Abstract
Background: The detection of circulating tumour cells (CTCs) from peripheral blood of cancer patients can be carried out by real-time PCR approaches using different gene expression levels of tumour cells and surrounding blood cells. Materials and Methods: Potential marker genes were first analyzed in a model system and then applied to 20 blood samples of adjuvant breast cancer patients and gene expression levels were correlated to tumour characteristics. Results: The mean of gene expression levels was found elevated for the four genes analyzed in the adjuvant breast cancer patient group in comparison to the samples of the group of healthy donors, but no correlation between gene expression and tumour characteristics could be detected as being statistically significant. Conclusion: The results demonstrated, that the employed methodology is functional, but has to be refined by certain approaches like simultaneously running a state-of-the-art system of CTC-detection comparing the results, and by an enlargement of patient collective and number of marker genes.
Despite improved diagnostic and treatment strategies, breast cancer is still the most frequent cancer and cause of cancer-related death in women worldwide. In 2004 more than 500,000 women died worldwide from this cancer entity and its secondary effects (1). Thereby, the median age of death is 68 years (2). Death is, in most cases, not caused by the primary tumour but by generation of remote metastasis growing in and destroying vital organs. The process of metastasis formation has been described extensively. Current models claim single tumour cells, dissolving from the primary tumour, travelling through the blood stream and lymphatic system, so called “circulating tumour cells” (CTCs) (3-5), settling down in remote organs and growing, forming new tumour mass (6). These cells also possess the ability to enter the bone marrow. They are then called “disseminated tumour cells” (DTCs) and are capable of forming tumour reservoirs within the bone marrow (7-12). The occurrence of CTCs/DTCs had also been linked to a worse outcome for affected patients (5, 13) and were, therefore, included into international tumour staging systems (14, 15). But the detection of these single tumour cells from blood or bone marrow respectively is still a technical challenge, as the number of these cells is rather small in comparison to the surrounding blood cells (1 tumour cell/1x106 blood cells) (16). Therefore, most of the actual methods for tumour cell detection use a first enrichment step before detection. Up until now, various detection methods are known (17, 18), most of which use the expression of cell surface epitope EpCAM on the surface of tumour cells but not on the blood cells but actually only one detection system reached the approval of the FDA: The CellSearch® System, distributed by Veridex LLC. (19). It is based on an immunomagnetic enrichment of tumour cells via EpCAM, followed by a staining of epithelial cell surface markers. The detection of typical epithelial genes is also the basis of a Real-Time PCR (RT-PCR) based approach for tumour cell search. Cytokeratins 8, 18 and 19 have already been demonstrated to be good marker genes for CTC-detection from peripheral blood of breast cancer patients (20-22), but for a more precise detection it would be necessary to use a set of marker genes, which might then also help characterize CTCs. A possible characterization in turn might help apply a more personalized treatment strategy, reducing side-effects while increasing treatment efficiency. The aim of this study was an analysis of genes that are included in the Oncotype DX® system (23). The Oncotype DX® test is up to now only applied to formalin-fixed cancer samples (24). We expanded the Oncotype DX® system for the detection of circulating tumour cells. Genes for estrogen (ER) and progesterone-receptor (PR) were tested, as well as the B-cell lymphoma associated Bcl2 gene, cathepsin L2 (CatL2), the human epidermal growth factor receptor 2 (Her2) and Ki-67 gene. Thereby, estrogen receptor is one of the most prominent breast cancer tumour markers. ER-positive tumours normally have a better prognosis (25) and can be treated by endocrine therapy using tamoxifen (26). The expression of progesterone receptor is mostly linked to estrogen receptor expression. A low PR expression in ER-positive tumours leads to a less favourable prognosis (27-29). The expression of Her2 is correlated to a worse prognosis, but Her2-positive tumours can be treated with the monoclonal antibody trastuzumab (herceptin) (30, 31), which results in improvements in disease-free and overall survival (32-34). Ki-67 is a gene marker for cell proliferation, also leading to worse prognosis, and is thereby important for cancer diagnosis (35-38). Bcl2 is another important independent prognostic marker in breast cancer diagnosis (39), involved in apoptotic pathways with high significance in triple-negative and metastatic (40) breast cancer. CatL2 has been described to be an important marker in primary breast cancer (41) and has been tested for tumorigenity in breast cancer cell lines (42). The genes used for this study were tested in a model system. Therefore, definite amounts of cells from breast cancer cell lines were added to blood samples form healthy donors. The best performing genes from this model were used to quantify tumour specific gene expression in blood samples of 20 adjuvant breast cancer patients.
