Background
Glioma is the most common aggressive cancer in the central nervous system (CNS) and represent 75% of primary intracranial tumors in adults [
1]. Many previous studies have shown that after surgery combined with radiotherapy and chemotherapy, glioma still has an aggressive tendency toward intracranial recurrence, and the growth rate after recurrence is accelerated, worsening patient prognosis [
2,
3]. Data have shown that the median recurrence period of glioblastoma (GBM) is 8–12 months, the median recurrence period of stromal glioma is 18–36 months, and the median overall survival (OS) of recurrent glioma is only 25–35 weeks [
4,
5]. Therefore, considering the poor prognosis of gliomas and the difficulty in monitoring glioma response and progression, it is necessary to establish an approach for evaluating the progression or survival of patients with glioma.
Advancement in the development of molecular biology techniques for glioma has led to the discovery of a series of biomarkers with practical value for diagnosis, differential diagnosis, and treatment [
6,
7]. Although these molecular markers have made great breakthroughs in the diagnosis and treatment of glioma, patient prognosis is still not optimistic, and there is no molecular biomarker that can accurately distinguish the subtypes of glioma or predict prognosis. The rapid development of liquid biopsy technology, especially relevant studies on circulating tumor cells (CTCs), shows that this technology is helpful for the clinical treatment and prognosis prediction of GBM patients [
8,
9]. Although CTC studies in glioma patients show potential clinical applications, there are still some questions that need to be answered, such as whether CTCs can represent the main nature of tumor cells to correctly reflect the clinical behavior of GBM patients with recurrence and metastasis and provide more accurate information for individual treatment and prognosis prediction.
Based on previous studies [
10,
11], a viable CTC detection method based on human telomerase reverse transcriptase (TBCD) was used in this study to evaluate the preoperative and postoperative CTC status of glioma patients. Next, the correlation between CTCs and clinical indicators was studied, and recurrence and prognosis were assessed. To better illustrate the clinical application value of CTCs in glioma, we analyzed the significance of the correlation between CTCs and patients’ macroimmunity in depth and proposed that the postoperative macroimmunity status of patients is closely related to CTCs and patient prognosis. To the best of our knowledge, this study is the first on the correlation between macroimmunity and CTCs in glioma patients, and we believe that our results can provide a new perspective for the clinical application of CTCs in glioma and even the treatment of glioma.
Methods
Collection of patient and clinical information
In this study, patients and healthy control volunteers all provided written informed consent, and peripheral blood collections were approved by the Ethics Committee of the Cancer Hospital, Chinese Academy of Medical Sciences. From October 2014 to June 2017, 106 patients newly diagnosed with glioma were enrolled, and the clinical information of the included patients was provided by the Department of Neurosurgery, Beijing Tiantan Hospital. Furthermore, another 31 patients were enrolled according to the screening conditions for RNA sequencing research. The clinical characteristics of the enrolled patients are shown in Table
1. All methods were performed in accordance with the approved guidelines.
Table 1
Patient Characteristics
Age (yr) |
Mean (range) | 42.63(13-70) |
Sex (%) |
Male | 37(34.9) |
Female | 69(65.1) |
Pathology (%) |
A | 19(17.9) |
O | 5(4.7) |
AO | 8(7.5) |
OA | 29(27.4) |
AOA | 15(14.2) |
GBM | 30(28.3) |
WHO Grade (%) |
1 | 2(1.9) |
2 | 37(34.9) |
3 | 31(29.2) |
4 | 36(34.0) |
KPS (%) |
60 | 2(1.8) |
70 | 10(9.5) |
80 | 25(23.6) |
90 | 66(62.3) |
100 | 3(2.8) |
Blood samples
To explore the significance of tumor reduction surgery on CTCs of glioma, we detected the patients’ CTC counts before surgery and compared them with their CTC counts at 7 days post-operation. All patients with gliomas were first treated and underwent surgery, and long-term follow-up was conducted after the surgery. Four milliliters of blood was collected from eligible patients or healthy donors (aged 20–50 years) in K2E (EDTA) tubes and stored at 4 °C in the lab within 2 h. The peripheral blood of another 31 glioma patients was collected for WBC RNA sequencing, and postoperative CTCs were also detected.
