The lipogenic enzyme fatty acid synthase (FAS) is up-regulated in a wide variety of cancers, and is considered a potential metabolic oncogene by virtue of its ability to enhance tumor cell survival. Inhibition of tumor FAS causes both cell cycle arrest and apoptosis, indicating FAS is a promising target for cancer treatment.
Here, we used gene expression profiling to conduct a global study of the cellular processes affected by siRNA mediated knockdown of FAS in MDA-MB-435 mammary carcinoma cells. The study identified 169 up-regulated genes (≥ 1.5 fold) and 110 down-regulated genes (≤ 0.67 fold) in response to knockdown of FAS. These genes regulate several aspects of tumor function, including metabolism, cell survival/proliferation, DNA replication/transcription, and protein degradation. Quantitative pathway analysis using Gene Set Enrichment Analysis software further revealed that the most pronounced effect of FAS knockdown was down-regulation in pathways that regulate lipid metabolism, glycolysis, the TCA cycle and oxidative phosphorylation. These changes were coupled with up-regulation in genes involved in cell cycle arrest and death receptor mediated apoptotic pathways.
Together these findings reveal a wide network of pathways that are influenced in response to FAS knockdown and provide new insight into the role of this enzyme in tumor cell survival and proliferation.
Up-regulation of fatty acid synthase (FAS), the enzyme responsible for the endogenous synthesis of palmitate, is increasingly recognized as a hallmark of cancer [1, 2]. While normal cells obtain most fatty acids from circulating lipids, tumor cells have developed an increased reliance on endogenous fatty acid synthesis to satisfy their metabolic needs [2]. This elevation of FAS occurs early in human cancer, is associated with aggressive forms of the disease, and is linked to poor prognosis [3–7]. Consequently, strategies to target FAS are becoming increasingly exploited as attractive approaches for cancer therapy.
FAS is critically important for tumor cell survival and function; providing the necessary fatty acids for membrane formation and signal transduction [2]. The targeted knockdown of tumor FAS by small molecule inhibitors or small interfering RNA (siRNA) has been shown to induce both cell cycle arrest and apoptosis in cultured cells and suppresses tumor growth in xenograft bearing mice [8–13]. This anti-tumor activity is linked to increased expression of p27kip1 [9, 14] and decreased Akt phosphorylation [15]. FAS inhibition can also lead to the transcriptional suppression of the Her2/neu gene suggesting an active role for FAS in gene regulation [16]. However, detailed understanding of the regulatory mechanisms linking inhibition of FAS to these anti-tumor effects remains elusive.
In the present study, we utilized siRNA, BeadArray technology, and pathway analysis to define the genome-wide changes that take place following knockdown of FAS (12–48 h). We have identified a core set of 279 genes representing the FAS knockdown signature in the MDA-MB-435 mammary carcinoma cell model. Functional classification of these target genes, combined with quantitative pathway analysis, revealed extensive changes in metabolism, cell survival/proliferation, DNA replication/transcription, and ubiquitin dependent protein degradation as a consequence of FAS inhibition. Taken together, our results provide a detailed overview of the anti-tumorigenic signaling network induced in tumor cells by the targeted knockdown of FAS.
MDA-MB-435 mammary carcinoma cells were selected as the model for defining the FAS knockdown signature. For our experiment, four independent siRNA duplexes targeting FAS (FAS #1-#4) were chosen based on there ability to knockdown the enzyme and to induce tumor cell apoptosis after 72 h [17]. Inhibition of FAS by each duplex was verified using 25 nM of siRNA as demonstrated by a decrease in FAS mRNA, protein, and fatty acid biosynthesis after 48 h relative to non-silencing control siRNA (Figure 1a–c). The non-silencing control siRNA was selected based on minimal cross reactivity with known targets and had no impact on FAS expression or activity when compared against Lipofectamine 2000 transfection alone (data not shown). Gene expression profiles were examined on two separate occasions following transfection with FAS siRNA. Treatment times (12, 24, 36 and 48 h) were chosen to capture early and late gene changes associated with the block in cell cycle progression and the advent of apoptosis occurring in response to knockdown of FAS. Abrogation of FAS (>70%) was verified within 12 h of transfection and persisted throughout the 48 h experiment. Significant changes in cell viability were not observed 48 h post-transfection indicating cells were viable during the time of gene analysis (data not shown).
FAS knockdown generates specific and time-dependent gene expression patterns. (a-c) Target mRNA, protein and fatty acid biosynthesis knockdown by FAS siRNA duplexes. MDA-MB-435 tumor cells were exposed to 25 nM of four different siRNA duplexes targeted against FAS (FAS #1-#4) or non-silencing control siRNA for 48 h. Efficiency of FAS knockdown was determined by measuring FAS mRNA (a), FAS protein (b) and fatty acid biosynthesis (c). β-tubulin served as a loading control for FAS protein expression. Values are the mean ± SE of two replicates per treatment. (d) Expression profile represents 279 genes differentially modified by 1.5 fold in response to knockdown of FAS (shown in hours on the X-axis). Horizontal lines represent the average expression of individual genes modified by at least 3 of the FAS siRNA duplexes. Red and green indicate increased and decreased expression, respectively, relative to non-silencing control siRNA.
The first approach we used in analyzing the effect of knockdown of FAS on gene expression was to identify target genes modified by at least 3 of the siRNA duplexes. The expression signature of each FAS siRNA was determined by identifying genes significantly changed over the time course using MB statistics and two way ANOVA analysis. For each siRNA duplex only genes with a p-value ≤ 0.05 and at least 1.2 fold changes in both biological replicates at a given time point were maintained for expression analysis, and are published as supporting information online (see Additional Files 1, 2, 3, and 4). The FAS knockdown signature was defined as the overlap of significant gene changes occurring in response to at least 3 of the siRNA duplexes with an average gene expression change 1.5 fold that observed in controls. This allowed us to identify gene changes specifically associated with knockdown of FAS, while at the same time eliminate potential off-target effects unique to the individual siRNA sequences. Using this approach, we identified 279 genes whose expression changed in response to knockdown of FAS (169 genes up-regulated and 110 down-regulated; see Additional File 5). Alterations in gene expression occurred as early as 12 h after knockdown of FAS with the majority of genes affected by 24 h (Figure 1d). These results show that inactivation of FAS has a profound affect on gene transcription.
Knockdown of FAS affected genes that regulate a variety of biological processes including cell proliferation, DNA replication, transcription and apoptosis (Table 1; see Additional File 5). We found that the abrogation of FAS enhanced the expression of several anti-proliferative genes including CCNG1, CDKN1A (p21cip 1), SPRY2 and SPRY4. This coincided with up-regulation of the cell surface apoptosis gene ANXA1, the mitochondrial apoptosis pathway gene APAF1, and the death receptors TNFRSF10B (TNF-related apoptosis-inducing ligand (TRAIL) receptor) and TNFRSF21 (TNF-α receptor). In addition, results showed knockdown of FAS up-regulates SGPL1, a key enzyme in sphingosine-1-phosphate metabolism whose increased expression also leads to accumulation of the pro-apoptotic signaling molecule ceramide [18]. This finding is consistent with reports that FAS inhibition up-regulates ceramide synthesis, which has been found to be a necessary step leading to the induction of tumor cell apoptosis following the loss of FAS [17].
Selected FAS target genes involved in proliferation/apoptosis, metabolism, transcription and protein ubiquitination.
The fold change values represent the average from at least 3 of the FAS siRNA duplexes compared to non-silencing control siRNA. Genes were classified using GO terminology from GeneSpring and the NCBI Nucleotide database. For detailed annotations of all 279 differentially expressed genes see Additional File 5.
Several of the genes significantly altered by knockdown of FAS are known to play a role in the regulation of lipid metabolism. Inactivation of FAS increased the expression of INSIG1, and the leptin receptors, LEPR and LEPROTL1, which block transcription, proteolytic cleavage and transcriptional activation of the sterol regulatory element-binding protein (SREBP) family of transcription factors that promote lipid biosynthesis (Table 1) [19, 20]. Additionally, loss of FAS led to down-regulation of ID2 and ID3, which are dominant negative helix-loop-helix (HLH) proteins that bind SREBP 1c and functionally repress FAS promoter activity [21]. ID2 and ID3 also repress expression of the cyclin-dependent kinase inhibitor p21cip1 [22, 23], thus, as expected the suppression of these genes in response to knockdown of FAS was coupled with up-regulation of the p21cip 1gene (Table 1). These data provide evidence of a direct link between regulation of FAS and control of cell cycle progression.
Another important biological category affected by knockdown of FAS is regulation of protein ubiquitination (Table 1). We found that knockdown of FAS altered the expression of several E2 ubiquitin conjugation enzymes (2 up-regulated) and E3 ubiquitin ligases (4 down-regulated and 6 up-regulated) which function to target proteins for degradation by the proteosome [24]. In addition, we identified up-regulation of 2 splice variants of UBE2V1, which are catalytically inactive E2 enzymes [25]. UBE2V1 variants result from the co-transcription of UBE2V1 with the neighboring upstream gene, Kua, which shares sequence consensus to fatty acid hydrolases (variant 2, Kua-UEV fusion gene [GI_40806191-I]; variant 4, Kua gene [GI_40806192-I]). This targeting of ubiquitination enzymes indicates that the knockdown of FAS affects the tumor cell proteome not only through changes in transcription, but also on the post-translational level.
The second approach we used to analyze the gene array results was to identify gene pathways coordinately up-regulated or down-regulated by knockdown of FAS using Gene Set Enrichment Analysis (GSEA). GSEA is a unique application that coordinates small changes in gene expression from a large number of functionally related genes in order to identify pathways that are significantly changed [26]. As expected, fatty acid metabolism (NES = -1.808, NOM p-val = 0.002, FDR q-val = 0.085) was significantly suppressed by knockdown of FAS (Figure 2). Fatty acid elongation (NES = -1.537, NOM p-val = 0.037, FDR q-val = 0.394) was also reduced, which is consistent with the loss of available palmitate.
Down-regulation of metabolic pathways by targeted knockdown of FAS. The figure displays select pathways found to be down-regulated in response to FAS siRNA treatment compared to non-silencing control siRNA. Significance was determined using a nominal p-value < 0.05 or FDR < 0.250. The expression levels of the genes significantly modified in the pathway are coded colorimetrically: red, high expression; blue, low expression. FAS siRNA treatments for each time point are ordered as follows (a and b indicate different biological replicates): FAS #1a, FAS #1b, FAS #2a, FAS #2b, FAS #3a, FAS #3b, FAS #4a, FAS #4b. For a complete list of all pathways down-regulated by knockdown of FAS, see Additional File 6.
The effects of knockdown of FAS on the expression of metabolic genes extended to pathways other than fatty acid metabolism (Figure 2). Inactivation of FAS down-regulated both the glycolysis/gluconeogenesis (NES = -1.560, NOM p-val = 0.029, FDR q-val = 0.377) and krebs-TCA cycle (NES = -1.737, NOM p-val = 0.014, FDR q-val = 0.094) pathways. Down-regulation of the krebs-TCA cycle was consistent with an overall suppression in mitochondrial genes involved in energy metabolism and oxidative phosphorylation (GO 005739; NES = -1.515, NOM p-val = 0.039, FDR q-val = 0.365). Furthermore, this down-regulation in genes regulating glucose utilization was coupled with a reduction in insulin signaling (NES = -1.635, NOM p-val = 0.005, FDR q-val = 0.280). This suggests that inhibition of FAS may restrict the ability of tumor cells to produce the energy necessary to thrive.
