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Table of Contents
Year : 2018  |  Volume : 1  |  Issue : 6  |  Page : 201-207

Assessment of microvascular patterns and density in glioblastoma and their correlation with matrix metalloproteinase-9, p53, glial fibrillary acidic protein, and Ki-67

1 Department of Pathology, Institute of Human Behaviour and Allied Sciences, Delhi, India
2 Department of Biostatistics and Medical Informatics, University College of Medical Sciences, Delhi, India

Date of Web Publication27-Dec-2018

Correspondence Address:
Dr. Sujata Chaturvedi
Department of Pathology, Institute of Human Behaviour and Allied Sciences, Dilshad Garden, Delhi - 110 095
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/glioma.glioma_31_18

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Background and Aim: Microvascular patterns (MVPs) and microvessel density (MVD) can influence the progression of glioblastomas. This study aims to study MVP and MVD using immunohistochemistry, and examine any correlation with the expression of matrix metalloproteinase-9 (MMP-9), p53, glial fibrillary acidic protein (GFAP), and Ki-67 labeling index (Ki-67 LI) in 24 cases of glioblastoma multiforme. Materials and Methods: MVPs and MVD were studied by a dual staining method using periodic acid–Schiff stain with CD34 (MVDCD34), CD31 (MVDCD31), von Willebrand factor (MVDvWF), and factor VIII (MVDFVIII). The expression of MMP-9, p53, GFAP, and Ki-67 LI was analyzed using immunohistochemistry. The Pearson coefficient of correlation and intraclass correlation were obtained using SPSS software. Results: Five distinct categories of MVP were found: Microvascular sprouting (MS)/simple vessels, vascular clusters (VCs), vascular garlands, glomeruloid tufts, and vasculogenic mimicry. Of the MVPs, MS was the most common pattern and was present in all cases. On calculating the Pearson's correlation coefficient, different MVPs gave varying results regarding their correlation with MMP-9, p53, GFAP, and Ki-67 LI. MSCD34, CD31, vWF showed significant correlation with MMP-9 and Ki-67 LI, while MSFVIII did not show any correlation with Ki-67 LI. Only VCCD34 had a correlation with Ki-67 LI. No correlation between any of the MVPs and GFAP and p53 was appreciated. MVD ranged from: CD34 (9.2–41.9/hpf), FVIII (6.05–40.5/hpf), CD31 (5.1–40.7/hpf), and vWF (8.7–35.5/hpf). MVDCD34 and MVDCD31 correlated with MMP-9 and Ki-67, whereas, MVDvWF and MVD FVIII correlated with MMP-9. Interobserver agreement was seen only in the assessment of MVD and the MS type of MVP. Conclusion: MVD and MVPs had correlation with MMP-9, p53, GFAP, and Ki-67. These results could impact the development of strategies using antiangiogenic therapies.

Keywords: Glioblastoma, microvascular patterns, microvessel density

How to cite this article:
Jha K, Pant I, Singh R, Bansal AK, Chaturvedi S. Assessment of microvascular patterns and density in glioblastoma and their correlation with matrix metalloproteinase-9, p53, glial fibrillary acidic protein, and Ki-67. Glioma 2018;1:201-7

How to cite this URL:
Jha K, Pant I, Singh R, Bansal AK, Chaturvedi S. Assessment of microvascular patterns and density in glioblastoma and their correlation with matrix metalloproteinase-9, p53, glial fibrillary acidic protein, and Ki-67. Glioma [serial online] 2018 [cited 2022 Nov 27];1:201-7. Available from: http://www.jglioma.com/text.asp?2018/1/6/201/248706

  Introduction Top

Angiogenesis plays a key role in disease progression in infiltrating astrocytic tumors.[1] A host of factors contribute to the process of angiogenesis including angiogenic factors and their receptors, adhesion molecules, β-catenin, regulators of extracellular matrix remodeling, and genetic alterations.[2] The degree of angiogenesis impacts both tumor biology and the aggressiveness of the tumor. Therefore, the quantitative assessment of angiogenesis in terms of microvessel density (MVD), has been studied in a variety of malignant tumors.[3],[4],[5] Its prognostic significance has been shown in carcinomas of the breast. In many other tumors, its role as a prognostic and predictive factor is yet to be established.[6] Thus, we attempted to study the quantitative and qualitative aspects of angiogenesis in terms of MVD and microvascular patterns (MVPs) in glioblastoma. We attempted to correlate MVD and MVP with the expression of markers of tumorigenicity (p53), tumor spread (MMP-9), glial differentiation, glial fibrillary acidic protein [GFAP], and the proliferation marker (Ki-67 labeling index [Ki-67 LI]).

