metabolite profiling reveals the glutathione biosynthetic pathway … · 2017. 12. 16. · cancer...

Cancer Biology and Translational Studies

Metabolite Profiling Reveals the GlutathioneBiosynthetic Pathway as a Therapeutic Targetin Triple-Negative Breast CancerAlexander Beatty1, Lauren S. Fink1, Tanu Singh1, Alexander Strigun2, Erik Peter2,Christina M. Ferrer3, Emmanuelle Nicolas1, Kathy Q. Cai1, Timothy P. Moran1,Mauricio J. Reginato3, Ulrike Rennefahrt2, and Jeffrey R. Peterson1

Abstract

Cancer cells can exhibit altered dependency on specific met-abolic pathways and targeting these dependencies is a promis-ing therapeutic strategy. Triple-negative breast cancer (TNBC) isan aggressive and genomically heterogeneous subset of breastcancer that is resistant to existing targeted therapies. To identifymetabolic pathway dependencies in TNBC, we first conductedmass spectrometry–based metabolomics of TNBC and controlcells. Relative levels of intracellular metabolites distinguishedTNBC from nontransformed breast epithelia and revealed twometabolic subtypes within TNBC that correlate with markers ofbasal-like versus non-basal–like status. Among the distinguish-ing metabolites, levels of the cellular redox buffer glutathionewere lower in TNBC cell lines compared to controls andmarkedly lower in non-basal–like TNBC. Significantly, thesecell lines showed enhanced sensitivity to pharmacologic inhi-

bition of glutathione biosynthesis that was rescued by N-acet-ylcysteine, demonstrating a dependence on glutathione pro-duction to suppress ROS and support tumor cell survival.Consistent with this, patients whose tumors express elevatedlevels of g-glutamylcysteine ligase, the rate-limiting enzyme inglutathione biosynthesis, had significantly poorer survival. Wefind, further, that agents that limit the availability of glutathi-one precursors enhance both glutathione depletion and TNBCcell killing by g-glutamylcysteine ligase inhibitors in vitro.Importantly, we demonstrate the ability to this approach tosuppress glutathione levels and TNBC xenograft growth in vivo.Overall, these findings support the potential of targeting theglutathione biosynthetic pathway as a therapeutic strategy inTNBC and identify the non-basal-like subset as most likely torespond. Mol Cancer Ther; 17(1); 1–12. �2017 AACR.

IntroductionMetabolic reprogramming is regarded as a hallmark of cancer

that contributes to the progression of the disease (1). Differentialmetabolic requirements for rapidly proliferating malignant cellsrelative to nontransformed counterparts suggest that targetingmetabolism may be a potential strategy for developing novelcancer therapies (2–4). A pivotal step in developing new thera-peutic approaches that exploit metabolic vulnerabilities is toidentify metabolic alterations that are relevant for a particularmalignancy. Advances in the field of metabolomics have made itpossible to measure a broad spectrum of cellular metabolites,providing an approach to identify the specific metabolic altera-tions associated with cancer (5, 6).

TNBC represents nearly 15% to 20% of all breast cancers butdoes not express the estrogen receptor (ER) or progesterone

receptor (PR) and lacks HER2 amplification, and, consequently,is not responsive to current targeted therapies. Thus, TNBCrepresents an unmet medical need and the standard of care forTNBC has remained relatively stagnant for many years. Cytotoxicchemotherapy is the primary treatment modality, and althoughchemotherapy has a fairly high response rate, the likelihood ofrelapse is substantial (7).Here,weusebroad-spectrummetaboliteprofiling in TNBC to subtype the disease based on metabolicheterogeneity and identify potential metabolic targets that can beexploited therapeutically.

Materials and MethodsCell lines and materials

Nine of the 12 TNBC cell lines, as well as the nontrans-formed, immortalized control breast cell line MCF10A wereobtained from the ATCC. Excluding BT-20 and MDA-MB-231,which were received in 2012, the cell lines were received in2011. MDA-MB-468, BT-474, and ZR-75-1 were obtained bythe Cell Culture Facility at Fox Chase Cancer Center from ATCCas part of the NCI-60 panel. The NCI-60 panel was obtainedin 2003. The two additional TNBC cell lines, CAL-148 andMFM-223, were purchased from the Deutsche Sammlung vonMikroorganismen und Zellkulturen GmbH in 2011. The sup-pliers routinely authenticate the cell lines by short tandemrepeat profiling though the cell lines were not authenticatedby our laboratory. All cell lines were amplified and frozenwithin 2 months of receipt. Cell lines are periodically tested

1Fox Chase Cancer Center, Philadelphia, Pennsylvania. 2Metanomics GmbH &Metanomics Health GmbH, Berlin, Germany. 3Department of Biochemistry &Molecular Biology, Drexel College of Medicine, Philadelphia, Pennsylvania.

Note: Supplementary data for this article are available at Molecular CancerTherapeutics Online (http://mct.aacrjournals.org/).