Cell and virus agent
The human glioma cell line U251 was obtained from the Cell Resource Center, Peking Union Medical College, and cultured using minimum essential medium with Earle's balanced salts (MEM-EBSS) containing 10% fetal bovine serum (FBS) at 37 °C in 5% CO2.
In this study, the oHSV1-hTERT-GFP virus was constructed as described in a previous study [
10]. Based on the selectivity of telomere activity, the oHSV1-hTERT-GFP virus captured telomere activity that is widely expressed in cancer cells and expressed green fluorescent protein (GFP).
Identification of samples with glioma
The peripheral blood of patients with glioma was collected (4 ml) and prepared with K
2E-EDTA anticoagulant tubes within 2 h of isolation. Details of the TBCD that we used have been described previously [
10,
11]. APC-anti-human CD45 (clone: HI30, Invitrogen, USA) was added to 10
7 cells/sample and incubated at room temperature for 30 min. Flow cytometry (BD, USA) was used to detect the GFP+/CD45- cell population, which was recorded as a positive result (Additional file
1: Fig S1A).
GFP fluorescence observation
U251 cells or patient samples were transfected with the viral agent and cultured in a 6-well plate for 24 h, and the cells were observed under a fluorescence microscope (Olympus).
Fluorescent in situ hybridization (FISH)
FISH was performed using a Vysis LSI 1p36 Microdeletion Region Probe (Abbott). DNA-FISH was applied to investigate the presence of CTCs. Peripheral blood samples (4 ml) from glioma patients were treated with a standard CTC testing procedure with some modifications. Peripheral blood samples were transfected with oHSV1-hTERT-GFP and cultured for 24 h. The cells were selected with an MS column by using anti-CD45 microbeads. The unlabeled cells were collected on a slide, and the slides were observed under a fluorescence microscope, confirming that the GFP+ cells were on the slide. Probes were used according to the manufacturer’s protocol. The prepared slides were scanned by fluorescence in situ scanner.
Identification of CTCs using ImageStreamX®
APC-CD56 antibody (clone: CMSSB, Invitrogen, USA) and Alexa Fluor 405 CD45 antibody (clone: HI30, Invitrogen, USA) were used to identify CTCs. The CTC images were detected by the ImageStreamX® Mark II system (Amnis) (Additional file
1: Fig S1B). CD45/GFP+/CD56+ cells in the blood samples were considered CTC-positive cells.
Next-generation sequencing (RNA-seq) and data processing
Peripheral blood was obtained from healthy controls (n=23) and untreated glioma patients (n=31). After red blood cell lysis buffer (Qiagen) was used to remove red blood cells, the total RNA of the remaining WBCs was extracted using TRIzol (Invitrogen). RNA-seq libraries were prepared from qualified samples using the NEBNext® Ultra™ RNA Library Prep Kit for Illumina (NEB) according to the manufacturer's instructions.
Clean reads in FASTQ files were quantified against an Ensembl catalog (GRCh37) at the transcript level using Salmon and aggregated to the gene level using tximport; transcripts per million reads (TPM) values were obtained with Salmon gene expression quantification software, and unexpressed genes were removed.
We defined genes positively correlated with CTCs as having a correlation coefficient
R > 0.4,
p < 0.05 (Cytoscape software) [
12], which presented biological role specificity for the most significant differentially expressed gene transcripts based on their functional and pathway enrichment probes. The GO database [
13] was used. Immune cells with differentially expressed genes were distinguished by using the “DESeq2” R package; genes with fold change > 2 and
p < 0.05 were defined as differentially expressed genes. Heatmaps were made by using the “Pheatmap” R package, and survival curves were evaluated with the log-rank test (two-tailed) by the Kaplan-Meier method. GO analysis and hierarchy relation analysis were conducted with the “topGO” R package.
For each given gene list, pathway and process enrichment analyses were carried out using Metascape [
14] with the following ontology sources: KEGG Pathway and GO Biological Processes.
