) and provided no funding and had no role in the design, preparation, or submission of this manuscript and did not employ any of the authors. This competing interest does not alter our adherence to PLOS ONE policies on sharing data and materials.

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

In this study, to advance the use of non-invasive liquid biopsy for monitoring cancer patients, we investigated the use of CCT2 as a marker for detection of rare cancer cells in blood. The use of CCT2 for the detection of CTCs is supported by our data showing that CCT2 protein levels are increased in tumor tissues compared to normal tissues and may be a better marker for metastatic tissues than CK. The addition of an anti-CCT2 antibody to the CSS platform resulted in improved CTC image analysis and increased detection of rare breast cancer and SCLC cells spiked into blood, which was confirmed in a pilot study of blood from SCLC patients. The detection of CK - /CCT2 + cancer cells with the CSS platform suggested that the use of CCT2 as a marker could help establish prognostic thresholds for cancers that currently lack validated liquid biopsy methods. Because CCT2 may drive biological processes that are independent of cancer lineage markers and increases with advanced cancer stage, CCT2 could be a novel marker for detection of CTCs that helps enumerate as well as inform on tumor metastatic potential.

The diagnosis of solid tumors is typically reached through tissue biopsy. However, new diagnostic methods that are non-invasive and provide a better representation of the whole tumor would improve patient management and monitoring. In recent years, new methods for ‘liquid biopsy’ have developed but few have yet to gain approval for clinical use by the U.S. Food and Drug Administration (FDA). Liquid biopsy is defined as a non-invasive body fluid collection of tumor information like CTCs or circulating tumor DNA (ctDNA) [ 22 ]. For diagnostic purposes, CTCs are of interest since these are shed from the active edges of tumors into the peripheral circulation and typically have short half-lives of 1.0–2.4 hours [ 23 ]. CTCs, therefore, provide current information about a patient’s tumor status [ 24 , 25 ]. Importantly, CTCs contain the genetic information that could be used to classify patient tumors [ 26 , 27 ]. Given this, the promising diagnostic potential of CTCs remains to be fully exploited. Most CTCs detection methodologies only provide information with the goal of enumeration. The gold standard for clinical circulating tumor cell (CTC) enumeration is the FDA-approved CellSearch ® System (CSS). CSS uses the epithelial markers, epithelial cellular adhesion molecule (EpCAM) and pan-CK 8, 18, and 19, for capture, enrichment, and staining of CTCs. The use of the CSS enabled the establishment of prognostic thresholds for the detection of CTCs in breast, prostate, and colon cancers [ 24 , 25 , 28 ]. Yet, liquid biopsy methods for the detection of CTCs are used in clinical settings as a complementary diagnostic to the mainstream tissue biopsy methods. Reasons include the lack of standardization and validation needed to implement liquid biopsies as routine in the clinic. Another reason is tumor heterogeneity, even within the same patient, and the lack of biomarkers for CTC detection to overcome this hurdle.

The demand for protein-folding increases during oncogenesis to support the unregulated growth and survival of cancer cells [ 1 – 3 ]. The molecular chaperone, CCT, interacts with numerous oncoproteins, suggesting that multiple cancer signaling pathways could converge through this chaperonin [ 4 ]. Unlike the ubiquitous heat shock proteins, CCT is a multi-subunit complex consisting of a double-ringed barrel formed with eight subunits (CCT1-8); proteins fold within an inner chamber in an ATP-dependent fashion [ 5 ]. While CCT could fold up to 10% of the normal cell proteome, it may have a heighten role during oncogenesis, supporting the growth of cancer cells through client proteins such cytoskeletal proteins (actin, tubulin), cell cycle mediators (cell division cycle protein 20 (cdc20), cyclin E, p53, etc.) [ 6 , 7 ], and growth/survival factors (Signal transducer and activator of transcription 3 (STAT3), Myelocytomatosis (MYC), etc.) [ 4 , 8 , 9 ]. Data from our lab and others revealed that the CCT2 subunit, or CCTβ, is linked to cancer progression and increased stage/aggressiveness of breast, lung, prostate, hepatocellular, gallbladder, and colonic cancer [ 10 – 18 ]. Additionally, we found that the level of CCT2 was independent of hormone receptor status in breast cancer [ 12 ], suggesting that CCT2 expression could be upstream of tumor lineage-defining markers. Overexpressing CCT2 in luminal breast cancer cells increased cell proliferation, spheroid growth, and anchorage-independent growth [ 19 ], while CCT2 depletion prevented tumor growth in a syngeneic model of triple-negative breast cancer (TNBC) [ 13 ]. Moreover, CCT2 expression is associated with chemoresistance, metastasis, and epithelial to mesenchymal transition (EMT) markers [ 20 , 21 ]. This suggests that CCT could enable oncogenic changes as CTCs are shed from tumors to seed distal sites, resulting in metastasis. Therefore, assessing CCT2 protein levels in cancer cells/tissues could provide clinicians with information on tumor progression and metastatic potential.

