A Systems Oncology Approach Identifies NT5E as ... - ACS Publications

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A Systems Oncology Approach Identifies NT5E as a Key Metabolic Regulator in Tumor Cells and Modulator of Platinum Sensitivity Ekaterina Nevedomskaya,*,†,∥ Richard Perryman,‡,§ Shyam Solanki,‡ Nelofer Syed,§ Oleg A. Mayboroda,† and Hector C. Keun*,‡ †

Center for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), L4-Q, PO Box 9600, 2300RC Leiden, The Netherlands ‡ Division of Cancer, Department of Surgery and Cancer, Imperial College London, Hammersmith Hospital, London W12 0NN, United Kingdom § Division of Brain Sciences, Department of Medicine, Imperial College London, London W12 0NN, United Kingdom S Supporting Information *

ABSTRACT: Altered metabolism in tumor cells is required for rapid proliferation but also can influence other phenotypes that affect clinical outcomes such as metastasis and sensitivity to chemotherapy. Here, a genome-wide association study (GWAS)guided integration of NCI-60 transcriptome and metabolome data identified ecto-5′-nucleotidase (NT5E or CD73) as a major determinant of metabolic phenotypes in cancer cells. NT5E expression and associated metabolome variations were also correlated with sensitivity to several chemotherapeutics including platinum-based treatment. NT5E mRNA levels were observed to be elevated in cells upon in vitro and in vivo acquisition of platinum resistance in ovarian cancer cells, and specific targeting of NT5E increased tumor cell sensitivity to platinum. We observed that tumor NT5E levels were prognostic for outcomes in ovarian cancer and were elevated after treatment with platinum, supporting the translational relevance of our findings. In this work, we integrated and analyzed a plethora of public data, demonstating the merit of such a systems oncology approach for the discovery of novel players in cancer biology and therapy. We experimentally validated the main findings of the NT5E gene being involved in both intrinsic and acquired resistance to platinum-based drugs. We propose that the efficacy of conventional chemotherapy could be improved by NT5E inhibition and that NT5E expression may be a useful prognostic and predictive clinical biomarker. KEYWORDS: O2PLS, chemometrics, cancer, metabonomics, transcriptomics, data integration, chemotherapy, cisplatin resistance

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such as metastasis and sensitivity to therapy.4,5 Expanding the knowledge of metabolic regulation would not only help to understand the origins of malignant transformations but also to unravel possibilities for specifically disrupting cancer metabolism and thus new therapeutic options.6 Metabolic reprogramming of a cancer cell is a complex process, the origin and regulation of which are poorly understood. The precise metabolic alterations in a given tumor depend on a variety of factors, including driver mutations, overall genomic landscape, and tumor micro-

INTRODUCTION

Cancer is characterized by a high degree of genetic heterogeneity not only between different tumor types but also even between different cell populations within one tumor.1 There are multiple mutations that can lead to a malignant cell, with multiple downstream pathways being affected.2 However, one common characteristic of cancer, irrespective of the causal mutations, is altered metabolism that supports tumor growth and persistence.3 Altered metabolism allows malignant cells to survive in the abnormal tumor microenvironment and cope with hypoxia, low pH, and low-nutrient content. Such adaptations are absolutely necessary for the survival of malignant cells and have been linked to clinical outcomes, © XXXX American Chemical Society

Received: September 7, 2015

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DOI: 10.1021/acs.jproteome.5b00793 J. Proteome Res. XXXX, XXX, XXX−XXX

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Journal of Proteome Research

level of metabolites in blood and urine.8 Collectively, 75 genes mentioned in these studies as being associated with metabolism were selected for further analysis. The list of the selected genes can be found in Supporting Information Table S3. Orthogonal projections to latent structures, OPLS,20 is an extension of a supervised multivariate analysis method, partial least squares (PLS),21 with an integrated orthogonal signal correction (OSC) filter.22 The special feature of OPLS is that it separates the variation of the data matrix X into the following parts: correlated to the response Y, systemically nonrelated (orthogonal) to Y, and the residual variance. Such a separation allows the sources of variation to be examined separately, improves the interpretability of the model, and makes the method widely applicable in the field of chemometrics and omics data analysis.23 O2PLS9,24 is a generalization of OPLS that allows prediction in both directions between multivariate matrices X and Y. The model is separated into the joint variation between the matrices and the orthogonal variation present in each of the matrices. O2PLS has been proposed as a method for integration of different omics data and successfully used in a number of applications.10−12 Gene expression and metabolite data were integrated using O2PLS modeling. X and Y data matrices were represented by mRNA and metabolite levels, respectively. Seven-fold crossvalidation was performed to assess the predictive ability of the model, and Q2 values were calculated for the model as a whole and for each of the variables in both matrices. The number of predictive O2PLS components was determined based on the scree plot of the PCA analysis of the matrix of covariance between X and Y. The number of orthogonal components was determined by maximizing joint Q2 values for prediction of both X and Y. Sample permutation was also performed in order to estimate the significance of the obtained Q2 levels. Predictive X and Y loadings represent covariance between gene expression and metabolomics data.