  Materials and Methods Top

Clinical details

This is a retrospective study of 24 histologically diagnosed cases of glioblastoma over a period of 1 year. Paraffin-embedded blocks with tumor samples were retrieved from the archives for further immunohistochemical evaluation. From the clinical records, gender, age, tumor site, and size were recorded [Table 1]. In these cases, glioblastoma had been diagnosed on the basis of the histopathological criteria of increased cellularity, distinct nuclear atypia, mitotic activity, endothelial proliferation, and necrosis.
Table 1: Demographic details of the patients

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Immunohistochemistry for microvessel density and microvascular patterns

Dual staining (periodic acid-Schiff with CD34, CD31, factor VIII, and vWF)

Sections were cut at a thickness of 4 μm from routinely prepared, formalin-fixed, and paraffin-embedded blocks. Dual staining was done according to the method described by Yue and Chen.[7] For CD34 staining, we used anti-CD34 mouse monoclonal antibody (Biogenex, clone QBEND/10, ready-to-use), anti-CD31 rabbit monoclonal antibody (Biocare, clone BC2, ready-to-use), anti-vWF rabbit polyclonal anti-human antibody (Dako, clone N/A, ready-to-use), and anti-factor VIII rabbit polyclonal antibody (Biocare, clone N/A, ready-to-use). Microvessels from nonneoplasic brain tissue adjacent to tumor served as internal morphological controls [Figure 1]A. Primary antibodies were omitted for the negative controls. Two observers (KJ and RS) made independent MVD and MVP observations on a twin header microscope to ensure similarity of fields observed.
Figure 1: (A) Cortical tissue as internal positive control for vessels (CD34 + PAS, ×100); (B) microvascular sprouting (CD34 + PAS, ×400); (C) vascular clusters (CD34 + PAS, ×400); (D) vascular garlands (CD34 + PAS, ×400); (E) glomeruloid tufts (CD34 + PAS, ×400); (F) vasculogenic mimicry (CD34 + PAS, ×400)

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Assessment of microvascular patterns

We have followed the criteria suggested by Chen et al.[8] Five different categories of MVP were defined: microvascular sprouts/simple vessels (MS), vascular clusters (VC), vascular garlands (VG), glomeruloid tufts (GT), and vasculogenic mimicry (VM). For vessels marked by antiendothelial cell (EC) antibodies, four types of formations were defined as follows: (1) microvascular sprouting (MS)/simple vessels, defined as delicate capillary-like vessels with a lumen, (2) vascular cluster (VC), defined as distinct focal aggregations of vessels without connective stroma, (3) vascular garland (VG), defined as clustered vessels arranged in garland like formation, with or without connective tissue stroma, and (4) GT, defined as clustered vessels embedded by connective tissue stroma with a glomerulus-like appearance (containing 15–100 nuclei). A stained lumen or a separate cell stained positive for the endothelial markers was regarded as a single countable microvessel. The vessels with muscular walls were not included in the study. The fifth type of pattern was VM, defined as vascular channels lined by cells that stained negative for the endothelial markers, but showed periodic acid–Schiff (PAS) positivity.

Microvessel density estimation

MVD measurement was performed after staining the sections with the four endothelial markers. Briefly, the stained sections were screened at ×100 magnification under a light microscope to identify the ten regions of the section with the highest number of microvessels. Microvessels were then counted in the selected areas at ×400 magnification (high-power field, hpf), and the average was counted. For detection of microvessels, a microvessel was defined as any brown, immunostained EC separated from adjacent microvessels, tumor cells, and other connective tissue elements. The MVD count was further grouped into three classes: low (<10/hpf), intermediate (10–20/hpf), and high (>20/hpf). The average MVD was then correlated with the immunohistochemical expression of MMP-9, p53, GFAP, and Ki-67 LI.