Materials and Methods
Cell line. Breast cancer cell line Cama-1 (ATCC: HTB-21: mammary gland adenocarcinoma; ATCC, Wesel, Germany) was cultured according to international standards. Cells were grown to sub-confluency and re-fed twice a week. For determination of cell numbers, cells were detached from the ground of the cell culture flask by addition of trypsin/EDTA (Biochrom, Berlin, Germany) and counted in a hemocytometer. Cell numbers required for the experiments were gained by dilution of the cell solution with PBS (Biochrom, Berlin, Germany) and added to the healthy blood samples after a trizol-step during RNA-isolation to prevent immunological reactions of the blood cells with the tumour cells (43). 0, 10, 100 and 1,000 cells per ml blood were added to different blood samples.
Blood samples. For the creation of the model system, 20 ml blood were withdrawn from healthy donors, diluted with PBS (Biochrom, Berlin, Germany) and carefully layered onto 20 ml histopaque 1077 (Invitrogen, Darmstadt, Germany). Density-gradient centrifugation was carried out at 400 × g for 30 min. Then buffy coat was carefully aspired, transferred into a fresh tube and diluted with PBS (Biochrom, Berlin, Germany) following another centrifugation step at 4°C, 250 × g for 10 min for washing the cell fraction. Supernatant was removed and harvested cell pellet was stored at -80°C until use.
For the collection of blood samples from adjuvant breast cancer patients written consent of the patients was obtained following the ethical vote LMU 148-12, conform to the Declaration of Helsinki. Patient blood samples were treated in the same way as described for healthy blood samples.
RNA isolation. Harvested cells pellets were thawed and 1 ml trizol LS (Invitrogen) was added to the cells. Then 0.2 ml chloroform (Merck, Darmstadt, Germany) were added and suspension was vortexed vigorously. Cell suspension was centrifuged at 12,000 × g, 4°C for 15 min, then supernatant was carefully aspired and transferred into a fresh tube. 1 ml isopropanol (Merck) was added and solution was incubated overnight at -20°C. The next day the solution was centrifuged again at 12,000 × g and 4°C for 10 min and the supernatant was removed and remaining RNA pellet was washed twice with 75% ethanol (Merck) by centrifugation at 12,000 × g, 4°C for 10 min. The pellet was air-dried and dissolved in 20 μl DEPC-treated water. RNA quantity was measured photometrically (Nanodrop, Implen, München, Germany) and quality was controlled by denaturing agarose gel electrophoresis.
Reverse transcription. For reverse transcription 4 μg of RNA were used, that had to be in a maximal volume of 6 μl. Reagents from the SuperScript III First Strand Synthesis Super Mix Kit (Invitrogen) were added in the order described in the manual and reaction steps were carried out as stated by the provider. CDNA samples were then kept on −20°C until further use in RT-PCR.
RT-PCR. 2 μl of the prepared cDNA were mixed with 18 μl of the target gene specific reaction master mix, prepared with 1μl of gene specific TaqMan-Primer (Hs99999905_m1 for GAPDH, Hs_03003631_g1 for 18S, Hs01556702_m1 for PR, Hs00174860_m1 for ER, Hs00170433_m1 for Her2, Hs01032443_m1 for Ki67, Hs00952036_m1 for CatL2, Hs01048932_g1 for Bcl2; ABI, Foster City, CA, USA) 10 μl TaqMan® Fast Universal PCR Mastermix (ABI, Foster City, CA, USA) and 7 μl d.water. Mastermix and cDNA were filled into the wells of a 96-well PCR Plate (MicroAmp® Fast Optical 96-well plate with barcode, ABI, Foster City, CA, USA). The plate was sealed with an optical adhesive cover (ABI) and placed into the 7500 Fast Real Time PCR system (ABI). Each gene was analyzed in quadruplicates and −RT and water controls were included. 18S and GAPDH were used as internal controls for the reaction. PCR was run in the following cycles: initial denaturation (95°C for 20 sec), 40 amplification cycles (95°C, 3 sec; 60°C, 30 sec). Fluorescence was displayed in the SDS 1.3.1 software.
