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Journal of Clinical Pathology logoLink to Journal of Clinical Pathology
. 2004 May;57(5):504–512. doi: 10.1136/jcp.2003.012963

Expression of vascular endothelial growth factor, hypoxia inducible factor 1α, and carbonic anhydrase IX in human tumours

A M Jubb 1, T Q Pham 1, A M Hanby 2, G D Frantz 1, F V Peale 1, T D Wu 3, H W Koeppen 1, K J Hillan 1
PMCID: PMC1770303  PMID: 15113858

Abstract

Aims: To measure vascular endothelial growth factor (VEGF-A) mRNA in a large, diverse cohort of tumours and to investigate whether VEGF-A expression is associated with markers of hypoxia, including hypoxia inducible factor 1α (HIF-1α) and carbonic anhydrase IX (CA9).

Methods: The expression of VEGF-A and CA9 was assessed in 5067 fresh frozen human tissue samples and 238 cell lines by DNA microarray analysis. In addition, tissue microarrays were constructed from 388 malignancies to investigate the expression of VEGF-A and HIF-1α by in situ hybridisation and immunohistochemistry, respectively.

Results: VEGF-A was significantly upregulated in primary malignancies of the breast, cervix, colon and rectum, oesophagus, head and neck, kidney, ovary, skin, urinary system, and white blood cells by DNA microarray analysis. However, VEGF-A expression only correlated with CA9 expression in renal tissues. In the tissue microarrays, HIF-1α positive cores showed a significant increase in VEGF-A expression in lung, ovary, soft tissue, and thyroid malignancies.

Conclusions: The expression of VEGF-A is upregulated in a large proportion of human malignancies, and may be associated with markers of hypoxia. VEGF-A expression can be induced in the absence of hypoxia and hypoxia does not always provoke VEGF-A upregulation in tumours.

Keywords: vascular endothelial growth factor, hypoxia inducible factor one alpha, carbonic anhydrase IX, angiogenesis, neoplasia

During tumorigenesis, solid lesions first undergo an avascular phase of growth, until the diffusion of oxygen and the exchange of waste and nutrients become rate limiting.1 The growth and survival of these small colonies (usually <1 mm3) is dependent both on their ability to promote angiogenesis and to adapt to hypoxic conditions.1–4 Indeed, even large tumours suffer from a structurally and functionally abnormal vasculature that results in regions of hypoxia, despite an increase in microvascular density.5

Environments with a low oxygen tension activate a series of transcriptional regulators in human cells, including hypoxia inducible factor 1 (HIF-1).6 HIF-1 is a heterodimeric transcription factor composed of HIF-1α and the constitutively expressed aryl hydrocarbon receptor nuclear translocator (also known as HIF-1β), which are basic helix–loop–helix–PAS domain proteins.7 Both the amount and activity of this heterodimer are kept to a minimum in normoxic cells by a series of oxygen dependent, post translational modifications to the HIF-1α protein.6 The best characterised regulatory mechanism is contingent on protein modification by oxygen dependent prolyl hydroxylases, which is required for binding to a von Hippel-Lindau (VHL) E3 ubiquitin protein ligase that targets HIF-1α for proteasomal degradation.8,9 Under hypoxic conditions, oxygen becomes rate limiting for prolyl hydroxylation,10 and the turnover of HIF-1α decreases accordingly, allowing HIF-1α to accumulate intracellularly.11

“Environments with a low oxygen tension activate a series of transcriptional regulators in human cells, including hypoxia inducible factor 1”

As part of an adaptive physiological response to hypoxia, HIF-1 upregulates the expression of genes that are involved in glycolysis,12–14 erythropoiesis,15 and angiogenesis.12–14,16 Specifically, the promoters 5′ to vascular endothelial growth factor (VEGF-A) and carbonic anhydrase IX (CA9) contain sequences that mediate hypoxia induced transcription and have a high degree of homology to known HIF-1 binding sites (hypoxia response elements).17–20 VEGF-A encodes a proangiogenic ligand, which can transduce angiogenesis through specific tyrosine kinase receptors, principally expressed by angioblasts and endothelial cells.21 The expression of VEGF-A is upregulated to varying degrees in a wide range of human malignancies during tumorigenesis.21 Signalling by growth factors (for example, epithelial growth factor, transforming growth factors α and β, interleukins 1β and 6, platelet derived growth factor, insulin-like growth factor I, and keratinocyte growth factor 1)22–24 and hormones (for example, thyroid stimulating hormone, adrenocorticotrophic hormone, and angiotensin II)25–27 is also implicated in increased transcription of VEGF-A, although the relative contribution of these two mechanisms has yet to be characterised extensively in different tumour types.

