Abstract
Background
Breast cancer is the leading cause of cancer-related deaths in Asia and is emerging as the commonest female malignancy. Angiogenesis or neovascularization is important for the growth and spread of malignant tumors, and quantitative assessment of angiogenesis may prove valuable in prognostication. This study was undertaken to quantify and explore angiogenesis with immunohistochemistry with CD 34, CD 105, and vascular endothelial growth factor (VEGF), as well as morphometric analysis and correlate with the grades of the invasive breast carcinoma.
Methods
Angiogenesis was assessed by morphometry and immunohistochemistry. Seventy cases of invasive ductal carcinoma (IDC) and twenty-five benign cases as controls were included in the study. Morphometry was performed on the CD34 and CD105 (Endoglin) stained representative histologic sections with the use of a computerized digital photomicrograph system using image analyzing software. Morphometric analysis and evaluation of vascular parameters, i.e. microvessel density (MVD), microvessel caliber (VC), and total microvessel boundary density (TVBD), were calculated. Semiquantitative assessment of angiogenesis of VEGF-stained sections was done by scoring. Immunohistochemical staining was correlated with the histological grade of the tumors. MVD, mean VC, TVBD with their mean values, SD, and range were calculated using Statistical Package for The Social Sciences (Version 20). One-way analysis of variance (ANOVA) with Tukey HSD was performed to assess the difference of the parameters for the groups. Spearman rank correlation coefficients ρ were calculated.
Results
The vascular parameters were significantly more in malignant lesions as compared to benign lesions and showed differences with increasing grade. Grades of breast carcinoma showed a mild positive correlation with VEGF (ρ = 0.467), MVD-CD34 (ρ = 0.422) and VC-CD34 (ρ = 0.482); and moderate positive correlation with TVBD-CD34 (ρ = 0.615), VC-CD105 (ρ = 0.527), and TVBD-CD105 (ρ = 0.354). When these parameters were compared with each other for all four groups, VEGF showed a mild positive correlation with MVD-CD34 (ρ = 0.295), TVBD-CD34 (ρ = 0.339), and TVBD-CD105 ((ρ = 0.277). MVD-CD105 showed a mild positive correlation with MVD-CD34 TVBD-CD105 also showed a strong positive correlation with MVD-CD34. VC-CD105 showed a moderate positive correlation with VC-CD34. CD 105 stained fewer but larger caliber vessels.
Conclusions
In this study, vascular parameters showed significant differences in three grades of IDC with CD34. Differences were seen in vascular parameters stained with CD105 in three grades of IDC. Expression of VEGF also showed significant differences with positive correlations in the three grades of IDC. CD34 highlighted both old and newly formed microvessels. CD 105 stained fewer but larger caliber microvessels. VC-CD105 can be an extremely useful adjunct along with VEGF and CD34 to study angiogenesis of vessels in IDC.
Keywords: Immunohistochemistry, Angiogenesis (neovascularization), Invasive ductal carcinoma, Vascular endothelial growth factor, Endoglin (CD105)
Introduction
Breast cancer is the most common invasive cancer in women comprising 25.2% of cancers diagnosed in women1 and a leading cause of cancer-related deaths in Asia.
The spectrum of this disease is different in developing countries, with younger (<50 years of age) patients being up to 25% in developing countries compared with developed countries (10%). Also, most breast cancer patients are diagnosed at a relatively late stage, and locally advanced cancers constitute over 50% of all patients managed.2
Breast carcinoma is heterogeneous, with some tumors behaving in an aggressive manner with poor prognosis while some tumors presenting in a higher stage have a better prognosis.3 There are many factors affecting prognosis in breast cancer. These include grade with the type of tumor, stage, mitotic figures, and hormone receptors. Factors still being investigated include microvessel density, transforming growth factor-α, bcl-2.4 Angiogenesis is a factor important for the growth and spread of malignant tumors.5
The growth of a tumor beyond a certain size requires angiogenesis, which may also permit metastasis. Quantifying angiogenesis by use of markers is being investigated with variable results. Quantitative association between microvascular characteristics and histological grade in invasive ductal carcinomas continues to remain uncertain.6
The marker most commonly being used for angiogenesis is CD34.7
Angiogenesis is regulated by vascular endothelial growth factor (VEGF). 8 Several studies have been conducted in the last decade to establish its role as a prognostic factor and emphasize its role in developing novel targeted therapy. VEGF is a mitogen for endothelium and increases vascular permeability, and induces proteolytic enzymes for remodeling of vessels. The use of microvessel density (MVD) and VEGF as a marker for angiogenesis can provide additional information on the prognosis of invasive breast carcinoma.9,10
CD105, a hypoxia-inducible protein expressed in angiogenic endothelial cells, is found abundantly in blood vessels in tumor tissues and has also been studied as a marker for angiogenesis.11 It marks activated endothelial cells and quantification of tumor microvessel density, as determined by immunohistochemical staining for CD105 may be an indicator of poor prognosis in many solid malignancies, including breast.12,13
This study was undertaken to quantify and explore angiogenesis with immunohistochemistry with CD 34, CD 105, and VEGF, as well as morphometric analysis and correlate with the grades of the invasive breast carcinoma.
