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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Cancer Res. Author manuscript; available in PMC 2012 February 1.
Published in final edited form as:
PMCID: PMC3071295

In vivo profiling of hypoxic gene expression in gliomas using the hypoxia marker EF5 and laser-capture microdissection


Hypoxia is a key determinant of tumor aggressiveness, yet little is known regarding hypoxic global gene regulation in vivo. We have employed the hypoxia marker EF5 coupled with laser capture microdissection to isolate RNA from viable hypoxic and normoxic regions of 9L experimental gliomas. Through microarray analysis, we have identified several mRNAs (including the HIF targets Vegf, Glut-1 and Hsp27) with increased levels under hypoxia compared to normoxia both in vitro and in vivo. However, we also found striking differences between the global in vitro and in vivo hypoxic mRNA profiles. Intriguingly, the mRNA levels of a substantial number of immunomodulatory and DNA repair proteins including CXCL9, CD3D and RAD51 were found to be downregulated in hypoxic areas in vivo, consistent with a pro-tumorigenic role of hypoxia in solid tumors. Immunohistochemical staining verified increased HSP27 and decreased RAD51 protein levels in hypoxic vs. normoxic tumor regions. Moreover, CD8+ T cells which are recruited to tumors upon stimulation by CXCL9 and CXCL10, were largely excluded from viable hypoxic areas in vivo. This is the first study to analyze the influence of hypoxia on mRNA levels in vivo and can be readily adapted to obtain a comprehensive picture of hypoxic regulation of gene expression and its influence on biological functions in solid tumors.

Keywords: mRNA, 9L rat glioma, Rad51, HSP27


Hypoxia/anoxia is a well-characterized component of the solid tumor microenvironment and has profound consequences for patient outcome. Hypoxic tumors are more resistant to radiotherapy and chemotherapy and patients with more hypoxic solid tumors fare worse compared to patients with better oxygenated tumors, irrespective of therapeutic modality (1, 2). At the cellular/molecular level, hypoxia promotes selection and clonal expansion of cells with inactivated p53 (3), increases metastatic potential (4, 5) and promotes genomic instability (6). These laboratory and clinical findings strongly suggest that hypoxia selects for a more aggressive tumor phenotype (7) and have stimulated efforts to develop accurate and tractable ways to image and quantify tumor hypoxia in human patients.

Several invasive and non-invasive methods currently exist for measuring hypoxia in solid tumors. While there is no consensus among hypoxia researchers on which method is considered the “gold standard” of hypoxia measurements in vivo, the two most widely used and referenced methods are polarographic electrodes which are invasively inserted into accessible tumors in patients under anesthesia, and bioreductive dyes including EF5 and Pimonidazole, which are injected into patients prior to surgery (2). EF5 is a 2-nitroimidazole that has been used for over 15 years in both in vitro and in vivo studies to measure hypoxia (8). The drug is reduced in regions of hypoxia allowing for covalent binding to cellular proteins. Binding is stable so that after the tumor is removed and sections are made they can be stained using anti-EF5 monoclonal antibodies conjugated to fluorochromes such as Cy3. By measuring EF5 immunofluorescence and then comparing it to a calibrated fluorescence scale that is generated using cells grown under specified O2 concentrations in vitro, the absolute pO2 present in tissues can be estimated (9).

During the last two decades, several molecular “hypoxia markers” have been identified (e.g., CA9, GLUT-1, VEGF), and are currently being investigated as qualitative and quantitative correlates of tumor hypoxia, but with mixed results (10). The lack of reliable hypoxia markers seems to stem from (a) the great intra- and inter-tumoral heterogeneity of hypoxia, (b) the co-regulation of the expression of such genes by factors other than hypoxia (e.g., oncogenes) and (c) the disparity in regulation of expression of specific genes in vitro vs. in vivo (10).