StudentT-test and one-way ANOVA statistical analysis was performed to compare gene expression differences between tumor and normal tissue, and among different cancer types. R software was used to calculate mean and SD for UCSC Xena-downloaded data. For RT-PCR analysis, relative gene expression was calculated by dividing a gene’s expression in that sample by the average expression of this gene in control samples, i.e., 2 -ΔΔCt /average (2 -ΔΔCt control sample) with T47D-lentiviral control (T47D-GFP) assigned as the control sample. A Log-rank test was used to compare cancer patient survival rates between high and low expression for CCT2 and CK. To investigate the association of CCT2 protein levels with CTC and active disease (time gap), Spearman’s correlation analysis and multiple linear regression analysis were performed. For regression analysis, we incorporated time gap as a dependent variable and CCT2 as an independent variable, and controlled for CTCs, age, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status. All statistical analyses were performed using Stata MP 15 (StataCorp LLC, 2019) or GraphPad Prism 9 software. All statistical tests were two-tailed with a significance level of α (type I error) <0.05.

To assess the presence of cancer markers on breast cancer cells that were enriched after processing in the CSS, cancer cells were recovered after processing and analyzed by flow cytometry (BD Cytoflex S flow cytometer). The CSS CXC kit (Menarini) was used in the CSS Autoprep with the adaptation that the DAPI reagent was removed before the CSS Autoprep cycle began and replaced with PBS. After recovering cells from the CSS magnest, the cells were stained for CD44 with BV421 mouse anti-human CD44 [G44-26] (BD Horizon) or the isotype control [KPL-1] (BD Horizon). The positive control was MDA-MB-231 cells alone (not in blood). The negative control was blood cells alone (not spiked with MDA-MB-231 cells). After staining for 20 min, the samples were analyzed in the BD Cytoflex S and data generated using FCS Express 6 software (De Novo).

To incorporate the anti-CCT2-PE antibody into the CSS protocol, we used the Veridex PDF “Guidelines for user-defined markers in CSS” [ 31 ]. The PDF can be found at https://www.cellsearchruo.com/sites/default/files/Guideline-for-Use-and-Optimization-of-User-Defined-Markers.pdf . For blood containing spiked cancer cells, anti-CCT2-PE antibody (LSBio) was added at 8 μg/mL using the Veridex PDF recommendations for the staining protocol associated with the CSS CXC kit (Menarini). Lower concentrations (1 μg/mL—4 μg/mL) were also tested with SCLC cell lines. For analysis, the spiked cancer cells were reviewed first using the CSS definition for CTCs which is based on using CK in the Fluorescein isothiocyanate (FITC) channel for identification, and second by the positive expression for CCT2 which is read in the PE channel. Criteria for the CCT2 positive analysis were as follows: 1) round positive signal in the PE channel, 2) PE signal that shows overlap with the DAPI signal, and 3) PE signal that did not show overlap with a CD45 signal if a CD45 signal was present. Cells with faint signal in CD45 channel that could be explained by bleed from the PE channel were not counted as CD45 positive. Cells with pixelated images or streaks in the PE channel were selected if there was a definitive outline between the stained cell and the background. Cells with punctate staining in the PE channel were selected only when the cell outline had a definitive difference from the background. Overlap with CK signal was not a requirement for CCT2 positive cell selection. For the pilot study of SCLC patients, samples were collected and processed according to the “Protocol for standard CTC capture and enumeration” mentioned above. Anti-CCT2 antibody was added at either 12 μg/mL or 24 μg/mL. The same criteria for selection of CTC and CCT2 positive CTC were used as mentioned previously in this section.