The aim of the study was to assess angiogenesis in invasive breast carcinoma by immunohistochemistry and morphometry, to compare the grades of invasive breast carcinoma by morphometric evaluation with immunohistochemical markers, CD34 and CD105, and to correlate grades of invasive breast carcinoma with VEGF by immunohistochemistry. We also sought to compare the endothelial markers for studying angiogenesis.
Material and methods
The study was approved by the Institutional Ethical Committee and patient consent for inclusion in study was also obtained. Seventy histopathologically confirmed cases of invasive ductal carcinoma (IDC) who underwent modified radical mastectomy (MRM) over a period of two years were included in the study. Twenty-five controls (benign cases) were included in the study. Clinicopathological data of each patient, including grade were collected. All consecutive cases of IDC where MRM was performed were included in the study. Those cases where adjuvant therapy had been given before MRM, recurrent carcinoma, and metastatic cases, were excluded from the study.
Histopathological analysis
Resection specimens were fixed in 10% buffered formalin and embedded in paraffin. Serial 5-mm sections were stained with hematoxylin and eosin (H&E) for pathological diagnosis, staging, and grading. The slides were independently reviewed by two pathologists. The tumors were staged according to TNM classification and graded according to Elston et al, modification of Bloom and Richardson’s criterion using three factors: (a) tubule formation, (b) nuclear pleomorphism, and (c) mitotic count.14
Immunohistochemistry
Two paraffin-embedded tissue blocks from each case with adjacent normal breast tissue were selected. Immunohistochemical staining for CD34, VEGF, and CD105 was performed using monoclonal antibodies on four μm thick tissue sections.
For ER, PR, and Her2neu scoring, Allred scoring,15 and ASCO guidelines16 were used.
Morphometry on CD34 and CD105 stained sections
Morphometry was done on the CD34 and CD105 stained representative histologic sections. We used a computerized digital photomicrograph system using a calibrated image analyzing software (Biowizard-4.1, Dewinter Optical, Inc., New Delhi, India; Fig. 1). Counting of the microvessels was performed by first scanning at low power ( × 100) magnification. Three areas showing the highest numbers of microvessels were selected as the “hot spots.” Five high-power fields ( × 400) in each of the hotspots were examined and recorded by three observers.
Fig. 1.
Measurement of microvessels parameters using Dewinter Biowizard 4.1 image capturing and analysing software.
Morphometric analysis
Morphometric analysis and evaluation of parameters, i.e. MVD, microvessel caliber (VC), and total microvessel boundary density (TVBD), were calculated and correlated with the histological grade of the tumors.
The following parameters were calculated for both benign and malignant cases.
Microvessel parameters MVD, VC, and TVBD were calculated using the following relations: MVD = (Total number microvessels in 5 high power 5 fields)/(Total area of the 5 fields); TVBD = π × MVD × VC.
VEGF scoring
The percentage of cancer cells having cytoplasmic staining for VEGF was noted. Staining was scored as follows: 0: No reactivity,1: Positive in less than 5% of tumor cells, 2: Positive between 5% and 50% of tumor cells,3: >50% positive.9 Interobserver bias was ruled out by coding and scoring by three independent observers.
Statistical analysis
The database was created in MS Excel (Version 2007) and analyzed using SPSS (Ver 20.0). MVD, mean VC, TVBD with their mean values, SD, and range were calculated. Benign cases were designated as grade 0 for the evaluation. Grade 1, 2, and 3 were assigned the groups 1, 2, and 3, respectively. For statistical significance p-value was considered at 5% level (p-value < 0.05).
One-way analysis of variance (ANOVA) with Posthoc Tukey Honest Significant Difference (Tukey HSD) was performed to assess the difference of the parameters for the groups. Spearman rank correlation coefficients ρ were calculated for (a) all 4 groups (n = 95) and (b) 3 IDC groups (n = 70).
Results
A total of 70 cases of IDC diagnosed at our tertiary care center were studied. The frequency of controls and malignant cases with age is shown in Fig. 2. Representative sections of benign, grades 1, 2, and 3 carcinomas stained with H and E, VEGF, CD34, and CD105 are shown in Fig. 3.
Fig. 2.
The frequency of benign and malignant cases in various age groups.
Fig. 3.
(I-IV): 3(I): Photomicrograph of H&E stained sections (×400) of benign (A) and 3 grades of IDC of the breast: (B) grade 1, (C) grade 2, and (D) grade 3. Numbers of cases are shown in the bracket for each group. Fig 3(II) (A-D): VEGF staining was graded as: Negative, 1, 2 and 3. Fig. 3 (III). (A-D) Photomicrograph of CD-34 immunostained sections (×400) of benign (A) and 3 grades of IDC of the breast: (B) grade 1, (C) grade 2, and (D) grade 3. Fig. 3 (IV).Photomicrograph of CD-105 immunostained sections (×400) of benign (A) and 3 grades of IDC of the breast: (B) grade 1, (C) grade 2, and (D) grade 3.
Table 1 shows the studied parameters in each group with mean values SD (Controls, Grade 1, 2, and 3) and p-values. Analysis of variance (ANOVA) showed differences in all parameters except MVD, CD105, and HER2neu when all groups were analyzed.
Table 1.