Immunohistochemical analyses for matrix metalloproteinase-9, p53, glial fibrillary acidic protein, and Ki-67 labeling index

Serial sections from matched specimens were selected for immunohistochemistry. The following antibodies were used with standard protocols: anti-MMP-9 rabbit monoclonal antibody (Biogenex, clone EP1255Y, ready-to-use), anti-p53 mouse monoclonal antibody (Biogenex, clone BP53-12, ready-to-use), anti-GFAP mouse monoclonal antibody (Biogenex, clone GA-5, ready-to-use), and anti-Ki-67 rabbit monoclonal antibody (Biocare, clone EPR3611, ready-to-use). As positive controls, previously tested known positive cases were taken. Negative controls were generated by omission of the primary antibody.[9] The expression of molecular markers was recorded using Allred score.[10] A score of 0–2 was regarded as negative, while a score of 3–8 was positive. For Ki-67 LI, the number of cells showing nuclear positivity was counted out of a total of 1000 tumor cells. Immunohistochemical analysis of MMP-9, p53, GFAP, and Ki-67 LI was performed.

Statistical analysis

Statistical analyses were performed using SPSS software, version 20.0 (SPSS, Inc., Chicago, IL, USA). P < 0.05 was defined as statistically significant. Associations between MVPs, MVD, and MMP-9, p53, GFAP, and Ki-67 LI were determined by the Pearson's correlation coefficient. Interobserver agreement in the assessment of MVD, and MVPs was assessed by intraclass correlation coefficient (ICC).

  Results Top

Clinical characteristics

The age of the patients ranged from 15 to 77 years. Of them, 16 were male and 8 were female. The size of the tumor received ranged from 2.5 to 8.5 cm. Different locations of the tumor are detailed in [Table 1].

Classification of microvascular patterns

All 24 cases were heterogeneous in the distribution and proportion of the five MVPs. The most prominent pattern detected was MS/simple vessels, present in all cases. The other patterns in decreasing order of frequency were: (1) VC, (2) VG, (3) GT, and (4) VM [Table 2] and [Figure 1], [Figure 2], [Figure 3].
Table 2: Classification of microvascular patterns by different markers

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Figure 2: (A) Microvascular sprouting (FVIII + PAS, ×400); (B) vascular clusters (FVIII + PAS, ×400); (C) vascular clusters, four vessels are seen separated by connective tissue pillar (FVIII + PAS, ×400); (D) Vascular garlands (FVIII + PAS, ×400); (E) glomeruloid tufts (FVIII + PAS, ×400); (F) vasculogenic mimicry (FVIII + PAS, ×400)

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Figure 3: (A) Microvascular sprouting (CD31 + PAS, ×400); (B) vascular clusters (CD31 + PAS, ×400); (C) vascular garlands (CD31 + PAS, ×400); (D) glomeruloid tufts (CD31 + PAS, ×400); (E) vasculogenic mimicry (CD31 + PAS, ×400);(F) microvascular sprouting (vWF + PAS, ×400); (G) vascular clusters (vWF + PAS, ×400); (H) vascular garlands (vWF + PAS, ×400); (I) glomeruloid tufts (vWF + PAS, ×400); (J) vasculogenic mimicry (vWF + PAS, ×400)

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Estimation of microvessel density by different markers

The MVD range (per hpf) given by CD34, FVIII, CD31, and vWF were 9.2–41.9, 5.1–40.7, 8.7–35.5, and 6.05–40.5, respectively. Majority of the cases fell into the high MVD count category [Table 3].
Table 3: Estimation of microvascular density by different markers

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Correlation between microvessel density and microvascular patterns with matrix metalloproteinase-9, p53, glial fibrillary acidic protein, and Ki-67 labeling index

MMP-9 and GFAP immunopositivity was observed in the cytoplasmic and fibrillary network. Ki-67 and p53 expression was nuclear [Figure 4]. In our study, whereas, MVDCD34 and MVDCD31 showed statistically significant association with MMP-9 and Ki-67 LI, MVDvWF and MVDFVIII showed association with only MMP-9. Among the various MVPs studied with different markers, MSCD34, CD31, vWF also showed a similar association with MMP-9 and Ki-67 LI, except MSFVIII, which did not show any association with Ki-67 LI. The rest of the patterns failed to show any association except for VCCD34 showing association with Ki-67 LI. No association was found between any of the vascular parameters and GFAP or p53 [Table 4].
Figure 4: (A) Strong Matrix metalloproteinase-9 expression in tumor cells, ×200; (B) nuclear p53 expression in tumor cells, ×400; (C) glial fibrillary acidic protein positivity in tumor, ×200; (D) Ki-67 expression in areas of highest proliferation, ×200

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Table 4: Pearson's correlation between microvascular density and microvascular patterns with matrix metalloproteinase-9, p53, glial fibrillary acidic protein, and Ki-67 labeling index; significance, P, two-tailed

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Comparison of different endothelial markers

The highest number of MVPs and MVD were detected by CD34. The rest of the endothelial markers gave comparable results [Table 3] and [Table 4].