The anti-tumorigenic effects of knockdown of FAS were traced to an up-regulation in genes that modify tumor proliferation (Figure 3). GSEA analysis showed that knockdown of FAS up-regulated genes involved in DNA damage signaling (NES = 1.809, NOM p-val = 0.000, FDR q-val = 0.068) and cell cycle arrest (NES = 1.504, NOM p-val = 0.036, FDR q-val = 0.163). Induction of cell cycle arrest was coupled with up-regulation of the p27 (NES = 1.582, NOM p-val = 0.009, FDR q-val = 0.128) and Rb (NES = 1.581, NOM p-val = 0.010, FDR q-val = 0.126) pathways consistent with our previous finding that FAS regulates the G1 checkpoint through these pathways [9]. Additionally, knockdown of FAS elevated the expression of several cyclin-dependent kinase inhibitors (p16INK4 (CDKN2A), p15INK4 (CDKN2B) and p21Cip 1) which function as negative regulators of the Rb pathway [27], demonstrating FAS induces a high degree of regulation over the G1/S transition (Figure 3). Results also showed that knockdown of FAS elevated genes involved in the G2 pathway including the checkpoint regulators CHEK2, WEE1 and PLK1 (Table 1; Figure 3). These data indicate that FAS controls tumor proliferation by regulating several aspects of the cell division cycle.
Up-regulation of cell cycle arrest and cell death pathways in response to knockdown of FAS. The figure displays select pathways found to be up-regulated in response to FAS siRNA treatment compared to non-silencing control siRNA. Significance was determined using a nominal p-value < 0.05 or FDR < 0.250. The expression levels of the genes significantly modified in the pathway are coded colorimetrically: red, high expression; blue, low expression. FAS siRNA treatments for each time point are ordered as follows (a and b indicate different biological replicates): FAS #1a, FAS #1b, FAS #2a, FAS #2b, FAS #3a, FAS #3b, FAS #4a, FAS #4b. For a complete list of all pathways up-regulated by knockdown of FAS, see Additional File 7.
The apoptotic effects of knockdown of FAS were recently shown to be blocked by co-treatment with the pan-caspase inhibitor z-VAD-fmk [28], suggesting a role for caspases in mediating FAS induced tumor cell death. Here, we found up-regulation of caspase 7 and caspase 8 (receptor-mediated apoptosis) in response to knockdown of FAS (Figure 3). This was accompanied by enhanced expression of pathways and genes that activate caspase 8 such as tumor necrosis factor (tnfr1, NES = 1.408, NOM p-val = 0.038, FDR q-val = 0.230; tnfr2, NES = 1.788, NOM p-val = 0.002, FDR q-val = 0.070; Figure 3). GSEA results also demonstrated compensatory up-regulation of NF-κB (NF-κB Induced, NES = 1.5, NOM p-val = 0.022, FDR q-val = 0.166) and the MAP kinase (NES = 1.539, NOM p-val = 0.016, FDR q-val = 0.133) survival pathways in line with previous reports (see Additional File 7; [14]).
The present study provides a comprehensive overview of the genomic changes accompanying knockdown of tumor FAS. To our knowledge, this is the first report demonstrating that targeted knockdown of a metabolic enzyme can influence overall energy producing pathways within the tumor cell. The major findings of this work are as follows: first, siRNA mediated knockdown of FAS affects the transcription of genes involved in tumor cell energy metabolism as demonstrated by down-regulation in lipid metabolism, glycolysis, krebs-TCA cycle and oxidative phosphorylation pathways. Second, we show that loss of FAS is anti-tumorigenic by up-regulating cell cycle arrest and death receptor mediated apoptosis pathways. Third, the inhibition of FAS leads to changes in genes that regulate transcription and ubiquitin-dependent protein degradation. Altogether, these findings provide novel insight into the cellular processes affected by knockdown of FAS in tumor cells.
A vital assumption in siRNA-mediated gene silencing is that knockdown of the target gene is exclusive, and without off-target consequences. Yet, increasing evidence has suggested siRNA can induce off-target effects that are unique to the individual siRNA duplex, but unrelated to the identity of the target gene [29]. These siRNA duplex specific off-target effects are thought to arise from unintended sequence homology with the off-target transcript. In this study, we addressed the concern of potential siRNA off-target effects by examining gene expression signatures in response to four siRNA duplexes targeting different regions of the FAS gene and focused our analysis on the common gene changes that were associated with knockdown of the enzyme. This eliminated off-target effects and enabled us to study gene changes specifically associated with knockdown of FAS. Altogether, evidence supporting the role of FAS inhibition in inducing the described genomic effects is as follows: first, we showed that FAS siRNA strongly abrogates the expression of the FAS gene, reduces FAS protein levels and blocks the biosynthetic activity of the enzyme, as indicated by reduced [14C]malonyl-CoA incorporation into palmitate. Second, pathway analysis demonstrated that fatty acid biosynthesis and metabolism are among the most significant pathways down-regulated in response to FAS siRNA. Third, up-regulation of the p27 and Rb gene pathways is consistent with our prior work showing knockdown of FAS elicits a G1/S arrest through these pathways [9]. We also found increased expression of the p21Cip 1gene which is up-regulated at the protein level in response to knockdown of FAS [14, 30]. Like p27, p21Cip1 acts as a negative regulator of the G1/S transition by inactivating the cyclin-dependent kinases that function to phosphorylate Rb and promote S phase entry [31].
Knockdown of FAS by siRNA or the small molecule inhibitor Orlistat induces apoptosis by activating caspase 8 (Knowles LM and Smith JW, unpublished observations). This is consistent with the current observation that siRNA targeting FAS up-regulates TNF-α and TRAIL death receptor mediated apoptosis pathways. In addition to TRAIL, cell death through FAS inhibition has also been linked to up-regulation of BNIP3 and DAP Kinase 2 [17]. These two proapoptotic genes were detected by microarray analysis 72 h after FAS inhibition, which explains why we did not observe similar changes as our study was limited to 48 h. A possible mechanism accounting for the ability of FAS inhibition to induce these apoptotic effects is the accumulation of ceramide, which has been shown to induce these death receptor pathways [32]. Ceramide accumulation is the result of malonyl-CoA build-up following inhibition of FAS [17]. Importantly, we have found that knockdown of FAS up-regulates SGPL1. Ceramide generation and promotion of cellular apoptosis have been observed in response to increased expression of SGPL1 [18], suggesting this enzyme may represent a leverage point by which knockdown of FAS influences ceramide production.
In addition to changes in proliferation and survival, knockdown of FAS also suppresses genes in the glycolysis, krebs-TCA cycle and oxidative phosphorylation metabolic pathways suggesting an overall reduction in cellular energy metabolism. Consistent with the down-regulation of oxidative phosphorylation gene expression, Chajès et al. [33] report impaired mitochondrial function and generation of reactive oxygen species in response to FAS inhibition. Targeted inactivation of ATP citrate lyase, a lipogenic enzyme which functions immediately up-stream FAS, has the opposite effect by stimulating mitochondrial function [34]. This is interesting as we have found that knockdown of FAS, but not of ATP citrate lyase, induces apoptosis (Knowles LM and Smith JW, unpublished observations); raising the question of whether the changes in cell survival and metabolism are linked together. Indeed, growing evidence suggests TNF-α may influence metabolism, as it can induce the down-regulation of genes involved in oxidative phosphorylation [35], inhibition of electron transport chain activity [36] and generation of reactive oxygen species [37]. Identifying whether induction of apoptosis and reduced energy metabolism are connected (via TNF-α or another molecular target) will be an important direction for future investigations, and may prove critical for understanding the anti-tumorigenic benefits of a knockdown in tumor FAS.
The overall goal of our study was to define the regulatory changes that take place following inhibition of tumor FAS. To this end, we have identified a number of target genes specifically modified by knockdown of FAS. Of particular interest among these are the dominant negative HLH transcription repressors ID2 and ID3, which function to bind and inactivate the basic HLH family of transcription factors [38], and therefore, are recognized for their ability to block cell differentiation and promote proliferation [39]. Like FAS, ID over-expression correlates with tumor development in an array of cancers [40, 41], and knockdown of ID2/ID3 induces growth arrest and apoptosis [39, 42]. Our finding that knockdown of FAS leads to the suppression of transcripts encoding ID2 and ID3 provides evidence of a link between FAS and ID expression. This link is further supported by observations that ID2/ID3 proteins can also suppress FAS gene expression by inactivating the basic HLH transcription factor SREBP1 c that controls FAS expression [21]. Further characterization of the molecular interplay between FAS and ID is needed to more fully understand the role of these in tumorigenesis.
In conclusion, this study provides a comprehensive database of genomic changes that occur in response to knockdown of FAS and confirms that FAS is central not only for tumor cell metabolism but also for tumor cell signaling. Further characterization of these signals on the protein level will offer a more complete understanding of the cellular targets and pathways affected by knockdown of FAS and may lead to new therapeutic strategies for tumor prevention and treatment.
The MDA-MB-435 mammary tumor cell line was obtained from Janet Price at the University of Texas Southwestern. MDA-MB-435 cells were maintained in Minimum Essential Medium Eagle (MEM) with Earl's Salts (Mediatech, Inc., Herndon, VA, USA) supplemented with 10% fetal bovine serum (Irvine Scientific, Santa Ana, CA, USA), 2 mM L-glutamine (Invitrogen Life Technologies, Inc., Carlsbad, CA, USA), MEM vitamins (Invitrogen Life Technologies, Inc.), nonessential amino acids (Mediatech, Inc.) and antibiotics (Omega Scientific, Inc., Tarzana, CA, USA). Cells were grown at 37°C under a humidified, 5% CO2 atmosphere.
Four individual FAS siRNA sequences corresponding to 5'-GAGCGUAUCUGUGAGAAACUU-3' (nucleotides 6241–6259; FAS#1), 5'-GACGAGAGCACCUUUGAUGUU-3' (nucleotides 1741–1749; FAS#2), 5'-UGACAUCGUCCAUUCGUUUUU-3' (nucleotides 1758–1776; FAS#3), 5'-UGACAUCGUCCAUUCGUUUUU-3' (nucleotides 6236–6254; FAS#4) were designed and synthesized by Dharmacon (Lafayette, CO, USA). MDA-MB-435 cells were plated at 7.81 × 103 cells/cm2 in 10-cm2 plates and grown for 24 h prior to transfection with 25 nM FAS#1, FAS#2, FAS#3, FAS#4 or non-silencing control duplex #2 (D-001210-02) siRNA in Opti-MEM medium (Invitrogen) using Lipofectamine 2000 reagent (Invitrogen). After 5 h, transfection medium was replaced with normal culture medium and cells were grown for 12–48 h post-transfection.
Total RNA was isolated from adherent cells using the Qiagen RNeasy kit (Qiagen, Inc, Valencia, CA, USA). Genomic DNA contamination was removed using RNase-Free DNase (Qiagen). RNA purity was assessed by A260/A280 absorption and RNA integrity was verified by agarose gel electrophoresis. cDNA was synthesized from 4 μg RNA using the Superscript III First-Strand Synthesis System (Invitrogen). Real-time quantitative PCR reactions were performed using Power SYBR® Green PCR Master Mix (Applied Biosystems, Foster City, CA, USA) on a Stratagene Mx3000p QPCR System (La Jolla, CA, USA). cDNA was amplified using forward and reverse primers for FAS (5'-AACTCCATGTTTGGTGTTTG-3' and 5'-CACATGCGGTTTAATTGTG-3') and normalized to the housekeeping gene P0 (5'CAAGACTGGAGACAAAGTGG-3' and 5'AATCTGCAGACAGACACTGG-3'). The formation of a single PCR product was verified using automated melting curve analysis.
Adherent cell populations were lysed in 2× SDS sample buffer, separated by SDS-PAGE and transferred to nitrocellulose as previously described [9]. Membranes were probed for FAS and β-tubulin protein expressions using anti-FAS (BD Transduction Laboratories, San Jose, CA, USA) and anti-β-tubulin (Upstate Biotechnology, Lake Placid, NY, USA) antibodies. Immunoreactivity was detected using anti-mouse IgG conjugated peroxidase and visualized by enhanced chemiluminescence.