Corresponding Author: Jeffrey R. Peterson, Fox Chase Cancer Center, 333Cottman Avenue, Philadelphia, PA 19111. Phone: 215-728-3568; Fax: 215-728-3574; E-mail: [email protected]

doi: 10.1158/1535-7163.MCT-17-0407

�2017 American Association for Cancer Research.

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for Mycoplasma contamination using 40,6-diamidino-2-pheny-lindole (DAPI) to stain DNA. Two different primary humanmammary epithelial cell (HMEC) isolates were obtained fromGIBCO (Details A10565) and Lonza (CC-2551), respectively,in 2012. Culture media for the cell lines can be found inSupplementary Table S1.

Metabolite profiling and normalizationCells (5–6 biological replicates) were harvested at a density of

approximately 70% to 80% at either 24 or 48 hours after seedingto lumox plates (Sarstedt) as previously described (8). Cells werewashed twice with 0.9%NaCl and extracted in dichloromethane:ethanol (0.82:1) as described previously (9). Extracts were flashfrozen in liquid nitrogen according to (10). Samples were ana-lyzed by gas chromatography–mass spectrometry (GC-MS) andLC/MS-MS as described elsewhere (11).

Metabolite profiling data were normalized against the medi-an in the pooled reference sample (generated by poolingsamples from additional biological replicates collected fromall investigated cell lines) to give Pool-normalized ratios (per-formed for each sample per metabolite) to compensate forinter- and intrainstrumental variability. Samples were alsosubjected to internal sample normalization to the medianmetabolite signal for each sample to account for differencesin the amount of applied sample material.

Metabolomics statistical analysisMultivariate statistical analysis was performed using Simca

Pþ software v13.0 (Umetrics) on the z-scores of the metabo-lites. All metabolite data were log10-transformed (to ensure anapproximate normal distribution), centered, and scaled to unitvariance. Scaling to unit variance introduced a common scalefor all metabolites independent of their absolute variance.Hierarchical clustering was conducted using R.utils and Hmiscwithin the statistical software package R (version 2.8.1). Thealgorithm for the calculation of the hierarchical clustering withstability information was taken from the pvclust-package (12).Hierarchical clustering used Ward method using the Spearmancorrelation between the metabolic profiles to determine thesimilarity between samples. Univariate "one-metabolite-at-a-time" analysis was performed by ANOVA, conducted using Rwith package nlme (13).

qPCRTotal RNA was isolated using an RNeasy Kit (Qiagen) and

reverse transcribed using Moloney murine leukemia virus reversetranscriptase (Ambion). TaqMan gene expression assays (LifeTechnologies) were used to amplify KRT5 (Hs00361185_m1),KRT14 (Hs00265033_m1), KRT17 (Hs01588578_m1), KRT23(Hs00210096_m1) cDNA. POLR2F was used as normalizer(F: TGCCATGAAGGAACTCAAGG, R:TCATAGCTCCCATCTGG-CAG). The slopes of the standard curves used to convert cyclethreshold values to quantities were between�3.2 and�3.7 cycle/log decade.

Viability assaysCompoundswere added24hours after cell plating andviability

was assessed at the specified time point using the CellTiter-Gloluminescent cell viability assay (Promega) according to the man-ufacturer's instructions. Data were normalized to vehicle-treatedcontrols and IC50 curves were generated using GraphPad Prism.

Viability assays were generally performed at least twice. In assaysthat included N-acetylcysteine (Sigma-Aldrich) treatment, themediumwas replaced every 48 hours except in RNAi experimentswhere additional N-acetylcysteine was added every 48 hours.CB-839 was obtained from Focus Biomolecules (14). Erastinwas purchased from Cayman Chemical (15), and BPTES wasacquired from Sigma (16).

siRNATransfections (DharmaFECT 1, Thermo Scientific) for siRNA

knockdown experiments used ON_TARGETplus SMART pools(Thermo Scientific) forGCLC andGCLM, or a nontargeting siRNApool (Dharmacon). Protein knockdown was assessed 72 hoursposttransfection using specific antibodies [GCLM (NBMP1-33405, Novus Biologicals) and GCLC (M01, clone 3H1,H00002729-M01 Abnova)].

Glutathione measurementsTotal glutathione (reduced plus oxidized forms)was quantified

using the GSH/GSSG-Glo Kit (Promega) according to the man-ufacturer's instructions. When comparing glutathione levels forvarious drug treatments, samples were normalized to parallel cellviability measurements using the CellTiter-Glo assay (Promega).For tumor samples, 50–100mg of tissue was homogenized in ice-cold PBS using dounce homogenizer. The homogenate was dilut-ed 7-fold and added to an equal volume of total glutathione lysisreagent. The remainder of the assay was carried out using themanufacturer's instructions.

ROS measurementsCellular ROS levels were measured using CellROX green

reagent (ThermoFisher Scientific) according to themanufacturer'sinstructions. Fields of fixed cells to be imaged were selected atrandom. Fluorescent micrographs were captured with a NikonEclipse TE2000 inverted microscope using Ocular software(QImaging). Image quantification was performed using ImageJ(NIH, Bethesda, MD).