environment. Integrating system-level omics data can shed light on the interconnectivity of those components and regulation of metabolism in cancer.7 We approached such integration using systematic joint analysis of metabolic and gene expression data. For this, we used prior knowledge of genetically influenced metabotypes8 from genome-wide association studies (GWASs) and a statistical integration method, O2PLS, for combining metabolic and gene expression data from the well-characterized NCI-60 cell line panel. O2PLS is a method of bidirectional statistical modeling between sets of multidimensional data that allows for focusing on the correlations of interest and improving the interpretability of those correlations.9 O2PLS has been successfully used for combining transcriptomic and metabolomic data,10 proteomic and metabolic profiling data,11 and data on different classes of lipid metabolites.12 Here, our initial hypothesis was that using O2PLS to conduct a joint analysis of gene expression and metabolic profiles in a panel of diverse cancer cell lines could identify a gene or geneset with significant influence over cancer cell metabolism. Since tumor metabolism has been previously described to be associated with therapy resistance, tumor subtypes, and outcome, we also hypothesized that the gene(s) thus defined through O2PLS analysis were related to such traits. Using this methodology, we identified the extracellular nucleotidase, NT5E, which encodes for an ecto-5′-nucleotidase as a major determinant of cancer cellular metabolism. NT5E (CD73) has been previously associated with an impaired antitumor immune response via production of extracellular adenosine that suppresses T-cells.13,14 Aberrant expression of NT5E has been shown in a number of cancers: melanoma,15 leukemia,16 glioma,17 colorectal,18 and breast, notably triple negative,19 cancers. Subsequent bioinformatic investigation confirmed that NT5E was differentially expressed in cancer cell lines depending on oncogene, epithelial−mesenchymal transition (EMT), or hormonal receptor status and that both expression and NT5E activity were correlated with sensitivity to a number of cytotoxic drugs. Importantly, we observed in several independent data sets that NT5E expression was elevated upon in vitro and in vivo acquisition of resistance to cisplatin in ovarian cancer cell lines. We confirmed experimentally that cisplatin resistance could be reversed in vitro by targeting NT5E. The translational significance of our findings was supported by the association of NT5E expression with survival in ovarian cancer patients and its increased expression upon cisplatin therapy. Collectively, our data indicate a role for NT5E in mediating sensitivity to platinum chemotherapy in tumors via a direct, cell-autonomous alteration in metabolism. We therefore suggest that NT5E inhibition may be a potential therapeutic strategy in platinumresistant disease or for potentiating a response in combination with platinum agents.

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Survival Analysis

A Cox proportional hazard model was used to obtain hazard ratios in each of the ovarian cancer data sets separately. Metaanalysis was done with R package survcomp;25 the set of Cox regression coefficients was combined using a random effects model. All analyses were performed using in-house scripts and publicly available packages in R statistical software.26 Scripts are available upon request. Gene set enrichment analysis was performed using GSEA software (http://www.broad.mit.edu/gsea/)27 using the collection of gene ontologies as gene sets. Reagents

Unless otherwise stated, reagents were obtained from Sigma (Sigma-Aldrich Ltd., Dorset, UK).

EXPERIMENTAL PROCEDURES

Data

Cell Culture

List and details of all of the publicly available data sets used for the analysis are available in Supporting Information Table S1.

PEO1 and PEO4 ovarian cancer cells were obtained from Euan Stronach, Imperial College London. BT549 and MCF7 breast cancer cells were obtained from the Developmental Therapeutics Program, National Cancer Institute. Cells were maintained in RPMI 1640 (phenol red; no glutamine; 10% FBS), supplemented with penicillin/streptomycin, L-glutamine, and NEAA (hereafter referred to as growth media), and incubated at 37 °C under 5% CO2.

Data Integration

To limit the number of genes used in the integrative analysis, we focused on genes that have been previously described to have an influence on metabolic profiles in humans. For this, we selected seven published GWAS studies (Supporting Information Table S2) that explored the influence of genetic loci on the B


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Figure 1. O2PLS analysis. Loadings plots of O2PLS model integrating gene expression (a) and metabolite data (b). In this display, the relative position of each point in the two loadings plots indicates if regression coefficients for a given pair of an mRNA probe and metabolite are correlated or anticorrelated to each other, whereas distance from the origin and color indicate the magnitude of the association and the magnitude of the contribution to Q2, respectively. Variables with Q2 values above 0.2 are labeled.