Healthy human blood was collected in CellSave vials (Menarini) by a commercial vendor (BioIVT) and shipped overnight. For breast cancer, T47D-CCT2 and MDA-MB-231 cells were spiked into blood at concentration of 1,000 cells per 10 mL of blood. For lung cancer, CRL 5903 and CRL 5853 cells were spiked in blood at a concentration of 100 or 1000 cells per 7.5 mL of blood. Cancer cells were collected from tissue culture conditions in a minimal volume of complete media at the desired concentration and then spiked into the CellSave blood vials. After overnight incubation at room temperature, the samples were run in the CSS using the CSS CXC kit according to the staining protocol for CCT2 listed below. Viability of cells after addition to CellSave vials, under blood-free conditions, was measured using Trypan blue (Gibco) according to the manufacturer’s protocol.

The protocol was adapted from Lowes et al [ 30 ]. In brief, for enrichment CSS reagents from the CSS CXC kit (Menarini) were used: dilution buffer (450 μL), anti-EpCAM ferrofluid (25 μL), and capture enhancement reagent (25 μL). Samples were incubated for 15 min and then added to a magnet holder and incubated for an additional 10 min. The supernatant was removed and discarded. Samples were then stained with the following CSS CXC kit (Menarini) reagents: nucleic acid dye (50 μL), staining reagent (51.5 μL), and permeabilization reagent (100 μL). Anti-CCT2-PE (5 μL) (LSBio: LS-C649415) was added and the sample was incubated for 20 min. After incubation, dilution buffer (500 μL) was added, and the sample was placed in a magnet holder for an additional 10 min. The supernatant was discarded. Samples were re-suspended in dilution buffer (350 μL) and transferred into CSS magnest cartridges and run in CSS Analyzer II. Using the CSS Analyzer II’s “Setup” tab, the magnest cartridges could be reconfigured with the “format sample” button. The magnest cartridge could then be labeled for detection with the CXC kit algorithm and the exposure time could be adjusted as described in Lowes et al [ 30 ].

RNA was isolated from cells using TRIzol ™ (Invitrogen) following the manufacturer’s standard protocol for RNA extraction. RNA was quantified using NanoDrop 8000 (ThermoFisher). cDNA was synthesized using the iScript ™ cDNA Synthesis Kit (Bio-Rad) following the manufacturer’s protocol. cDNA was diluted 1:5 and mixed with a Fast SYBR ™ Green Master Mix (Applied Biosystems) according to manufacturer’s recommendations and then run in the Quantstudio PCR for 40 cycles at 95°C for 3 sec, and 62°C for 30 sec. Primers were designed using the NCBI nucleotide database, S1 Table .

Cells were collected, stained, and analyzed by flow cytometry (BD Cytoflex S flow cytometer) as follows. Staining for membrane surface molecules included use of the following antibodies: EpCAM-PE (phycoerythrin): [VU-1D9] (Abcam) and isotype control [MOPC-21] (BD Pharmingen); E-cadherin-PE: CD324 [DECMA-1] BioLegend) and isotype control [A95-1] (BD Pharmingen); and N-cadherin-APC (Allophycocyanin): CD325 [8C11] (BioLegend) and isotype control [ 27 – 35 ] (BD Pharmingen). Staining was performed following standard methods. Cells were stained for 30 min (EpCAM) and 20 min (E-cadherin and N-cadherin). For intracellular staining of CCT2 we used the antibody CCT2-PE: [NP_006422] (LSBio) and the isotype control [MOPC-21] (LSBio), following the method in ThermoFisher Scientific’s “Protocol A: two-step protocol: intracellular (cytoplasmic) proteins” and incubating the antibody for 70 min. When optimizing the CCT2 intracellular stain for CSS Autoprep conditions, we adjusted the antibody staining protocol for CCT2-PE to match the 20 min in the CSS Autoprep automated conditions. All data was generated using FCS Express 6 software (De Novo).