Gene expression profiling of tumors has shown promise in the detection of patient populations with markedly distinct prognostic outcomes in retrospective and prospective studies (11, 12). Specific tumor gene expression “signatures” that can be used to predict patient outcome with considerable reliability is expected to revolutionize medicine in the next few decades (13). The regulation of mRNA and miRNA expression by hypoxia has been studied extensively in vitro (14, 15). Gene expression analyses using oligonucleotide-based microarrays following exposure of cells to hypoxia and comparison to normoxic controls, have confirmed the central role of the hypoxia inducible factors HIF-1 and HIF-2 in mediating a large portion of hypoxic mRNA regulation (15, 16). Despite this progress, there have been no studies reporting a comprehensive analysis of hypoxia’s influence on gene expression in vivo due to problems with mRNA integrity following immunohistochemical staining of frozen tissue. Since hypoxia in tumors exists in a background of additional physiological stresses (e.g., low nutrients, low pH, infiltrating immune cells), it is likely that the intricate interplay of these stresses on gene expression profiles in vivo will not be recapitulated faithfully by in vitro experiments utilizing these stresses individually (17). These confounding factors likely contribute to the lack of correlation between expression of known hypoxia-regulated genes with immunohistochemical detection of these markers in areas of hypoxia in vivo (17, 18).

Here, we report the identification of the hypoxic gene expression profile in a solid tumor using a prototypical hypoxia marker EF5, Laser Capture Microdissection (LCM) and Microarray Gene Expression Profiling (EF5-LCM-MGEP). We have utilized LCM to isolate total RNA from normoxic and hypoxic regions in the rat 9L glioma tumor model grown in its isogenic host (Fischer rat). Microarray analysis of the isolated RNA generated an in vivo hypoxia expression profile. For comparison, we also analyzed RNA samples isolated from rat 9L glioma cells in culture exposed to hypoxia or normoxia. Microarray results for select endogenous hypoxia-related markers were validated through qPCR. Our analysis reveals some major differences between in vitro and in vivo gene expression signatures, including a pronounced repression of mRNAs coding for immune system and DNA repair-related proteins in hypoxic areas of these solid tumors.

Materials and Methods

Cell Culture and in vitro hypoxic treatments

9L gliosarcoma cells were grown in MEM media with 12% newborn calf serum with twice-weekly transfer. To initiate the experiment, cells were either incubated at ~20% O2 (“Normoxia”) or 0.2% O2 (“Hypoxia”) for 6h or 16h. Cells were subsequently washed with HBSS (Invitrogen), detached with 0.25% trypsin-EDTA and resuspended in RNA lysis solution (Ambion) at a concentration of 50,000cells/100µl lysis solution. The lysate was stored at −80°C prior to RNA isolation.

Animal Model

All animal experiments were performed in accordance with NIH guidelines and with the approval of the University of Pennsylvania Animal Use Committees (IACUC). Six week old male Fischer 344 rats (F344/Ncr, NCI-Frederick, MD) were inoculated via subcutaneous injection on the hind flank with 9L cells (106) to generate passage ‘zero’ tumors (p0). The tumor was excised, cut into 1–2mm3 pieces and used to generate subsequent passages. The epigastric tumors were initiated by tying p5 or p6 tumor pieces onto the epigastric artery-vein pair as previously described(19).

EF5 Injection

Once 9L epigastric tumors attained a size of 1–1.5g, animals were anesthetized with 2.5% isoflurane and maintained at 35–37°C with a heated pad. The EF5 bolus was administered via tail vein catheter at a dose of 30 mg/kg. Three hours after EF5 administration, 1.0ml Hoecsht (10mM) was injected via tail catheter. After 5 min the tumor was excised under 4% isoflurane in air and embedded in 3% low melt agarose (Biorad Laboratories). The embedded tumor was then frozen on a dry ice cooled aluminum block and stored at −80°C.