Transient NT5E Knockdown and Cisplatin Sensitivity

SDS-PAGE running buffer (Tris 25 mM, SDS 17 mM, glycine 200 mM) and then transferred on to a nitrocellulose membrane in transfer buffer (Tris 25 mM, glycine 187 mM, 20% methanol). Membranes were stained with ponceau to ensure that protein was present, and then the membrane was washed using wash buffer (1× PBS in dH2O with 0.5% Tween). The blots were exposed to blocking solution (wash buffer with 5% powdered milk) and then probed with the following antibodies in blocking solution: rabbit anti-CD73 mAb (1:800, Abcam PLC, Cambridge, UK), donkey anti-rabbit mAb linked to a horseradish peroxidase (HRP) reporter (1:5000, Abcam PLC, Cambridge, UK), mouse anti-β-actin mAb (1:10000, SigmaAldrich Ltd., Dorset, UK), and goat anti-mouse mAb linked to a HRP reporter (1:20000, Abcam PLC, Cambridge, UK). The blots were exposed to enhanced chemiluminescent solution (Supersignal West Pico Chemiluminescent substrate by PIERCE, Thermo Fisher Scientific UK, Hemel Hempstead, UK) for 5 min, and immunoreactive bands were detected on a Kodak 4000MM image station (Kodak Ltd., Herts, UK).

NT5E expression was knocked down using the siPORT protocol. The siRNA’s used were either Silencer Select validated siRNA targeting NT5E (Life Technologies Ltd., Paisley, UK; sense: 5′-GUAUCCAUGUGCAUUUUAAtt-3′, antisense: 5′-UUAAAAUGCACAUGGAUACgt-3′) or AllStars negative control siRNA (QIAGEN Ltd., Manchester, UK). Briefly, siPORT was diluted 1:20 in Opti-MEM and incubated at room temperature for 10 min. AllStars negative control siRNA and NT5E siRNA were diluted in Opti-MEM to a final concentration of 10 nM. siPORT (Life Technologies Ltd., Paisley, UK) was then added to each siRNA and incubated at room temperature for 10 min. Either AllStars negative control siRNA or siRNA targeting NT5E transfection solution (20 μL) was then added to each well of a 96-well plate. Six thousand cells were added to each well of the 96-well plate, and media was added to a total volume of 100 μL. Cells were incubated at 37 °C under 5% CO2 for 24 h, at which point the media was replaced with growth media supplemented with cisplatin (0 μM, 2.5 μM). The cells were incubated at 37 °C under 5% CO2 for 72 h, at which point 7.5 μL of CCK8 solution was added; then, they were placed back in the incubator. After 4 h, the color change was analyzed on a Synergy H1 hybrid plate reader (BioTek UK, Bedfordshire, UK) at a wavelength of 450 nm.

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RESULTS

NT5E Is a Major Determinant of Metabolism in Cancer Cells

Transcriptomics data for 60 of the NCI-60 cell lines was obtained using the Agilent-012391 whole human genome oligo microarray G4112A chip (41000 probes); metabolite levels were available for 154 uniquely identified metabolites (Supporting Information Table S4) across 58 cell lines. To reduce dimensionality in the analysis and to maximize the likelihood of identifying biologically meaningful associations, when integrating mRNA and metabolic data we focused on genes previously shown to be related to metabolic traits on a systemic level (genetically influenced metabotypes). Genetic variation in a number of genes (Table S2) was previously associated with the concentration of metabolites in biofluids in GWASs.8 We limited our analysis to these GWAS-based genes, which corresponded to 75 unique genes in NCI-60 gene expression data, which in turn equated to 119 mRNA probes. The matched gene expression and metabolite levels data for 58 cell lines were used as X and Y inputs for O2PLS analysis, respectively. The optimal O2PLS model comprised 2 predictive and 3 Y- and 3 X-orthogonal components. The amount of variation in mRNA data that correlated to metabolites was found to be 12%, and the amount of variation in metabolite data correlated to gene expression was 27%. The overall

Western Blot for NT5E Protein

Cells (2.5 × 105) were added to each well of a 6-well plate, transfected using the siPORT protocol mentioned above, using 50 nM AllStars negative control siRNA or siRNA targeting NT5E in a total of 2.5 mL of media, and left for 24 h. The media was then removed and replaced with 2.5 mL of normal growth media. After 72 h, the media was removed and the cells were lysed using RIPA solution (RIPA buffer with 1× protease cocktail inhibitor) and centrifuged at 15000 rpm for 15 min, and the protein supernatant removed. The amount of protein was determined by BCA assay (Thermo Fisher Scientific UK, Hemel Hempstead, UK) with standard protein controls and analyzed on a Synergy H1 hybrid plate reader (BioTek UK, Bedfordshire, UK) at a wavelength of 562 nm. Western blot was carried out using a standard SDS polyacrylamide gel electrophoresis (PAGE) protocol. Fifteen micrograms of protein was separated by gel electrophoresis using a 10% polyacrylamide/bis-acrylamide gel (Tris 600 mM, SDS 6 mM, 0.75% weight/volume APS, 0.05% TEMED) in C