MDA-MB-231 cells (ATCC ® HTB-26 ™ ) were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) (Corning) and supplemented with 10% fetal bovine serum (FBS) (Gemini) and 1% Penicillin-Streptomycin (P/S) (Corning). T47D cells (ATCC ® HTB-133 ™ ) were transfected with a lentiviral plasmid to express CCT2 with a FLAG tag (DYKDDDDK) hereafter referred to as T47D-CCT2, as previously described [ 13 ]. T47D cells were cultured in Roswell Park Memorial Institute Medium (RPMI-1640) (Corning) supplemented with 10% FBS (Gemini), 1% P/S (Corning), and 0.2 units/mL human recombinant insulin (Santa Cruz). 0.5 μg/mL puromycin dihydrochloride (ThermoFisher) was added to maintain plasmids.

Slides with FFPE tissues were received from three sites. 1) US Biomax Inc: normal breast tissue microarray (TMA) BRN801b. 2) ORMC: MBC patients from the pilot study described above. 3) AdventHealth: surgical breast cancer archival tissues TMA (>10 yrs) from 28 patients with G1-G3 invasive ductal carcinoma. Slides were processed by standard immunohistochemistry (IHC) methods for CCT2 and STAT3 staining [ 11 ]. Anti-CCTβ antibody [amino acids 277 and 473 of Human TCP1 beta] (LifeSpan Biosciences) and STAT3 antibody [E121-21] (Abcam) were used. Slides were stained and scored for the CCT2 stain by an independent and identification-blinded pathologist, as described previously [ 12 ]. Images were taken using the BZ-X800 Keyence.

Blood samples in CellSave vials (Menarini) were stored at room temperature and run in CSS according to the manufacturer’s protocol for the CSS CTC Kit (Menarini) for CTC analysis within 96 hours of blood draw. CTCs were selected based on the standard CSS criteria for CTC, which is met if the cell is 1) positive for DAPI (4′,6-diamidino-2-phenylindole) staining, 2) positive for CK-FLU (Cytokeratin-fluorescein-conjugated (green)) staining and the CK-FLU stain overlaps with 50% of the DAPI stain, and 3) negative for cluster of differentiation 45 (CD45) staining.

For the metastatic breast cancer (MBC) study, participants included 38 female patients aged 18 years or older, diagnosed with MBC arising from a prior stage 1, 2, or 3 of disease with paraffin-embedded formalin fixed (FFPE) tissue block available to produce slides for histology. Patients with de novo MBC were excluded. Our study size met the parameters established in a sample size estimation based a range of Pearson correlation (R) using the wp.correlation function in the R package (WebPower). If R is > = 0.5 (medium effect), then CCT2 expression in CTCs is as clinically relevant as in tumor tissue for prognosis prediction. To detect such a level of effect size (power 0.8, alpha 0.05), we would need 28 patients. If R is > = 0.6 (medium-large effect), we would need 18 patients. The study was conducted observing the human subject protection criteria for the Orlando Regional Medical Center (ORMC) and the University of Central Florida (UCF) and was approved by separate Institutional Review Board (IRB) committees at ORMC and UCF. Informed consent agreements were obtained from all participating subjects. Patients received standard treatments and follow-up at ORMC for breast cancer, at which time two 10 mL of blood in CellSave tubes (Menarini) were drawn and four slides of tissue were obtained from the pathology department at ORMC. Deidentified samples and data on diagnosis, treatment, and recurrence were acquired and processed by the lab at UCF following the Federal Privacy Regulations for protected health information. For the SCLC study, deidentified patient blood from four participants, 7.5–10 mLs of blood in CellSave tubes, was acquired from a commercial source (BioIVT).