Z-score analysis was conducted with the “GOplot” R package. All genes in the genome were used as the enrichment background. Terms with a
p value < 0.01, a minimum count of 3, and an enrichment factor > 1.5 (the enrichment factor is the ratio between the observed counts and the counts expected by chance) were collected and grouped into clusters based on their membership similarities.
GSEA was performed using the “GSVA” package, which uses the C7 immunologic signature gene sets and Hallmark gene sets downloaded from the Molecular Signatures Database (MSigDB) [
15,
16]. Estimating the Proportion of Immune and Cancer cells (EPIC) [
17] was used to analyze peripheral blood cell distribution.
GSEA enrichment analysis between immune-related gene sets and NETS was conducted by enrichment score in peripheral blood genes compared with different CTCS levels in the two groups. The immune gene set was also derived from a publication published by Bindea G. et al. [
18]. Using the gene expression signatures of NETs, the GSEA implementation method relies on the R package “GSEABase,” resulting in an enrichment score ch gene set and NE values for the immune-related phenotypes of the 20 peripheral blood transcriptomes generated using the above methods.
Statistics
The data were analyzed by generalized linear regression ANOVA in a cell line model and clinical samples. The cutoff value was determined by the Youden index and ROC curve. The Spearman correlation coefficient was used to assess correlations with clinical information. The Kaplan-Meier method was used to estimate the event time distribution, and the log-rank test was used for comparison. A multivariate Cox proportional hazards regression model was used to check the clinical descriptive information. SPSS 24.0 and GraphPad Prism 7.0 were used for data analysis. All the statistical tests were two-sided, with p values of less than 0.05 considered significant.
Discussion
In recent years, with in-depth research on the mechanism of tumor metastasis, great progress has been made in the use of CTC detection as a new noninvasive diagnostic method. Although the systemic metastasis of glioma is very rare, some studies have reported the successful isolation and identification of glioma CTCs from peripheral blood [
20‐
28]. The presence of CTCs in gliomas is a common phenomenon, although the reported detection rates vary to a certain extent (20–80%). However, the most prominent difference between central nervous system tumors and epidermal malignant tumors is the lack of EpCAM expression, which makes the detection of glioma CTCs challenging and requires other strategies [
27].
In this study, an hTERT-based detection method for circulating tumor cells in glioma patients was established. It has been reported that hTERT expression is increased in commonly used glioma cell lines (U251, U373, and U87) as well as in clinicopathological sections [
25,
29]. Based on previous studies, anti-CD45 antibodies can be used to reduce the background of CTC detection and to effectively reduce false positives in CTC detection [
30]. Here, for 52 healthy individuals and 106 patients with glioma, the TBCD method was used for glioma sample (ROC) curve analysis. The participants who provided each of the 4-ml blood samples had > 1 under the threshold of CTC, and the sensitivity of the difference between glioma patients and healthy individuals reached 0.830 and 0.865, respectively, showing that the detection method showed very high efficiency.
Compared with other CTC detection methods, the CTC detection method for glioma based on the hTERT promoter has several advantages. First, it does not depend on the expression of tumor cell surface markers, such as EpCAM and GFAP; thus, the CTC tracer is independent of the expression levels of cell surface markers and specific molecular types. Second, since the vector requires infection and replication in active cancer cells to obtain GFP expression, this method labels only viable cells, thus obtaining more reliable CTC assessment results. Although studies have reported high hTERT expression in glioma using an adenovirus vector tracer [
25], CTCs can be isolated via our strategy by flow cytometry and enable a further single-cell molecular analysis of informatics analyses to determine more accurate individualized treatment measures for patients.