FAS activity was determined by measuring the incorporation of [14C]malonyl-CoA into cellular fatty acids as previously described [9]. Briefly, MDA-MB-435 cells were lysed in 20 mM Tris (pH 7.5), 1 mM dithiothreitol, and 1 mM EDTA by sonication. Insoluble material was removed by centrifugation (14,000 rpm) for 15 min at 4°C. The resulting lysates (70 ug) were incubated in reaction buffer containing 500 μM NADPH, 166.6 μM acetyl CoA, 100 mM KCl and 0.4 μCi [14C]malonyl-CoA (GE Healthcare, Piscataway, NJ, USA) for 25 min at 37°C. Reactions were chased with 25 nM cold malonyl-CoA for 15 min and terminated by the addition of chloroform:methanol (1:1). The chloroform extract was dried under N2 and extracted with water-saturated butanol. The butanol extract was evaporated under N2, and labeled fatty acids were detected using a Beckman model LS 5000CE Scinillation Counter (Beckman Coulter, Inc., Fullerton, CA, USA).
MDA-MB-435 cells were transfected with siRNA on two separate occasions to generate independent measurements for each treatment, and monitored for whole genome expression at 12 h intervals over a 48 h time period. Total RNA was isolated and examined for purity/integrity as described above. Labeled cRNA was prepared from 500 ng RNA using the Illumina® RNA Amplification Kit from Ambion (Austin, TX, USA). The labeled cRNA (1500 ng) was hybridized overnight at 55°C to the Sentrix® HumanRef-6 Expression BeadChip (>46,000 gene transcripts; Illumina, San Diego, CA, USA) according to the manufacturer's instructions. BeadChips were subsequently washed and developed with fluorolink streptavidin-Cy3 (GE Healthcare). BeadChips were scanned with an Illumina BeadArray Reader and hybridization efficiency was monitored using BeadStudio software (Illumina) to measure internal controls built into the Illumina system.
Expression data was filtered to identify genes expressed on the BeadChip at >0.99 confidence (16,585 genes), and normalized per chip using non-linear Normalize.quantiles or VSN normalization to remove array to array variability (Bioconductor Project [43]). The efficiency of both normalization methods were validated by measuring gene to gene correlations across BeadChips according to the methods of Ploner et al. [44] using R 2.0.1 software (R Development Core Team, Vienna, Austria).
Differentially expressed genes that changed over time in response to each of the four FAS siRNA duplex were identified using two independent methods. In the first method, per chip normalized Normalize.quantiles data was imported into the GeneSpring GX 7.3.1 software package (Agilent Technologies, Palo Alto, CA, USA), and per gene normalized to the appropriate biological control for each time point. Genes that significantly changed over time in response to each FAS siRNA duplex were compared against control using a two-way ANOVA with a Benjamini & Hochberg FDR of 0.05. In the second method, VSN normalized data was imported into the Bioconductor Timecourse package [43]. Genes regulated over time by each FAS siRNA duplex in both biological replicates were determined using mb. long() statistics, with an mb>0 used as criterion for the identification of significantly regulated genes. Genes identified as significant using these two complementary approaches were combined, and genes with a p-value ≤ 0.05 and with 1.2 fold changes in both the biological replicates of each duplex at a given time point, were maintained for expression analysis. Genes identified as significantly modified by each of the 4 FAS siRNA duplexes were then overlapped using venn diagrams to identify a set of genes modified by 3 or more of the siRNA treatments. Expression profiles of the core gene list were averaged, and genes modified by 1.5 fold were visually examined and ordered by hierarchical clustering using the Pearson Correlation similarity measurement (GeneSpring).
For pathway analysis, data from the 16,585 genes expressed in MDA-MB-435 cells was exported from GeneSpring, filtered for duplicate symbols and analyzed using GSEA software (Broad Institute) according to published methods [26]. Briefly, data was overlapped on 522 gene sets (s2.symbols.gmt) downloaded with the GSEA package and measured for the enrichment of genes at the top or bottom of the gene list to determine their correlation with the gene set's phenotype. The GSEA parameters used included: metric for ranking genes, signal2noise; enrichment statistic, weighted; permutation type, phenotype; permutation number, 1000; and gene set size restrictions, 4 minimum, 500 maximum. Gene sets significantly modified by FAS siRNA treatment were identified using a nominal p-value < 0.05 and a multiple hypothesis testing FDR < 0.25. NES represents the enrichment of genes in the designated GSEA gene set, ranked according to the overrepresentation of genes at the top or bottom of the list, normalized to gene set size.
We wish to thank Kang Liu from the Burnham Institute for Medical Research Microarray Facility for BeadChip hybridizations and Roy Williams for help with gene analysis. This work was supported by Department of Defense Grant W81XWH-04-1-0515, and National Institutes of Heath Grant R01 CA108959 (to J.W.S.).
12864_2006_881_MOESM1_ESM.xls Additional file 1: Genes significantly modified by FAS siRNA duplex #1 (≥1.2 or ≤0.83 fold). The fold change values represent the average from 2 replicates of FAS siRNA duplex #1 compared to non-silencing control siRNA. Significance was determined using a p-value of < 0.05, with a 1.2 fold change cutoff for both biological replicates at a given time point. (XLS 328 KB)
12864_2006_881_MOESM2_ESM.xls Additional file 2: Genes significantly modified by FAS siRNA duplex #2 (≥1.2 or ≤0.83 fold). The fold change values represent the average from 2 replicates of FAS siRNA duplex #2 compared to non-silencing control siRNA. Significance was determined using a p-value of < 0.05, with a 1.2 fold change cutoff for both biological replicates at a given time point. (XLS 372 KB)
12864_2006_881_MOESM3_ESM.xls Additional file 3: Genes significantly modified by FAS siRNA duplex #3 (≥1.2 or ≤0.83 fold). The fold change values represent the average from 2 replicates of FAS siRNA duplex #3 compared to non-silencing control siRNA. Significance was determined using a p-value of < 0.05, with a 1.2 fold change cutoff for both biological replicates at a given time point. (XLS 404 KB)
12864_2006_881_MOESM4_ESM.xls Additional file 4: Genes significantly modified by FAS siRNA duplex #4 (≥1.2 or ≤0.83 fold). The fold change values represent the average from 2 replicates of FAS siRNA duplex #4 compared to non-silencing control siRNA. Significance was determined using a p-value of < 0.05, with a 1.2 fold change cutoff for both biological replicates at a given time point. (XLS 382 KB)
12864_2006_881_MOESM6_ESM.xls Additional file 6: GSEA pathways down-regulated in response to knockdown of FAS. The table displays all pathways found to be down-regulated in response to FAS siRNA treatment compared to non-silencing control siRNA. Significance was determined using a nominal p-value < 0.05 or FDR < 0.250. (XLS 17 KB)
12864_2006_881_MOESM7_ESM.xls Additional file 7: GSEA pathways up-regulated in response to knockdown of FAS. The table displays all pathways found to be up-regulated in response to FAS siRNA treatment compared to non-silencing control siRNA. Significance was determined using a nominal p-value < 0.05 or FDR < 0.250. (XLS 24 KB)
JS and LK conceived and designed the study. LK carried out the siRNA transfections, validated target knockdown, and prepared RNA for BeadArray expression profiling, performed data analysis and drafted the manuscript. JS supervised the coordination of the study and participated in manuscript preparation. All authors read and approved the final manuscript.
A compact VEGF signature associated with distant metastases and poor outcomes | BMC Medicine | Full Text
Tumor metastases pose the greatest threat to a patient's survival, and thus, understanding the biology of disseminated cancer cells is critical for developing effective therapies.
Microarrays and immunohistochemistry were used to analyze primary breast tumors, regional (lymph node) metastases, and distant metastases in order to identify biological features associated with distant metastases.
When compared with each other, primary tumors and regional metastases showed statistically indistinguishable gene expression patterns. Supervised analyses comparing patients with distant metastases versus primary tumors or regional metastases showed that the distant metastases were distinct and distinguished by the lack of expression of fibroblast/mesenchymal genes, and by the high expression of a 13-gene profile (that is, the 'vascular endothelial growth factor (VEGF) profile') that included VEGF, ANGPTL4, ADM and the monocarboxylic acid transporter SLC16A3. At least 8 out of 13 of these genes contained HIF1α binding sites, many are known to be HIF1α-regulated, and expression of the VEGF profile correlated with HIF1α IHC positivity. The VEGF profile also showed prognostic significance on tests of sets of patients with breast and lung cancer and glioblastomas, and was an independent predictor of outcomes in primary breast cancers when tested in models that contained other prognostic gene expression profiles and clinical variables.
These data identify a compact in vivo hypoxia signature that tends to be present in distant metastasis samples, and which portends a poor outcome in multiple tumor types.
This signature suggests that the response to hypoxia includes the ability to promote new blood and lymphatic vessel formation, and that the dual targeting of multiple cell types and pathways will be needed to prevent metastatic spread.
Metastases are the main cause of mortality for patients with breast cancer. The molecular biology behind metastasis is complex and likely requires changes in cell cycle regulation [1], the repertoire of expressed proteases and protease inhibitors [2], proteins that promote autocrine growth loops, and/or proteins that cause an epithelial-to-mesenchymal transition [3]. To make matters more complicated, it is clear that metastasis biology is in part governed by non-tumor cells including fibroblasts [4], endothelial cells [5], and myoepithelial cells [6]. For example, recent evidence suggests that tumor endothelial cell interactions are important for determining patient outcomes as evidenced by the promising results from clinical trials that use bevacizumab, a monoclonal antibody directed against vascular endothelial growth factor (VEGF) [7, 8].
Genomic profiling of human tumors and model systems has identified important features concerning metastasis biology. First, it has been shown that the expression profile of primary tumors without metastases can be highly predictive of the development of future metastases [9–13]. Second, cell lines can be selected that have specific end-organ tropisms with distinct expression profiles [14, 15]. Finally, cell line and murine models have demonstrated many different genes as being important for breast tumor metastasis, including Twist [16], Snail [3], and CXCL12 [17]. In this paper, we compare primary breast tumors, regional metastases, and distant metastases with each other and show that distant metastasis samples are distinct and provide unique signatures that predict poor outcomes in primary tumors.
One hundred and forty-six patients represented by 161 breast tumor specimens (with 23 paired tumor samples) and 10 normal breast samples (195 total microarrays) were profiled. Most of these samples appeared in previous publications [18–20], with 39 being new to this study, and all of which were collected using institutional review board-approved protocols. The clinical information for all samples is in the table in Additional file 1. Included within the 161 profiled tumors were 134 primary tumors, nine regional metastases and 18 distant metastases. Patients were heterogeneously treated in accordance with the standard of care dictated by their disease stage, estrogen receptor (ER) and HER2 status.
Total RNA isolation and microarray protocols are described in Hu et al [21]. Each sample was assayed versus a common reference sample [22]. The microarrays used were Agilent Human oligonucleotide microarrays that were scanned on an Axon GenePix 4000B, analyzed with GenePix Pro 4.1, and Lowess normalized. All microarray data have been deposited into the GEO under the accession number of GSE3521.
The background-subtracted, Lowess-normalized log2 ratio of Cy5 over Cy3 intensity values were filtered to select genes that had a signal intensity of > 30 units in both the Cy5 and Cy3 channels. Only genes that met these criteria in at least 70% of the 195 microarrays were included for subsequent analysis. Next, each patient was classified according to the following metastasis scoring system (MetScore): MetScore = 1 were patients that had a primary tumor and were clinically node negative (N = 0) and distant metastasis negative (M = 0); MetScore = 2 were patients that had a regional metastasis (N = 1–3) and no distant metastasis (M = 0); MetScore = 3 were patients with confirmed distant disease at the time of diagnosis (M = 1 and any N) or that were represented by an actual distant metastasis sample. We next performed a multi-class significance analysis of microarrays (SAM) using a single sample from each patient, biasing the sample selection to use the actual regional or distant metastasis samples (146 arrays, see Additional file 1). We identified the gene set that was associated with the MetScore 1-2-3 distinction, which gave 1195 genes at a false discovery rate of 5%. This gene set was next used in a one-way average linkage hierarchical cluster using the program 'Cluster' [23], with the data being displayed relative to the median expression for each gene using 'Java Treeview' [24].