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Journal of Proteome Research Table 1. Correlation Analysis of NT5E Expression and Sensitivity to Chemotherapeutic Drugs in the NCI-60 Panel name

a

Pearson correlation coefficient

p adjusteda

p −6

inosine-glycodialdehyde tetraplatin 5,6-dihydro-5-azacytidine nitrogen mustard hydrochloride dichloroallyl-lawsone fluorodopan daunorubicin hydrochloride melphalan

−0.59 −0.52 −0.49 −0.48 −0.48 −0.47 −0.47 −0.45

1.00 × 10 3.00 × 10−5 0.0001 0.0001 0.00015 0.0002 0.0002 0.0004

0.0001 0.004 0.01 0.01 0.02 0.02 0.02 0.05

iproplatin diaminocyclohexyl-Pt-II cis-diamminedichloroplatinum(II) (cisplatin) carboplatin

−0.43 −0.43 −0.26 −0.21

0.0006 0.0007 0.05 0.1

0.08 0.08 1 1

Bonferroni corrected.

proportion of variance predicted in cross-validation (Q2 values) was 4 and 12% for mRNA probes and metabolites, respectively. To confirm the significance of this level of predictivity, the null distribution of the model Q2 was estimated using 1000 data sets with random sample permutations: none of the permuted models produced Q2 values exceeding those of the initial model. The cross-validation procedure in combination with the generation of random null models indicated that the statistical model was not overfitted. Subsets of variables in each matrix with the highest Q2 values, which contributed the most to the to model predictivity, were identified and are displayed in Figure 1. As can be seen from the scores plot, the three probes for the NT5E gene have the highest predictive Q2 value. This suggested that, of the panel selected, the expression of this gene was the most strongly associated with variation in the levels of metabolites across cancer cell lines in the NCI-60 panel. Using OPLS regression to create a model linking intracellular metabolite variation specifically to NT5E expression, we then identified metabolites that were correlated explicitly to NT5E, namely, cholesterol, glycerol, guanine, guanosine, inosine, inositol 1-phosphate, N-acetylneuraminic acid, phosphate, and uracil (Supporting Information Figure S1). Significantly, inosine concentration has been previously shown to be associated with NT5E nucleotide polymorphism in a GWAS study,28 in support of our observations. Validation of the relationship between NT5E expression and metabolite levels was performed using an independent data set that characterized consumption and release (CORE) profiles of metabolites from media across the NCI-60 cancer cell lines.29 As anticipated, NT5E expression showed significant positive correlation to inosine (among other metabolites) and negative correlation to a number of nucleoside-monophosphates (Supporting Information Figure S2). In conclusion, NT5E not only is associated with certain metabolic traits on a systemic level but also determines the metabolic phenotype among cancer cell lines.

Sensitivity to two nucleoside analogues, azacytidine and inosine-glycodialdehyde, exhibited the most significant correlations to NT5E expression. As the sensitivity to these chemotherapeutic agents is related to the expression of nucleoside transporters,31 it is possible that elevated levels of extracellular nucleosides associated with increased NT5E expression leads to competition for cellular uptake, thus resulting in less of the chemotherapeutics entering the cell and decreased potency. Interestingly, we also observed that sensitivity to a number of platinum-based drugs was significantly (four of five compounds were significant in a univariate test, and tetraplatin was significant after stringent Bonferroni correction) negatively correlated with the expression of NT5E gene (Table 1). Using the CORE data set, we were further able to confirm that the metabolites associated with NT5E expression were also associated with the sensitivity to platinum agents (Supporting Information Figure S3). We have previously reported associations between a number of the metabolites (inosine, guanine, guanosine, uracil, and cholesterol) found to be associated with NT5E expression in our current analysis and platinum sensitivity using a completely different bioinformatics approach of data integration based on overrepresentation.32 These complementary results obtained using different bioinformatics approaches suggest the existence of a specific metabolic phenotype that is associated with the sensitivity to cytotoxic agents, such as, for instance, platinumbased drugs. We also investigated the degree to which the link between NT5E and sensitivity to cytotoxic agents could be explained by differences in growth rates of the cell lines. Although NT5E expression correlated with the doubling times of the cell lines and negatively with platinum sensitivity (Supporting Information Figure S4a,b), this correlation could not be completely explained by growth rates. Within a homogeneous group of fast-growing cell lines, there was a considerable range of NT5E expression, which did not correlate with cell growth but did correlate significantly with drug potency (Supporting Information Figure S4c,d).