High expression of VEGF-A has been associated with a worse survival and an increased incidence of disease recurrence in many malignancies, including cancers of the breast,28–30 colon and rectum,31–35 ovary,36,37 kidney,38 cervix,39 and head and neck.40 HIF-1α activation correlates with a worse prognosis and resistance to treatment in ovarian, head and neck, and oesophageal cancer.41,42 Similarly, CA9 expression is an adverse prognostic indicator in patients with invasive breast carcinoma,43 nasopharyngeal carcinoma,44 cervical carcinoma,45 and non-small cell lung cancer.46 Therefore, accurate and reliable assays of VEGF-A expression and hypoxia have assumed prognostic and therapeutic importance. Nevertheless, none of the techniques used to measure VEGF-A expression has been widely applied, prohibiting reliable interlaboratory comparisons.

The aim of our study was to measure VEGF-A mRNA values in a large, diverse cohort of malignancies. In addition, we sought to investigate the published correlations between VEGF-A expression and markers of hypoxia.

METHODS

DNA microarray experiments

The Gene Logic® (Gaithersburg, Maryland, USA) database of Affymetrix® HG-U133 GeneChip® probearray data was screened for probes that corresponded to the VEGF-A and CA9 mRNA sequences (Genbank accession numbers M32977 and NM_001216, respectively). Probes with the highest proportion of samples called “present” were chosen for downstream analysis on 7579 fresh frozen human tissue samples and 225 cell lines. (Gene Logic performed tissue sample preparation and data analysis.) Probeset 210512_s_at was chosen to represent VEGF-A expression and probeset 205199_at to represent CA9 expression.

Tissue microarray experiments

Tissue culture

The human tumour cell lines 786-0 (renal cell adenocarcinoma); A673 (rhabdomyosarcoma); SK-MES-1 (lung squamous cell carcinoma); SW480, KM12, HCT15, HCT116, and COLO205 (all human colorectal adenocarcinomas); SkBr3, MDA-MB-453S, MDA-MB-231, MCF7, and MDA-MB-175-VII (all breast adenocarcinomas); and CALU6 (anaplastic carcinoma) were obtained from the American Tissue Culture Collection (Manassas, Virginia, USA). H322 (lung non-small cell carcinoma) was obtained from the National Cancer Institute (Bethesda, Maryland, USA). Chinese hamster ovary (CHO) cells transfected with human VEGF-A were established as described previously.47 Cell lines were cultured in vitro according to the protocols, harvested after the first passage, fixed in 10% neutral buffered formalin, and embedded in paraffin wax.

Selection of human tissues

Formalin fixed, paraffin wax embedded tissue cassettes and corresponding haematoxylin and eosin (H&E) stained sections were retrieved from the histopathology archives at Genentech, South San Francisco, California, USA and the Leeds Teaching Hospitals NHS Trust, UK. (Appropriate ethical approval was obtained for all research conducted using human tissues.) H&E sections were reviewed (KJH) and representative regions of malignant cells were annotated on the slide to aid tissue microarray (TMA) core sampling.

Preparation of synthetic control blocks

cDNA probe templates were generated from human kidney marathon-ready cDNA (BD Clontech, Palo Alto, California, USA). Primers were designed to amplify a 604 bp fragment of VEGF-A mRNA. Sense and antisense primers contained T7 and T3 RNA polymerase initiation sites, respectively (sense, GGATTCTAATACGACTCACTATAGGGCGGGCCTCCGAAACCATGAACT; antisense, CTATGAAATTAACCCTCACTAAAGGGATCCTCCTGCCCGGCTCAC). Each 50 μl polymerase chain reaction (PCR) contained 0.5 ng of cDNA, 33 ng of each primer, 0.6mM dNTPs (0.15mM each dATP, dCTP, dGTP, dTTP), 1× polymerase mix, 1× buffer, and 1.0M GC-Melt (BD Clontech). The thermal cycling conditions were: 94°C for five minutes, followed by 30 cycles of 94°C for one minute and 68°C for 30 seconds. Sense and antisense RNA fragments were transcribed from this template using T7 and T3 RNA Megascript kits, respectively (Ambion, Austin, Texas, USA). Fragments were embedded in 2% agarose at a concentration of 5 μg/ml, and processed as described previously.48 Blank 2% agarose controls were processed in an identical manner.

