|Year : 2020 | Volume
| Issue : 1 | Page : 32-37
Interlinked expression of tumor attributes: Can their evaluation serve as a noninvasive paradigm for prognosis in glioblastoma?
Puneet Gandhi1, Richa Khare1, Nitin Garg2, Sandeep K Sorte3
1 Department of Research, Bhopal Memorial Hospital and Research Centre, Bhopal, Madhya Pradesh, India
2 Department of Neurosurgery, Bhopal Memorial Hospital and Research Centre, Bansal Hospital, Bhopal, Madhya Pradesh, India
3 Department of Neurosurgery, Bhopal Memorial Hospital and Research Centre, Bhopal, Madhya Pradesh, India
|Date of Submission||18-Nov-2019|
|Date of Acceptance||04-Dec-2020|
|Date of Web Publication||2-Jul-2020|
Dr. Puneet Gandhi
Department of Research, Bhopal Memorial Hospital and Research Centre, Bhopal, Madhya Pradesh
Source of Support: None, Conflict of Interest: None
Background: Glioblastoma (GB) is one of the deadliest brain cancers with a bleak prognosis. It is characterized by highly proliferating tumor cells influenced by surrounding stroma, with a constant detectable communication between the tumor and its environmental components, which can be assessed in terms of specific proteins.
Patients and Methods: To experimentally establish this cellular dialog, we undertook to evaluate plasma expression of three proteins: human telomerase reverse transcriptase (hTERT), chitinase-like protein (YKL-40), and tissue inhibitor of metalloproteinase-1 (TIMP-1) in 66 individuals inclusive of thirty primary and two secondary GB patients and 34 age-matched controls, with reference to proliferation, angiogenesis, and extracellular matrix (ECM) degradation biomarkers. A follow-up at 3 months postsurgery for marker profile was done for GB.
Results: Plasma levels of markers were higher in therapy-naive patients than in controls (P < 0.0001). In a 3-month follow-up, these levels were found to be higher in patients who survived for ≤4 months than nine patients who had lower values with better survival. A strong correlation existed between hTERT, YKL-40, and TIMP-1 levels; Cox regression analysis revealed higher levels of these circulating markers as poor prognostic indicators. The sensitivity of these markers increased to 96.9% in combination, thereby improving the accuracy of prognosis. Confounders, i.e. age, site, and extent of resection, had no effect on the prognostication of these markers.
Conclusion: These experimental blood-based data define the influence of angiogenesis and ECM remodeling on proliferation of GB relative to poor survival, using a panel of biomarkers. Evaluating the disease outcome noninvasively can pave the way for designing intervention strategy to control angiogenesis and ECM degradation and in turn tumor progression.
Keywords: Biomarkers, circulatory, glioblastoma, noninvasive, prognosis
|How to cite this article:|
Gandhi P, Khare R, Garg N, Sorte SK. Interlinked expression of tumor attributes: Can their evaluation serve as a noninvasive paradigm for prognosis in glioblastoma?. Int J Neurooncol 2020;3:32-7
| Introduction|| |
Glioblastoma (GB) is very aggressive and heterogeneous, accounting for 52% of all primary brain tumors, and with a largely unchanged median survival over the past decade. The need for validating blood-based molecular prognostic markers in GB is underlined by the sample restriction of tissue markers.
In this clinical backdrop, the objective of the study was to characterize the tumor attributes, namely rate of proliferation in terms of circulatory human telomerase reverse transcriptase (hTERT), angiogenic status by chitinase-like protein (YKL-40), and tissue inhibitor of metalloproteinase-1 (TIMP-1) for extracellular matrix degradation, in GB samples. This would aid in identifying a novel biomarker panel that can improve overall survival (OS) by patient-specific adjuvant therapy.
| Patients and Methods|| |
On the basis of neurological symptoms followed by radiological diagnosis, 32 patients suspected of high-grade glioma, who were taken up for this pilot study. The recruited individuals were asked to sign the consent form for voluntary participation, as approved by the institutional ethics committee of the hospital (IEC/21/Res/11); sample collection was then done as per standard protocol. Subsequent to histological diagnosis, thirty patients were isocitrate dehydrogenase (IDH) negative and therefore designated as GB (not otherwise specified), whereas n = 2 were IDH-mutant type. GB samples were normalized against 34 controls, including 15 control tissue samples resected during brain surgery for reasons other than malignancy. In follow-up, blood samples were collected at 3 months for analysis of candidate marker panel.