The mean values of Age, Tumor size, ER positivity, PR positivity, HER2 positivity, VEGF scoring, MVD-CD34, VC-CD34, TVBD-CD34, MVD-CD105, VC-CD105, TVBD-CD105 with standard deviation(SD) and range for the benign breast and the 3 grades of IDC. p-values of ANOVA test for the parameters the groups are presented.
| S No | Parameters (unit) | Benign (N = 25) | Grade-1 (N = 18) | Grade-2 (N = 31) | Grade-3 (N = 21) | p-value (ANOVA) |
|---|---|---|---|---|---|---|
| 1 | Age (Year) | 40.1 ± 12.8 (18–68) | 57.2 ± 8.7 (42–72) | 51.1 ± 11.0 (30–78) | 50.1 ± 10.3 (33–66) | <.001∗ |
| 2 | Tumor size (cm) | 3.91 ± 1.01 (2.5–6.0) | 3.75 ± 1.17 (1.5–7.5) | 4.19 ± 1.16 (2.0–6.0) | .394 | |
| 3 | ER (negative = 0, positive = 1) | 1.00 ± 0.00 (1-1) | 0.65 ± 0.49 (0–1) | 0.33 ± 0.48 (0–1) | <.001∗ | |
| 4 | PR (negative = 0, positive = 1) | 0.72 ± 0.46 (0–1) | 0.48 ± 0.51 (0–1) | 0.29 ± 0.46 (0–1) | .024∗ | |
| 5 | HER2 (negative = 0, positive = 1) | 0.39 ± 0.50 (0–1) | 0.45 ± 0.51 (0–1) | 0.43 ± 0.51 (0–1) | .916 | |
| 6 | LN status (% of positive cases (Positive cases/Total) | 27.7% (5/18) | 64.5% (20/31) | 66.7% (14/21) | .0214 (Chi square test) | |
| 7 | VEGF (Score-0-3) | 1.24 ± 0.66 (0–2) | 1.61 ± 0.61 (0–2) | 1.90 ± 0.87 (0–3) | 2.29 ± 0.72 (1–3) | <.001∗ |
| 8 | MVD-CD34 (mm−2) | 140 ± 16 (114–173) | 154 ± 20 (116–190) | 166 ± 40 (113–289) | 197 ± 61 (124–342) | <.001∗ |
| 9 | VC-CD34 (μm) | 7.87 ± 0.86 (6.30–9.92) | 9.80 ± 0.83 (8.33–11.44) | 9.51 ± 1.18 (7.45–12.24) | 10.00 ± 1.71 (7.61–14.07) | <.001∗ |
| 10 | TVBD-CD34 (mm−1) | 3.45 ± 0.51 (2.47–4.61) | 4.71 ± 0.61 (3.56–5.92) | 4.90 ± 1.16 (3.50–8.63) | 6.21 ± 2.37 (3.26–11.65) | <.001∗ |
| 11 | MVD-CD105 (mm−2) | 101 ± 16 (71–148) | 100 ± 35 (44–182) | 101 ± 31 (41–173) | 112 ± 43 (41–191) | .577 |
| 12 | VC-CD105 (μm) | 9.52 ± 1.42 (7.28–12.06) | 11.80 ± 1.70 (9.82–15.43) | 11.98 ± 2.34 (8.19–17.69) | 13.25 ± 3.03 (8.99–20.07) | <.001∗ |
| 13 | TVBD-CD 105 (mm−1) | 3.00 ± 0.66 (2.03–5.29) | 3.65 ± 1.25 (1.15–6.08) | 3.68 ± 0.91 (1.71–5.65) | 4.60 ± 1.90 (1.94–8.40) | <.001∗ |
N=Number of sample. For ER, PR, and HER2, the positive cases are assigned the numerical value 1, and the negative cases are assigned the numerical value 0.∗ = p <0.05.
The P-value for the pair of groups by Tukey HSD test is shown in Table 2.
Table 2.
The p-values of Post hoc Tukey HSD test for the significant parameters in ANOVA test (p <.05) i.e. Age, VEGF scoring, MVD-CD34, VC-CD34, TVBD-CD34, VC-CD105, TVBD-CD105, ER positivity and PR positivity, showing comparison between two groups are presented.
| Parameters | Benign VS Grade-1 | Benign VS Grade-2 | Benign VS Grade-3 | Grade-1 VS Grade-2 | Grade-1 VS Grade-3 | Grade-2 VS Grade-3 |
|---|---|---|---|---|---|---|
| Age | <.001∗ | .002∗ | .014∗ | .248 | .194 | .989 |
| VEGF | .371 | .007∗ | <.001∗ | .545 | .028∗ | .267 |
| MVD-CD34 | .656 | .071 | <.001∗ | .727 | .005∗ | .029∗ |
| VC-CD34 | <.001∗ | <.001∗ | <.001∗ | .845 | .958 | .483 |
| TVBD-CD34 | 017∗ | .001∗ | <.001∗ | .962 | .004∗ | .005∗ |
| VC-CD105 | .007∗ | <.001∗ | <.001∗ | .993 | .177 | .179 |
| TVBD-CD105 | .312 | .162 | <.001∗ | .998 | .078 | .044∗ |
| ER | .015∗ | <.001∗ | .028∗ | |||
| PR | .226 | .017∗ | .321 |
∗ = p <0.05.