Mouse xenograftsOrthotopic xenografts were generated by implanting 2.5 mil-

lion MDA-MB-231 cells in 100 mL PBSmixed with 100 mL growthfactor–reduced Matrigel (Corning) into the fourth inguinal fatpad of 4- to 6-week-old female NOD.Cg-PrkdcscidIl2rgtm1Wjl/SzJ(NSG) mice. After 14 days, animals were randomized into treat-ment groups. L-BSO (Sigma) was administered via the drinkingwater (20 mmol/L) ad libitum as reported previously (17, 18).Vehicle (control group) or 200 mg/kg sulfasalazine (Sigma) in0.1 N NaOH (pH 7.5) was dosed by intraperitoneal injectiononce daily. Vehicle or CB-839 was delivered twice daily byintraperitoneal injection at a dose of 10 mg/kg in a solution of25% (w/v) hydroxypropyl-b-cyclodextrin (Roquette) in PBS.The Fox Chase Cancer Center Institutional Animal Care and UseCommittee approved all animal procedures.

Tissue preparation, histology, IHCTumor sections were fixed in 10% formalin for 24 to 48 hours,

dehydrated and embedded in paraffin. Immunohistochemicalstaining was carried out using standard methods. Sections wereincubated overnight with primary antibodies to anti-humanKi-67 (Clone MIB-1; DAKO) and cleaved caspase-3 (Cell Signal-ing Technology; #9661) at 4�C in a humidified chamber.

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Micrographs were taken with a Nikon DS-Fi1 camera (Melville)on a Nikon Eclipse 50i microscope and quantified using a Scan-Scope CS5 slide scanner (Aperio).

ResultsMetabolite profiling reveals two major TNBC metabolicsubtypes

We used untargeted mass spectrometry–based metaboliteprofiling to examine relative levels of cell-associated metabo-lites in a panel of 15 TNBC cell lines and noncancerous con-trols. To capture the diversity of TNBC phenotypes, 12 widelystudied cell lines were chosen that included representativesfrom each of the six gene expression-based subtypes of TNBCdefined previously (19). Control cells were the immortalizedbut nontransformed MCF10A breast epithelial cell line and twoindependent isolates of primary HMEC. Proliferating cell cul-tures were rapidly harvested, extracted, and intracellular meta-bolites were quantified. We conducted unsupervised hierarchi-cal clustering based on 155 robustly quantifiable metabolites toexamine the relationship among the cell lines (Fig. 1A). Rep-licate measurements of the same cell line clustered more closelytogether than any two cell lines, demonstrating that biologicalvariation between cell types exceeds variation among replicates(Fig. 1B; larger version in Supplementary Fig. S1). Cells clus-tered into three metabolic subtypes (MSTs), with control cells(MST1) segregating from two TNBC cell line clusters (MST2 andMST3; Fig. 1B). Coclustering of the immortalized control cellline MCF10A with the primary HMEC cells suggests that themetabolic distinction between cancer versus nontransformed,proliferating cells is more significant than that between immor-talized and primary cells.

We conducted principal component analysis to evaluate inter-and intra-cell line variability (Fig. 1C). HMEC cells were excludedfrom this model due to their extreme segregation from theremaining cell lines. We generally observed close clusteringamong replicates demonstrating technical robustness of themeasurements. Second, we observed a strong segregation of theMCF10A control cells (black) from the TNBC cell lines (Fig. 1C),suggesting a prominent distinction in the metabolic phenotypeof cancer versus control cells.

To independently assess the robustness of the metabolic sub-types defined by hierarchical clustering, we performed an addi-tional principal component analysis on the replicate measure-ments from all 15 cell types (TNBC and controls; Fig. 1D). MST1cells (black) clearly segregated from the TNBC cell lines, althoughthe HMECs and MCF10A clustered independently, perhapsreflecting metabolic differences between primary and immortal-ized control cells, respectively. In addition, the distinctionbetween MST2 and MST3 was preserved in this analysis, consis-tent with the hierarchical clustering analysis. Overall, this sub-typing analysis revealed that TNBC cells aremetabolically distinctfrom nontransformed breast epithelia, and identified two majorTNBC metabolic subtypes.

TNBC metabolic subtypes are distinguished by expressionof basal cytokeratins

To identify genes differentially expressed between the twoTNBC metabolic subtypes, we examined publicly availablegene expression data (GSE10890) that included 10 of the TNBCcell lines in our panel. The most differentially expressed gene was

KRT5 (cytokeratin 5), which had significantly higher expression(adjusted P ¼ 0.000025) in cell lines of MST3 than MST2(Supplementary Fig. S2A). Quantitative RT-PCR confirmed higherexpression of KRT5 in MST3 (Fig. 1E; Welch t test, P ¼ 0.002).Cytokeratin 5 is a clinically relevant marker that is used to dis-tinguish basal-like from non-basal-like breast cancers (20, 21),suggesting that the metabolic subtypes might segregate accordingto basal-like status. This hypothesis was supported by mRNAexpression of additional basal cytokeratins (KRT14, KRT17,KRT23), which were more highly expressed in MST3 than MST2(Fig. 1E; Fisher combined probability test, one-tailed, P ¼0.00038). Furthermore, we compared the MST group member-ship of each cell line to its gene expression-based subtypeas defined by Pietenpol and colleagues (19) and found that MST2was enriched for non-basal-like (mesenchymal, mesenchy-mal stem-like, luminal androgen receptor, unclassified) gene-expression subtypes whereas MST3 correlated with basal-like(basal-like 1, basal-like 2, immunomodulatory) subtypes (Sup-plementary Fig. S2B; Fisher exact test, P ¼ 0.046). Taken together,these observations support an underlying metabolic basis forthe distinction between basal-like and non-basal-like TNBC.