Tissue microarray construction

TMAs were constructed to represent the case series using a Beecher Instruments’ microarrayer (Silver Spring, Maryland, USA), as described previously.49 In total, 388 tissues were sampled (three adrenal, 83 breast, two endometrium, three kidney, three liver, 78 lung, 71 ovary, four pancreas, 78 soft tissue, and 63 thyroid), in addition to the cell pellets and synthetic control blocks described above. Tissue sampling was undertaken in triplicate to provide representative data on the parent block50; synthetic controls and cell pellets were sampled in duplicate. Sections, 3 μm thick, were cut from the recipient blocks and mounted on to glass slides. H&E staining for verification of the histology (KJH) was performed on the first section cut from each TMA block.

Immunohistochemistry

Tissue sections were dewaxed and heat mediated antigen retrieval was performed in target retrieval solution (Dako Cytomation, Carpinteria, California, USA). Immunohistochemistry (IHC) was carried out as described previously,51 using a primary antihuman HIF-1α monoclonal antibody at 1 μg/ml, (clone H1α67; Novus Biologicals, Littleton, Colorado, USA). Negative control slides were incubated with a mouse IgG2b immunoglobulin culture supernatant (Dako Cytomation) at 1 μg/ml. Cases were assigned as positive for HIF-1α if one or more of the three cores contained neoplastic cells with positively staining nuclei (KJH).

In situ hybridisation

For each riboprobe to be synthesised, 12 μl (125 mCi) of [α33P]-UTP (Amersham Biosciences, Piscataway, New Jersey, USA) was speed vacuumed until dry. Each aliquot was reconstituted in 1× buffer, 4.5mM dithiothreitol, 0.23mM rNTPs (0.08mM each rATP, rCTP, rGTP), 2.3μM rUTP, 1.4 U/μl RNAse inhibitor, 0.05 μg/μl cDNA template, and 0.7 U/μl of either T7 (sense probe) or T3 (antisense probe) RNA polymerase (Promega, Madison, Wisconsin, USA). In vitro transcription took place over one hour at 37°C. Samples were then treated with 0.05 U/μl DNase (Promega) for 15 minutes at 37°C and purified over RNeasy mini columns (Qiagen, Valencia, California, USA). Formalin fixed, paraffin wax embedded tissue sections were dewaxed and treated with proteinase K (20 μg/ml in 2× SSC) at 37°C for 15 minutes. Hybridisation and washes were carried out as described previously.48,52,53

Tissue sections were dehydrated, air dried, and exposed to a phosphorscreen for 16 hours at room temperature. Immediately after incubation, the phosphorscreen was scanned with a Typhoon 9410 (Amersham Biosciences, Piscataway, New Jersey, USA). Slides were then dipped in NBT2 nuclear track emulsion (Eastman Kodak, Rochester, New York, USA), exposed for four weeks at 4°C, developed, and counterstained with H&E. Background subtraction, gridding, and analysis of the phosphorimages were undertaken with Phoretix Array v.3 (Nonlinear Dynamics, Newcastle upon Tyne, UK). Subsequently, the TMAs were reviewed (KJH) by bright/dark field microscopy for verification of hybridisation. Cores were scored semiquantitatively on a scale of 0 (no expression) to 3 (very strong signal), according to the overall intensity of the hybridisation signal.

Statistical analysis

Statistical analysis was performed using SPSS for Windows (version 11.0; Chicago, Illinois, USA). The Mann-Whitney U test was used to assess the differences between the median values of continuous datasets. Large datasets were subjected to false discovery rate controlling procedures as indicated.54 Significance was assumed if the two sided p value was < 0.05 and the false discovery rate controlling procedure did not discount the finding.