The expression of IDH, p53, and epidermal growth factor receptor (EGFR) in formalin-fixed paraffin-embedded (FFPE) tissues of GB as per the World Health Organization guideline , was carried out as described below. Sections of tissue were probed with primary antibody IDH1 (Santa Cruz Biotechnology, US, 1:1500 dilution), p53 (Bethyl Laboratories, US, 1:1500 dilution), EGFR (Bethyl Laboratories, US, 1:2000 dilution), hTERT (Abcam, US, 1:2000 dilution), TIMP-1 (Biorbyt, US, 1:2000 dilution), and YKL-40 (Antibodies Online, US, 1:2000 dilution). The sections were then treated with host-specific fluorescein isothiocyanate-tagged compatible secondary antibody (Santa Cruz Biotechnology, US), counterstained, and mounted with antifade. Imaging and its analysis were done according to the procedure described earlier.
Estimation of human telomerase reverse transcriptase, chitinase-like protein, and tissue inhibitor of metalloproteinase-1
Plasma hTERT (ng/ml; MyBioSource, US), YKL-40, and TIMP-1 concentrations (ng/ml; R and D Systems, US) were determined by sandwich enzyme-linked immunosorbent assay using commercial kits according to the manufacturers' protocol. All samples were analyzed in duplicates. The levels were measured as absorbance at 450 nm with the correction wavelength set at 540 nm for YKL-40 and TIMP-1.
The Mann–Whitney U-test was applied to determine the differences in levels of the three circulating markers between GB and control groups. Spearman's rho coefficient was used to calculate the correlation between concentrations of these markers in blood with the OS and also to check their correlation with each other. To evaluate the biomarker performance, receiver operating characteristic (ROC) curve, area under the curve (AUC) of ROC, sensitivity, specificity, as well as likelihood ratios were calculated to best differentiate GB cases from controls. To establish the accuracy of the panel, CombiROC software (https//:combiroc.eu) was used and the predictive probability of the panel as a single marker was subjected to ROC analysis. Survival curves were estimated using the Kaplan–Meier log- rank test to enumerate OS of GB patients. Univariate and multivariate Cox proportional hazards regression models provided the prognostication power of the panel. The OS of the patients was defined as the time lapse between the date of initial diagnosis and the date of demise. The influence of covariates (age, site, and extent of resection [EOR]) on hTERT, YKL-40, and TIMP-1 expression was checked by multivariate analysis, and adjustment, if any, was done by logistic regression. A two-tailed P < 0.05 was taken considered for statistical significance, and GraphPad Prism version 8.2.1 (GraphPad Software, San Diego, CA, US) was used.
| Results|| |
The median age of the GB group was 49 years. Postoperative clinical findings recorded 40% lesions as frontal (n = 13) and 60% as nonfrontal (n = 19) tumors. Clinical case summary described 6.25% of patients with gross total resection (n = 2) and 93.75% of patients with a subtotal resection (n = 30). A comparison of magnetic resonance imaging of GB cases did not provide any indication on differential tumor progression or its proliferation rate. Of a total of 32 GB patients, only 9 underwent adjuvant therapy and lived for more than 4 months. The rest of the patients deferred adjunctive treatment. Therefore, at the end of the study, the median OS in GB was 4 months and the interquartile range was 9.25 months.
Quantification of molecular markers by immunofluorescence-based immunohistochemistry
Positive expression of EGFR and p53 confirmed the group as GB at the molecular level. The expression of hTERT in GB FFPE tissue was calculated at 25.76% (10.5%–44.75%), for YKL-40, it was 7.1% (2.1%–17%), and TIMP-1 was 8.34% (3.4%–12%), respectively, which reflected in the image analysis [Appendix 1 and [Figure 1]. The Mann–Whitney test revealed that hTERT, YKL-40, and TIMP-1 expression was significantly higher in GB (P< 0.0001) as compared to controls. An inverse correlation of hTERT (r =−0.6558, P < 0.0001), YKL-40 (r =−0.6872, P < 0.0001), and TIMP-1 (r =−0.6591, P < 0.0001) was obtained in respect of OS when analyzed using Spearman's rho coefficient.