NT5E Expression and Related Cellular Metabolic Phenotypes Are Associated with Chemosensitivity in Cell Lines

NT5E Is Upregulated in the Models of Intrinsic and Acquired Platinum Resistance in Ovarian Cancer

Using the data on the sensitivity of NCI-60 cell lines to a panel of 118 mechanism-of-action drugs,30 we found that NT5E expression was negatively correlated with sensitivity to a number of the drugs (namely, high expression of NT5E is associated with low potency of the drug; Table 1).

The observed correlation between NT5E expression and sensitivity to platinum-based drugs is of particular interest with respect to ovarian cancer, for which treatment options are currently limited and typically confined to chemotherapy.33 D


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Figure 2. NT5E expression in ovarian cancer cell lines with acquired and intrinsic resistance to cisplatin. (a) Boxplot of NT5E expression in two groups of ovarian cell lines: intrinsically sensitive and resistant to cisplatin. (b) Fold change of NT5E expression in resistant cell lines (PEO4, A2780CP, CHICisR, M41 CisR, TYKnuCisR, and OV90C) relative to their parental sensitive counterparts (PEO1, A2780, CHI, M41, TYKnu, and OV90). Standard deviation of biological replicates is shown where applicable.

relatively high expression of NT5E (as determined in NCI-60 cell line panel data). As we would predict from this, both cell lines showed relatively limited sensitivity to cisplatin (with BT549 being more sensitive than MDA-MB-231). Silencing of NT5E expression led to increased sensitivity to cisplatin in both of these cell lines (Figure 3a,b), supporting our hypothesis that high NT5E expression is causally involved in intrinsic cisplatin insensitivity. Next, we examined two ovarian cancer cell lines that originated from the same patient tumor before and after the clinical acquisition of resistance to cisplatin: PEO1 (sensitive) and PEO4 (resistant).36 Western blot analysis confirmed that the resistant cell line, PEO4, had a higher NT5E protein expression than the sensitive PEO1 cell line (Figure 3e), consistent with our observations on the transcriptional level (Figure 2b). Silencing of NT5E in PEO1 cell line slightly, though not significantly, increased sensitivity to cisplatin (Figure 3c), whereas in the resistant PEO4 cell line it significantly increased sensitivity to the drug (Figure 3d). These results support our hypothesis that NT5E is involved not only in intrinsic but also in acquired resistance to cisplatin. Furthermore, they suggest that interfering with NT5E expression can resensitize resistant cancer cells to cisplatin.

To assess whether NT5E expression is associated with resistance to cisplatin treatment, we evaluated expression in the publicly available panel of ovarian cancer cell lines for which sensitivity to cisplatin was determined.34 Consistent with our previous observations, we found that NT5E expression was elevated in the subgroup of ovarian cancer cell lines resistant to cisplatin (t-test, p = 0.006; Figure 2a). However, the observed correlations are indicative of intrinsic sensitivity of the cell lines to the drugs, whereas the major challenge in ovarian cancer is the resistance to chemotherapy that develops after the initial response to the therapy in the majority of patients.35 We analyzed several publicly available expression data sets of ovarian cancer cell lines that are sensitive to platinum and resistant derivatives (in vitro acquired resistance), as well as sensitive and resistant cells derived from the same patient (in vivo acquired resistance). NT5E expression was elevated in five of six cell lines with acquired platinum resistance compared to expression in their sensitive counterparts across independent studies (Figure 2b). NT5E Silencing Increases Sensitivity to Platinum

Given our findings, we sought experimental validation and functional confirmation of the association between NT5E and platinum sensitivity. In an initial experiment, we characterized two breast cancer cell lines, BT-549 and MDA-MB-231, with E


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Figure 3. Effect of NT5E silencing on cisplatin sensitivity in breast and ovarian cancer cell lines. Bar graphs show cell number relative to control in MDA-MB-231 (a), BT-549 (b), PEO1 (c), or PEO4 (d) cells transfected with NT5E siRNA (white bar) or with scrambled control (black bar) upon treatment with 0 or 2.5 μM of cisplatin. Error bars show standard deviation over biological replicates (n = 5 for breast cancer cell lines; n = 3 for ovarian cancer cell lines). Below: Western blot analysis of the efficiency of knockdown. (e) Western blot analysis of NT5E protein level in PEO1 and PEO4 cell lines (representative of 3). n.s., not significant; *, p < 0.05; **, p < 0.005 (independent t-test).