Tumor Sectioning

See supplementary information


LCM sections were fixed for 1.5 min, 1.5 min. and 5 min with ice-cold 70%, 85% and 100% EtOH respectively followed by a quick rinse in 100% EtOH at room temperature, then dried. Tissue sections were incubated in a humidified chamber for 30 min at 4°C with 150µl antibody solution consisting of 540µl Rinse solution, 30µl Cy3-ELK3-51 antibody (EF5 antibody, 4mg/ml) and 30µl RNasin (40U/µl, Promega Corp., WI). Rinse solution contained 3ml RNase-free H20, 32µl 1M HEPES, 160µl 20X Earle’s NaCl and KCl salt and 40µl RNasin. Slides were washed 6X with 100µl rinse solution and with increasing ice-cold EtOH concentrations (70%, 85% and 100%). Slides were dipped into room-temperature 100% EtOH, allowed to dry and stored in sterile 50ml tubes with desiccant (4A molecular sieve, Aldrich) prior to LCM. If kept more than 1.5 hr before LCM, the tubes were stored in dry ice, prior to returning them to RT, where the slides were dipped into 100% ethanol at RT and dried. For co-detection of hypoxia and HSP27, Rad51 and CD8+ cells, frozen tumor sections were incubated with the corresponding primary antibodies following incubation (anti-HSP27; Cell Signaling) or together (anti-Rad51; Thermo Scientific, HIS52 and anti-Rat CD8; Biolegend) with the Cy3-ELK3-51.

Laser Capture Microdissection

LCM was performed using the PALM system from Zeiss (LCM Facility, Children’s Hospital of Philadelphia). Stained 9L rat gliosarcoma tumor sections mounted onto Zeiss membrane slides were kept on dry ice and dehydrated in 100% EtOH just prior to use to optimize excision and limit RNA degradation. EF5 fluorescence was detected with the Texas Red filter and indicated areas of hypoxia while the antibody’s absence corresponded to normoxic regions. Tissue displaying necrotic morphology based on fragmented appearance of nuclei (Hoechst 33342) was avoided. Once sufficient normoxic sample had been excised (~100ng), the tissue was dissolved in 100µl lysis solution and stored at −80°C pending RNA isolation. Hypoxic tissue was collected and processed similarly.

RNA Isolation

See supplementary information.

mRNA Microarrays

Total RNA was linearly amplified using the NuGEN Ovation WT-Pico kit. Briefly, total RNA (2ng/sample) was reverse transcribed to generate cDNA that incorporated an RNA primer binding sequence. After second-strand synthesis, cDNA templates were amplified by ribo-SPIA. The resulting amplified cDNA was fragmented, biotinylated and hybridized to Rat Gene 1.0ST Arrays (Affymetrix). Microarrays were stained with streptavadin-phycoerythrin and incubated with biotinylated anti-streptavadin to enhance fluorescence signal detection. Comparison of signal intensity yielded relative gene expression under hypoxia/normoxia.

qPCR Analysis

see supplementary information.

Microarray Data Analysis and Statistics

All analyzed microarray data are in accordance with the MIAME guidelines and microarray data has been deposited at the GEne Expression Omnibus (GEO) site (Samples GSM637895-GSM 637912). Affymetrix.cel files were imported into Partek Genomics Suite (v6.5b, Partek Inc., St. Louis, MO). Robust Multichip Average (RMA) normalization was applied. Global sample variability was assessed by Principal Components Analysis (PCA). To identify differentially expressed genes, a 3-way mixed-model ANOVA was calculated, with the factors oxy state (H or N) sample type (vivo, vitro 6, vitro16) and batch/tumor. An interaction term between oxygenation state and sample type was also included as were pairwise contrasts of H vs. N for each of the sample types. P-values resulting from the ANOVA but not the pairwise comparisons were corrected for multiple testing using the step-up method of Benjamini and Hochberg as implemented in Partek (20). For each of the sample types the appropriate pairwise contrast was filtered for significance (p-value <.05) and then by fold change to yield 2 gene lists, one for those genes that were >2x higher in hypoxia and then for those genes that were <−2x lower in hypoxia. This yielded 6 gene lists which were then compared using Venn diagrams, one for all of the lists with genes up in hypoxia and a second for the lists containing genes down in hypoxia. For the gene list used in the hierarchical clustering, the most significant genes (false discovery rate 20%) for the interaction term between sample type and oxy state were selected. This yielded 49 genes. To generate Heat Maps for the three experimental conditions, separately, for up and down changes, the significant gene lists described for each of the three comparisons (cutoffs 2 fold, p < 0.05) were combined. Log2 ratios for each tumor pair were used to hierarchically cluster genes (188 genes from the union of up lists, 549 from the union of down lists) and samples. .