Scatter plots of MVD-CD34 and MVD-CD105; VC-CD34 and VC-CD105; TVBD-CD34 and TVBD-CD105 for each sample with grade shown in Fig. 4.
Fig. 4.
(I-III). Scatter plots of a) MVD-CD34 and b) MVD-CD105, a) VC-CD34 and b)VC-CD105 a) TVBD-CD34 and b) TVBD-CD105 with grades. Benign samples are assigned as grade-0.
CD34 IHC
There was a significant difference among the groups in MVD-CD34, VC-CD34, and TVBD-CD34. The difference in MVD-CD34 was significant between Grade0 vs Grade3, Grade1 and Grade3, and Grade2 vs Grade3. The vascular parameters were significantly more in malignant lesions as compared to benign lesions and showed significant differences with increasing grade. VC of the vessels, when stained with CD34, was significantly different between the benign, i.e. group 0 and all grades of IDC.
CD105 IHC
MVD in the sections stained by CD105 did not show a significant difference between the three groups. This was due to a smaller number of vessels stained by CD105. VC and TVBD showed a significant difference in the groups. VC-CD105 showed a significant difference between groups, Grade0, and each malignant grade; however, the difference was not statistically significant between the three grades of IDC. TVBD showed a significant difference in tumors of grade 0 and grade 3 and grade 2 and grade 3.
VEGF expression
Positive VEGF staining was seen in 89 cases (93.68%), while 6 (6.315%) were negative for VEGF. VEGF expression score was found to be negative in 6 (6.315%), score 1 in 28 ((29.47%), score 2 in 44 (46.315%), and score 3 in 17 (17.89%) of tumors. VEGF expression correlated with increased vascularity of the tumors.
Comparison of CD34 and CD105
Irrespective of the grades, MVD-CD34 was significantly larger than the MVDCD105, TVBD-CD34 was significantly larger than the TVBD-CD105, but VC-CD34 was significantly smaller than the VC-CD105, i.e., CD105 stained a smaller number but greater caliber microvessels.
Correlation with grade
Spearman's correlation coefficients between grades and morphometric parameters with CD34, CD105, and VEGF were analyzed for: a) all four groups and b) three IDC groups and are shown in Table 3 and Table 4, respectively.
Table 3.
Spearman's correlation coefficients ‘ρ’ of the four groups with microvessel parameters and age are presented. Numerical values for each grade is assigned as; Benign=0, Grade1 = 1, Grade2 = 2 and grade3 = 3 for the evaluation of Spearman's ρ.
| Spearman's 'ρ' (4 groups, n = 95) | Grade | Age | VEGF | MVD-CD34 | VC-CD34 | TVBD-CD34 | MVD-CD105 | VC-CD105 | TVBD-CD105 |
|---|---|---|---|---|---|---|---|---|---|
| Grade | 1.000 | ||||||||
| Age | .200 | 1.000 | |||||||
| VEGF | .467∗∗ | .116 | 1.000 | ||||||
| MVD-CD34 | .422∗∗ | .226∗ | .295∗∗ | 1.000 | |||||
| VC-CD34 | .482∗∗ | .323∗∗ | .204∗ | .107 | 1.000 | ||||
| TVBD-CD34 | .615∗∗ | .306∗∗ | .339∗∗ | .798∗∗ | .634∗∗ | 1.000 | |||
| MVD-CD105 | .058 | .007 | .200 | .369∗∗ | -.099 | .198 | 1.000 | ||
| VC-CD105 | .527∗∗ | .294∗∗ | .083 | .049 | .687∗∗ | .445∗∗ | -.151 | 1.000 | |
| TVBD-CD105 | .354∗∗ | .219∗ | .277∗∗ | .369∗∗ | .323∗∗ | .439∗∗ | .785∗∗ | .408∗∗ | 1.000 |
∗ = p < .05, ∗∗ = p < .01, Controls are assigned as grade-0.
Table 4.
Spearman's correlation coefficients ‘ρ’ of the three malignant groups with microvessel parameters and age are presented. Numerical values for each grade is assigned as; Grade1 = 1, Grade2 = 2 and grade3 = 3 for the evaluation of Spearman's ρ.
| Spearman's 'ρ' (3grades, n = 70) | Grade | Age | VEGF | MVD-CD34 | VC-CD34 | TVBD-CD34 | MVD-CD105 | VC-CD105 | TVBD-CD105 |
|---|---|---|---|---|---|---|---|---|---|
| Grade | 1.000 | ||||||||
| Age | –.249∗ | 1.000 | |||||||
| VEGF | .329∗∗ | –.063 | 1.000 | ||||||
| MVD-CD34 | .268∗ | .012 | .211 | 1.000 | |||||
| VC-CD34 | .035 | .098 | .003 | –.147 | 1.000 | ||||
| TVBD-CD34 | .225 | .005 | .168 | .823∗∗ | .364∗∗ | 1.000 | |||
| MVD-CD105 | .101 | .011 | .286∗ | .399∗∗ | –.114 | .279∗ | 1.000 | ||
| VC-CD105 | .219 | .067 | –.234 | –.181 | .506∗∗ | .126 | –.189 | 1.000 | |
| TVBD-CD105 | .179 | .085 | .174 | .330∗∗ | .106 | .327∗∗ | .887∗∗ | .233 | 1.000 |
∗ = p < .05, ∗∗ = p < .01.