NT5E Expression Predicts Disease Outcome in Ovarian Cancer Patients and Is Elevated upon Treatment with Platinum-Based Agents

relevance of NT5E, we conducted a meta-analysis of survival outcomes in six cohorts of ovarian cancer patients. NT5E was significantly associated with overall survival of ovarian cancer patients (overall hazard ratio 1.21, p = 0.005; Figure 4). Using publicly available gene expression data, we also examined changes in NT5E expression in patient biopsies obtained before and after treatment with either carboplatin or paclitaxel.38 We found that NT5E levels were significantly elevated in the post-treatment samples compared to that in the pretreatment samples in patients that had been treated with carboplatin or a combination of paclitaxel/carboplatin (p = 0.0003 in a paired t-test; Figure S5). NT5E levels remained

Sensitivity to chemotherapy is a crucial factor influencing patient survival in ovarian cancer, particularly since debulking surgery is often suboptimal, especially in advanced cancers.37 Association of NT5E expression and drug sensitivity is of potential clinical significance since platinum- and taxane-based drugs are the major chemotherapeutic agents used for treatment of ovarian cancer.33 NT5E expression is strongly negatively associated with sensitivity of cancer cell lines to platinum-based drugs (Table 1). To evaluate the translational F


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NT5E expression has been previously shown to correlate positively to a known component of cell migration (PLAU) in breast cancer tumors42 and implicated in cell adhesion in glioma.17 To investigate whether NT5E is involved in EMT, we explored whether its expression was correlated with known markers of EMT43 in the NCI-60 panel. There was a strong positive correlation of NT5E expression and expression of genes associated with EMT (Supporting Information Figure S8a), such as the well-known mesenchymal markers, SLUG and Fibronectin (Supporting Information Figure S8b,c). Similarly, there was a moderate negative correlation to the expression of genes that decrease during EMT. This suggests that high NT5E expression might be associated with a more mesenchymal, and thus more aggressive, metastatic phenotype. In order to get a global view on the potential pathways associated with NT5E expression, we performed correlation and geneset enrichment analysis of gene expression across the Cancer Cell Line Encyclopedia (CCLE) panel. Gene Ontology terms related to extracellular matrix, regulation of cell migration, and cell junctions were enriched among the genes positively correlated with NT5E expression (Supporting Information Table S5). Nucleic acid-related metabolic processes, mRNA processing and splicing, and DNA repair and replication processes were enriched among the genes negatively correlated with NT5E expression (Supporting Information Table S5). There is a continuous effort to stratify different cancers into subtypes based on their genomic architecture that can be differentially managed in the clinic.44 We investigated whether NT5E exhibits various levels of expression in cell lines depending on their mutational status. A panel of 474 cancer cell lines from CCLE45 was used, for which both mutational and gene expression data are available. We found that NT5E expression was significantly elevated in the cell lines that have an oncogenic mutation in either KRAS and/or BRAF, compared to wild type, whereas expression was lower in cells with mutations in the tumor suppressors PTEN and/or TP53 compared to wild type (Supporting Information Figure S9). This result is in accordance to the results of in vivo transcriptomics analysis, in which NT5E expression was reported to be upregulated in BRAF mutant serous ovarian tumors compared to that in wild-type tumors.46 These findings suggest that NT5E elevation may contribute to any differential

Figure 4. Meta-analysis of survival data. Forest plot of NT5E expression association with survival in six ovarian cancer studies. Solid blue lines denote hazard ratios (HRs) and 95% confidence intervals; boxes denote the relative influence of each study over the results; diamond marks the overall HR and its 95% confidence interval.

unchanged upon treatment with paclitaxel alone (Figure S5). This suggests, in line with our hypotheses, that NT5E upregulation is part of the tumor cell response to stress induced by exposure to platinum-based chemotherapy. NT5E and Cancer Subtypes

NT5E showed differential expression throughout the NCI-60 panel (Figure S6), with one of the highest amounts of variation being in breast cancer cell lines (Figure 5a). This variation corresponded to key clinical subtypes of breast cancer: estrogen receptor positive (ER+) and negative (ER−) disease. NT5E was also found to exhibit higher expression in androgeninsensitive prostate cancer cell lines compared to that in those that are androgen-sensitive39 (Figure 5b). Ovarian tumors can also be characterized by hormone receptor status.40 Using the gene expression data from The Cancer Genome Atlas (TCGA), we stratified ovarian cancer patients into estrogen-positive or -negative based on the median expression of estrogen-receptor alpha gene (ESR1). We found that the expression of NT5E was higher in tumors with ESR1 expression below the median compared to that in tumors with ESR1 expression above the median, i.e., an inverse association between NT5E and ESR1 expression (p < 10−5; Figure 5c). Examination of other data sets showed that silencing of estrogen receptor (ER) in breast cancer cells induces upregulation of NT5E expression (Supporting Information Figure S7), which is consistent with previous reports.41