RESULTS

DNA microarray data

VEGF-A expression

When compared with normal tissue, median VEGF-A expression was significantly greater in primary malignancies of the breast (1.4 fold), cervix (1.4 fold), colon and rectum (2.2 fold), oesophagus (2.0 fold), head and neck (1.6 fold), kidney (3.2 fold), ovary (3.5 fold), skin (1.6 fold), urinary system (3.1 fold), and white blood cells (7.8 fold; including acute lymphocytic leukaemia and chronic myeloid leukaemia spleen samples) (fig 1; table 1). Malignancies of the kidney showed the greatest range and maximum expression of VEGF-A (fig 1). VEGF-A was further upregulated in metastatic colorectal tumours when compared with primary malignancies with no evidence of metastasis (table 1). This association was not seen in other tumour types. In contrast, significant reductions of VEGF-A, relative to normal tissue, were noted in primary malignancies of the lymphoid system (median 0.24 of normal), prostate (0.65 of normal), stomach (0.77 of normal), testis (0.29 of normal), and thyroid (0.44 of normal) (fig 1; table 1).

Figure 1.

Figure 1

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Electronic northern plot of the relative degrees of vascular endothelial growth factor A (VEGF-A) mRNA expression in different tissues and pathological states, determined by DNA microarray analysis (probeset 210512_s_at). The box represents the interquartile range, divided by a median line. Green, normal tissue; red, malignant tissue; blue, non-malignant disease. The Mann-Whitney U test was used to assess the differences between the median values of normal and primary malignant tissue: *p < 0.05; **p < 0.01; ***p < 0.0001. CNS, central nervous system; WBC, white blood cells.

Table 1.

Relative degree of vascular endothelial growth factor A mRNA expression determined by DNA microarray analysis (probe 210512_s_at) in different tissues and pathological states