At present, the reported studies on CTCs of glioma focus on GBM, while there are few studies on other pathological types [
20,
27]. In this study, the total detection rate of CTCs in glioma patients was 83.02% (
n=88/106), which was the highest reported to date, and CTCs of different pathological subtypes were widely present in peripheral blood regardless of the degree of malignancy. This finding suggests that the presence of CTCs is a common feature of gliomas and is not unique to GBM. In the traditional monitoring of clinical response and glioma indicators, pseudoprogression or radiation necrosis may affect treatment judgment and lead to overtreatment [
31,
32]. Because CTCs are widely present in peripheral blood, the number of CTCs can indirectly reflect the number and tumor load of intracranial tumor cells. Our results showed that at the 52-month follow-up after surgical treatment, there was a significant positive correlation between postoperative CTC levels and glioma DFS and OS. These results suggest that the CTCs detected by the TBCD assay are independent of the tumor characteristics of the patients and can be used as a supplement independent of other clinical and prognostic indicators.
Subsequently, we analyzed the relationship between systemic immune status and CTCs. In this study, we focused for the first time on CTCs associated with the peripheral immune system as a factor for poor prognosis in glioma. Until now, little has been known about the mechanism by which CTCs rely on the circulatory system to escape the killing attacks of various immune cells, migrate or return to their original sites, and implant there. A recent study has shown that neutrophils are the main white blood cells that interact with CTCs in mouse models and patients [
33]. An increasing number of studies have shown that neutrophils play a role in all stages of tumor progression [
34,
35]. Allen et al. revealed the remodeling effect of the tumor on systemic immunity through studies on a variety of animal models [
36]. Among them, SB28 glioblastoma, located in the brain, has a greater impact on systemic immunity than other tumors. Therefore, through the analysis of the systemic immunity of glioma patients and the correlation between systemic immunity and CTCs, the ability of clinical evaluation and prediction can be further improved.
Although there has been some understanding of the relationship between CTCs and systemic immunity, there are few reports on the association of neutrophils or systemic immune status with glioma CTCs. Our study data showed that postoperative CTC status highly correlated with the overall immunity of the body. On the one hand, the peripheral immune state is characterized by the promotion of the secretion of the inflammatory cytokine TNF-α through neutrophil degranulation or activity enhancement, and TNF-α is associated with the activation of neutrophil extracellular traps (NETs) in the inflammatory response [
37,
38]. Regarding glioma patients, the level of CTCs and the expression of NETs-related genes showed obvious convergence, and we thus infer that there is a close relationship between glioma CTCs and NETs, which is associated with a reduction in cell-killing ability, leading to immune escape and a poor prognosis. On the other hand, the analysis of RNA sequencing results also showed T-cell-mediated immune cell damage and decreased lymphocyte immune regulation ability. Additionally, this study showed a correlation between hypoxia and CTCs in the systemic immune state. CTCs in the systemic immune state also face adaptive changes in the hypoxic environment. In the peripheral blood of glioma patients, CTCs significantly correlated with the increased expression of HIF-1α in the systemic immune system. HIF-1α may act as a protective factor for peripheral CTC survival, preventing apoptosis in the peripheral circulation of CTCs and thereby shortening the predicted OS times of patients [
39].
Another interesting phenomenon in this study is that although CTCs can be detected in glioma patients both before and after surgery, the patient prognosis was not related to preoperative CTC levels but was significantly related to postoperative CTC levels. There are few studies on the correlation between preoperative CTCs and the prognosis of patients with glioma, but Lynch D. et al. also showed no correlation between CTCs and PFS or OS in a small cohort of patients with glioma [
40]. We speculate that CTCs are likely to interact with the immune system, especially the inflammatory response, resulting in a poor patient prognosis. At the tumor site, the inflammatory response is a negative prognostic factor for gliomas [
41,
42], and the release of inflammatory factors tends to accelerate glioma progression [
43]. However, preoperative tumor-related factors in glioma patients, including pathological grade, lesion site, tumor location, mass size, and edema degree, did not affect inflammatory factors [
44]. Therefore, although CTCs exist in glioma patients before surgery, they have no effect on the prognosis of glioma patients due to the systemic noninflammatory immune environment. These results suggest that the systemic immune status should also be considered in the clinical treatment of glioma patients and that targeted systemic immunomodulatory therapy, especially of neutrophils associated with NETs formation, may benefit patients clinically.
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