Figure 5. NT5E expression and cancer subtypes. NT5E expression according to sex-hormone sensitivity in (a) breast cancer ER+ (white) and ER− (gray) cell lines (error bars indicate standard deviation across multiple probes), (b) prostate cancer cell lines that are androgen-sensitive (AS, white) or -insensitive (AI, gray), and (c) ovarian cancer tumors stratified by estrogen-receptor level. G


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indicating the importance of the immune response.42 By contrast, we show that sensitivity and, more importantly, acquired resistance to cisplatin can be modulated in vitro, which suggests that there are mechanisms other than immunosuppression by which NT5E can influence chemosensitivity and resistance. Analysis of the clinical data for ovarian cancer patients provides translational validity of the findings. Surprisingly, a recent paper Virtanen et al. has shown that extracellular adenosine, generated by NT5E, can be rapidly taken up by cells, inhibiting cell invasion and motility as well as phospho-AMPK1α.57 The latter, if confirmed in our cell lines system, might explain NT5E’s involvement in cisplatin resistance: phospho-AMPK1 inhibits mTOR, the inhibition of which has been shown to sensitize resistant ovarian cancer cells to cisplatin.58 It is also possible that NT5E could regulate the availability of nucleotide triphosphates required for DNA synthesis and repair processes.59 Our gene set enrichment analysis also shows correlation between NT5E expression and processes related to mRNA and DNA metabolism, processing, and replication. However, the exact mechanism that leads to the effect we observe is still to be elucidated. Furthermore, NT5E is differentially expressed in cell lines and tumors that correspond to different subtypes of cancer and can be potentially used for identifying more invasive, metastasis-prone subtypes of various cancers. NT5E is highly expressed in cell lines corresponding to the more aggressive ER-negative breast tumors compared to that in ER-positive cells. In a large panel of cancer cell lines, it is differentially expressed between cell lines harboring KRAS/BRAF or TP53/ PTEN mutations. It has been shown for a number of cancer subtypes that KRAS and TP53 mutations are mainly mutually exclusive and represent two distinct tumor origins.60,61 Recently, ovarian cancers have been classified into two distinct groups: Type I with frequent KRAS/BRAF mutations and Type II characterized by TP53 aberrations.62 Such classification has therapeutic implications, with Type I being less responsive to platinum-based chemotherapy.63,64 Additionally, Ras oncogenes have been associated with clinical and experimental resistance to platinum-based drugs in ovarian cancer.65,66 Our analysis suggests that high NT5E expression is associated with a more mesenchymal phenotype of cancer cells due to the correlation of expression to known markers of EMT, such as TWIST, SNAIL, SLUG, and Vimentin. Association of NT5E expression and EMT has also recently been shown for ovarian cancer patients.52 EMT has been implicated in resistance to cisplatin.67,68 This is in contrast to the recent paper by Miow et al.,34 in which epithelial ovarian cancer cell lines are shown to be more resistant to cisplatin than mesenchymal cell lines. While NT5E expression in these ovarian cell lines is consistent with our hypothesis of high NT5E expression in cisplatin-resistant cell lines, the association and precise definition of the epithelial/mesenchymal status might need further refinement. Collectively, this suggests that NT5E expression is an important marker relevant for subtyping and assessing differences in chemosensitivity in various types of cancer.

response to chemotherapy associated with genomic subtypes of disease.