Metabolites distinguishing the metabolic subtypesWe examined metabolites driving the separation of the MSTs

by performing an ANOVA to compare the relative levels ofindividual metabolites in each subtype. Comparing the TNBCMSTs (MST2 and MST3) to the noncancerous MST1 revealedseveral metabolites that distinguished cancer from normalepithelial cells (Fig. 2A and B, complete data provided inSupplementary Table S3). Among the most increased metabo-lites in TNBC compared with control were polyunsaturatedfatty acids including docosahexaenoic acid, arachidonic acid,and dihomo-gamma-linolenic acid. The most decreased meta-bolites in TNBC included the branched chain amino acidsvaline and leucine as well as the aromatic amino acid trypto-phan (Fig. 2A and B).

The reduced form of the antioxidant glutathione was themetabolite with the largest difference in abundance betweenMST2 andMST3 (Fig. 2C; Supplementary Table S3). Furthermore,both TNBC MSTs had lower levels of glutathione relative tonontransformed cells (MST1; Fig. 2D). MST2 had on average15.3-fold lower glutathione, and MST3 had 2.4-fold lower glu-tathione relative to MST1. The reduced:oxidized glutathionebalance (GSH:GSSG) was also significantly lower in MST2 com-pared with MST3 (Supplementary Fig. S3A), although theNADPH/NAPþ ratio was not significantly different when mea-sured separately (Supplementary Fig. S3B). The levels of theglutathione precursors cysteine and glutamate were also lowerin MST2 relative to MST3 by 2.5- and 1.7-fold, respectively(Fig. 2C; Supplementary Table S3). Finally, 5-oxoproline, ametabolite involved in glutathione salvage, was lower in TNBC(both MST2 and MST3) compared with MST1, with MST2 againexhibiting a more robust decrease (Fig. 2C; SupplementaryTable S3). These observations demonstrate lower glutathioneand its precursors in TNBC, particularly in MST2 and are consis-tent with elevated oxidative stress.

Glutathione biosynthesis is required to suppress ROS inTNBC cells and is associated with poor patient prognosis

We hypothesized that low glutathione in TNBC, and particu-larly in MST2, reflected dependence on glutathione biosynthesis

Targeting Glutathione Addiction in Breast Cancer

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Figure 1.

Metabolite profiling reveals three metabolic subtypes (MSTs). A, Schematic of metabolite profiling workflow. B, Unsupervised hierarchical clusteringbased on the relative levels of 155 intracellular metabolites. Three major MSTs are indicated. A more detailed version with bootstrap analysis of theclusters is available in Supplementary Fig. S1. C, PCA plot showing individual cell line replicates. The scores plot for the first two principal componentsencompasses 18.2% and 17.3% of total variance (13 components calculated in total by SIMCA accounted for 83.4% of the variance). D, PCA plot of allreplicates of all 15 cell lines color-coded by MST. The scores plot for the first two components encompasses 27.7% and 13.2% of total variance (85.1%;14 total components in total). Details corresponding to PCA loading of Fig. 1C and D are given in Supplementary Table S2. E, Box and whiskers plotsrepresenting mRNA expression of the indicated genes in cell lines of MST2 (blue) and MST3 (red). Whiskers represent the maximum and minimum values.BT-549 was excluded from the comparison for KRT23 because expression was below detection.

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Figure 2.

ANOVA analysis identifies metabolites differentially expressed between the metabolic subtypes. Volcano plots displaying the absolute values of the tvalues for each of 155 intracellular metabolites from ANOVA analyses versus the log2-transformed relative fold change for each metabolite for (A) MST1compared to MST2, (B) MST1 compared to MST3, and (C) MST2 compared to MST3. Metabolites are color coded by metabolite category and selectedmetabolites are individually labeled. D, Relative levels of reduced glutathione (GSH) in the three metabolic subtypes. Colors denote MSTs as definedin Fig. 1. Error bars represent relative standard deviation.






to maintain adequate antioxidant capacity. To test this, we mea-sured the effect of buthionine sulphoximine (BSO) on cell via-bility across the panel. BSO irreversibly inhibits g-glutamylcys-teine ligase (GCL), the first and rate-limiting enzyme in glutathi-one biosynthesis, leading to rapid depletion in cellular glutathi-

one (Supplementary Fig. S3C; ref. 22). Consistent with ourhypothesis, the average IC50 for BSO in cell lines of MST2 wassignificantly lower compared with the other subtypes (Fig. 3A;Welch t test, P ¼ 0.01). MST3 showed intermediate sensitivity toBSO, whereas the MST1 cell line MCF10A was relatively resistant.