Relationships between the gene expression data and the MetScore classification was further examined using a 10-fold cross-validation (CV) analysis to identify a set of genes that might distinguish a MetScore group from the others. 10-fold CV using five different statistical predictors including PAM [25], a k-nearest neighbor classifier with either Euclidean distance or one-minus-Spearman-correlation as the distance function, and a class nearest centroid metric with either Euclidean distance or one-minus-Spearman-correlation as the distance function, were used as described in Chung et al [26]. We performed 10-fold CV using the five different statistical predictors with the reported CV prediction accuracies being the average of the five predictors (Tables 1, 2, 3 and 4).
Cox proportional hazards models for relapse-free survival using the NKI 295 patient test data set – model containing the clinical variables and the VEGF-profile as a continuous variable
For the VEGF profile, an average expression value across all 13 genes (RRAGD, FABP5, UCHL1, GAL, PLOD, DDIT4, VEGF, ADM, ANGPTL4, NDRG1, NP, SLC16A3 and C14ORF58) was determined and the patients were placed into a three-group classification based their 13-gene average log2 expression ratio from the University of North Carolina (UNC) training data set and using the cut off values (-0.63/0.08) that were identified using X-tile [27] and relapse-free survival as the endpoint. Analyses using the VEGF profile and the training set cutoffs were also applied to an independent test set of 295 patients assayed on Agilent microarrays (that is, NKI295 [28]), to a set of lung carcinoma samples from Bhattacharjee et al [29], and to the glioblastoma sample set from Nutt et al [30]. To perform these across-data set analyses, for the NKI295 dataset we used the log ratio of red channel intensity versus green channel intensity and the data was median centered for every gene across the 295 arrays. The Netherlands Cancer Institute (NKI) dataset was then distant weight discrimination (DWD) normalized [31] with the UNC training dataset after collapsing by NCBI Entrez GeneID; after DWD normalization, the NKI data was also column standardized. For the Affymetrix datasets the probe level intensity .CEL files were processed by robust multi-chip average. The probe sets' log intensity was median centered for every gene across all the arrays. The Affymetrix datasets were also DWD normalized relative to the UNC training data after collapsing by NCBI Entrez GeneID, and were column standardized.
First, each sample was assigned an 'intrinsic subtype' as described in Hu et al [18], where a centroid was created for each of the following intrinsic subtypes: Basal-like, Luminal A, Luminal B, HER2-enriched and Normal-like. Next, we tested for associations between a tumor's intrinsic subtype, the VEGF profile and other published expression profiles implicated in metastasis biology that included a) the 70-gene outcome predictor developed by van't Veer et al [10, 11], b) the 'wound-response' profile [32], c) the hypoxia-induced cell line signature [33], d) the 11-gene BMI/stem cell signature [34], e) a bone metastasis signature [14], f) a lung metastasis signature [15], and g) the expression profiles of HIF1α, Snail [3] and Twist [16]; we extracted as many genes as was possible from our microarrays for each predictor and followed the classification scheme described by the authors. For the bone metastasis signature [14], we created an average value for each patient using the 43 genes that were highly expressed in the cell line derivatives that metastasized to the bone; we performed a similar analysis for the lung metastasis signature [15].
Lastly, for the 11-gene stem cell signature [34], we created an average value across all 11 genes. We also created a 'glycolysis-profile' by starting with the nine glycolysis genes/probes present on the array, then filtering for probes that showed > 30 intensity units in both channels, and then selecting for 70% good data across all samples; next we selected the subset of glycolysis gene probes that passed filtering and showed a Pearson correlation of greater than 0.4, which resulted in the selection of six out of nine glycolysis genes (GPI, PKM2, PFKP, PGK1, GAPD, ENO1), which were then used to create an average profile for each patient.
We examined correlations between profiles using multiple methods (Additional file 1): for quantized profile testing, Chi-squared analysis and Fischer's exact test were used. For continuous variable testing, ANOVA analyses were performed. Finally, we also performed a calculation of the Cramer's V statistic for the evaluation of the strength of association between two quantized variables (see Oh et al [19]).
Univariate Kaplan-Meier analysis was performed with a log-rank test using WinSTAT for excel. Multivariate analysis of the NKI295 test set using Cox proportional hazards modeling was conducted in SAS version 9.1; a Cox hazard model was tested that included estrogen receptor status (coded as positive vs. negative), tumor size (coded as ≤ 2 cm vs. > 2 cm), lymph node status (codes as 0, 1–3, > 3 positive nodes or M = 1), age (continuous variable, formatted in decades), grade (coded as grade 1 vs. 2, and grade 1 vs. 3), and treatment (coded as yes if treatment with chemo and/or hormonal therapy, no if no adjuvant therapy was given), and the VEGF profile of low, intermediate or high as a single categorical variable. Another Cox model was also tested that included all the clinical variables, the VEGF profile, and other expression predictors [11, 13, 18, 19, 28, 35].
In situ hybridization (ISH) on tissue microarrays containing 250 different human breast tumors (not related to the 146 patients used for microarray analysis) was performed as previously described [36]. Briefly, digoxigenin (DIG)-labeled sense and anti-sense RNA probes are generated by PCR amplification of approximately 450 bp products with the T7 promoter incorporated into the primers; the primer sequences used for amplification were VEGF (Forward-tctccctgatcggtgacagt, Reverse-tcgaaaaactgcactagagacaa), ANGPTL4 (Forward: gggaatcttctggaagacctg, Reverse-tacacacaacagcaccagca) and ADM (Forward-gtgtttgccaggcttaagga, Reverse-tcggtgtttccttcttccac). In vitro transcription was performed with a DIG RNA-labeling kit and T7 polymerase according to the manufacturer's protocol (Roche Diagnostics, Indianapolis, IN). Immunohistochemistry (IHC) was performed for HIF1α using Mouse Anti-Human HIF1α (BD Biosciences #610958) according to the protocol from Vleugel et al [37]; the tumors were scored for perinecrotic and diffuse staining as described in Vleugel et al.
To identify gene expression patterns associated with breast cancer metastases, we performed 195 microarrays representing 134 primary tumors, nine regional metastases and 18 distant metastasis specimens (146 different patients and 10 normal breast tissues). Each patient was classified according to a MetScore, which is roughly analogous to stage except that tumor size was not considered (see Methods). As expected, this scoring system was highly predictive of patient outcomes (Figure 1A and 1B). Using the MetScore classifications, we performed CV analyses to determine if any MetScore group might be distinct relative to the others. Low accuracy rates (56% to 65%) for the prediction of MetScore 1 vs. MetScore 2 specimens were observed; however, when MetScore 1 vs. MetScore 3 (80% to 85%) or MetScore 2 vs. MetScore 3 samples (81% to 83%) were compared, high accuracy rates were obtained, which suggests that MetScore 3 was the most distinct group.
Next, we performed a multi-class significance analysis of microarray [38] analysis using a single sample from each of the 146 patients and the MetScore 1-2-3 grouping and obtained a 1195 genes at a 5% false discovery rate. This gene set was then used in a one-way average linkage hierarchical clustering analysis (Figure 2 and Additional file 2) where the samples were first ordered according to MetScore, and then according to their correlation to the average profile (that is, centroid) of the MetScore 3 class. This analysis demonstrates that some MetScore 1 and 2 samples actually have a MetScore 3 profile; a similar result has been shown before by Ramaswamy et al [9].
One-way average linkage hierarchical cluster analysis. One-way average linkage hierarchical cluster analysis of the gene set associated with MetScore status. One hundred and ninety-five microarrays, representing 146 tumors and 10 normal breast samples were analyzed using the 1195 gene MetScore gene set. Overview of the complete cluster diagram (the full cluster diagram can be found as Additional file 2). The tumors were ordered according to their MetScore, and then according to their increasing correlation to the Metscore3 centroid within each group. Clinical regional node status, distant metastasis status, ER, PR, and intrinsic subtype are shown. A) FOS-JUN gene expression cluster, B) fibroblast/mesenchymal cell cluster, C) CXCL12 gene expression cluster, D), immune-cell/HLA cluster, E) VEGF profile.
The gene expression patterns from this SAM analysis were complex and there were few, if any, that directly correlated with a simple progression from MetScore 1 to 2 to 3. Included within this gene set were many clusters and/or gene sets that have been identified previously, including a luminal/ER+ pattern [11, 39, 40] and a proliferation signature [41, 42], both of which are integral parts of a gene expression assay that predicts the likelihood of recurrence in ER+ and patients treated with tamoxifen [13]. In addition, many other biologically important gene sets were identified, including an 'immediate early' gene cluster containing c-FOS and JUNB (Figure 2A) [43], a set of fibroblast genes containing PLAU, THSB2 and multiple collagen genes (Figure 2B), a set of immune cell genes (Figure 2D), and a gene set containing CXCL12 (Figure 2C); CXCL12 was the top-ranked gene from this SAM analysis and was recently identified as a chemokine whose high expression promotes tumor cell proliferation, migration and invasion [17]. Analysis of these individual clusters by EASE [44], with both EASE score and Bonferroni < 0.05 used as the cut off, identified many significant gene ontology categories that included 'transcription regulation' and 'DNA/nucleic acid binding' for the FOS-JUN cluster, while the fibroblast cluster was over-represented for 'extracellular matrix', 'cell adhesion and communication', 'organogenesis', 'development', and 'regulation of protease activity'. The CXCL12 cluster was over-represented for 'cell adhesion', 'cell migration' and 'extracellular matrix'. Lastly, a small 13-gene cluster containing VEGF, Adrenomedulin (ADM) and Angiopoietin-like 4 (ANGPTL4) was identified as the 'VEGF-profile' (Figure 2E), which is discussed below in greater detail.
Our previous work identified five 'intrinsic' subtypes of breast cancer that are of prognostic and predictive value [18, 41, 45]. Subtype classification of the tumors using the centroid predictor from Hu et al [18] showed significant outcome predictions (Figure 1C and 1D). A Chi-squared test (p = 0.0006) showed that intrinsic subtype was significantly correlated with MetScore, with the Basal-like and HER2-enriched groups being the most frequent in MetScore 3 and with no Luminal A samples being in the MetScore 3 group. Correlations between tumor subtype and stage have been described [46, 47], and were recapitulated here.
A small cluster of genes containing VEGF was identified (Figure 2E) that showed high expression in MetScore 3 tumors. This gene cluster contained several secreted proteins that have been implicated in endothelial cell (VEGF and ANGPTL4), lymphatic cell (ADM) and smooth muscle cell (GAL) dynamics. As a step in evaluating this profile, we performed ISH to determine what cell type was producing VEGF, ANGPTL4 and ADM. In the vast majority of cases that showed strong ISH positivity (which totaled approximately 10% of the 250 tumors tested), it was the tumor cells themselves that produced the mRNA for these three genes, and typically all three were produced (Figure 3). In a few cases, both tumor and fibroblasts showed ISH positivity, but this was rare.
As a second step in the evaluation of the VEGF profile, we created an average expression ratio of the 13 genes for each patient and looked for correlations with outcome. By dividing the patients into low, intermediate and high-expression groups using relapse-free survival (RFS) and cutoffs determined by X-tile [27], we saw that the VEGF profile was prognostic of RFS (Figure 4A) and overall survival (data not shown) with the high expression portending a poor outcome. Rank order expression classifications (two or three groups) were also robust methods of predicting outcomes (Additional file 3). Applying the VEGF profile classification rules to an independent test set of 295 patients (that is, NKI295) [10, 28] also significantly predicted outcomes (Figure 4B), as did rank order classifications (Additional file 3). This classification rule was also of prognostic value on a set of lung carcinoma samples (Figure 4C and Additional file 3E and 3F), although there were too few 'low' samples to be included into the Kaplan-Meier plot analysis, and on a set of patients with glioblastoma (Figure 4D and Additional file 3G and 3H); we noted that two genes (ANGPTL4 and C14ORF58) were not found on the Affymetrix platform for lung and glioblastoma test data sets. However, the Pearson correlation is 0.992 (UNC training dataset) and 0.986 (NKI295 test dataset) respectively between the average of the 13 genes and that of the 11 genes (omitting ANGPTL4 and C14ORF58). We repeated the survival analysis for the UNC dataset and NKI test set again using the 11 genes and the results were very similar (data not shown).