When all four studied groups were considered, grades showed mild positive correlation with VEGF, MVD-CD34, and VC-CD34 and moderate positive correlation with TVBD-CD34, VC-CD105, and TVBD-CD105. When only three IDC groups were analyzed, grades showed a mild positive correlation with VEGF, MVD–CD34, and TVBD-CD34.
When these parameters were compared with each other for all four groups, VEGF showed a mild positive correlation with MVD-CD34, TVBD-CD34, and TVBD-CD105. MVD-CD105 showed a mild positive correlation with MVD-CD34. TVBD-CD105 also showed a strong positive correlation with MVD-CD34. VC-CD105 showed a moderate positive correlation with VC-CD34.
Correlation with lymph node status
In the present research, 39 (55.7%) cases were node-positive (5 of grade 1, 20 of grade 2, and 14 were grade 3), while 31 cases were node negative (13 of grade 1, 11 of grade 2, and 7 of grade 3). We found significant differences between grades and lymph node status among the groups, (p < 0.05) (Table 1) When grades were compared, there was a significant difference in grade 1 vs grade 2 (p = 0.0131) and grade 1 vs grade 3 (p = 0.0154); however, the difference between grade 2 and grade 3 was not significant, (p = 0.8729) (Table 1). The mean values of microvascular parameters with standard deviation based on Lymph node (LN) status with p-values of the unpaired t-test are presented in Table 5. The microvascular parameters did not show any significant differences with LN positivity status (Table 5).
Table 5.
Mean with SD of the vascular parameters for ER, PR, HER2 and LN negativity and positivity with p-values of unpaired t-test. ∗-difference is significant, N-number of samples.
|
Sl No. |
Parameters |
ER-negative (N = 25) Mean ± SD |
ER-positive (N = 45) Mean ± SD |
p-value (t-test) |
| 1 | MVD-CD34 (mm−2) | 214 ± 62 | 171 ± 31 | <.001∗ |
| 2 | VC-CD34 (μm) | 10.01 ± 1.70 | 9.39 ± 1.60 | .133 |
| 3 | TVBD-CD34 (mm−1) | 6.69 ± 2.27 | 5.02 ± 1.16 | <.001∗ |
| 4 | MVD-CD105 (mm−2) | 117 ± 43 | 108 ± 40 | .383 |
| 5 | VC-CD105 (μm) | 12.87 ± 3.20 | 13.06 ± 2.83 | .798 |
| 6 |
TVBD-CD105 (mm−1) |
4.60 ± 1.75 |
4.26 ± 1.42 |
.381 |
|
Parameters |
PR-negative (N=36) Mean± SD |
PR-positive (N=34) Mean± SD |
p-value (t-test) |
|
| 1 | MVD-CD34 (mm−2) | 209 ± 62 | 172 ± 29 | .002∗ |
| 2 | VC-CD34 (μm) | 9.92 ± 1.72 | 9.43 ± 1.59 | .221 |
| 3 | TVBD-CD34 (mm−1) | 6.53 ± 2.33 | 5.03 ± 0.87 | <.001∗ |
| 4 | MVD-CD105 (mm−2) | 123 ± 43 | 97 ± 35 | .007∗ |
| 5 | VC-CD105 (μm) | 12.57 ± 2.86 | 13.52 ± 3.18 | .193 |
| 6 |
TVBD-CD105 (mm−1) |
4.76 ± 1.70 |
4.00 ± 1.35 |
.043∗ |
|
Sl No. |
Parameters |
HER2-negative (N=40) Mean± SD |
HER2-positive(N=30) Mean± SD |
p-value (t-test) |
| 1 | MVD-CD34 (mm−2) | 187 ± 44 | 204 ± 68 | .209 |
| 2 | VC-CD34 (μm) | 9.37 ± 1.37 | 10.26 ± 1.96 | .029∗ |
| 3 | TVBD-CD34 (mm−1) | 5.50 ± 1.66 | 6.54 ± 2.36 | .034∗ |
| 4 | MVD-CD105 (mm−2) | 114 ± 42 | 110 ± 40 | .689 |
| 5 | VC-CD105 (μm) | 12.61 ± 2.85 | 13.52 ± 3.22 | .216 |
| 6 |
TVBD-CD105 (mm−1) |
4.40 ± 1.56 |
4.50 ± 1.70 |
.799 |
|
Sl No. |
Parameters |
LN –negative (N=31) Mean± SD |
LN –positive (N=39) Mean± SD |
p-value (t-test) |
| 1 | MVD-CD34 (mm−2) | 208 ± 61 | 186 ± 50 | .102 |
| 2 | VC-CD34 (μm) | 9.47 ± 1.38 | 9.84 ± 1.80 | .355 |
| 3 | TVBD-CD34 (mm−1) | 6.22 ± 2.20 | 5.74 ± 1.91 | .332 |
| 4 | MVD-CD105 (mm−2) | 119 ± 45 | 109 ± 40 | .329 |
| 5 | VC-CD105 (μm) | 12.37 ± 3.09 | 13.26 ± 2.96 | .225 |
| 6 | TVBD-CD105 (mm−1) | 4.42 ± 1.43 | 4.45 ± 1.69 | .938 |
∗ = p <0.05.