Interobserver agreement

The interobserver agreement was found in the counting of MVD by CD34 (ICC = 0.784, P < 0.01), factor VIII (ICC = 0.989, P < 0.01), and CD31 (ICC = 0.993, P < 0.01). Among the various MVPs, there was agreement in the assessment of MS by all the markers. Rest of the patterns showed variable results [Table 5].
Table 5: Assessment of interobserver agreement

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  Discussion Top

Angiogenesis is important in the pathogenesis of a variety of nonpathologic and pathologic conditions including malignancies.[11] Marked heterogeneity is seen in the organization, structure, function, gene expression, and antigen composition of tumor vessels. This is quite different from normal vasculature. It was Judah Folkman who first proposed in 1971, that tumor growth and metastasis are dependent on angiogenesis, and blocking angiogenesis could be the key to block the progression of the disease.[12] Although there are several studies on MVD in glioblastoma,[9],[13],[14],[15] very few articles mention the qualitative aspects of MVPs.[8],[16]

Glioblastoma is a highly vascularized tumor, and microvascular proliferation forms an integral part in the progression of low-grade glioma to glioblastoma.[14] The vascular networks show heterogeneity in morphology, as well as, in the mechanisms leading to their formation and development.[17] The evidence of angiogenesis playing a critical role in the biological behavior of these tumors and patients' prognosis has necessitated studies on the basic mechanisms of vascularization.[18] For almost 30 years, sprouting angiogenesis was considered the exclusive mechanism of vascularization in tumors. However, several additional mechanisms such as intussusceptive angiogenesis, vessel co-option, glomeruloid angiogenesis, recruitment of bone marrow-derived endothelial progenitor cells, and VM have been identified.[19],[20],[21],[22]

Growing tumors have been found to increase their blood supply and meet increased oxygen and nutritional demands by the following mechanisms: formation of new capillary buds from preexisting vessels (sprouting);[20] lining along the preexisting blood vessels (vessel co-option);[21] migration of angioblasts/endothelial progenitor cells from bone marrow under the influence of growth factors, (also known as vasculogenesis);[20],[23] splitting of preexisting vessel with the formation of connective columns (tissue pillars) in the lumen of the vessel (intussusceptive microvascular growth [IMG]);[24] remodeling of existing vasculature to form complex glomeruloid bodies (glomeruloid angiogenesis); and finally,[22] the tumor cells themselves dedifferentiating and acquiring an endothelial phenotype forming PAS-positive vascular patterns (VM).[25]

IMG has been found to be a faster, more economical way of increasing the complexity and density of the tumor microvessel network, independent of EC proliferation.[24] It has been found that this process sets into play once the sprouting of blood vessels has begun. It has been suggested that the absence of EC proliferation in IMG implies that neovascularization by this mechanism would be resistant to angiosuppressive treatment.[22]

Maniotis et al.[26] first described VM in 1999 in uveal melanoma. It has since been observed in a variety of tumors such as carcinoma of breast, prostate, ovary, lung, synovial sarcoma, rhabdomyosarcoma, pheochromocytoma, and glioblastomas.[7] The discovery of VM has challenged the hypothesis that angiogenesis is the only means by which tumor acquires a blood supply. According to Folberg et al.,[27] the mere detection of VM anywhere in the tumor sections has a prognostic significance. In a recent study by Wang et al.,[28] the presence of VM has been proposed as an adverse prognostic factor for postoperative survival in newly diagnosed cases of glioblastoma.

The studies have found the presence of “classic” (capillary-like) and “bizarre” (GT, VCs, and vascular garlanding), vascular patterns along with VM in glioblastoma.[8],[9],[16] In consonance with these studies, our study also confirms the presence of these patterns. Our findings suggest that MS and simple vessels form the predominant pattern in these tumors. The 24 cases in our study had a variable composition of the bizarre patterns. Chen et al.[8] in their study found that the cases with fewer microvascular sprouts and VCs had a significantly higher number of GTs and VM. However, we found that the bizarre vascular patterns comprised only up to 10% of the total MVP found in most of the cases. VM was found in approximately 50% of our cases, in contrast to 38.1% in another study by Liu et al.[25] and 28.75% in the study by Wang et al.[28] In the study by Liu et al.,[25] MVD was lower in VM positive cases. No such observation was seen in our study. In concordance with the study by Wang et al.,[28] we found a few vessels lying closely approximated together, separated either by a common connective tissue wall or juxtaposed together [Figure 2]C and [Figure 3]B, pointing toward intussusception being the likely underlying mechanism.