A multivariate Cox proportional hazards analysis on the NKI295 test set using RFS was performed using clinical variables and the VEGF profile, and it was determined that the VEGF profile was a significant predictor of RFS (Table 1). In Fan et al [48], we evaluated the prognostic powers and concordance across multiple expression predictors including the intrinsic subtypes, the NKI 70 gene signature, a microarray-based version of the Genomic Health Inc. Recurrence Score, and the wound-response profile using this same NKI patient data set, and we have also identified other profiles of prognostic significance including an estrogen pathway [19] and p53 mutation profiles [35]; therefore, we performed a Cox proportional hazards analysis (Table 2) with backwards variable selection (Table 3) to evaluate a model that contained all of the aforementioned gene expression predictors and clinical variables. The final model contained both clinical parameters and multiple gene expression predictors including the VEGF profile (Table 3). Similar results were obtained when using time to distant metastasis formation, or overall survival (data not shown), or when treating the VEGF profile as a continuous variable (Table 4).
A biological implication of the VEGF profile is that it is related to a tumor's response to hypoxic conditions, which historically has been referred to as the Warburg effect [49, 50]. A central tenant of the Warburg effect is that a tumor's metabolism becomes more dependent upon glycolysis due to anaerobic conditions. To examine glycolysis using a genomic approach, we created a 'glycolysis-profile' using the six most highly correlated glycolytic enzyme probes (GPI, PKM2, PFKP, PGK1, GAPD, ENO1, Figure 5A); the VEGF profile and the six best glycolysis probes were highly correlated (p < 0.001, Table 5).
VEGF profile, glycolysis and HIF1α gene expression analyses. A) Gene expression for the VEGF profile (plus average values), for the six glycolysis genes and glycolysis centroid, HIF1α and fibroblast centroids are shown across the 146 patient UNC training data set with the tumors ordered according to their VEGF profile average values. B) Similar analysis as presented in A except the data set is the NKI patient test set.
HIF1α is a known regulator of VEGF expression, and therefore we determined that HIF1α mRNA gene expression was correlated with the VEGF profile (p = 0.0004; Table 5); in addition, 'perinecrotic' HIF1α IHC staining as defined by Vleugel et al [37] was also assayed on a subset of 66 of these tumors and was correlated with expression of the VEGF profile (ANOVA p-value = 0.018, data not shown), while a 'diffuse' HIF1α IHC profile was not. Next, the promoter region of each of the genes in the VEGF profile was examined using the program rVISTA [51] and showed that DDIT4, VEGF, NDRG1, SLC16A3, PLOD, ADM, ANGPTL4 and C14ORF58 all had hypoxia response elements within 2000 bp upstream of their start codons; it is already known that many of these genes including VEGF [52], ADM [53], and DDIT4 [54] are HIF1α-regulated. Nearly identical genomic results were also obtained from the NKI295 test set (Figure 5B).
A fibroblast/mesenchymal signature was another profile that changed with MetScore (Figure 2B), and thus to examine the potential fibroblast cell content present within each MetScore group we determined each patient's average expression value of the genes contained with the cluster presented in Figure 2B. This gene set contains fibrillin, fibroblast activation protein alpha, six collagen protein subunits and versican, which are genes and/or proteins that are typically produced by fibroblast and/or mesenchymal cells [55]. This analysis shows that the fibroblast profile is correlated with intrinsic subtype (Table 5, p = 0.012) and that the MetScore 3 samples had the lowest average expression compared with the MetScore 1 and 2 samples (ANOVA p-value = 0.005, data not shown). Pathological examination of H&E sections of the distant metastasis samples also supports this conclusion and shows scant admixed mesenchymal cells in the distant metastasis samples versus their primaries that show abundant admixed mesenchymal cells (Figure 6).
H&E images of a primary breast tumor taken from a MetScore 3 patient. Showing a prominent admixed stromal component comprised of fibroblasts and myofibroblasts in the primary tumor. The fibroblast/myofibroblast component is markedly diminished in the distant metastatic sites (B and C) as compared with the primary tumor (A). Magnification 200×.
We examined whether the intrinsic subtypes, the MetScore classification, and the VEGF signature correlated with any of the following expression profiles that have been associated with metastatic potential: a) the NKI 70-gene predictor [10, 11], b) the 'wound-response' profile [32], c) a cell line-derived hypoxia profile [33], d) an 11-gene BMI/stem cell signature [34], e) a bone metastasis signature [14], f) a lung metastasis signature [15], g) a hypoxia metagene [56], and h) the expression profile of three individual genes (HIF1α, Snail [3], and Twist [16]). These analyses identified a large amount of concordance across profiles (Table 5). For example, the breast tumor subtype was significantly correlated with the bone and lung profiles, Snail expression, and the 11-gene stem cell signature; in particular, the bone and lung profiles were associated with both ER-negative subtypes (Basal-like and HER2-enriched), and Snail expression and the 11-gene stem cell signature were the highest within the Basal-like subtype. Similar results were also observed when the VEGF profile was compared with the other profiles. Two 'hypoxia signatures' have been described and shown to be of prognostic value across a variety of tumor types including breast [33, 56]; the large signature of Chi et al [38] showed a four-gene overlap with the VEGF profile (ADM, NDRG1, DDIT4 and ANGPLT4) while the 'hypoxia-metagene' of Winter et al [56] showed a three-gene overlap (VEGF, NDRG1 and ANGPLT4); as might be expected, all three of these profiles were correlated (Table 5, p < 0.0001).
We took a genomics approach to study metastasis biology and classified patients with breast cancer according to the presence and location of their metastases (that is, MetScore). The resulting analyses showed that the most distinct group with the most distinguishing features were the distant metastases; few differences were seen between primary tumors and regional metastases, as has been shown before [57]. When the set of genes that were correlated with MetScore was determined, many previously known gene sets were identified including proliferation [58], ER status [11, 39, 40], and fibroblast and/or mesenchymal genes [55, 59]. Notable distant metastasis features included the low expression of fibroblast genes (and a corresponding paucity of fibroblasts as defined by histological examination) and the high expression of the VEGF profile. The VEGF profile represents a in vivo defined gene expression program that includes a combination of cell-intrinsic and cell-extrinsic factors. The cell-extrinsic factors have known roles as inducers of endothelial cell growth (VEGF and ANGPTL4), inducers of lymphatic vessel growth (ADM) [60], and smooth muscle cell dynamics (GAL); thus, the expression of this gene set would appear to increase the likelihood of tumor survival by causing de novo vessel formation and providing a dual conduit for metastatic spread. The cell-intrinsic factors include the high expression of SLC16A3, whose function is to efflux the lactic acid out of the cell that occurs during high glycolytic activity, and the expression of NDRG1, which is a known hypoxia-inducible gene [61, 62]. In addition, the tumors that highly express the VEGF profile also highly express glycolytic enzymes. In total, our data suggests poor-outcome distant metastasis samples have the intrinsic ability to promote vessel formation, the intrinsic ability to live under anaerobic conditions, and have lost dependence upon fibroblasts.
Many genomic profiles for breast tumor metastasis biology have been identified, and we therefore compared them with each other and determined that significant correlations exist. In particular, all metastasis profiles tested correlated with 'intrinsic subtype'. For example, the Basal-like subtype showed significant correlation with the 11-gene stem cell profile, the lung and the bone metastasis profiles (consistent with these observations, one of the MetScore 3 Basal-like patients had distant metastases present in the bone, lung and liver). The Basal-like subtype also showed high expression of Snail, and Basal-like tumors have been shown to have other features of epithelial-mesenchymal transition [63] including vimentin expression [64].
The VEGF profile showed very significant prognostic value when using primary tumors, even when tested in models that contained many other expression predictors and clinical variables. We also believe it possible that the VEGF profile may have predictive value for angiogenesis inhibitors because it contains VEGF and ANGPTL4, which are inducers of angiogenesis. How, or if, the VEGF profile is correlated with response to angiogenesis inhibitors remains to be determined; however, our profile does suggests that effective anti-angiogenesis therapies for patients who express this profile may need to extend beyond VEGF to include the simultaneous targeting of ANGPTL4 and/or ADM.
We thank J. Chuck Harrell for reviewing this manuscript. This work was supported by funds for CMP from the NCI Breast SPORE program to UNC-CH (P50-CA58223-09A1), by NCI (RO1-CA-101227-01), by the Breast Cancer Research Foundation and by the V Foundation for Cancer Research. LAC was supported in part by the National Institutes of Health (M01RR00046), and OIO was supported by a Disparities Center Project Grant NIEHS P50 ES012382.
CMP and ZH have filed a patent application for the use of the VEGF profile for breast cancer prognosis.
ZH, CF, XH, CL, DSO, SS, RW, FI and CMP made substantial contributions to the concepts, acquisition and analysis of the data. ZH, DSO, MGE, LAC, OIO, MVDR and CMP contributed significantly to the drafting of the manuscript and its intellectual content. All authors have read and approved the final manuscript.
Dengue is emerging as one of the most abundant vector-borne disease globally. Although the majority of infections are asymptomatic or result in only a brief systemic viral illness, a small proportion of patients develop potentially fatal complications. These severe manifestations, including a unique plasma leakage syndrome, a coagulopathy sometimes accompanied by bleeding, and organ impairment, occur relatively late in the disease course, presenting a window of opportunity to identify the group of patients likely to progress to these complications. However, as yet, differentiating this group from the thousands of milder cases seen each day during outbreaks remains challenging, and simple and inexpensive strategies are urgently needed in order to improve case management and to facilitate appropriate use of limited resources. This review will cover the current understanding of the risk factors associated with poor outcome in dengue. We focus particularly on the clinical features of the disease and on conventional investigations that are usually accessible in mid-level healthcare facilities in endemic areas, and then discuss a variety of viral, immunological and vascular biomarkers that have the potential to improve risk prediction. We conclude with a description of several novel methods of assessing vascular function and intravascular volume status non-invasively.
Dengue is the most important arboviral infection affecting humans, and presents a major challenge for public health services worldwide. Infection can be caused by any of four dengue virus serotypes (DENV1 to 4), transmitted by Aedes mosquitoes. Over half the world’s population is thought to live in areas at risk for transmission, and recent estimates suggest that around 400 million infections occur annually, of which 100 million are clinically apparent [1]. The clinical phenotype varies from a mild self-limiting febrile illness through to severe and occasionally life-threatening disease. Symptomatic disease typically follows three phases: an initial febrile phase lasting 3 to 7 days; a critical phase around defervescence, during which complications appear in a small proportion of patients; and a spontaneous recovery phase. Complications primarily affect the vascular system, and include an unusual plasma leakage syndrome that may result in hypovolaemic shock - the potentially fatal dengue shock syndrome (DSS); a coagulopathy sometimes accompanied by bleeding; and organ impairment [2]. Because these severe manifestations occur relatively late in the disease course, often when the infecting virus is no longer detectable in plasma, immune-mediated mechanisms are postulated to play a significant role in pathogenesis [3]. Currently no vaccine is available, and neither antiviral drugs nor immunomodulatory agents have been shown to be effective in reducing morbidity or improving disease outcome [4]-[7]. However, with good supportive care (primarily judicious use of parenteral fluid therapy to offset plasma volume losses due to leakage), mortality rates of less than 1% are possible even among DSS cases [8],[9].