Correlation with ER, PR, and Her2neu
The mean values of microvascular parameters with standard deviation based on ER, PR, Her2 neu with p-values of the unpaired t-test are presented in Table 5. For ER-positive malignant samples, MVD-CD34 and TVBD-CD34 were significantly smaller than (p < .001) than the ER-negative ones, unlike other microvascular parameters. In the case of PR-positive malignant tumors, MVD-CD34, TVBD-CD34, MVD-CD105, and TVBD-CD105 were found significantly smaller (P < .05) than the PR-negative samples; whereas VC-CD34 and VC-CD105 did not show any significant variation with PR status. VC-CD34 and TVBD-CD34 showed significantly larger values for Her2neu positive cases than the Her2neu negatives, but other microvascular parameters did not exhibit any significant changes between Her2neu positive and Her2neu negative cases. No significant difference was found between triple-negative cases and others. Triple-negative cases had higher MVD-CD34 (212 vs 189 triple-negative vs others) and MVD CD 105 (126 vs 111), but it was not significant (p > 0.3).
Discussion
Angiogenesis is crucial for the growth, spread, and metastases of breast cancer, and the correlation between angiogenesis and clinical and morphological parameters may indicate the prognostic value of angiogenesis in invasive ductal breast carcinomas.17, 18, 19
In the present study, the vascular parameters were studied in breast carcinoma using immunohistochemistry and morphometry, and their relationship with grades was explored.
In our study, MVD-CD34 showed a significant difference with increasing grades. These findings are in accordance with Safwat et al.7 Ding et al.20 Beresford et al21 and Dales et al.22 Gabriel et al23 found that MVD-CD105 was greater in grade III than in grade II, signifying an increase in the vascular neoformation indicating that the vascular specificities of CD34 and CD105 are different.
In this study, VEGF staining was seen in 93.68% of samples and in all grades of IDC, which is slightly higher than Safwat et al7 who found VEGF positivity in 92% of the tumors. Positivity for VEGF has ranged from 51 to 92% in various studies (Adams et al24 (80%), Al Harris et al25 (61.5%) and Nieto et al26 (51%)).
In the present research, VEGF correlated with grades of IDC similar to Zhou et al10 and Callagy et al.27 Safwat et al7 found that expression of VEGF was found to be negative in 8%, weak in 0%, moderate in 12%, and strong in 80% of breast carcinomas. Also, Zhou et al10 found the expression of VEGF negative, weak, moderate, and strong in 29 (23.8%), 38 (31.1%), 34 (27.9%), and 21 (17.2%) of tumor tissues, respectively.
VEGF expression showed both cytoplasmic and membranous staining in this study. Similar findings were observed by Karavasilis et al.28
These findings have also been reported by Safwat et al7 and Adams et al.25 VC of the vessels when stained with CD34 was significantly different between the benign i.e. group 0 and all grades of IDC. TVBD also showed a significant difference between benign and malignant groups and was significant between group 1 and 3, and 2 and 3. Expression of MVD, Vascular Caliber, and VEGF expression may give important information about the behavior of IDC.
In our study, MVD-CD34 and TVBD-CD34 were significantly larger in both ER and PR negative cases, MVD-CD105 and TVBD-CD105 were significantly larger in PR negative cases, and VC-CD34 and TVBD-CD34 were significantly larger in HER2 positive cases.
Triple-negative tumors had higher MVD-CD34 and MVD CD105 than other types, but it was not significant.
Workers have studied the relationship of vascular parameters with lymph node status in IDC and have found varying results. Our results are comparable to Tsutsui et al29 who did not find any correlation between MVD and lymph node status. Our study is in contrast to studies by Horak et al who found that the number of blood vessels per unit area significantly correlated with lymph node metastases30 and Popiela18 who suggested the number of CD34 microcapillaries as a predictor of the development of lymph node metastases in female IDC.
Several researchers have evaluated MVD in IDC, but very few have studied VC. We have studied the vascular caliber of vessels in addition to other parameters in this research. It has shown a significant difference with the increasing grade of the tumor.
Larger VC helps in increasing the supply of blood, and therefore, nutrients to the most active area of the tumor. Therefore, it is not only the number of vessels but also the caliber of the vessels, which is important in angiogenesis.
In the present research, we found that CD105 was expressed in larger caliber vessels. VC-CD105 was expressed significantly higher than VC-CD34. As vascular caliber also affects the supply of blood in addition to the number of vessels, we suggest that VC-CD105 is an additional factor in prognostic evaluation.
CD105 does not highlight all the blood vessels. The mean diameter of the vessels stained by CD105 was significantly larger than the vessels stained by CD34. These larger vessels have been stained preferentially by CD105 in our study.
According to Nassiri et al31, the endothelial cells of tumors are more productive than endothelial cells of normal tissue, and thus, they express elevated CD105 levels.