Gene Ontology Analysis

see supplementary information


Selection of 9L rat tumor model for in vivo hypoxia studies

In seeking an appropriate animal model in which to study gene expression under defined conditions of tissue hypoxia, we considered the following parameters: the tumor should exhibit properties similar to human tumors, have a well-defined hypoxic response and be of sufficient size to allow large tissue areas to be collected by LCM. The 9L gliosarcoma grown in its isogenic host (Fischer rat) as an epigastric pedicle has been well-characterized by us (19) and fulfilled several of these parameters (Fig. 1A). This tumor: (a) is isogenic to the Fischer rat, allowing for interrogation of the influence of the immune system in hypoxic gene expression (b) has a well-developed stromal support and has been described as pathophysiologically similar to human glial and sarcoma tumors, and (c) is one of a handful of rodent tumors with inter-tumor variation in hypoxia-mediated radiation response similar to human tumors, as measured by EF5 binding and radiation response (21). 9L tumors develop relatively normal stroma with diffuse, scattered, rather than central, necrosis (22) (Fig. 1B).

Figure 1
A. The 9L glioma epigastric tumor model. B. Macroscopic staining of 9L tumor showing areas of viable hypoxic cells (red). C. Schematic of LCM-MGEP procedure: Left panel: 9L rat glioma tissue before and after LCM. (i) Uncut normoxic region. (ii) Normoxic ...

Isolation of high quality RNA from hypoxic regions of rat glioma xenografts following LCM

Initially, we attempted to isolate total RNA from tumors following EF5 staining protocols long-established by our group (21). However, we found that previously established fixation methods and the extensive time of incubation with blocking solution and anti-EF5 antibody led to significant RNA degradation which was not acceptable for subsequent microarray gene expression analysis. Therefore, considerable effort was devoted to optimize the protocol for EF5 staining. We adopted ethanol fixation and a very fast 'quick-stain' (45 min for the whole procedure), using highly purified, Cy3-labelled anti-EF5 antibodies and excess of RNase inhibitor to overcome this limitation.

EF5 antibody binding was detected using the Texas Red filter. Regions of high fluorescence corresponded to regions of hypoxia whereas areas with minimal fluorescence corresponded to regions of normoxia. Areas without fluorescence corresponded to regions of normoxia. To ensure that EF5-negative regions consisted of viable, normoxic tumor cells and not necrotic areas (which do not metabolize EF5 to the active nitroimidazole), we stained serial sections placed adjacent to the EF5 stained regions with Hoechst 33342 dye. Regions with necrotic tissue have a distinct morphology with a more sparse arrangement of nuclei that also exhibit a smaller and fragmented morphology. We avoided such areas in the corresponding EF5 slide and collected normoxic tissue from areas devoid of these characteristics. Using this approach, we collected separately approx. 100 ng. of normoxic and hypoxic tissue from 2 sections each and repeated the process for three different 9L tumors (Fig. 1C). The quality of the isolated RNA was analyzed using the Agilent Bioanalyzer (Penn Microarray Facility). A RNA Integrity Number (RIN) of 7 was deemed of sufficient quality for further amplification and array analysis. As shown in Fig. 1C (right and bottom panels), the RNA isolated in this manner had clearly visible bands representing the large ribosomal RNA (28s and 18s) as well as small RNA (5s, tRNA).