Figure 3.

Low glutathione levels reflect increased glutathione consumption in order to reduce reactive oxygen species. A, BSO dose–response curves showing relativecell viability after 7 days (left), cell lines ranked by BSO IC50 value (middle) and comparison of the average BSO IC50 value for each MST (right). Errors bars fordose–response curves show standard deviation. All other error bars represent standard error of the mean. B, BSO dose–response curves for tworepresentative cell line of MST2 (BT-549, HCC1187) in the presence or absence of 5 mmol/L N-acetyl cysteine (NAC). C, Scatter plot of log-transformedIC50 values for BSO versus log-transformed IC50 for H2O2 in cell viability assays for representative members of MST2 (HCC1187, BT-549, BT-20, MDA-MB-231),colored blue, and of MST3 (HCC70, HCC1806, MDA-MB-468), colored red. The Spearman r and associated P-value from a Spearman correlation areshown. The BSO IC50 values correspond to those reported in A. D, Cellular ROS levels were measured by CellROX for individual cells of each specified typeand are grouped by MST in (E). F, Relative viability of BT-549 cells after siRNA knockdown of GCLC in the presence or absence of a sublethal dose of BSO (1mmol/L). Western blots show knockdown of GCLC 72 hours after transfection. GAPDH serves as a loading control. G, BSO dose–response curves showingrelative cell viability and relative glutathione levels (black; oxidized plus reduced forms) after 72 hours of treatment for the indicated cell lines.

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Confirming that BSO toxicity is a result of inadequate redoxbuffering capacity, the thiol-based ROS scavenger N-acetylcys-teine suppressed the decreased viability associated with BSOtreatment in multiple cell lines (Fig. 3B; Supplementary Fig.S3D). In addition, the sensitivity of the cell lines to BSO correlatedsignificantly with sensitivity to an exogenous source of ROS,hydrogen peroxide (Spearman correlation, P ¼ 0.006; Fig. 3C).Furthermore, MST2 cells showed increased ROS levels comparedwith MST3 cells (Fig. 3D and E), which could be reduced by N-acetylcysteine (Supplementary Fig. S3E), consistent withincreased ROS stress. We also assessed BSO toxicity in twonon-TNBC breast cancer cell lines. Although the luminal B sub-type cell line BT474was largely resistant to BSO, the luminal A cellline ZR-75-1 was BSO-sensitive (Supplementary Fig. S3F), sug-gesting that glutathione dependence may not be exclusive toTNBC. Nevertheless, we observed an addiction to glutathionebiosynthesis in TNBC cell lines of MST2.

In addition, we performed siRNA knockdown of the geneencoding either the catalytic (GCLC) or modulatory (GCLM)subunit of GCL in representative MST2 cell lines. Transienttransfection with the corresponding siRNA resulted in signifi-cant protein reduction at 72 hours (Fig. 3F; Supplementary Fig.S4A), but only a moderate decrease in glutathione at this timepoint (Supplementary Fig. S4B). While neither siRNA treatmentwas sufficient to significantly reduce cell viability (Supplemen-tary Fig. S4A), knockdown of GCLC (Fig. 3F), but not GCLM(Supplementary Fig. S4C) was synthetically lethal with lowdose BSO. To examine the relationship between glutathionedepletion and loss of cell viability, we analyzed the effect ofBSO dose on glutathione levels and cell viability in represen-tative BSO-resistant (HCC1806) and sensitive (MDA-MB-231)TNBC cell lines. While HCC1806 cells did not exhibit a loss ofcell viability until glutathione levels were decreased to approx-imately 2% of control levels, MDA-MB-231 cells began toshow decreased cell viability when glutathione levels werelowered to approximately 12% (Fig. 3G). Thus, BSO resistancereflects diminished glutathione dependence, rather than inabil-ity of BSO to deplete glutathione in these cells.

To gain insight into the potential clinical relevance of gluta-thione dependence in breast cancer, we evaluated GCLC/GCLMexpression in breast tumors from The Cancer Genome AtlasResearch Network (23). Using the cBioportal analysis tool(24, 25), we found that 13% (123/960) of all breast cancersamples had copy number amplification or overexpression ofeither GCLC or GCLM and this was increased to 25% (25/99) inthe triple-negative subset. Importantly, GCLC/GCLM amplifica-

tion or overexpression was associated with shorter disease-freesurvival (P ¼ 0.006; Fig. 4, left) and overall survival (P ¼0.00038; Fig. 4, right), consistent with a selection for increasedglutathione biosynthesis in order to support tumorigenesis.