CD34 highlighted both old and newly formed microvessels. CD 105 stained less number but larger caliber microvessels. The use of both CD34 and CD105 can be valuable in studying the behavior of IDC.
Limitation of the study: The study did not include the follow up of the patients for correlation of angiogenesis and prognosis.
Conclusions
In this study, vascular parameters showed significant differences in three grades of IDC. Differences were seen in vascular parameters stained with CD105 in three grades of IDC. Expression of VEGF also showed significant differences with positive correlations in the three grades of IDC. CD34 highlighted both old and newly formed microvessels. CD 105 stained fewer but larger caliber microvessels. VC-CD105 can be an extremely useful adjunct along with VEGF and CD34 to study the behavior of IDC.
Disclosure of competing interest
The authors have none to declare.
Acknowledgement
This paper is based on Armed Forces Medical Research Committee Project No. 4820/2016 granted and funded by the office of the Directorate General Armed Forces Medical Services and Defence Research Development Organization, Government of India.
References
- 1.Ghoncheh M., Pournamdar Z., Salehiniya H. Incidence and mortality and epidemiology of breast cancer in the world. Asian Pacific J Cancer Prev. 2016;17(S3):43–46. doi: 10.7314/apjcp.2016.17.s3.43. [DOI] [PubMed] [Google Scholar]
- 2.Giuliano A.E., Edge S.B., Hortobagyi G.N. Eighth edition of the AJCC cancer staging manual: breast cancer. Annals of Surgical Oncology. 2018;25:1783–1785. doi: 10.1245/s10434-018-6486-6. Springer New York LLC. [DOI] [PubMed] [Google Scholar]
- 3.Hayes D.F. Prognostic and predictive factors revisited. Breast. 2005;14(6):493–499. doi: 10.1016/j.breast.2005.08.023. [DOI] [PubMed] [Google Scholar]
- 4.Adam Maciejczyk A. New prognostic factors in breast cancer. Adv Clin Exp Med. 2013;22(1):5–15. [PubMed] [Google Scholar]
- 5.Arnes J.B., Stefansson I.M., Straume O., et al. Vascular proliferation is a prognostic factor in breast cancer. Breast Cancer Res Treat. 2012;133(2):501–510. doi: 10.1007/s10549-011-1785-7. [DOI] [PubMed] [Google Scholar]
- 6.Murray J.C., Fox S.B. vols. 29–46. Humana Press; 2003. Microscopic assessment of angiogenesis in tumors. (Angiogenesis Protocols). [Google Scholar]
- 7.Safwat M.D., Habib F., Elayat A., Oweiss N., Reffat S., Algaidi S. Morphometric and immunohistochemical study of angiogenic marker expressions in invasive ductal carcinoma of human breast. Folia Morphol (Warsz) 2009;68(3):144–155. [PubMed] [Google Scholar]
- 8.Choi W.W.L., Lewis M.M., Lawson D., et al. Angiogenic and lymphangiogenic microvessel density in breast carcinoma: correlation with clinicopathologic parameters and VEGF-family gene expression. Mod Pathol. 2005;18:143–152. doi: 10.1038/modpathol.3800253. [DOI] [PubMed] [Google Scholar]
- 9.Valković T., Dobrila F., Melato M., Sasso F., Rizzardi C., Jonjić N. Correlation between vascular endothelial growth factor, angiogenesis, and tumor-associated macrophages in invasive ductal breast carcinoma. Virchows Arch. 2002;440(6):583–588. doi: 10.1007/s004280100458. [DOI] [PubMed] [Google Scholar]
- 10.Zhou S., Wang G.-P., Liu C., Zhou M. Eukaryotic initiation factor 4E (eIF4E) and angiogenesis: prognostic markers for breast cancer. BMC Cancer. 2006;6:231. doi: 10.1186/1471-2407-6-231. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Duff S.E., Li C., Garland J.M., Kumar S. CD105 is important for angiogenesis: evidence and potential applications. FASEB Journal. 2003;17(9):984–992. doi: 10.1096/fj.02-0634rev. [DOI] [PubMed] [Google Scholar]
- 12.Fonsatti E., Altomonte M., Nicotra M.R., Natali P.G., Maio M. Endoglin (CD105): a powerful therapeutic target on tumor-associated angiogenetic blood vessels. Oncogene. 2003;22(42):6557–6563. doi: 10.1038/sj.onc.1206813. [DOI] [PubMed] [Google Scholar]
- 13.Charpin C., Dales J.P., Garcia S., et al. Neoangiogenesis by CD31 and CD105 expression evaluation in breast carcinoma tissue microarrays. Clin Cancer Res. 2004;10:5815–5819. doi: 10.1158/1078-0432.CCR-04-0021. [DOI] [PubMed] [Google Scholar]
- 14.Elston C.W., Ellis I.O. Pathological prognostic factors in breast cancer. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology. 1991;19(5):403–410. doi: 10.1111/j.1365-2559.1991.tb00229.x. [DOI] [PubMed] [Google Scholar]
- 15.Qureshi A., Pervez S. Allred scoring for ER reporting and it's impact in clearly distinguishing ER negative from ER positive breast cancers. J Pak Med Assoc. 2010 May;60(5):350–353. [PubMed] [Google Scholar]