Tissue Diagnosis N Median Interquartile range Mann-Whitney U
p v normal p v metastatic
Adipose Normal 37 366 259–591
Lipomas and lipomatosis 7 189 116–969 0.377
Adrenal Normal 10 499 249–616
1° malignancy, no metastasis* 10 337 242–576 0.257
Benign tumours 4 374 59–755 0.671
Blood vessels Normal 11 113 74–281
Bone Normal 2 157 125–189
1° malignancy, no metastasis* 25 191 121–632 0.517 0.767
Metastatic disease 7 225 118–820 0.380
Benign tumours 5 85 65–122 0.190
Bone marrow Normal 5 185 126–289
Breast Normal 33 166 129–211
1° malignancy, no metastasis* 276 240 160–362 0.0001 0.197
Metastatic disease 21 187 96–412 0.495
Fibroadenomas 9 177 159–243 0.450
Cystosarcoma phyllodes 3 341 0.041†
Cell lines Normal 99 81 44–156
Untreated malignant 110 108 62–263 0.001
Cervix Normal 66 217 163–280
1° malignancy, no metastasis* 20 294 182–451 0.011 0.699
Metastatic disease 4 322 219–437 0.105
Central nervous system Normal 160 111 78–157
1° malignancy, no metastasis* 21 113 58–607 0.651 0.386
Meningiomas 10 48 27–135 0.009
Colorectal Normal 222 150 112–203
1° malignancy, no metastasis* 158 331 238–466 <0.0001 0.001
Metastatic disease 34 479 291–725 <0.0001
Benign tumours 19 255 202–358 0.0003
Endometrium Normal 7 452 238–563
1° malignancy, no metastasis* 96 330 212–507 0.455
Metastatic disease 12 396 246–476 0.735
Gallbladder Normal 8 607 506–666
Head and neck Normal 14 140 110–179
1° malignancy, no metastasis* 30 228 145–308 0.002 0.326
Metastatic disease 22 316 153–421 0.003
Benign tumours 21 244 140–384 0.005
Heart Normal 15 345 305–456
Kidney Normal 76 462 382–532
1° malignancy, no metastasis* 85 1461 383–2435 <0.0001 0.936
Metastatic disease 6 1407 166–2985 0.170
Oncocytomas 8 778 492–1017 0.01
Other benign tumours 4 162 111–230 <0.0001
Liver Normal 43 533 390–583
1° malignancy, no metastasis* 18 435 315–617 0.282 0.130
Metastatic disease 7 341 126–478 0.020†
Lung Normal 100 438 354–567
1° malignancy, no metastasis* 123 390 223–591 0.101 0.820
Metastatic disease 5 348 107–894 0.588
Lymphoid Normal 44 230 162–309
1° malignancy, no metastasis* 78 55 29–100 <0.0001 0.024†
Metastatic disease 3 225 89–432 0.828
Muscle (skeletal) Normal 31 389 280–587
Myometrium Normal 140 718 531–888
1° malignancy, no metastasis* 2 1201 779–1623 0.156 0.079
Metastatic disease 7 246 100–561 0.009
Leiomyoma 50 485 289–712 <0.0001
Neuroendocrine 1° malignancy, no metastasis* 14 236 158–374 0.166
Metastatic disease 3 373 332–393
Oesophagus Normal 18 271 181–493
1° malignancy, no metastasis* 17 544 388–843 0.006 0.923
Ovary Normal 117 95 62–166
1° malignant disease 94 329 202–523 <0.0001 0.764
Metastatic disease 61 356 185–532 <0.0001
Benign tumours 48 136 69–201 0.089
Pancreas Normal 34 446 319–512
1° malignancy, no metastasis* 52 428 320–517 0.818 0.768
Metastatic disease 20 430 363–510 0.962
Adenomas 5 428 264–2812 0.669
Placenta Normal 4 62 42–69
Prostate Normal 34 795 469–1083
1° malignancy, no metastasis* 65 520 267–803 0.004 0.665
Metastatic disease 3 668 300–922 0.404
Benign prostatic hypertrophy 31 464 267–618 0.003
Prostatic intraepithelial neoplasia 4 1374 749–1574 0.092
Skin Normal 55 120 87–140
1° malignant disease 38 191 108–331 <0.0001 0.645
Metastatic disease 22 166 87–355 0.025†
Small intestine Normal 129 230 181–301
1° malignancy, no metastasis* 6 196 105–281 0.245 0.134
Inflammatory bowel disease 4 97 77–251 0.041†
Adenomatous polyps 6 202 145–278 0.417
Soft tissue 1° malignancy, no metastasis* 58 300 156–596 0.275
Metastatic disease 15 464 216–764
Benign tumours 39 149 91–242
Stomach Normal 23 374 284–465
1° malignancy, no metastasis* 57 288 215–366 0.009 0.141
Metastatic disease 13 193 112–373 0.012
Testis Normal 15 177 94–199
1° malignancy, no metastasis* 18 52 22–161 0.010 0.465
Thymus Normal 65 25 19–34
1° malignancy, no metastasis* 3 28 21–341 0.395
Thyroid Normal 11 1212 567–1388
1° malignant disease 26 534 293–973 0.003 0.242
Metastatic disease 9 464 226–601 0.001
Nodular hyperplasia 15 668 592–914 0.002
Benign tumours 8 1448 867–1388 0.206
Urinary Normal 7 170 115–421
1° malignancy, no metastasis* 24 531 281–868 0.005 0.817
Metastatic disease 3 579 131–2033 0.210
White blood cells Normal 79 21 9–45
1° malignancy, no metastasis* 6 164 117–240 0.001
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*Primary malignant disease with no evidence of distant metastasis; †These p values are not considered significant by the false discovery rate controlling procedure,54 with a desired false discovery rate <0.05.

VEGF-A v CA9 expression

The degree of CA9 expression showed a linear correlation with that of VEGF-A only in renal tissue (R2  =  0.54; fig 2A). The correlation was strongest in neoplastic tissues (R2  =  0.38), with a weaker association in normal kidney (R2  =  0.26), and no correlation in non-malignant disease (R2  =  0.01). Correlations were not apparent in breast, colorectal, lung, ovary, pancreas, prostate, thyroid, or urinary tissues (fig 2B).

Figure 2.

Figure 2

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Scatter plots comparing the expression of vascular endothelial growth factor A (VEGF-A) and carbonic anhydrase IX (CA9) mRNA, assessed by DNA microarray in (A) kidney tissues and (B) breast, colorectal, lung, ovary, pancreas, prostate, thyroid, and urinary tissues.