These findings led us to speculate whether vascular parameters have significance apart from tumor grade. Several trials have evaluated the impact of antiangiogenic treatment on the overall survival of glioblastoma patients, but with inconsistent results.[29] Alternate mechanisms of vascularization operating during tumor proliferation and growth may be responsible for the failure of antiangiogenic therapy. Due to the existence of multiple vascularization mechanisms and angiogenic signaling pathways, the inhibition of a single signaling pathway likely triggers alternative vascularization mechanisms.[22] A review of the literature shows that some key genes have been studied in the molecular pathways involved in the development and proliferation of vascular networks in glioblastoma.[20] Identifying the key molecular pathways in the above-mentioned vascularization mechanisms will be a first step in providing a more targeted antivascular therapy in glioblastoma.

Matrix metalloproteinases (MMPs) have been recognized as modulators of tumor microenvironment.[30] The formation of new blood capillaries is dependent on the extracellular matrix, which serves as structural support for existing and developing vessels and matrix metalloproteinases such as MMP-2 and 9.[20] MMPs are known to affect vascular density and regulate vessel branching in glioblastoma. These zinc-dependent endopeptidases have been implicated in modulating the vascular functionality by regulating pericyte activation and recruitment.[30],[31] In the present study, a statistically significant association was found between MMP-9 and MVD. A significant relationship was seen in the expression of MMP-9 with microvascular sprouts and simple vessels. However, the complex patterns failed to show an association with MMP-9 expression. In the study done by Zhang et al.,[32] inhibition of MMP-2 and MMP-9 expression led to suppressed VM channel and mosaic vessel formations in melanoma. No association was seen between VM formation and MMP-9 expression in our study.

Ki-67 LI is a proliferation marker that denotes higher grade and aggressiveness of malignant gliomas.[33] TP53 mutations bear a favorable prognostic impact irrespective of whether the glioblastoma is primary or secondary.[34] Chen et al.[8] observed a potential relationship between expression of Ki-67 and p53 with microvascular heterogeneity. Correlation between Ki-67 LI and MVD has been studied in other tumors with variable results.[35],[36],[37] In our study, the Ki-67 index showed a similar relationship as MMP-9 with MVD and MS, and p53 expression was not associated with the vascular parameters studied. We also studied the expression of GFAP in the glioblastoma cells. It is a well-known fact that the malignant astrocytomas are often GFAP negative and seem to lose GFAP expression with the progression of the grade. It has also been found that GFAP-negative tumor cells proliferate more rapidly as compared to the GFAP-positive tumor cells. A strong negative correlation between loss of GFAP expression and dedifferentiation of malignant tumor cells has also been documented by several in vitro studies.[38] Vascularization and loss of GFAP expression are seen with the progression of the malignant phenotype of the tumor cells. In the current study, no association was seen in the vascular parameters and GFAP expression.

Often, when MVD and MVPs are analyzed manually by multiple observers, interobserver variability yields variable results.[9] The selection of vascularized fields for quantification likely plays an important role in this variability. The “hot spot” method yields better results as compared to the random selection of vascular areas within the tumor.[9] Preusser et al.[9] state that highest agreement is achieved when different observers counted MVD in the same area. However, observer agreement on vascular patterns was poor in their study. In our study, there was better agreement in counting MVD and microvascular sprouts (statistically significant). There was no agreement in the assignment of VC, VG, GT, and VM in the present study. One of the possible reasons for the absence of such agreement is the presence of many intermediate formations in vascular patterns as has been mentioned in other studies.[8]

Researchers have been somewhat divided on their views about the selection of the most suitable endothelial marker.[6],[16] We used four endothelial markers to study MVD and MVPs. In our hands, we found CD34 to be the best endothelial marker. Using CD34, we found the vessels were better highlighted and had finer detail.

This preliminary study revealed varying MVD and MVPs assessed by different immunohistochemical markers; their correlation with MMP-9, p53, GFAP, and Ki-67; and inter-observer agreement pattern in the assessment of MVD and MVPs. As antiangiogenic therapeutic strategies are being developed for glioblastoma, further studies that standardize the vascular parameters such as MVD and MVPs are needed. Our study was limited by a small sample size and the lack of follow-up data.

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Conflicts of interest

There are no conflicts of interest.

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  [Figure 1], [Figure 2], [Figure 3], [Figure 4]

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]


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