RNA amplification and hybridization to gene expression arrays

Due to the small amount of RNA isolated from the in vivo samples, a high-efficiency linear amplification step was necessary prior to hybridization to the gene microarray (for technical consistency, the same amplification method was performed using the same amount of input RNA from the in vitro samples). For this, 100 ng of RNA was amplified using the NuGEN Ovation WT-Pico kit. Following the amplification step, the cDNA was hybridized to the Affymetrix Rat Gene 1.0ST Array for 16h. Analysis of the expression signal was performed by the Penn Microarray Facility using established methods (Affymetrix Expression Console), and statistical tests for significant differences between conditions were performed using Partek Genomics Suite. An aliquot from each amplification reaction was preserved for subsequent verification of select upregulated or downregulated genes with Real-Time quantitative PCR (qPCR).

Analysis of gene expression in response to hypoxia in vitro

To compare the gene expression profile of in vivo isolated RNA from hypoxic and normoxic areas to that obtained after in vitro exposure to hypoxia or normoxia, we exposed 9L rat cells to hypoxic conditions using an established hypoxia control system (23). In vivo, tumor cells can be exposed to a wide range of oxygen tensions, ranging from normoxia (5–8% O2 for most tissues) to complete anoxia for different periods of time. Since analysis of all possible combinations of pO2 and time of exposure would be prohibitively time-consuming and costly, we chose to use stringent hypoxic conditions (0.2% O2) for 6h and 16h. These conditions are routinely used for in vitro analysis of hypoxic gene expression (24). Furthermore, clinical studies using EF5 in human patients with glioblastomas indicate that pO2 levels in this range exist in these tumors (25). Total RNA isolation, analysis of its quality and hybridization to oligonucleotide arrays were performed right after treatment as described above for in vivo-isolated RNA.

Principal Components Analysis (PCA) and supervised clustering of gene expression changes of hypoxic vs. normoxic conditions in vivo and in vitro

As a first step in the analysis of the in vivo and 6h and 16h in vitro samples (thereby referred to as IVV, IVR6 and IVR16 respectively), we performed PCA using all three of the independent replicate samples for each condition. PCA is a mathematical algorithm which allows a 3-dimensional graphical representation of variation within a dataset by reducing dimensionality of the data (26). As shown in Fig. 2A, the 3 biological replicates of each experimental group largely clustered together in 3-dimensional space, with the exception of one of the IVV replicates which appeared to diverge from the other two IVV samples. This analysis indicates that there may be higher variability of gene expression pattern in vivo compared to that in vitro and that the different duration of hypoxic exposure results in distinct clustering of expressed genes.

Figure 2
A. Principal Component Analysis of gene expression in the in vivo (IVV), in vitro 6h (IVT) and in vitro 16 (IVT16) samples. B. Clustering of 49 upregulated and downregulated genes with >2-fold change that exhibit significant differences in hypoxic ...

Further analysis of gene expression between each group was performed using supervised clustering of the top upregulated and downregulated mRNAs, as described in the “Methods” section. As shown in Fig. 2B, the two IVR samples showed similar clustering of these specific mRNAs. In contrast, in the IVV sample, there was a more pronounced increase in the expression of several upregulated mRNAs and there was a distinct cluster of mRNAs whose expression was significantly downregulated in vivo (blue), but upregulated or not changed in vitro. A similar trend was observed when the results were analyzed without filtering for level of induction, but only for statistical significance (p<0.05; Fig. S1A). Indeed, analysis of the top 20 downregulated and upregulated mRNAs (i.e., the 20 genes demonstrating the highest fold-increases or decreases with a p<0.05) (Table 1) shows that whereas in vitro, mRNAs displayed a maximum of 2-fold (6h) or 2.4-fold (16h) decreases under hypoxia, in vivo, the decreases in mRNA levels ranged from 7.0-fold to 3.6-fold (Table 1C). Among the upregulated mRNAs with >2-fold changes, 17 were found to be upregulated by all 3 conditions whereas 13 were upregulated only in vitro but not in vivo (Fig. 2C). Remarkably, 115 genes were significantly upregulated in vivo but not by either in vitro condition. Also surprising was the finding that there were no commonly repressed genes among the three conditions. In stark contrast, 539 genes were significantly downregulated (>2-fold decreases) in hypoxic compared to normoxic regions in vivo (Fig. 2C). Similar patterns were observed in the analysis of common and unique mRNAs among the 3 datasets filtered only for statistical significance (p <0.05, no fold-change restriction). Here too, the number of significantly upregulated and downregulated mRNAs in hypoxic vs. normoxic areas in vivo was substantially larger than those in the two in vitro samples. (Fig. S1B).