Tissue microarray data

VEGF-A expression

To corroborate the Gene Logic data on tissue homogenates, we undertook an investigation of VEGF-A expression by in situ hybridisation (ISH) on a series of human tumour samples. VEGF-A expression was seen in the stromal and/or neoplastic cell populations of positive (score 1–3) TMA cores covering a range of malignancies (fig 3). The ISH score given by the pathologist correlated closely with the quantitative signal obtained by phosphorimaging, which has been used for the remainder of the analysis (fig 4). The VEGF-A antisense control recorded a positive signal by phosphorimaging, when compared with the VEGF-A sense and blank agarose negative controls (fig 5). Of the cell pellets, the VEGF-A transfected CHO cells showed the highest quantitative signal intensity, with lower signals in 786-0, A673, SKMES, SW480, and KM12 cells (fig 5). The remaining cell lines expressed little or no VEGF-A.

Figure 3.

Figure 3

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Tissue microarray cores illustrating vascular endothelial growth factor A (VEGF-A) in situ hybridisation (ISH) and hypoxia inducible factor 1α (HIF-1α) immunohistochemistry. Brightfield and darkfield images of VEGF-A ISH demonstrate silver grains overlying neoplastic cells (red arrows). (A) Renal adenocarcinoma; (B and E) lung squamous cell carcinoma; (C and D) lung adenocarcinoma. Bars: 100 μm except for high power images (20 μm). Low power and high power images of HIF-1α immunohistochemistry show nuclear localisation of the diaminobenzidine chromagen in stromal (D) and neoplastic (A, B, and E) cell populations. VEGF-A expression can be seen in both HIF-1α positive (A, B, and E) and negative (C and D) tissue cores.

Figure 4.

Figure 4

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Box plots showing the distribution of quantitative data obtained by phosphorscreen imaging of vascular endothelial growth factor A (VEGF-A) in situ hybridisation studies, classified according to the histopathologist’s score. The box represents the interquartile range, divided by a median line. Whisker lines delineate the number of cases that lie within 1.5 box lengths from the upper or lower extremes of the interquartile range. Empty circles represent outliers found within 1.5 and 3 box lengths from the upper or lower extremes of the interquartile range. Solid circles mark extreme values outside of this range.

Figure 5.

Figure 5

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Quantitative vascular endothelial growth factor A (VEGF-A) mRNA expression assessed by in situ hybridisation phosphorimage analysis in a series of cell pellets and synthetic controls. VEGF-A-S, VEGF-A sense; VEGF-A-AS, VEGF-A antisense.

VEGF-A v HIF-1α expression

Of 1025 tumour cores, 316 (31%) scored positive for HIF-1α expression in the nuclei of the stromal and/or neoplastic cell populations. All nine cores of renal cell carcinoma and follicular adenocarcinoma of the thyroid were positive, compared with only 5% of breast ductal adenocarcinomas, 4% of mucinous ovarian adenocarcinomas, 6% of gastrointestinal stromal tumours, and no adenocarcinomas of the endometrium or neuroendocrine tumours of the lung (table 2). HIF-1α positive cores showed significantly higher VEGF-A expression than did HIF-1α negative cores in adenocarcinomas of the lung (1.6 fold), serous adenocarcinomas of the ovary (4.5 fold), liposarcomas (20.5 fold), rhabdomyosarcomas (4.8 fold), and medullary carcinomas of the thyroid (1.7 fold) (table 2).

Table 2.

Relative VEGF-A mRNA expression determined by quantitative in situ hybridisation in different tumour types