- 16.Wolff A.C., Hammond M.E.H., Allison K.H., et al. Human epidermal growth factor receptor 2 testing in breast cancer: American society of clinical oncology/college of American pathologists clinical practice guideline focused update. Arch Pathol Lab Med. 2018 Nov;142(11):1364–1382. doi: 10.5858/arpa.2018-0902-SA. [DOI] [PubMed] [Google Scholar]
- 17.Olewniczak S., Chosia M., Kwas A., Kram A., Domagała W. Angiogenesis and some prognostic parameters of invasive ductal breast carcinoma in women. Pol J Pathol. 2002;53(4):183–188. [PubMed] [Google Scholar]
- 18.Popiela T.J., Sikora J., Klimek M., et al. The analysis of CD34 antigen immunoreactivity level in invasive ductal breast cancer with respect to the presence of lymph node metastases. Neuroendocrinol Lett. 2008;29(4):443–446. [PubMed] [Google Scholar]
- 19.Şener E., Şipal S., Gündoğdu C. Comparison of microvessel density with prognostic factors in invasive ductal carcinomas of the breast. Turk Patoloji Derg. 2016;32(3):164–170. doi: 10.5146/tjpath.2016.01366. [DOI] [PubMed] [Google Scholar]
- 20.Ding S., Li C., Lin S., et al. Comparative evaluation of microvessel density determined by CD34 or CD105 in benign and malignant gastric lesions. Hum Pathol. 2006;37(7):861–866. doi: 10.1016/j.humpath.2006.02.006. [DOI] [PubMed] [Google Scholar]
- 21.Beresford M.J., Harris A.L., Ah-See M., Daley F., Padhani A.R., Makris A. The relationship of the neo-angiogenic marker, endoglin, with response to neoadjuvant chemotherapy in breast cancer. Br J Cancer. 2006;95(12):1683–1688. doi: 10.1038/sj.bjc.6603491. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Dales J.P., Garcia S., Carpentier S., et al. Long-term prognostic significance of neoangiogenesis in breast carcinomas: comparison of Tie-2/Tek, CD105, and CD31 immunocytochemical expression. Hum Pathol. 2004 Feb;35(2):176–183. doi: 10.1016/j.humpath.2003.10.008. [DOI] [PubMed] [Google Scholar]
- 23.Gabriel C.N., Cristina B.B., Arlete H., Lígia Maria B.C. Immunohistochemical evaluation of the microvascular density through the expression of TGF-beta (CD 105//endoglin) and CD 34 receptors and expression of the vascular endothelial growth factor (VEGF) in oligodendrogliomas. Neuropathology. 2008;28:17–23. doi: 10.1111/j.1440-1789.2007.00825.x. [DOI] [PubMed] [Google Scholar]
- 24.Adams J., Carder P.J., Downey S., et al. Vascular endothelial growth factor (VEGF) in breast cancer: comparison of plasma, serum, and tissue VEGF and microvessel density and effects of tamoxifen. Cancer Res. 2000;60(11):2898–2905. [PubMed] [Google Scholar]
- 25.Al-Harris E.S., Al-Janabi A.A.A., Al-Toriahi K.M., Yasseen A.A. Over expression of vascular endothelial growth factor in correlation to Ki-67, grade, and stage of breast cancer. Saudi Med J. 2008;29(8):1099–1104. [PubMed] [Google Scholar]
- 26.Nieto Y., Woods J., Nawaz F., et al. Prognostic analysis of tumour angiogenesis, determined by microvessel density and expression of vascular endothelial growth factor, in high-risk primary breast cancer patients treated with high-dose chemotherapy. Br J Cancer. 2007;97(3):391–397. doi: 10.1038/sj.bjc.6603875. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Callagy G., Dimitriadis E., Harmey J., Bouchier-Hayes D., Leader M., Kay E. Immunohistochemical measurement of tumor vascular endothelial growth factor in breast cancer. A more reliable predictor of tumor stage than microvessel density or serum vascular endothelial growth factor. Appl Immunohistochem Mol Morphol. 2000;8(2):104–109. doi: 10.1097/00129039-200006000-00004. [DOI] [PubMed] [Google Scholar]
- 28.Karavasilis V., Malamou-Mitsi V., Briasoulis E., et al. Angiogenesis in cancer of unknown primary: clinicopathological study of CD34, VEGF and TSP-1. BMC Cancer. 2005;5:25. doi: 10.1186/1471-2407-5-25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Tsutsui S., Kume M., Era S. Prognostic value of microvessel density in invasive ductal carcinoma of the breast. Breast Cancer. 2003;10(4):312–319. doi: 10.1007/bf02967651. [DOI] [PubMed] [Google Scholar]
- 30.Horak E.R., Leek R., Klenk N., et al. Angiogenesis, assessed by platelet/endothelial cell adhesion molecule antibodies, as indicator of node metastases and survival in breast cancer. Lancet. 1992 Nov 7;340(8828):1120–1124. doi: 10.1016/0140-6736(92)93150-l. PMID: 1279332. [DOI] [PubMed] [Google Scholar]
- 31.Nassiri F., Cusimano M.D., Scheithauer B.W., et al. Anticancer Res. 2011;31(6):2283–2290. [PubMed] [Google Scholar]