Table 1
Top 20 upregulated or downregulated mRNAs (based on fold-change) after exposure to 6h hypoxia (A), 16h hypoxia (B) or in hypoxic vs normoxic areas (C).

Changes in mRNA levels in response to hypoxia in vitro and differences in mRNA levels in hypoxic vs. normoxic areas in vivo

Both in vitro conditions generated results that are in agreement with those previously reported by several studies. For example, well-known hypoxia and HIF-regulated mRNAs such as Pgf, CA9, PDK1, Ndrg1, etc., were significantly upregulated by in vitro hypoxia at both time points (Table 1 A and B) (27). Interestingly, from all the repressed genes, under both conditions, only one (Cyp1a1) has been previously shown to be downregulated by hypoxia (28) (Table 1).

Of the 20 top in vivo up-regulated genes, 14 are known hypoxia-regulated genes, a finding that validates our EF5-LCM-MGEP approach. Notably, Bnip3, a gene shown in several studies to be upregulated by HIF-1 and HIF-2 and to mediate hypoxia-induced apoptosis and autophagy (29,30) was the mRNA with the highest hypoxic upregulation. This was followed by Heme Oxygenase I (decycling), which mediates hypoxia-induced metabolic changes in cells, the transcription factor ATF3, which is activated in response to endoplasmic reticulum stress and hypoxia and HSP27 (Hsbp1), a small heat shock protein, known to be a direct HIF transcriptional target and to confer anti-apoptotic properties to tumor cells under hypoxia (31, 32) (Table 1C). However, our analysis also revealed many mRNAs upregulated in hypoxic areas including cyr61, Jmjd1a and Znf609, that have not been previously directly implicated in hypoxic gene regulation. The in vivo analysis has revealed for the first time a number of mRNAs whose expression was markedly lower in hypoxic compared to normoxic areas. Intriguingly, the majority of these mRNAs appear to play a role in immune system functions, specifically in lymphocyte and leukocyte-mediated immunity and antigen presentation. These gene products include: Ly6C, CD3D, CXCL9 (3335). A recent study found that increased expression of CD3D mRNA associated positively with increased survival in patients with metastatic melanoma. Another interesting finding was the downregulation of mRNA levels of several pro-apoptotic (e.g., Ddx60) and DNA repair proteins (e.g., Rad51) (36).

To verify the results obtained from the gene microarray analysis, we performed qPCR on total mRNA isolated in vitro and in vivo. As shown in Fig. 3A and B, the levels of Vegf, Pdk1, Glut1 and CA9 were all significantly upregulated by both 6h and 16h of hypoxia in vitro. The magnitude of the increases was larger based on qPCR and is within the range of published studies. Expression of Vegf and Glut1 was also found to be significantly upregulated in the in vivo samples (Fig. 3C).

Figure 3
qPCR analysis of levels of specific mRNAs whose levels were found to be upregulated by microarray analysis in samples IVR6h (A), IVR16h (B) and IVV (C). Error bars represent S.E. values (N=3). For (c), N=2 for VEGF and Glut1 and N=1 for PDK1 and CA9.

Gene Ontology analysis of in vivo hypoxia-regulated genes

To further analyze the effects of hypoxia on cellular processes and pathways in the solid tumors, we retrieved gene ontology (GO) annotations using mRNAs with >2.5-fold changes in normoxic vs. hypoxia areas using the GOrilla application (Technion University)(37). As expected from the analysis of individual genes and from previous published in vitro studies, the top pathways whose genes were upregulated by hypoxia included glycolysis and glycolysis-related processes, hypoxic response, stress response and angiogenesis (Suppl. Table S1). In contrast, pathways which included genes with decreased expression included those involved in immune system processes such as antigen recognition and chemotaxis, cell cycle progression, DNA synthesis, cell killing and DNA repair (Suppl. Table SII).