Although the vast majority of symptomatic infections do not progress to severe disease, areas of high dengue transmission can have seasonal epidemics, which can quickly overwhelm health service capacity. Thus, the ability to identify patients at high risk of progression, who are likely to benefit from close observation and early intervention with supportive therapy, has become the focus of intense research efforts in recent years. In 2009, the World Health Organization (WHO) revised the classification system for dengue, defining two major entities - dengue and severe dengue - to replace the more complicated dengue fever/dengue haemorrhagic fever (DF/DHF) system used previously [10]. The new classification also encompasses a set of `warning signs’ intended to help clinicians identify patients likely to develop complications during the critical phase of the illness (Figure 1). These signs and symptoms were derived in part from a dataset describing almost 2,000 patients with dengue across Asia and Latin America, but owing to the small number of patients progressing to severe disease during the study, further work is needed to validate the findings [11].
Dengue disease phases and potential complications. Reproduced, with permission of the publisher, from the WHO publication [12] (Figure 2.1; p. 25).
This review will focus on the current knowledge of factors associated with disease progression, either considered in terms of DHF/DSS or dengue with warning signs/severe dengue. We concentrate first on readily accessible parameters, that is, the clinical features of the disease and the conventional investigations available in most mid-level healthcare facilities, before discussing a variety of potential viral, immunological and vascular biomarkers that have been proposed. Finally we conclude by describing a number of novel non-invasive methods of assessing vascular function or intravascular volume status, and consider their potential relevance for dengue.
Epidemiological studies have identified a number of risk factors for more severe dengue disease, including secondary infection [13], the two extremes of age [14],[15], pregnancy [16], under-nutrition and over-nutrition [17], and the presence of comorbidities such as diabetes and hypertension in adults [18]. Male patients are often overrepresented in dengue epidemics, but female sex has been associated with more severe disease [19]-[21]. Several groups have attempted to identify the clinical symptoms and signs occurring during the acute illness that are associated with outcome. In a prospective study of 700 Vietnamese children with confirmed dengue, gastrointestinal symptoms and hepatomegaly were more prominent among those classified as having DHF, and a peak haematocrit above 50% was associated with DSS [22]. Clinical bleeding manifestations are highly variable, although skin petechiae are commonly observed across the severity spectrum, and the tourniquet test, one of the original diagnostic criteria for DHF, has been shown to differentiate poorly between DF and DHF [23],[24]. In Singapore, where dengue is predominantly an adult disease, a decision tree involving clinical bleeding, high serum urea, low serum protein and low lymphocyte proportion was developed to predict likely progression to DHF among hospitalised patients; sensitivity and negative predictive value were high, but specificity was poor [25]. The decision tree was subsequently validated in another Singaporean cohort, and has been suggested as a tool to guide triage of patients who require hospitalisation versus outpatient treatment [26].
A number of haematological parameters have been considered as potential predictors, most commonly the platelet count [27]. Platelet counts tend to fall over the illness course, reaching a nadir shortly after defervescence, before demonstrating a rapid recovery response [28]. Lower counts (<50× 109/l; normal range 150 to 400 × 109/l) are seen more frequently in severe disease [29], and are considered a risk factor for bleeding [30]. However, the correlation between thrombocytopenia and haemorrhage is weak, with lower platelet counts correlating more closely with the severity of vascular leakage in one study [31].
Although a rapid decrease in platelet count concurrent with an increase in haematocrit is presented as one of the warning signs for likely progression to shock in the 2009 WHO dengue classification [32], specific values have not been defined as yet. Given the wide range of normal platelet values and the fact that the nadir occurs relatively late in the disease course, efforts have been made to develop prognostic algorithms that combine the platelet count with other haematological indices during the febrile phase. Using classification and regression tree analysis, algorithms based on platelet count, total white cell count, monocyte percentage and haematocrit level, obtained within 72 hours of fever onset, identified Thai children who went on to develop DSS with good sensitivity (97%) but limited specificity (48%) [33]. The rate of change in serial haematological markers, particularly the platelet count, may also carry important prognostic information, and efforts are in progress to develop models that incorporate longitudinal data from repeated sampling to see if this improves the accuracy of risk prediction.
Currently, clinical identification of vascular leakage is difficult until or unless DSS develops. The most common method of monitoring leakage relies on identification of relative haemoconcentration, determined by tracking changes in serial haematocrit measurements, with a rise of more than 20% from baseline considered evidence of significant leakage. However, this method can be rather insensitive, particularly if the patient is receiving parenteral fluid therapy, and it is also limited by the fact that an individual's baseline value is rarely known. Thus, although assessment of haemoconcentration can be helpful for case management, it is often only possible to appreciate the true severity of haemoconcentration once the acute illness has resolved and the haematocrit has returned to normal for that individual. Although comparison with age- and sex-matched population haematocrit values is recommended, such values are rarely available in dengue endemic areas [11].
Hepatic involvement is common in dengue, and liver enzymes are frequently elevated in infections of all severity grades. More marked derangements are usually associated with more severe disease profiles [34],[35], but discrimination between severe and non-severe dengue has proved to be poor [36]. In addition, liver enzyme levels tend to peak late in the disease course, typically during the second week, limiting their usefulness as prognostic markers [35]. Hypoproteinaemia is well recognised during the critical phase, and correlates with the severity of leakage [37],[38]; however, like haemoconcentration, hypoproteinaemia is difficult to identify without serial measurements or a known baseline value for an individual. Proteinuria also occurs, and marked increases in fractional clearances of several endogenous proteins have been documented among children with DSS [37]. The urine protein/creatinine ratio (UPCR) has been suggested as a possible predictor of outcome, with higher peak UPCRs observed in adult DHF cases compared to DF cases [38]. However, a study describing the magnitude and kinetics of urinary albumin excretion in a large paediatric population found that although albumin excretion was increased in the confirmed dengue patients, with a significant time-trend showing peak values during the critical phase, discrimination between patients of different severity was poor, and albuminuria was not useful in predicting the development of complications [39].
Studies using ultrasound have demonstrated that pleural effusions, ascites and gall bladder wall oedema are common during the critical phase, and correlate with disease severity [40]-[42]. In addition, serial ultrasound studies indicate that subclinical plasma leakage can be detected as early as days 2 to 3 of fever [43], and is better at predicting likely disease progression than other markers of plasma leakage such as haematocrit and albumin measurements [44]. Gallbladder wall oedema appears to precede the development of ascites and effusions, and may therefore be a helpful early predictor of outcome. Thus ultrasonography can be a useful monitoring tool, and where available, should be considered in the overall assessment during the febrile phase. However, there are certain limitations,particularly the lack of defined normal ranges for the parameters of interest, the variability in measurements obtained by different operators, and the lack of specificity of the findings. For example, gallbladder wall thickness may be increased in other infections and also in the postprandial state [45]. Finally, as yet, the availability of portable ultrasonography and the technical expertise necessary to interpret the images obtained remains relatively restricted in the resource-limited areas where dengue is endemic.
In the face of the rapidly expanding global pandemic, many research groups are attempting to assess a variety of potential biomarkers for associations with dengue disease progression, and only a selected overview is possible here. Only human studies will be reviewed, as the true relevance of even the most sophisticated model systems to human dengue disease remains uncertain.
The observations that a) severe disease is more common in secondary infections with a different dengue virus (DENV) serotype and b) severe clinical manifestations typically occur when the viral load is in steep decline [46],[47], support the idea that the pathogenesis of severe dengue is at least partly immune-mediated. Antibody-dependent enhancement, whereby cross-reactive antibodies from a previous infection fail to neutralise the current serotype, but instead enhance viral uptake into susceptible cells, is thought to result in a higher blood and/or tissue viral burden and a more intense immune response with increased release of vasoactive mediators [48],[49]. Higher viraemias, considered to reflect a greater dengue-infected cell mass, have been linked to severe disease [49],[50], with early peak viral loads appearing to correlate with subsequent disease severity. However, serotype differences are apparent, with patients infected with DENV1 sometimes displaying extremely high plasma viraemia levels with relatively mild clinical disease [51],[52]. The duration of viraemia has not shown a clear association with disease severity. In fact, rapid viral clearance, represented by the steepness of the decline in viraemia, has been demonstrated more commonly in DHF cases and in secondary infections, possibly as a result of a primed adaptive immune response [50]. Soluble non-structural protein-1 (NS1) levels generally correlate with viraemia levels, and are usually higher in patients with severe than in those with uncomplicated dengue. Higher levels of NS1 within 72 hours of fever onset predicted patients who developed DHF at defervescence in one study [53], and it has been suggested that NS1 may play a pathogenic role in the capillary leakage process, through activation of complement and generation of anaphylatoxins and the terminal complement complex SC5b-9 [54]. Plasma levels of NS1 and SC5b-9 have been shown to correlate with disease severity, and were present in the pleural fluid of patients with DSS, along with the anaphylatoxin C5a. Cross-reactive NS1 antibodies have been shown invitro to target membrane-bound NS1, resulting in complement-mediated cytolysis and endothelial cell disruption [55],[56]. However, the kinetics of NS1 antigenaemiaare known to be influenced by several factors, including the infecting serotype, with lower levels seen in DENV2 than in DENV1 infections after adjusting for viral load, and the host immunity, with faster clearance times seen in secondary than in primary infections. Thus, the magnitude of NS1 antigenaemia alone is not sufficient to explain outcome, and this would probably affect the overall utility of NS1 as a potential biomarker [52],[57].
Numerous studies have investigated associations between altered levels of circulating cytokines/chemokines and complement activation markers with dengue severity. The findings from a selection of representative studies are presented in Table 1[58]-[68]. However, interpretation of the data is difficult as most of the studies are small, and confounded by serotype variations and differences in plasma viraemia levels, the age and immune status of the patients, and the timing of sampling during the evolution of the infection. In addition, it is possible that the parameters of interest are influenced by the degree of capillary leakage, making comparisons of plasma measurements between severity groups unreliable.
Elevated levels in patients with DSS compared with those with DF, which correlated with markers of disease severity [58]. IL-10 correlated with degree of thrombocytopenia [49],[65]
Raised levels in severe dengue versus mild disease. Positive correlation with disease severity. sTNFRspredicted children who subsequently went on to develop shock [64],[67]
DHF was associated with earlier peak IFN-γ levels [49]. Associated with development of severe disease [59]
Compared with healthy control, MIP-1b and G-CSF were elevated in patients with uncomplicated dengue; MCP-1 in dengue with warning signs; and IP-10 in both groups [62]
Higher levels were demonstrated in DF/DHF compared with OFI, and correlated with dengue severity [54]
Reduced during acute phase in patients with dengue compared with healthy controls, and correlated with thrombocytopenia [66]
No difference was demonstrated between the severity grades for IL-1b but there was significantly lower levels of IL-2 and EGF in DSS compared with DF [58]
One study demonstrated reduced VEGF levels in DSS compared with DF from 2 days before to 2 days after defervescence [58]. Other studies have shown elevated VEGF levels in DHF compared with DF[69], and a further study demonstrated elevated levels of free VEGF in DHF at the time of plasma leakage, and reduced levels of VEGFR2 [63]
Reduced levels were found in patients with DHF compared with those with DF and healthy controls [68]
As the vascular system is targeted in dengue infections, there may be a stronger biological rationale for investigating markers specifically implicated in vascular pathology. Soluble intercellular adhesion molecule-1 (sICAM-1) and soluble vascular cell adhesion molecule-1 (sVCAM-1) have been shown to be increased in patients with dengue compared with controls, and to correlate with disease severity [70]. Similarly, sVCAM-1, sICAM-1 and E-selectin were raised in patients with dengue compared with patients with other febrile illness 1 day prior to defervescence [71]. Soluble thrombomodulin, a non-specific marker of endothelial activation, was also found to be increased 2 to 3 days before defervescence in that study, and to correlate well with disease severity [71]. Angiopoietin-2 is a protein secreted from endothelial cells in response to inflammatory stimuli, which antagonises angiopoietin-1, a protein required to maintain effective microvascular barrier function. In one study, reduced levels of angiopoietin-1 and increased levels of angiopoietin-2 were demonstrated in dengue cases at hospital admission compared with levels obtained at discharge and also compared with levels seen in healthy controls, and these changes correlated with plasma leakage and disease severity [72].