Evaluation. The CT, ΔCT, ΔΔCT and RQ-values generated from fluorescence intensities (RQ-values were generated as described in Livak et al. (44)) displayed by SDS software were exported to Microsoft Excel™ and respective graphs were drawn. Statistical analysis was performed via SPSS V.22.
Results
Of the six genes (ER, PR, Ki-67, Her-2, Bcl2 and CatL2) analyzed in the model system of blood samples from healthy donors spiked with Cama-1 breast cancer cell line cells, PR and Bcl2 did not perform very well, so they were excluded from the analysis of patient samples.
Average RQ-values of adjuvant breast cancer group vs. CTC-negative control group.
Comparing the average RQ-values between the group of adjuvant breast cancer patients and the healthy control group (negative for CTCs), it is striking that RQ-values are much higher in the patient group, as CatL2 is almost 100x higher expressed in the patient samples, for Her2 and Ki67-6x and 16x higher expression values could be found in the group of adjuvant breast cancer samples. But the greatest difference is seen for ER: It's expression is nearly 350x higher in patient samples, than in the control group. These differences in gene expression could be attributed to the presence of CTCs expressing the analysed genes (Table I, Figure 1). However looking at the single patients in order to correlate their RQ-values to tumour characteristics no coherence could be found (Table II), as for example patient no. 5, who has a really small tumour without lymph node affection and low grading has the highest RQ-values for all four genes examined. On the other hand, patient no.6, with a pT2 tumour and lymph node affection has rather high RQ-values as well, so that it is not yet possible to assign certain tumour characteristics to RQ-values.
Also the statistical analysis of patient subgroups concerning age, tumour size, lymph node involvement, grading and ER/PR status in respect to RQ-values did not show any significance (Table III) within the tested patient samples.
It might be interesting to mention, that three patients (Patients 6, 7 and 9) with Her2-negative primary tumours showed a clear expression of Her2 in the RT-PCR assay, meaning that they might have Her2-positive CTCs. This phenomenon is well known as “Her2-Switch”, which stands for the fact, that single tumour cells, after leaving the primary tumour, undergo various genetic changes and in consequence have a different phenotype as the primary tumour cells (45, 46).
Discussion
The results demonstrate, that a certain increase of gene expression due to the presence of CTCs can be detected through the RT-PCR methodology, but from the data presented herein, no conclusion can be drawn from the RQ-values measured from the PCR experiments to tumour characteristics. These results are in accordance to a number of recent studies, which found differences in gene expression between breast cancer patients and healthy control samples, but could not relate these differences to tumour characteristics (47-49). It will be a future challenge to find a correlation between these two data sets. Therefore it would be certainly helpful, to analyze a larger cohort of patient samples, to have a better coverage of different tumour sizes, grading etc. Furthermore it could make sense, to run a gold standard method, like the CellSearch® system simultaneously to the RT-PCR and compare the results of both methods in order to find any coherences. Additionally more marker genes could be implicated in the analysis to be able to detect CTCs with even more sensitivity. These further improvements of the presented method could help refine tumour diagnostics by having a simple and cost-efficient technique to detect and characterize CTCs, that will thereby gain in importance for tumour characterization.
Graphical display of average RQ-values of adjuvant breast cancer group vs. CTC-negative control group.
To further take into account phenomena like the Her2-switch or epithelial-mesenchymal transition (EMT), in which gene expression patterns are changed between the primary tumour and the minimal residual disease, it is extremely important, not only to detect but additionally to characterize CTCs, so that respective modes of tumour treatment could be applied, preventing survival of tumour cells with altered characteristics. RT-PCR could be rather useful for such a characterization of CTCs, and therefore it is important to test a large set of marker genes to be able to create a set of the most important genes, which could hopefully bring diagnosis and treatment one step ahead.
Composition of single patient data in comparison to RQ values.
Statistical evaluation of RQ-values in different patient subgroups.
Acknowledgements
The Authors thank T. Thormeyer for helping with statistical analysis.
- Received April 18, 2016.
- Revision received May 20, 2016.
- Accepted May 23, 2016.
- Copyright© 2016 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved
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