|Figure 1: Receiver operating characteristic curve analysis (a-c): (a) human telomerase reverse transcriptase, (b) chitinase-like protein, and (c) tissue inhibitor of metalloproteinase-1 (d) showing the optimal cutoff value for enhanced sensitivity using CombiROC analysis of the markers human telomerase reverse transcriptase, chitinase-like protein, and tissue inhibitor of metalloproteinase-1|
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Plasma levels of human telomerase reverse transcriptase, chitinase-like protein, tissue inhibitor of metalloproteinase-1
Median levels of plasma hTERT, YKL-40, and TIMP-1 in GB were calculated at 4478 ng/ml, 116.53 ng/ml, and 107.069 ng/ml, respectively [Table 1]. These values emerged as substantially higher when compared to controls, with P < 0.0001 for each marker, in the patient group, when nonparametric unpaired t-test was applied. On evaluating these levels, it was found that the concentrations of these markers were lower in patients completing adjuvant therapy; their survival was greater than the median OS as compared to their counterparts [Table 2]. Furthermore, a highly significant positive correlation between tissue and systemic expression of these markers was obtained [Appendix 1 and [Table 1].
|Table 1: Demographic and clinical characteristics with reference to plasma concentrations of the molecular markers in the study cohort|
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|Table 2: A comparison of plasma levels of molecular markers in glioblastoma patients grouped according to their adjuvant therapy status|
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Receiver operating characteristic analysis
AUROC plots provided an optimal cutoff for hTERT, YKL-40, and TIMP-1 in plasma with 100% specificity to best predict survival. The AUCs for hTERT, YKL-40, and TIMP-1 were 0.8594, 0.9654, and 0.8977, with thresholds at 1420 ng/ml, 41.98 ng/ml, and 69.81 ng/ml, respectively. These threshold values were equated for survival analysis by the log-rank Mantel–Cox test [Table 3] and [Figure 1]a, [Figure 1]b, [Figure 1]c.
|Table 3: The cutoff values for defining sensitivity and specificity of circulatory levels in therapy-naive glioblastoma|
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Patients with plasma hTERT, YKL-40, and TIMP-1 concentrations exceeding their optimal cutoffs were found to differ significantly from the lower ones, and they had a shorter survival period. Therefore, it can be concluded that these markers can serve as survival indicators [Figure 2].
|Figure 2: Survival curve analysis in glioblastoma patients (a) human telomerase reverse transcriptase, (b) chitinase-like protein, and (c) tissue inhibitor of metalloproteinase-1; red indicates worse prognosis, whereas green represents favorable prognosis|
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Cox regression analysis
Further, the relation of OS with circulatory measures of hTERT, YKL-40, and TIMP-1 was calculated applying univariate and multivariate Cox regression models to identify the plausible prognostic factors for GB. Univariate and multivariate Cox regression model analysis delineated shorter patient survival to be linked to elevated plasma levels of hTERT (P = 0.0371), YKL-40 (P = 0.0246), and TIMP-1 (P = 0.0295). Patients with higher plasma concentrations of these molecules were likely to have a worse outcome, indicating the target proteins to be good prognostic biomarkers.
The effect of age, site of the tumor, and EOR as confounding factors were checked by multivariate analysis. Higher levels of hTERT, YKL-40, and TIMP-1 in plasma were not associated with age (P = 0.1842, P = 0.2518, and P = 0.6197) or EOR (P = 0.6565, P = 0.8911, and P = 0.0909). Furthermore, hTERT (P = 0.7416) and TIMP-1 (P = 0.8559) levels did not correlate with the tumor site, whereas YKL-40 was found to be associated (P = 0.0452). Therefore, after adjustment, the value for YKL-40 was obtained as P = 0.512. Results indicate that these circulatory molecules are independent of confounders, establishing their utility for precise prognosis.
The correlation of preoperative plasma levels of all three markers with OS was analyzed, and a significant inverse correlation of hTERT (r =−0.6456, P < 0.0001), YKL-40 (r =−0.5902, P = 0.0004), and TIMP-1 (r =−0.3772, P = 0.0333) was obtained with OS. Their intercorrelation was also analyzed by Spearman's rho correlation coefficient. A highly significant positive correlation of hTERT was observed with YKL-40 (r = 0.463, P = 0.0075) and TIMP-1 (r = 0.435, P = 0.0127), respectively. Similarly, YKL-40 was also significantly positive correlated with TIMP-1 (r = 0.368, P = 0.0377). Patients who received adjuvant therapy had better survival than those who deferred therapy (r = 0.6304, P = 0.0002). This analysis indicated that the events, i.e. tumor proliferation, angiogenesis, and extracellular matrix (ECM) degradation known to be correlated, can be monitored in terms of our target markers.