In addition to markers of endothelial activation, a few studies have examined factors more directly indicative of disruption of the endothelial barrier. Although morphological examination has revealed only minor non-specific changes in microvascular structure, increased circulating endothelial cells (CECs) have been demonstrated in patients with dengue, with the levels correlating with severity [70],[71]. However, even if confirmed to be useful for risk prediction, the techniques required to measure CECs are complex and assay standardisation is difficult, making it unlikely that CEC measurement could be widely deployed in endemic areas. Damage to the endothelial surface glycocalyx layer, a negatively charged gel-like structure that lines the luminal surface of the microvasculature and constitutes the primary permeability barrier, has also been implicated in the pathogenesis of severe dengue. Both the DENV envelope protein and NS1 are known to bind to heparan sulphate, a major constituent of the glyocalyx layer, potentially resulting in shedding of this layer, with consequent alterations in microvascular permeability [73],[74]. Preliminary evidence suggests that circulating heparan sulphate levels are raised during the early febrile phase of dengue [75], although further studies are required to determine whether these early increases in heparan sulphate levels predict which patients are likely to develop severe disease.
Along similar lines, microparticles are submicron vesicles that are shed from the surface of cell membranes in response to activation, injury and apoptosis. It is becoming increasingly apparent that far from being simple markers of cell activation, microparticles play a critical role in the pathophysiology of various disease states. Endothelial microparticles are emerging as potential biomarkers for cardiovascular diseases, diabetes and sepsis, conditions in which vascular inflammation and injury are key to patient outcomes [76]-[78]. Currently, the role of endothelial microparticles in the pathogenesis of the microvascular dysfunction in dengue is not known. However in one study, platelet derived microparticles(PMPs) rich in IL-1b correlated with signs of vascular permeability in patients with dengue, and PMPs were found to induce vascular permeability invitro in an IL1-dependent manner [79].
As outlined earlier, the defining feature of severe dengue is disruption of vascular barrier function, resulting in plasma leakage, intravascular volume depletion, and eventually cardiovascular collapse and death if appropriate fluid therapy is not instituted promptly. Current techniques to identify and monitor leakage rely on surrogate markers of intravascular volume depletion, including serial haematocrit measurements, and close observation of cardiovascular indices, particularly the pulse pressure and heart rate [10]. However, these parameters are relatively insensitive, and show little change until the critical phase of the infection (Figure 1), whereas evidence from serial ultrasound studies indicates that plasma leakage actually starts during the febrile phase [44]. It is possible that many patients experience some degree of plasma leakage, and that only a minority become haemodynamically unstable when a significant volume of fluid has extravasated, overwhelming that individual’s intrinsic compensatory homeostatic mechanisms. Several new techniques designed to evaluate endothelial dysfunction, monitor the microcirculation, and assess intravascular volume status have been developed in recent years, and have the potential to be useful tools for predicting outcome in dengue.
Greater understanding of normal microvascular physiology together with increasing recognition of the importance of endothelial dysfunction in systemic disease has led to the development of endothelial function testing in a variety of medical disciplines. Available techniques include venous plethysmography, assessment of flow-mediated dilatation of the brachial artery and peripheral artery tonometry (PAT). The latter two techniques evaluate endothelial function through monitoring the reactive hyperaemia (RH) response following occlusion of a proximal artery.
In PAT, changes in amplitude of the digital pulse volume are postulated to reflect nitric oxide bioavailability. The increase in blood flow following an occlusion test is thought to stimulate production and release of endothelial-derived nitric oxide, causing vasodilation. The test is easy to apply, requires less staff training and has better inter-user variability than techniques to assess brachial artery dilatation, and has shown good reproducibility [80],[81].
Longitudinal studies involving serial RH assessments have demonstrated that altered endothelial responses can be identified several years prior to the occurrence of adverse cardiovascular events [82],[83]. More recently, endothelium-dependent microvascular reactivity was shown to be impaired in acute sepsis in proportion to overall disease severity, and the degree of impairment at baseline predicted subsequent deterioration in organ function [84]. Studies in Plasmodium falciparum malaria have demonstrated endothelial dysfunction and decreased nitric oxide bioavailability with more marked changes in severe compared with mild malaria, and this work has led onto therapeutic intervention trials with L-arginine [85],[86]. To date, results of clinical endothelial function tests have not been reported in dengue, but there are ongoing studies in Vietnam and Singapore. Preliminary results indicate that dysfunction occurs early, during the febrile phase, raising the possibility that this technique might be useful in predicting outcome. However, drawbacks include the need for a degree of patient cooperation, meaning that it is difficult to use the technique in younger children and unstable patients, and, at least at present, the cost of the disposable probes, which is likely to limit widespread application in resource-poor settings.
Microcirculatory dysfunction causing tissue hypoperfusion is considered to play a major role in the pathophysiology of multiorgan failure in severe bacterial infections [87]. Early-onset multiorgan failure is thought to be mediated by inadequate tissue perfusion, whereas organ failure occurring later in the course of severe sepsis is likely to be related to mitochondrial failure and cell death [88]. In line with this hypothesis, haemodynamic optimization has been found to be beneficial in the early but not late stages of septic shock [89].
In the past two decades, bedside real-time imaging of the microcirculation has become possible with videomicroscopy using orthogonal polarisation spectral imaging (OPS) and side-stream darkfield imaging (SDF) [90]. It has been postulated that during the earliest phase of incipient shock, impaired microcirculatory flow and tissue hypoperfusion can be detected prior to the onset of overt cardiovascular collapse [91]. A decrease in the number of perfused capillaries, together with reduced flow rates and heterogeneity of the microcirculation, is associated with increased severity of both sepsis and malaria, and these changes are strong predictors of outcome [91],[92]. Microcirculatory changes occur early in the course of the disease and are independent of arterial blood pressure, and their persistence is associated with mortality [91],[93]. At present, alterations to microcirculatory flow patterns in dengue are largely unknown. In one case study from Brazil involving two patients with DSS, very abnormal microcirculatory patterns were identified, which improved following resolution of shock [94]. Our group in Vietnam is currently investigating the microcirculation using SDF technology in a large study of patients with suspected dengue, carrying out serial examinations from the early febrile phase throughout the evolution of the illness to see if the technique has prognostic value (Figure 2).
Side-stream darkfield imaging (SDF) in dengue. Example of still SDF images from (a) a patient with dengue in the late febrile phase, showing reduced total vessel density and perfused vessel density, plus extravasated red cells in the left upper and lower quadrants; (b) the same patient in the recovery phase with normal vessel density, flow rate and perfusion indices.
The ability to detect and measure changes in intravascular volume early in the evolution of dengue, particularly during the compensated phase of shock, would be of real benefit for patient management. The compensatory reserve index (CRI) is a new computational algorithm that uses feature analysis of the arterial pulse waveform to track real-time changes in central blood volume [95]. Arterial waveform data are processed by the algorithm to calculate a CRI value between 0 and 1, where 1 represents normovolaemia and 0 represents the point of decompensation [96]. The CRI has been shown to correlate with estimated volume changes in a human disease model that simulates hypovolaemia due to haemorrhage, and to detect changes that occur during the compensatory phase of shock, preceding changes in conventional haemodynamic parameters. In addition, the index has been shown to distinguish between individuals with high and low tolerance to intravascular volume loss, and may therefore be helpful in identifying patients who are more vulnerable to shock. One advantage of this technique is that the CRI can be obtained from any standard monitor that generates a pulse waveform, including a finger pulse oximeter, and it is therefore completely non-invasive and relatively inexpensive. Studies to assess the utility of the CRI in predicting likely progression to shock, and to track fluid resuscitation status in patients with established shock, are ongoing in Southeast Asia.
Another non-invasive bedside technique with potential for monitoring intravascular volume is portable echocardiography [97]. Echocardiograms have the additional benefit of providing a functional cardiac assessment as well as an estimate of intravascular volume status, and are increasingly being used for haemodynamic assessment in patients with septic shock [98]. Cardiac involvement is now recognised as a feature of dengue [99], particularly in severe cases where myocardial impairment probably acts in concert with hypovolaemia to compound haemodynamic instability [100]. In such cases, assessing collapsibility of the inferior vena cava may be useful in predicting which patients are likely to be fluid-responsive. Although this method has been used mainly for ventilated patients [101], evidence is emerging that it may also be useful in spontaneously breathing patients [102]. Cardiac dysfunction probably also plays a role in less severe dengue disease. In one paediatric study, reduced cardiac output during the febrile phase was identified as being related both to reduced preload and to left ventricular impairment, with no apparent change in blood pressure [103]. As portable devices become increasingly accessible worldwide, inclusion of an echocardiographic assessment to assist with dengue risk prediction is likely to become a feasible option in many mid-level healthcare facilities.
Predicting outcome in dengue remains challenging, and the search for more robust methods continues. Although warning signs are considered a key component for early recognition of potentially severe disease, the current evidence for any particular clinical or laboratory marker is weak. A major global study currently in progress, aiming to recruit 10,000 to 12,000 suspected dengue cases during the early febrile phase, should provide useful information for risk prediction(ClinicalTrials.govID:NCT01550016). However, given the rather broad range of signs and symptoms seen in dengue, inclusion of one or more specific biomarkers is likely to be needed in order to develop a robust algorithm. A number of viral, immunological and endothelial biomarkers have been proposed from small studies, and there is hope that, if these arevalidated in a large patient cohort, algorithms incorporating such biomarkers might prove sufficiently sensitive and specific to be clinically useful, particularly in endemic areas where the case burden is high. However, as the vascular system is the essential target in severe dengue, alternative approaches that focus primarily on the clinical vascular function tests described above could also prove to be helpful. Although these are relatively expensive and limited to centres with specialist expertise at present, if these are shown to be reliable, their non-invasive nature makes them an attractive option, especially for repeated assessments on children, and costs are likely to come down if the techniques are widely deployed. Finally, ongoing research efforts directed towards identifying robust early predictors for severe disease are crucial to ensure that if effective antiviral or disease-modifying drugs do become available in the future, these agents can be used to maximal advantage for the patients most at risk.
Both authors designed the outline of the article. SY wrote the initial draft, and BW revised and expanded the manuscript. Both authors have seen and approved the final manuscript.
SY is a Clinical Research Fellow studying dengue as part of the Imperial College Wellcome Trust Clinical PhD programme, and is based at the Oxford University Clinical Research Unit (OUCRU) in Vietnam. BW is the head of the Dengue Research Group at OUCRU, and Professor of Tropical Medicine at Oxford University, UK.
Lin CF, Lei HY, Shiau AL, Liu HS, Yeh TM, Chen SH, Liu CC, Chiu SC, Lin YS: Endothelial cell apoptosis induced by antibodies against dengue virus nonstructural protein 1 via production of nitric oxide. J Immunol. 2002, 169: 657-664.View ArticlePubMedGoogle Scholar
Hedblad B, Ogren M, Janzon L, Isacsson SO, Lindell SE: Low pulse-wave amplitude during reactive leg hyperaemia: an independent, early marker for ischaemic heart disease and death. Results from the 21-year follow-up of the prospective cohort study `Men born in 1914’, Malmo, Sweden. J Intern Med. 1994, 236: 161-168.View ArticlePubMedGoogle Scholar
The Ebola virus disease (EVD) outbreak in the three West Africa countries of Guinea, Liberia, and Sierra Leone, was declared by the World Health Organization (WHO) as a public health event of international concern in August 2014. The disease, which has caused more than 10,000 deaths from over 25,000 cases, has thrived on a failed disease control system, national denial, and a poor and fragile healthcare delivery system. The slow and initially uncoordinated national and global response turned the outbreak into an unprecedented humanitarian disaster. Prevention and control of future outbreaks depend on improving and upgrading disease surveillance into a responsive component of a reliable and efficient health care delivery system. Appropriate capacity building with a conducive operating environment, which has been lacking in the past few decades, will be key to the health system strengthening.