Expression of HSP27 and Rad51 in hypoxic and normoxic areas of 9L tumors

To test if the expression of select proteins correlates with that of their corresponding mRNAs, we analyzed the expression patterns of two proteins whose mRNAs were found to be significantly upregulated (HSP27-Hsbp1; 4.7-fold increase, p=0.002) or downregulated (Rad51; 3.0-fold decrease, p=0.01) in hypoxic vs. normoxic areas, respectively (Table 1 and supplementary data). As shown in Fig. 4A and Suppl. Fig. S1, expression of HSP27 was rather heterogeneous throughout the tumor, but primarily localized at or near areas of hypoxia (red and orange in combined images). In contrast, the expression of Rad51 was found to be primarily absent from hypoxic areas, and was highest in areas of normoxia (Fig. 4B). Interestingly, the anti-Rad51 antibody also appeared to stain blood vessels, which in their majority are normoxic. While this pattern of expression of Rad51 has not been previously reported, interrogation of a publicly available gene expression database (Novartis BioGPS) showed that expression of Rad51 mRNA is highest in endothelial cells, reaching levels 9 times higher that the median expression in other cell lines or tissues (Fig. 4C).

Figure 4
Immunohistochemical detection of proteins with mRNA levels altered by hypoxia in 9L tumors. Staining for HSP27 (A) (Alexa-labelled anti-HSP27 antibody; green), a protein whose mRNA was upregulated in hypoxic areas in vivo and RAD51 (B), a protein whose ...

Since several mRNAs which were found to be downregulated in hypoxic areas code for proteins with roles in T cell recruitment (e.g., CXCL9, Hcst/DAP10), we analyzed the relationship between CD8+ cells and hypoxic areas in the rat 9L gliomas. Immunohistochemical staining with an Alexa 488-labelled anti-CD8 antibody, revealed the presence of several CD8+ cells with strong membrane staining in the tumor (Figs. 5A–C), although the strongest staining was in the tumor periphery (not shown). Co-staining for EF5 adducts revealed that the vast majority of CD8+ staining was found outside hypoxic areas (19.7% of cells in normoxic areas vs. 5% in hypoxic areas were CD8+) and was concentrated primarily in the vicinity of Hoechst-perfused blood vessels (Figs. 5A and 5C).

Figure 5
A. Staining with an anti-CD8 Alexa 488-labelled antibody identifies cytotoxic T cells infiltrates. CD8+ cells are concentrated in highly vascularized areas (intense blue staining-Hoechst 33342) B. Higher magnification (400X) indicates CD8+ cells (green) ...


Direct, in vivo interrogation of the effects of components of the tumor microenvironment on gene expression offers significant advantages over similar in vitro approaches. First, EF5-LCM-MGEP offers the opportunity to analyze the effects of one stress (in this case low oxygen tension) in a physiological setting which invariably includes additional parameters such as nutrient availability, low pH and high interstitial pressure, all of which are known to influence gene expression in tumor cells (38, 39). Second, as our results indicate, this type of analysis has the potential to uncover stress responses that might be unanticipated or simply could not be interrogated under in vitro conditions. In our study, we observed a pronounced downregulation of a large number of mRNAs associated with immune system regulation and function in hypoxic areas, raising the intriguing possibility that hypoxic tumor areas represent an immunoprivileged tumor niche. If confirmed in other types of tumors, this finding would predict a new resistance factor for hypoxic tumors.