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DISCUSSION In the present study, we identified NT5E (CD73) as a major determinant of metabolism in cancer cell lines and demonstrated its importance for survival of ovarian cancer patients and resistance to chemotherapy, in particular, platinum-based treatment. While the role of NT5E in immunosuppression has been widely reported,13,14,47 our work suggests an additional, cell-autonomous role for NT5E in intrinsic sensitivity to chemotherapy and acquired resistance. We also show that for ovarian cancer cell lines the in vivo acquired resistance to cisplatin can be reversed by blocking the activity of NT5E. Ovarian cancer is generally characterized by very poor prognosis, with the treatment options being limited and consisting of cytoreductive surgery followed by platinum- or taxane-based treatment. Despite the initial response to chemotherapy in the majority of cases, resistance to therapy develops and disease recurs. Identification of new targets for overcoming resistance could, therefore, have a major impact on patient survival. NT5E expression has been implicated in a number of cancers: melanoma,15 colorectal cancer,18 chronic lymphocytic leukemia,16 glioma,17 and breast cancer.19,42,48,49 The mRNA expression and methylation status of NT5E have been correlated with the clinical survival outcome19,42 in breast cancer patients. Antibody therapy against NT5E was suggested as a possible treatment in breast cancer14 and has been shown to suppress metastasis50 and tumor angiogenesis.51 Recently, NT5E expression has been shown to be negatively associated with ovarian cancer outcome in serous high-grade ovarian tumors.52 This confirms our observation of NT5E as a prognostic marker in a broader selection of ovarian cancer patients. Importantly, we show that NT5E expression is also upregulated in ovarian tumors upon chemotherapy. We investigated if upregulation of NT5E expression was a characteristic feature of acquired resistance to cisplatin in ovarian cancer cell lines and if inhibiting its activity could reverse the resistance. We show, using publicly available data sets, that in isogenic pairs of cell lines originated from in vivo and in vitro acquired resistance NT5E expression is increased upon the acquisition of resistance in the majority (with one cell line as an exception) of cell lines and is consistent through multiple data sets available. The reverse association of NT5E expression in cell lines and sensitivity to chemotherapy was confirmed experimentally. We also demonstrated in vitro that silencing NT5E can reverse the acquired resistance to cisplatin. To the best of our knowledge, there are no previous reports regarding the role of the enzyme in sensitivity to chemotherapy in ovarian cancer. Despite the suggested importance of metabolism in cancer cells, its relationship to drug sensitivity is rarely discussed.53 Our results, as well as previous observations by Cavill et al.,32 suggest that metabolism, and the nucleotide salvage pathway in particular, can play a pivotal role. Previously, the role of NT5E in promoting the survival of cancer cells and metastasis was attributed to its immunosuppressive role due to extracellular accumulation of adenosine in various cancers,13,54 including ovarian.55,56 Recently, it has been shown that the inhibition of NT5E does not influence the sensitivity to a chemotherapeutic drug, namely, docetaxel, in vitro in breast cancer cell lines; however, it is efficient in vivo,

Limitations of the Study

The variables entering the initial O2PLS model were limited by the number of genes described to be associated with metabolic profiles in GWAS studies as well as by the metabolomics data publicly available to date. O2PLS analysis could be redone with a larger panel of genes and metabolites, possibly bringing new H


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CCLE and TCGA investigators for making their data accessible. We also thank Dr. Euan Stronach for providing the PEO1/ PEO4 cell lines. S.S. was supported by a Biotechnology and Biological Sciences Research Council(BBSRC)/AstraZeneca CASE award (BB/I532588/1). R.P. was supported by an Medical Research Council (MRC) Advanced Masters Studentship (MR/J015938/1).

knowledge to metabolic control in cancer. For selective interference experiments, work in other cell lines and ultimately in vivo experiments would add further confidence to our findings.

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CONCLUSIONS Using a systems oncology approach that integrates knowledge of metabolic alterations on a systemic level (through GWAS studies) with omics data on a cellular level, we identified NT5E as a major determinant of metabolism in cancer cell lines. Although a role for NT5E in cancer is known, our data demonstrates an involvement in sensitivity and acquired resistance to chemotherapy, which is independent of the immune system. The strong influence of NT5E on cellular metabolism suggests that metabolism and regulation of extraand intracellular metabolic homeostasis are pivotal in NT5Epromoted metastasis and treatment resistance. Taking all of our data together, we propose that NT5E could represent a novel target for treating platinum resistant ovarian cancer.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jproteome.5b00793. OPLS loadings (Figure S1); correlation of NT5E and CORE metabolites (Figure S2); correlation of NT5E and sensitivity to tetraplatin (Figure S3); correlation of NT5E, platinum sensitivity, and cell growth rates (Figure S4); change of NT5E expression upon platinum therapy (Figure S5); NT5E expression across the NCI-60 cell line panel (Figure S6); NT5E expression upon ESR1 silencing (Figure S7); NT5E expression and epithelial− mesenchymal transition (Figure S8); NT5E expression and cancer subtypes (Figure S9) (PDF) List of publicly available data sets used in the analysis (Table S1) (XLS) List of GWAS studies used for the selection of genes for the analysis (Table S2) (XLS) List of the genes used for data integration (Table S3) (XLS) List of metabolites used in the analysis (Table S4) (XLS) Results of Gene Set Enrichment Analysis (Table S5) (XLS)

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REFERENCES

AUTHOR INFORMATION

Corresponding Authors

*(E.N.) E-mail: [email protected]. Tel.: +31 (0) 20-5127920. *(H.C.K.) E-mail: [email protected]. Tel.: +44 (0) 207594-3161. Present Address ∥

(E.N.) Department of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors thank the developers of NCBI GEO database and all of the contributors of the public datasets used, as well as I


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