Tissue Tumour type VEGF-A phosphorimage volume (relative units) U test
HIF-1α positive HIF-1α negative
N* Median IQR N* Median IQR
Adrenal Carcinoma 3 (1) 26832 25861–27526 6 (2) 40350 21383–62429 0.4386
Breast Ductal adenocarcinoma 9 (7) 6982 3448–8322 165 (78) 6492 2759–10674 0.8651
Endometrium Adenocarcinoma 0 6 (2) 18942 5773–42790
Kidney Adenocarcinoma 9 (3) 194897 169937–263585 0
Liver Hepatocellular carcinoma 2 (1) 37978 22612–53344 6 (2) 12847 8069–19191 0.0956
Lung Adenocarcinoma 71 (30) 40213 21246–67767 44 (21) 24625 13594–53635 0.0394†
Neuroendocrine tumour 0 15 (5) 26564 20574–41287
Squamous cell carcinoma 35 (17) 41311 29085–72728 41 (18) 40082 18356–76064 0.3842
Ovary Adenocarcinoma 3 (1) 19842 17747–46596 3 (1) 40991 29618–42359 0.5127
Adenocarcinoma: endometrioid 25 (14) 20039 14073–40037 31 (15) 16686 10488–39839 0.4633
Adenocarcinoma: serous 18 (10) 128343 38207–185213 55 (22) 28502 19224–61354 0.0038
Adenocarcinoma: mucinous 1 (1) 5256 24 (12) 4286 1785–6829 0.7815
Adenocarcinoma: clear cell 6 (3) 117882 109480–136541 27 (10) 35733 19849–107339 0.0842
Pancreas Adenocarcinoma 6 (3) 18282 11131–33890 5 (2) 35693 23563–44115 0.1003
Soft tissue Angiosarcoma 4 (2) 1490 743–10969 20 (7) 504 269–2223 0.1879
Fibrosarcoma 0 11 (4) 2394 1275–5204
Gastrointestinal stromal tumour 1 (1) 10779 16 (6) 3678 1091–16290 0.4142
Leiomyosarcoma 4 (2) 12920 2420–34709 14 (5) 12097 10030–44381 0.3956
Liposarcoma 3 (1) 18772 12333–24699 25 (9) 915 116–3224 0.0067
Malignant fibrous histiocytoma 11 (5) 9165 2893–12657 32 (13) 6527 3195–11739 0.6361
Rhabdomyoscarcoma 7 (3) 13459 7475–73528 39 (15) 2792 1423–5678 0.0011
Synovial sarcoma 4 (2) 13932 8009–35790 29 (11) 4600 3096–8918 0.1856
Thyroid Carcinoma 38 (15) 30073 22677–46610 72 (26) 37024 21798–57202 0.2659
Carcinoma: anaplastic 3 (3) 13371 13196–33180 3 (4) 9073 5359–27547 0.3092
Carcinoma: medullary 9 (3) 29941 26268–143511 26 (9) 17788 12459–45316 0.0326†
Carcinoma: poorly differentiated 11 (4) 17925 15558–45240 11 (4) 42054 26370–52769 0.1396
Adenocarcinoma: follicular 9 (3) 83909 52089–98721 0
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*N, number of cores with number of cases in parentheses; †These p values are not considered significant by the false discovery rate controlling procedure,54 with a desired false discovery rate <0.05.

HIF-1α, hypoxia inducible factor 1α; IQR, interquartile range; VEGF-A, vascular endothelial growth factor A.

DISCUSSION

The measurement of VEGF-A expression in tumours has become an important methodological issue in determining prognosis,28–40 and may affect the sensitivity of individual tumours to targeted antibody and small molecule treatments. At present, there is a large body of literature describing the changes in VEGF-A expression during tumorigenesis, using many different techniques, such as ISH,48,55 IHC,30,37,40,56,57 enzyme immunoassay,58 reverse transcription-PCR,36,40 and western blotting.40 However, none of the techniques used to measure VEGF-A expression has been widely applied,59 and there is no accepted gold standard. This limits the external validity of studies and prohibits reliable interlaboratory comparisons. In addressing this concern, we used both DNA microarray and TMA technology to investigate the expression of VEGF-A in a large series of diverse human tumours. Furthermore, the data yielded information on the proportion of tumours in which the upregulation of VEGF-A expression is associated with markers of hypoxia.