Accuracy can be improved considerably by combining multiple markers, whose prognostic performance can be accessed through ROC curves. Analyzing the target molecules as a panel by CombiROC enhanced the sensitivity of our biomarkers. Individually, the sensitivity of hTERT, YKL-40, and TIMP-1 in the prognosis of GB was 76.47%, 94.12%, and 70.59%, respectively; it increased to 96.9% in combination, which is better than the AUC of our best marker [Figure 1]d. Hence, it is suggested that this panel can be used to enhance the accuracy for early prognosis of enhanced tumor proliferation and angiogenesis.
| Discussion|| |
The interaction between proliferating cells and various dynamics in the tumor vicinity is a decisive aspect of GB progression and depends majorly on two important factors, namely angiogenesis and ECM. In light of this fact, monitoring the angiogenic response by YKL-40 and ECM degradation as TIMP-1 is relevant to glial tumor proliferation and progression.
The histological characteristic of GB is increased neoplastic proliferation of glial cells which run parallel to angiogenesis and reorganization of ECM; hence, it can be sequentially tested in terms of circulating hTERT concentration. The work of Macarthur et al. assessing telomerase activity in brain-derived circulating tumor cells (CTCs) of 10 patients with high-grade glioma indicated higher telomerase activity in CTCs of 72% preradiotherapy patients which reduced postradiotherapy. This corroborates our results, wherein plasma levels of hTERT were elevated in all 32 GB patients and decreased by two-fold in 28.14% of live patients' postsurgery. The other two blood-based studies quantifying hTERT protein are also published from our laboratory, one where we have discussed low plasma hTERT levels in a GB patient with prolonged progression-free survival. The other extended study has established the correlation of elevated plasma concentrations of hTERT with OS in both low- and high-grade glioma patients, presenting hTERT as an independent prognosticator. Thus, this marker can be used to differentiate survival, pre- and postsurgical intervention, and adjunct therapy.
Since tumor cells undergoing active proliferation during Oncogenesis have increased telomerase activity, they are dependent on the development of new vasculature (angiogenesis), which in turn requires the disruption of ECM to sprout forth. Establishing this link, our results point to concomitantly increased levels of YKL-40 and TIMP-1 with the higher expression of hTERT in GB patients.
The secreted glycoprotein YKL-40 produced by tumor cells and inflammatory cells plays a pivotal role in the regulation of tumor angiogenesis. Findings of Zhang et al. usingin vitro andin vivo assays have suggested a role for YKL-40, indicating a direct relation between its expression and blood vessel density. On similar lines , elucidated mechanistically an angiogenic signature for YKL-40 in GB. In accordance with our results, two scientific groups have reported higher serum/plasma levels of YKL-40 to be associated with poor prognosis of GB patients., However, the present investigation is a first biomarker study to experimentally establish a direct linkage of angiogenesis to proliferation in GB, indicating YKL-40 as a target for angiogenesis inhibitors. In support of our observation, Boisen et al. have hypothesized that antiangiogenic drug bevacizumab works better in patients with newly diagnosed GB and low baseline plasma YKL-40 levels.
Aberrantly proliferating glial cells are also known to modify their phenotype for interacting with their surrounding environment and disrupt the ECM of the brain, partly through tumor-secreted matrix metalloproteinases. Therefore, quantifying the levels of TIMP-1 in circulation was taken to reflect on the state of ECM, a factor-promoting tumor proliferation in GB. Laboratory-based evidence given by Aaberg-Jessen et al. indicated that TIMP-1 frequently overexpresses in malignant GB. Two other studies in glioma patients and not exclusively on a GB cohort also suggest that TIMP-1 levels correlate with poor prognosis., However, a recent study conducted by Rojiani et al. proposed that TIMP-1 performs multiple functions within the tumor environment including stimulation of angiogenesis and proliferation of cells, and elevated TIMP-1 messenger RNA levels are associated with aggressiveness and poor clinical outcome in patients with brain metastasis. Given its potential growth-promoting abilities, TIMP-1 may actually be contributing to tumor proliferation and angiogenesis when upregulated, which is reflected in our obtained r values when Spearman correlation was applied.
| Conclusion|| |
Clinically, a single biomarker is unlikely to have sufficient sensitivity or specificity for use as a stand-alone prognosticator. Hence, a panel of markers can be more effective in delineating prognosis in GB as each individual patient's tumor shows variation in expression of molecular markers, which is mirrored in the clinical course of disease. Based on the output of this study, we can conclude that a crosstalk between tumor proliferation, angiogenesis, and ECM degradation exists in GB. At a first glance, the chosen proteomic marker-panel offers hope of enhanced prognostic accuracy of the tumor attributes, monitoring progression noninvasively, followed by personalized intervention during follow-up.
Financial support and sponsorship
Madhya Pradesh Biotechnology Council, Bhopal, Project # 249.
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3]