Data was collected from University of California Santa Cruz (UCSC) Xena at xena.ucsc.edu using the combined cohort of “The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and Genotype Tissue Expression (GTEx) samples” dataset [ 29 ]. From the available samples, 17,200 from TCGA and GTEX were used for analysis. The initial analysis examined genetic expression of CCT1-CCT8 using RNA-Seq by Expectation-Maximization (RSEM) expected count (DESeq2 standardized) expression between normal tissue (GTEx) and primary cancer (TCGA) in overall cancer, and then in brain, breast, colon, and lung cancer separately. Secondary analysis of UCSC Xena focused on the expression of the CCT2 and the CK genes Keratin 8 (KRT8), Keratin 18 (KRT18), and Keratin 19 (KRT19). Phenotype/sample types that were included were: “Metastatic”, “Normal tissue”, “Primary tumor”, and “Solid tissue normal”. This data was analyzed through R software for dataset mean and standard deviation (SD). Data for the KMplots was collected at kmplot.com using an mRNA gene chip for breast cancer. Genes used were CCT2 (201946_s_at) and “mean expression of multiple genes” for CK (KRT8: 209008_x_at; KRT18: 201596_x_at; and KRT19: 201650_at). Hazard ratios and Log-rank p values were calculated by kmplot.com software. mRNAseq data of breast cancer cell lines from the Broad Institute Cancer Cell Encyclopedia (CCLE) were collected by searching for E-cadherin (CDH1), N-cadherin (CDH2), EpCAM, and vimentin (VIM) expression in breast cancer.

Results

Bioinformatics-based evaluations of CCT2 or CK/KRT gene expression in normal compared to cancer tissue To evaluate CCT gene expression in tumor tissues compared to normal tissues, the eight CCT subunits (CCT1-8) were analyzed in the UCSC Xena Browser using the cohort of “TCGA, TARGET, and GTEx samples” dataset [29]. All eight CCT subunits showed significant (p< 0.0001) upregulation in RNAseq expression in cancer compared to normal tissue, with the strongest upregulation seen in CCT2 and CCT3, S1A Fig. These results were consistent when broken down by cancer type; brain, breast, colon, and lung, S1B–S1E Fig. CK is a standard diagnostic marker for epithelial cancers and CK8, CK18, and CK19 are used in the CSS when visualizing for CTCs. Therefore, to investigate CCT2 and CK/KRT gene expression correlations with tumors and normal tissue, we used the UCSC Xena dataset to focus on CCT2, KRT8, KRT18, and KRT19. UCSC Xena separates normal tissue from healthy patients (GTEx dataset) and normal tissue from cancer patients (TCGA dataset) by calling them “normal tissue” and “solid tissue normal” respectively. The pair-wise comparison showed that the average CCT2 gene expression in primary tumor and metastatic tissue was significantly higher than in normal tissue and solid tissue normal (mean±SD: 12.42±0.76 and 12.51±0.72 compared to 11.51±0.62 and 11.86±0.42 respectively, p<0.0001 for all pairwise comparisons). Meanwhile, KRT8 and KRT19 gene expression in metastatic tissue showed significant downregulation compared to normal tissue from health patients (mean±SD: 8.51±2.78 vs 9.68±3.51, p<0.0001, and 4.19±3.77 vs 8.47±3.53, p<0.0001) for KRT8 and KRT19 respectively, Fig 1A. CCT2 also demonstrated the lowest variance of the four markers. Full statistical analysis is shown in S2 Table. To determine how CCT2 and CK/KRT correlated with overall survival (OS), we used KMplotter to assess these relationships for breast cancer, Fig 1B. Results showed statistically significant inverse correlations between CCT2 high and OS in breast cancer (p = 6.4e-05). High KRT8/18/19 expression also demonstrated an inverse correlation with the patient OS but was not statistically significant and only the upper quartile survival could be calculated because both cohorts did not reach a median survival. These findings support that CCT2 expression is upregulated in cancer and metastatic tissues compared to normal tissue, while KRT8/19 showed downregulation in metastatic tissue, and that CCT2 expression correlated inversely with patient OS. PPT PowerPoint slide

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TIFF original image Download: Fig 1. Bioinformatic and histological analysis of CCT2 in cancer patients. (A) UCSC Xena analysis from “TCGA, TARGET, GTEx” dataset (n = 17,200) comparing mRNA expression of CCT2, KRT8, KRT18, and KRT19 in metastatic tissue (orange), normal tissues/GTEx (green), primary tumor (blue), and solid tissue normal/TCGA (purple). * = p<0.05, ** = p<0.005, *** = p<0.0001. (B) KMplotter analysis of overall survival (OS) with low vs. high CCT2 mRNA expression and low vs high mRNA expression of the mean of KRT8, KRT18, and KRT19 combined in breast cancer patients; n = 4,929. Hazard ratios (HR) and log-rank p-values, as calculated by kmplot.com software, are listed on the KmPlots. https://doi.org/10.1371/journal.pone.0264651.g001