This in vivo approach of gene expression analysis is not without some limitations. For example, without further analysis, it cannot be determined whether the observed decreases in gene expression of immune response gene products under hypoxia is the result of direct downregulation of transcriptional programs or the consequence of over/under-representation of one type of lymphocyte or macrophage over another in hypoxic regions. Tumor-Associated Macrophages (TAMs) have been shown to segregate into two broadly-defined classes: The pro-oxidant, cytotoxic and anti-tumorigenic type, M1 and the pro-angiogenic pro-tumorigenic type M2 (40, 41). Our findings indicate that expression of genes associated with the M1 phenotype (Ly6C, Cxcl9, Cxcl10) (42, 43) are significantly under-represented in hypoxic vs. normoxic areas. This could arise either from (a) an active repression of M1-associated genes under hypoxic stress, or (b) exclusion of M1 or attraction of M2 in hypoxic compared to normoxic tumor areas. Our findings that hypoxic areas are largely devoid of CD8+ cells (cytotoxic T lymphocytes) argue for the latter mechanism. However, it is formally possible that CD8 expression may also be downregulated by hypoxia, even though it did not appear in our list of downregulated mRNAs. Intriguingly, in a recently published study, Doedens et al. reported that TAMs under hypoxic conditions negatively regulate T cell function through HIF-1α (44). Although in this study the localization of T cells in solid tumors with relation to hypoxic areas was not analyzed, these results suggest that hypoxia may have additional repressive effects on T-cell activity, thereby promoting tumor aggressiveness. Additional studies with specific markers and immunohistochemical localization of the two types of macrophages and T cells with respect to hypoxic regions in vivo would help elucidate the precise mechanism and these studies are currently under way in our laboratory.

Our analysis has revealed a significantly higher number of mRNAs affected by hypoxia in vivo compared to in vitro. The increased number of upregulated mRNAs in vivo vs. in vitro is in agreement with other studies demonstrating a more robust gene upregulation in response to stresses in vivo. For example, Khodarev et al., demonstrated that ionizing radiation induced significantly higher up-regulation of genes in xenografts than in in vitro cultures (45) and Camphausen et al demonstrated that the tumor microenvironment can exert substantial influence on gene expression in gliomas in response to ionizing radiation (46). While several mechanisms could underlie this difference for hypoxia, it is noteworthy that there appears to be a trend towards higher number of upregulated mRNAs from 6h hypoxia to 16h hypoxia (from 37 to 66, respectively). It is possible that in vivo, where moderate hypoxic conditions can persist for more than 16h without significantly affecting cell survival, the prolonged exposure to low oxygen can result in a higher number of upregulated genes.

Identification of an mRNA “hypoxic signature” in solid tumors has important implications with clinical significance. For example, several proteins such as GLUT-1, VEGF, Osteopontin, etc. have been shown to correlate with, and predict a poorer patient prognosis (47). More recently, hypoxia-induced miRNAs such as mir210 have been associated with increased metastasis and poorer patient outcome (48, 49). These studies are almost invariably initiated by in vitro analyses of gene expression patterns and subsequent immunohistochemical analysis (in the case of proteins) or PCR analysis (in the case of miRNA) in patients’ samples. The EF5-LCM-MGEP method offers a more systematic approach in which multiple targets are identified and validated in vivo and then a select few targets of interest are moved to the clinic. Our approach is readily amenable to further analysis of gene expression to include miRNA and protein expression using miRNA microarrays and proteomics respectively. Preliminary studies in our laboratory have demonstrated feasibility for the analysis of miRNAs using a similar approach and we are in the process of obtaining the hypoxic miRNA expression profile in human solid tumors. It is therefore conceivable that in the near future, a systems-level analysis of the hypoxic response in solid tumors will be feasible which could uncover novel targets for therapeutic intervention.

Supplementary Material


We thank Luca Bernabei and Xiangyang Yang for expert technical help. We also thank Andy Minn (University of Pennsylvania), Rob Bristow (University of Toronto) and Edith M. Lord (University of Rochester Cancer Center) for helpful discussions. This work was supported by an ORISE fellowship from the Oak Ridge National Laboratory to D.M. and by pilot funds from the Department of Radiation Oncology, UPenn to C.K.


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