Combinatorial approaches to suppress glutathionebiosynthesis reduce tumor cell growth in vitro and in vivo

Glutathione is a non-ribosomally synthesized tripeptide com-posed of glutamate, cysteine and glycine. Glutamate and cys-teine are substrates for the first and rate-limiting step in gluta-thione biosynthesis (Fig. 5A). On the basis of the sensitivity ofTNBC cells to glutathione depletion, we hypothesized thatlimiting the availability of these amino acid precursors mayenhance BSO toxicity. Inhibitors of the cystine/glutamate anti-porter xCT sulfasalazine (SASP) and erastin were used to limitcysteine availability (26, 27). System xCT transports cystine intothe cell, in exchange for glutamate, where it is converted tocysteine that can be utilized for glutathione biosynthesis andother functions. In addition, xCT inhibitors may lower cellularglutathione by promoting glutathione secretion (28). To limitglutamate, we used small-molecule inhibitors of glutaminase(CB-839 and BPTES; refs. 14, 16). Glutaminase deaminatesglutamine into glutamate, which can be used for glutathionebiosynthesis and for xCT-mediated cystine uptake in addition toanaplerosis of the citric acid cycle (29).

The combination of CB-839 with BSO, at doses at which theindividual agents had minimal effect on cell viability, inducedcell death in the non-basal–like MDA-MB-231 cells and also inthe basal-like HCC1806 cells (Fig. 5B). Furthermore, subtoxicdoses of BSO in MDA-MB-231 potentiated toxicity of BPTES(Fig. 5C) or erastin (Fig. 5D) by over 100- and 10-fold, respec-tively. To quantitatively evaluate these combinations, we sys-tematically measured the effect of subtoxic BSO on the potencyof glutaminase inhibitors and xCT inhibitors in both non-basal–like (red) and basal-like (blue) TNBC cell lines usingCellTiter-Glo viability assays (Fig. 5E, complete dose–responsecurves in Supplementary Fig. S5A and S5B). CB-839 and BPTESmore selectively enhanced BSO in the non-basal–like cells andwe investigated whether this was associated with glutaminedependence and/or a further reduction in glutathione levels.We cultured representative cell lines in media containingincreasingly lower amounts of glutamine (Fig. 5F). Strikingly,MDA-MB-468 cells were insensitive to glutamine depletion andthis correlated with the lack of BSO enhancement by CB-839and BPTES (Fig. 5E). Conversely, glutamine dependence ofthe other cell lines correlated with enhancement of BSO toxicity

Figure 4.

GCLC/GCLM overexpression oramplification is associated with worseprogression-free and overall survival inbreast cancer patients. Kaplan–Meiercurves showing disease-free survival(left) and overall survival (right) inbreast cancer patients included in theprovisional TCGA invasive breastcarcinoma dataset. Data was analyzedusing cBioportal and a z-scorethreshold of �2.0.






by glutaminase inhibitors. Furthermore, glutamine depletionsignificantly reduced cellular glutathione levels (Fig. 5G), sug-gesting that glutamine and glutaminase activity are required, inpart, to maintain cellular glutathione levels. Consistent with thismodel, BSO toxicity was enhanced when cells were culturedunder reduced glutamine conditions (Fig. 5H and I) and thetoxicity of CB-839 could be partially suppressed by the antiox-idant N-acetylcysteine (Fig. 5J).

While sensitizing BSO doses generally enhanced the potencyof inhibitors of glutaminase and xCT, some cell lines showedstrong BSO interactions with only one of the two amino acidprecursor pathways (e.g., HCC1806 and MDA-MB-468). Thisallowed us to test whether the differential sensitivity to gluta-minase and xCT inhibitors is associated with their differentialability to deplete glutathione in each cell line. Consistent withthis hypothesis, in HCC1806 cells, CB-839, and BSO, but noterastin and BSO, resulted in a significantly lower level of totalglutathione compared with BSO treatment alone (Student ttest, P ¼ 0.008; Fig. 5K). Conversely, in MDA-MBA-468 cells,erastin and BSO significantly enhanced glutathione depletioncompared with BSO alone (P ¼ 0.038), whereas CB-839 plusBSO did not (Fig. 5L). These data support the hypothesis thatthe enhanced growth suppression by these combinations is dueto glutathione depletion and suggest that different cell linesmay exhibit different limiting substrates for glutathione bio-synthesis. Limiting substrate was not predicted by the relativeexpression level of glutaminase (GLS1) or the xCT subunitSLC7A11 measured previously (30). In contrast, BSO toxicitywas significantly enhanced by both SASP and CB-839 in MDA-MB-231 cells (Fig. 5M). The enhancement of BSO by thesecompounds was selective because in a screen of 23 inhibitors ofmetabolic enzymes, the toxicity of only three compounds wereenhanced by BSO by >2-fold and the most strongly enhanced ofthese was SASP (Supplementary Fig. S5C).