Optimization of CCT2 staining in the CSS A limitation with standard CTC detection methods is the reliance on epithelial markers. Such methods could miss cancer cells with hybrid (mixed epithelial and mesenchymal) or mesenchymal features. To include cells in our study that also had mesenchymal markers, we used breast cancer cell lines in which we had previously evaluated CCT2 levels and then determined their expression of epithelial and mesenchymal markers. T47D cells, representative of early-stage luminal A breast cancer, have lower protein levels of CCT2 compared to TNBC cells, like MDA-MB-231 [12, 13, 19]. In our published study of the role of CCT2 in the proliferation of cancer cells, we generated T47D cells that expressed exogenous CCT2 tagged with FLAG (T47D-CCT2) [13]. We assessed these T472-CCT2 cells for SNAI1 (SNAIL), TWIST, EpCAM, vimentin, E-cadherin, and N-cadherin mRNA expression, Fig 3A. SNAIL and TWIST are transcription factors that are associated with the activation of EMT. EpCAM and E-cadherin are epithelial markers, while vimentin and N-cadherin are mesenchymal markers. Statistically significant increases in TWIST (p = 0.0023), N-cadherin (p = 0.0170), and E-cadherin (p = 0.0215) mRNA were present in the T47D-CCT2 cells compared to the lentiviral control cells, with a significant decrease in vimentin (p = 0.0194) mRNA. EpCAM levels decreased, and SNAIL was increased, but neither of these were statistically significant. Therefore, since T47D-CCT2 cells expressed some mesenchymal characteristics without complete loss of epithelial ones, we used these cells for subsequent experiments. According to the online database CCLE, RNAseq expression of EMT markers indicated that T47D cells are more epithelial-like, and TNBC MDA-MB-231 breast cancer cells are more mesenchymal-like (S3 Fig); hence, we also used MDA-MB-231 cells in our experiments. We examined the membrane protein levels of EpCAM, E-cadherin, and N-cadherin by flow cytometry for both cell lines. T47D-CCT2 cells had detectable levels of EpCAM and E-cadherin and lower N-cadherin, Fig 3B. MDA-MB-231 were N-cadherin positive with a slightly lower expression of EpCAM and E-cadherin than T47D-CCT2, Fig 3C. In total, these data demonstrate that these two cell lines represent a range of epithelial and mesenchymal features. PPT PowerPoint slide

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TIFF original image Download: Fig 3. EpCAM, vimentin, E-cadherin, and N-cadherin expression in MDA-MB-231 and T47D cells. (A) RT-PCR data comparing lentiviral control (T47D-GFP) (black) with T47D-CCT2 (grey) for expression of EMT genetic markers: SNAIL and TWIST, epithelial markers: EpCAM and E-cadherin, and mesenchymal markers: vimentin and N-cadherin. p-values are shown on the graph. (B) Flow cytometry data detecting surface expression of EpCAM, E-cadherin, and N-cadherin protein in T47D-CCT2 cells (green) compared to isotype controls (grey). (C) Flow cytometry data detecting surface expression of EpCAM, E-cadherin, and N-cadherin proteins in MDA-MB-231 cells (red) compared to isotype controls (grey). All experiments were performed in duplicate. https://doi.org/10.1371/journal.pone.0264651.g003 To validate the anti-CCT2 antibody for intracellular staining, various antibody concentrations were tested in MDA-MB-231 cells and T47D-CCT2 cells to differentiate specific and non-specific signals under the conditions similar to those occurring during automated CTC collection and staining with the CSS. Saturation of the response was achieved around 20 μg/mL of antibody, S4 Fig. To optimize the cell-based signal for CCT2 in the CSS Analyzer II, we manually performed the intracellular staining for CCT2 in cells [30] and adjusted the exposure time in the CSS Analyzer II and the anti-CCT2 antibody concentration. A 0.2 second exposure time showed the least background noise, while both 8 μg/mL and 12 μg/mL of anti-CCT2 antibody resulted in a detectable signal, S5 Fig. We chose 8 μg/mL as the anti-CCT2 antibody concentration moving forward.

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