The World Health Organization (WHO) declared the 2014 Ebola virus disease (EVD) outbreak a public health event of international concern 8 months after the index case occurred and 5 months after it was officially reported [1]. Three countries (Guinea, Liberia, and Sierra Leone) bore the greatest burden of the epidemic, while three other West African countries (Mali, Nigeria, and Senegal) successfully contained the importation of the disease. In addition to the outbreaks in West African countries, the disease was also imported to the USA, UK, and Spain [2]. During the same period, another EVD outbreak unrelated to the West African outbreaks and which was successfully contained occurred in the Democratic Republic of the Congo (DRC) [3]. As of April 15, 2015, the reported number of cases associated with the West African epidemic, which has gone on for the last 16 months, is 25,826 cases with 10,704 deaths [4].
This commentary focuses on issues, generally not publicly discussed, but which have perennially hampered the ability of many African countries to effectively control disease outbreaks. Addressing these issues, and the lessons learned from the ongoing EVD epidemic, will equip African countries to prevent a repeat of the current humanitarian disaster.
The index case of the current West African EVD outbreak is considered to be a 2-year-old boy infected in Meliandou, near Gueckedou, in Guinea, in December 2013 [5]. The WHO was notified late in March 2014, by which time, Guinea had recorded 49 cases and 29 deaths [6]. On August 8, 2014, with a total of 1,779 cases and 961 deaths in Guinea, Liberia, Sierra Leone, and Nigeria [1], the WHO declared the EVD outbreak a public health event of international concern, that is, an ‘extraordinary event’ and a public health risk to other States [1].
The Nigerian index case of EVD was an individual who had visited and cared for a sibling who died from the disease in Liberia [7,8]. He travelled to Lagos, Nigeria, on a commercial flight, arriving on July 20, 2014, by which time he was acutely ill, and was admitted to a private hospital with fever, vomiting, and diarrhea [7,8]. He was initially treated for malaria having denied any exposure to EVD. His failure to respond to malaria treatment and his arrival from an EVD-affected country led to a suspicion of a case of EVD. Subsequent laboratory tests confirmed the diagnosis, and local public health authorities were alerted of the case of EVD [7,8]. The patient died on July 25, 2014.
The limited spread of EVD in Nigeria was a combination of fortuitous circumstances and the institution of a rapid and aggressive public health and infection control measures. The index case, a diplomat, was sick on arrival in Lagos, at a time when doctors were on strike and government hospitals closed. He was subsequently admitted to a private hospital, where he was the source of infection for nine of the hospital workers, and who were then sources of infection for five others [7,8]. This is in addition to two of the protocol officers who attended to him at the airport. One of those EVD infected protocol officers later escaped the Lagos Emergency Operations Centre (EOC) contact tracing network and travelled to Port Harcourt, where he initiated another chain of infections, involving three cases [7,8].
On July 23, 2015, the Nigerian authorities declared an EVD emergency, and activated the EOC to rapidly respond to the outbreak. In collaboration with partner agencies, the government, using existing public health assets, was able to set up six response teams, namely i) point of entry, ii) epidemiology/surveillance, iii) case management/infection control, iv) laboratory services, v) social mobilization, and vi) management/coordination, to rapidly contain the epidemic. When WHO declared Nigeria free of EVD [9] on October 20, 2014, the EOC had identified 894 contacts in three states (Lagos, Rivers, and Enugu), making approximately 18,500 face-to-face contact visits. There were a total of 20 confirmed or probable EVD cases, of whom eight died [7].
Therefore, despite the success of Nigeria in containing the EVD outbreak, the country had committed certain errors which cannot be repeated if future epidemics are to be rapidly contained. In these instances, there was a delay in providing funds for the early and crucial activities of the EOC. Official pre-outbreak information provided to the public was incorrect, creating a false sense of confidence regarding government preparedness for controlling EVD.
Since 1976, when EVD was first discovered in Africa, there have been 24 other outbreaks, aside from the current West African outbreak, with a total of 2,900 cases and 1,583 deaths [10]. As of April 15, 2015, the current outbreaks have accounted for around ten times this number: 25,826 cases and 10,704 deaths [4]. Several reasons have been given for the current unprecedented and prolonged EVD outbreak. These factors include, among others, i) the unreliable disease surveillance system which failed to detect and notify the outbreak at onset; ii) an official national and community denial of the occurrence of the epidemic; iii) the fragile and weak heath care delivery system which was incapable of managing the cases and containing the epidemic; iv) cultural practices associated with unsafe burial ceremonies; and v) ease and rapid mass movement of people between rural areas and urban centres, as well as across largely uncontrolled international borders.
Initial international response to the outbreak was described by Médecins Sans Frontières as “slow and derisory, ..... irresponsible” [11]. When international response was eventually in place, it was uncoordinated, making the initial global effort seem like a “consortium of confusion”.
Having failed to apply standard infection control guidelines to contain the epidemic, frantic efforts were made at global level. The focus temporarily shifted to using available, but unapproved, therapies on compassionate grounds [12]. Therefore, efforts were redoubled to find the ‘magic bullet’, namely drugs and vaccines, for the treatment of cases and to stem the tide of the epidemic. It is becoming clear, with the reduction in number of cases in Liberia and Sierra Leone, that this ‘magic bullet’ to bring the epidemic under control does not consist of drugs or vaccines, but rather a combination of aggressive contact tracing, isolation, and treatment of cases, safe burial ceremonies, and provision of adequate and correct information.
Since 1976, when an EVD outbreak was first reported, Africa has battled with no less than 24 episodes of EVD outbreaks [10]. These outbreaks should have provided Africa with opportunities for developing appropriate capacity and enhancing competence needed to deal with any EVD or other disease outbreaks. However, in all the reported EVD outbreaks occurring in Africa for last 38 years, Africa has been at the receiving end of international aid and assistance.
These long years of international aid and assistance for capacity building of Africans in disease control and response, have not actually yielded the desired result on building human resource capacity. This failure to build appropriate capacity is aptly illustrated by an extract from the published diary of a WHO expert, who is assisting with the control of several EVD and other viral hemorrhagic fever outbreaks in Africa. He wrote “We received the first emails on the Guinea event on Friday 14 March. During the weekend, I was travelling in the DRC for a training course on how to take blood samples in Ebola. It was a 3-day training course” [13]. It is inconceivable to think, and inconsolable to know, that training in ‘how to take blood samples in Ebola’ is the type of external technical assistance offered to health workers in DRC, a country where 38 years ago, the first EVD case was detected, and which has brought under control at least seven other EVD outbreaks [14]. DRC nationals have provided and continue to provide technical assistance to other African countries on EVD control.
By its nature and character, international assistance or aid, be it for economic development or research collaboration, tends to benefit the donor more than the recipient. A Director of a UK funded research center in Africa, wrote this piece in an annual report of the center: “Perhaps the most important achievement of the Unit during the past five years has been the training of a group of dedicated scientists and clinicians who have gone on to establish their own highly successful groups elsewhere in the tropics and in the UK and who continue to maintain the high reputation of the UK in tropical medicine” [15].
This is not an isolated case. A cursory review of the activities of foreign funded research centres in Africa revealed that scientists of the donor countries have benefitted more than their African counterparts in areas of research publication, competition for grants, and international recognition [16]. In many international conferences where issues concerning diseases of African origin and which are endemic in Africa, most, if not all the researchers and paper presenters are non-Africans. The disease outbreaks in Africa have become opportunities for foreign researchers to fine-tune their skills, enabling them to solve African problems, doing for Africa what African scientists should be doing for Africa.
Meanwhile, African scientists remain dependent on the crumbs from the tables of external donors; reduced to mere sample collectors and impotent contributors to disease prevention and control efforts locally or globally. If the next epidemic is not to catch Africa unprepared, this character and nature of external aid and assistance for research collaboration with Africa must change. To reduce dependence on external aid, Africa must reorganize her priorities, and invest more funds in research and research capacity building. African governments must create a conducive environment for African scientists to function with relevance, effectively and productively, freed from the bondage of dependency on foreign external support. On their part, developed countries should ease off on their dominance and control of the processes of global disease surveillance and create an atmosphere for genuine collaboration based on mutual appreciation and respect [17]. Only then can developing countries truly ‘own’ the processes of establishing a sustainable and reliable disease surveillance system for the prevention and control of disease and contribute to global health.
Even as agencies, foundations, governments, and institutions outside Africa are holding meetings to assess the lessons learnt from the EVD disaster and map out strategies for better response and control of future epidemics, EVD devastated African countries are enmeshed in other battles – misuse of EVD funds [18,19] and the inability to fully utilize aid and resources donated by international agencies. Therefore, is Africa prepared for the next epidemic? Has Africa learnt important lessons from the current EVD outbreak in West Africa? Will Africa still be helpless and totally dependent on international agencies for assistance to control any future epidemic? Will the scientists and governments of the developed world continue to so control the processes of a sustainable global disease surveillance system, leaving no meaningful role for participants from the developing countries? The answers of a pessimist would be no to the first two and yes to the last two questions. For the opposite to occur, Africa must accept that her self-imposed poverty status is not due to a lack of resources, but rather to their misuse, and that it can be reversed. This requires purposeful leadership and rightly guided followership. The African researcher or scientist must evaluate the needs of the society and become relevant to the society through adaptation and development of technologies that positively impact on the daily life of citizens. The poor standard of living and status of life in Africa places an extra burden on the African researcher or scientist that transcends the confines of learning and expertise. The researcher or scientist must take on the extra roles of guide, teacher, mentor, and beacon to society. Therein lies the true relevance of the African scientist or researcher. International aid will not build the capacity for such desired relevance; it must be funded with home-based resources and through governments with the right priorities.
OT graduated with a veterinary degree in 1971 from the Ahmadu Bello University Zaria, before obtaining a PhD in Virology in 1976 from the University of Ibadan, both in Nigeria. He was a faculty member of the Department of Virology, University of Ibadan, from 1971–1994, becoming Professor of Virology in 1981 and Head of the Department of Virology and Director of the Postgraduate Institute for Medical Research and Training at the College of Medicine, University of Ibadan, between 1986 and 1993. During his period at the University of Ibadan, he was involved in disease investigations and control of epidemics, elucidating the properties of new viruses, and in the training of students at undergraduate and postgraduate levels. In 1994, he joined the WHO Africa Region, where he served as the Regional Virologist and Laboratory Coordinator up till 2004. During this 10 year period, he was involved in the setting up of the African Regional Polio Laboratory Network, enhancing national scientific and research capacity for diagnosis and control of polio and viral hemorrhagic diseases (Yellow fever, Marburg, EVD, etc.) in Africa. He served as pioneer Vice Chancellor from 2004–2011 at the Redeemer’s University, a private faith-based institution in Nigeria. He serves on various national and international bodies (WHO, IOM, etc.) concerned with national and global control of infectious diseases. He currently serves as the President of the Nigerian Academy of Science.
WHO. Statement on the 1st meeting of the IHR Emergency Committee on the 2014 Ebola outbreak in West Africa. 2014. www.who.int/mediacentre/news/statements/2014/ebola-20140808/en/.
Niki is your virtual friend to help you book cabs, order food, recharge, pay postpaid, electricity, datacard, DTH bills and much more simply via chatting. She is an Artificial Intelligence (AI) powered chatbot that wants to be your simplified personal assistant. Niki is free to use, and you pay only for the services used, and make the most out of the offers available. You will always get responses with lightning speed. \n\n\n\n Show me movies running tomorrow Hi ! Let me assist you with movie ticket booking. You can type <b>cancel</b> at any point to exit this conversation Your city picked is Secunderabad. You can change it anytime by telling me another city. You can ask me for: No movie found running in theatre in Secunderabad for date: 26 Nov 2017. Please try changing some filters. cancel