A key aim of our study was to appraise the prevalence and degree of VEGF-A upregulation in a large series of diverse tumour types. DNA microarray analysis demonstrated a significant increase in median VEGF-A expression in many prevalent types of malignancy (fig 1; table 1). The data fall within the range of VEGF-A expression reported for these tumour types in the literature.28–40,48 In addition, there is a significant increase in VEGF-A expression in colorectal adenomas compared with normal colorectal tissue, which has been reported previously by our group48 and by independent investigators.60 However, not all tumours upregulate VEGF-A expression at the mRNA level. For example, DNA microarray analysis demonstrated a significant downregulation of VEGF-A expression in benign prostatic hypertrophy and primary malignancies of the lymphoid system, prostate, stomach, testis, and thyroid. Furthermore, the Gene Logic series of metastatic breast cancers did not show a significant increase in VEGF-A expression when compared with primary malignancies or normal breast. These observations are not supported by the literature,61–68 which may be a reflection of publication bias towards positive findings, demographic differences, or experimental idiosyncrasies. Limitations of the Gene Logic data include low sample numbers for certain tumour types. Moreover, many samples of normal tissue were obtained from tumour resection margins, which may be under the influence of paracrine and hormonal factors secreted by the tumour. For example, in testicular cancer this adjacent tissue frequently shows other histopathological changes. Nevertheless, analyses on subsets of normal tissue of the colon, ovary, breast, lung, and cervix showed no significant differences in VEGF-A expression between tissue from patients with or without cancer (data not shown). This paper also focuses on the expression of VEGF-A mRNA, in contrast to the published literature, which has relied more heavily on immunohistochemistry.61–66 Although mRNA transcript abundance is not always a true reflection of changes in protein expression, the interpretation of immunohistochemistry is complicated by the specificity and sensitivity of the antibody used. In addition, it is possible that tumours in which VEGF-A is not upregulated trigger the angiogenic switch by either downregulating antiangiogenic factors or upregulating other proangiogenic factors, circumventing their requirement for VEGF-A. For example, angiopoietin-2, a proangiogenic ligand, is upregulated in thyroid cancers and thrombospondin-1, an antiangiogenic extracellular matrix component, is downregulated.61

Take home messages.

  • Vascular endothelial growth factor A (VEGF-A) expression is upregulated in a large number of human malignancies, and may be associated with markers of hypoxia

  • VEGF-A expression can be induced in the absence of hypoxia and hypoxia does not always provoke VEGF-A upregulation in tumours

“DNA microarray analysis demonstrated a significant downregulation of vascular endothelial growth factor A expression in benign prostatic hypertrophy and primary malignancies of the lymphoid system, prostate, stomach, testis, and thyroid”

In the canonical model outlined above, the expression of VEGF-A is a result of increased transcriptional activity in response to HIF-1α activation under hypoxic conditions.6 However, the data presented here indicate that a large proportion of malignancies exhibit VEGF-A upregulation in the absence of these markers of hypoxia. Although the canonical model holds true in some tumours, it seems short sighted to view it as the only means of VEGF-A upregulation in human cancer. In adenocarcinomas of the kidney, the relatively strong association of HIF-1α and CA9 with VEGF-A may result from reduced HIF-1α turnover as a result of mutations or promoter methylation of the VHL gene, reported in 56% of sporadic clear cell carcinomas.66 A similar association between HIF-1α and VEGF-A expression was seen for lung tumours, serous adenocarcinomas of the ovary, liposarcomas, rhabdomyosarcomas, and medullary carcinomas of the thyroid. However, the last three tumour types were not well represented in our study and our findings need to be confirmed in a larger series. Furthermore, we suggest that many tumours may exploit hypoxia independent mechanisms to increase their expression of VEGF-A, which is also regulated by certain growth factors and/or hormones known to be manipulated during tumorigenesis.25–27

In summary, the data presented here represent the largest single series of human tissues and pathologies profiled for VEGF-A expression in a uniform and quantitative manner. The findings may prove useful in establishing a reference for VEGF-A expression in malignant disease.

Acknowledgments

We gratefully acknowledge the assistance of P Tobin, B Wright (Department of Pathology, Genentech Inc), K Jung (Department of Bioinformatics, Genentech Inc), and M Ostland (Department of Biostatistics, Genentech Inc).

Abbreviations

  • CA9, carbonic anhydrase IX

  • CHO, Chinese hamster ovary

  • H&E, haematoxylin and eosin

  • HIF-1α, hypoxia inducible factor 1α

  • IHC, immunohistochemistry

  • ISH, in situ hybridisation

  • PCR, polymerase chain reaction

  • TMA, tissue microarray

  • VEGF-A, vascular endothelial growth factor A

  • VHL, von Hippel-Lindau

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