We next investigated whether these combinations couldsuppress the growth of orthotopic MDA-MB-231 xenografts.Consistent with previous studies (31), BSO as a single agentshowed no tumor suppression activity (Fig. 6A). However,treatment of tumor-bearing mice with 20 mmol/L BSO (in thedrinking water) only suppressed tumor total glutathione to39 � 7% of control levels (Fig. 6B) whereas suppression ofglutathione by >85% was required to trigger loss of MDA-MB-231 cell viability in vitro (Fig. 3G). The combination of BSO andCB-839 did not further suppress glutathione levels over BSOalone (Supplementary Fig. S5F) and also did not suppresstumor growth (Supplementary Fig. S5D and S5E), althoughthis may be due to inadequate glutaminase inhibition becauseCB-839 was administered at a lower dose than in prior reports(14, 32). Alternatively, the lack of activity may reflect a dif-ferential requirement for glutamine created by the in vivoenvironment (33, 34). Consistent with the combined abilityof BSO plus SASP to reduce glutathione levels in culturedcells (Fig. 5M), mice treated with this combination had 25%of the glutathione of control-treated mice after 7 days oftreatment (Fig. 6C) and, importantly, grew more slowly(Fig. 6D) and had smaller tumor masses (Fig. 6E) at the endof the experiment, consistent with a previous report (31).However, treated mice did exhibit weight loss compared withcontrols (Fig. 6F). Tumors treated with BSO and SASP showedfewer Ki67-positive cells compared with control tumors(41 � 2% vs. 55 � 3%, Student t test, P ¼ 4.1 � 10�9), and

both these groups showed little to no staining for an apoptoticmarker (cleaved caspase-3; 0.1% of cells in both groups;Fig. 6G). These findings support the ability of cotreatment withBSO and SASP to reduce glutathione synthesis over BSOalone and reduce tumor growth in vivo.

DiscussionWe applied broad-spectrum metabolomics to identify meta-

bolic dependencies in TNBC, leading to four main insights intothis poorly understood cancer. First, we observe two distinctmetabolic phenotypes within TNBC that each differ from that ofuntransformed mammary epithelial cells. Second, the two TNBCmetabolic subtypes correlate significantly with clinically relevantmarkers of basal-like status. Third, non-basal-like TNBC cellsgenerally exhibit a relatively greater reliance on glutathione bio-synthesis that canbe targeted by the clinical agent BSO. Fourth, wehave identified rational drug combinations that have thepotentialto robustly improve treatment efficacy and broaden the applica-bility of anti-glutathione therapy in TNBC (31). Overall, our workdemonstrates how untargeted metabolomic analysis can identifydifferentially expressedmetabolites that reflect underlying cancer-specific metabolic pathway dependencies that can point towardnew therapeutic strategies. In addition, our data support anemerging and important distinction between basal-like andnon-basal-like TNBC (35) and provides a metabolomics resourcefor the discovery of additionalmetabolic adaptations that supportthe proliferation of each subtype.

Themetabolites that we identified as distinguishing TNBC cellsfrom nontransformed breast epithelial cells were largely consis-tent with previous studies (36–39). Importantly, most of thesestudies were performed using primary breast tumor tissue, sug-gesting that the TNBC cell line panel accurately recapitulatedmetabolic alterations observed in vivo. The polyunsaturated fattyacids docosahexaenoic acid (DHA) and arachidonic acid (AA)were elevated in TNBC, in agreement with reports that DHA andAA are increased in primary breast tumors (36, 37). b-Alanine, abreakdown product of pyrimidines and carnosine, was alsoelevated in TNBC as reported for ER-negative breast cancer(38). In addition, we identified decreases in a number of aminoacids, particularly branched chain amino acids, in TNBC, consis-tent with increased amino acid catabolism. A decrease in trypto-phan is consistent with increased expression and activity ofindoleamine 2,3 dioxygenase (IDO), the rate-limiting enzymein tryptophan catabolism that produces the immunomodulatorymetabolite kynurenine. Kynurenine and IDO are increased in ER/PR-negative compared to ER-positive breast tumors (39).

TNBC cell lines, particularly those of MST2, exhibitedalterations consistent with elevated oxidative stress. Mostnotably, the redox buffer reduced glutathione was decreasedin TNBC cell lines, and robustly decreased in MST2. Lowglutathione may reflect a reduced ability of TNBC to maintainadequate glutathione reserves relative to elevated demandfor redox buffering capacity. Interestingly, our results differfrom previous clinical studies reporting that reduced gluta-thione levels were generally increased in numerous cancertypes including breast (40). In addition, breast tumors, andspecifically ER/PR-negative tumors, exhibited elevated gluta-thione (39). This discrepancy may reflect a specific featureof non-basal-like TNBC, a subtype that was not specificallycharacterized in the prior studies, or could reflect the

Beatty et al.





Figure 5.

Treatment combinations that limit glutathione biosynthesis inhibit TNBC cell growth. A, Schematic representation of druggable metabolic pathwaysinvolved in glutathione biosynthesis. B, Light micrographs of HCC1806 (top) or MDA-MB-231 (bottom) treated as indicated for 7 days. The dose of CB-839was 200 nmol/L and BSO dose was 40 mmol/L for HCC1806 and 10 mmol/L for MDA-MB-231. Scale bar represents 200 mm. Dose–response curvesshowing relative cell viability versus dose of (C) BPTES or (D) erastin in the presence or absence of a subtoxic dose of BSO. E, Heat map showingfold-enhancement of growth suppression by the indicated inhibitor in the presence of BSO. The values correspond to the log2-transformed ratio ofthe IC50 of each agent in CellTiter-Glo cell viability assays divided by its IC50 in the presence of a sensitizing dose of BSO (Supplementary Fig. S5A and S5B).Positive values denote enhancement of toxicity. F, Cell viability of the indicated cell lines after culturing for 7 days in media containing theindicated concentration of glutamine. G, Relative levels of total glutathione after culturing HCC1806 cells for 24 hours in media containing thespecified concentration of glutamine. (Continued on the following page.)






influence of non-cell-autonomous factors on glutathionelevels in vivo.

Our finding that TNBC is sensitive to anti-glutathione ther-apy is consistent with recent publications (14, 41, 42). Two ofthese studies identified a dependence of TNBC on availabilityof the glutathione precursors glutamate and cysteine, respec-tively (14, 42). TNBC cell lines were sensitive to glutaminaseinhibitor, CB-839, which suppressed tumor growth in vivo (14).Similarly, the cysteine/glutamate antiporter xCT inhibitor sul-

fasalazine robustly inhibited proliferation, decreased glutathi-one content, and increased ROS levels (42). Our results suggestthat the effects of CB-839 and sulfasalazine may be at leastpartially mediated by restricting glutathione biosynthesis.

Several agents designed to exacerbate oxidative stress incancer cells and promote cell death are in preclinical or clinicaldevelopment (43). Here, we present an approach to stratifyTNBC to predict which will be most responsive to anti-gluta-thione therapeutics. Unexpectedly, the metabolic subtypes that

(Continued.) (H) and (I) show relative cell viability of cell cultures after growing for 7 days with the indicated concentration of BSO and either high orlow glutamine, as shown. J, Dose-dependent effect of CB-839 on the viability of HCC1806 cells in the absence or presence of N-acetylcysteine.Relative total glutathione levels after 24 hour of treatment of (K) HCC1806, (L) MDA-MB-468, or (M) MDA-MB-231 with the designated agents. For(K) and (L), doses of BSO, erastin, and CB-839 were 8, 200, and 40 nmol/L, respectively. For (M), BSO plus SASP, 5 mmol/L BSO and 200 mmol/L SASPwere used, whereas for BSO plus CB-839, 1 mmol/L BSO and 40 nmol/L CB-839 were used.

Figure 6.

Treatment combinations limit glutathione biosynthesis in vivo. A, Median tumor volume over time for mice treated with 20 mmol/L BSO in the drinkingwater or control water. B, Masses of the tumors in (A) following dissection at the end of the study. C, Total glutathione measured from lysates oftumors treated with BSO in combination with SASP or with vehicle alone. One tumor was analyzed from each mouse. Corresponding tumor growth isshown in (D) and final tumor masses in (E) for all tumors. A larger number of mice was used in the treatment group due to anticipated toxicity thoughonly one mouse was culled for significant body weight loss and was excluded from the data. Lines represent the median values. F, Mean body weightsof mice measured over the treatment period in (D). G, Percentage of Ki-67-positive or caspase 3-positive cells (less than 0.2% in either condition) inMDA-MB-231 tumor sections.

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we defined correlated with basal-like versus non-basal-likeTNBC. Because basal-like status is routinely determined byimmunohistochemical staining of tumor biopsies, existingclinical practice can be used to stratify TNBC patients andpotentially identify those most likely to respond to therapytargeting glutathione biosynthesis. Furthermore, the correla-tion between metabolic subtype and basal-like status suggeststhat similar metabolic distinctions could exist in other cancersthat are divided into basal-like and non-basal–like types.

Disclosure of Potential Conflicts of InterestUlrike Rennefahrt is a Senior Scientist at Metanomics Health GmbH/

Metanomics GmbH. No potential conflicts of interest were disclosed by theother authors.

Authors' ContributionsConception and design: A. Beatty, L.S. Fink, U. Rennefahrt, J.R. PetersonDevelopment of methodology: A. Beatty, L.S. Fink, A. Strigun, E. Peter,C.M. Ferrer

Acquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): A. Beatty, L.S. Fink, T. Singh, A. Strigun, E. Peter,C.M. Ferrer, E. Nicolas, K.Q. Cai, T.P. Moran, U. RennefahrtAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): A. Beatty, U. Rennefahrt, J.R. PetersonWriting, review, and/or revision of the manuscript: A. Beatty, L.S. Fink,A. Strigun, E. Peter, C.M. Ferrer, M.J. Reginato, U. Rennefahrt, J.R. PetersonAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): A. Beatty, K.Q. CaiStudy supervision: J.R. Peterson

AcknowledgmentsWe gratefully acknowledge Drs. Balachandran, Golemis and Connolly for

comments on the manuscript.

The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.

Received May 8, 2017; revised September 6, 2017; accepted October 5, 2017;published OnlineFirst October 11, 2017.

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Published OnlineFirst October 11, 2017.Mol Cancer Ther Alexander Beatty, Lauren S. Fink, Tanu Singh, et al. CancerPathway as a Therapeutic Target in Triple-Negative Breast Metabolite Profiling Reveals the Glutathione Biosynthetic

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