Transketolase Regulates the Metabolic Switch toControl Breast Cancer Cell Metastasis via thea-Ketoglutarate Signaling PathwayChien-Wei Tseng1,2,Wen-Hung Kuo3, Shih-Hsuan Chan1,2,4, Hong-Lin Chan5,King-Jen Chang6, and Lu-Hai Wang1,2
Abstract
Although metabolic reprogramming is recognized as a hall-mark of tumorigenesis and progression, little is known aboutmetabolic enzymes and oncometabolites that regulate breastcancer metastasis, and very few metabolic molecules have beenidentified as potential therapeutic targets. In this study, thetransketolase (TKT) expression correlated with tumor size in the4T1/BALB/c syngeneic model. In addition, TKT expression washigher in lymph node metastases compared with primary tumoror normal tissues of patients, and high TKT levels were associatedwith poor survival. Depletion of TKT or addition of alpha-keto-glutarate (aKG) enhanced the levels of tumor suppressors succi-nate dehydrogenase and fumarate hydratase (FH), decreasingoncometabolites succinate and fumarate, and further stabilizingHIF prolyl hydroxylase 2 (PHD2) and decreasing HIF1a, ulti-
mately suppressing breast cancer metastasis. Reduced TKT oraddition of aKG mediated a dynamic switch of glucose metab-olism from glycolysis to oxidative phosphorylation. Variouscombinations of the TKT inhibitor oxythiamine, docetaxel, anddoxorubicin enhanced cell death in triple-negative breast cancer(TNBC) cells. Furthermore, oxythiamine treatment led toincreased levels of aKG in TNBC cells. Together, our study hasidentified a novel TKT-mediated aKG signaling pathwaythat regulates breast cancer oncogenesis and can be exploited asa modality for improving therapy.
Significance: These findings uncover the clinical significanceof TKT in breast cancer progression and metastasis and demon-strate effective therapy by inhibiting TKT or by adding aKG.Cancer Res; 78(11); 2799–812. �2018 AACR.
IntroductionPatients with breast cancer have a 5-year survival rate over 90%;
however, for patients with distant metastasis, their survival ratedecreases to only about 25% because of the lack of effectivestrategies against breast cancer metastasis and recurrence (1).Tumor cells with altered metabolic program have high require-ments of glucose metabolism for rapid proliferation. Despitesome studies aiming at elucidating the correlation betweenaberrant metabolic behavior and tumor progression, how meta-bolic processes regulate breast cancer cells growth and metastasisis not fully understood.
A number of studies show that oncogenic signaling in cancersdrives metabolic reprogramming to generate large amounts ofbiomass during rapid tumor growth (2). For example, HIF1a
elevates the expression of glycolytic enzymes, including aldolaseA, phosphoglycerate kinase 1, and pyruvate kinase (3). In addi-tion, a number of studies revealed that genetic defects in TCAcycle enzymes, such as succinate dehydrogenase (SDH) andfumarate hydratase (FH), were also associated with tumor pro-gression (4, 5).
In this study, we used a proteomic approach to identifycertain differentially expressed metabolic enzymes involved intumor progression such as aldolase A (ALDOA), triose phos-phate isomerase (TPIS), a-enolase (ENOA), transketolase(TKT), and pyruvate dehydrogenase E1 (ODPB). Among them,TKT is a metabolic enzyme involved in the nonoxidative branchof the pentose phosphate pathway (PPP) and connects PPPwith glycolysis. Previous studies revealed that TKT was associ-ated with metastasis of ovarian (6) and esophageal (7) cancers,as well as poor patient survival (6, 7). To date, no study hasreported the effect of TKT-regulated metabolic signaling ontumor metastasis in breast cancer.
In this study, we reveal clinical significance and regulatorymechanism of TKT in progression and metastasis of breast cancervia alpha-ketoglutarate (aKG) signaling. TKT plays importantroles in regulating dynamic switch of glucose metabolism. Thecombined therapy based on the new targets TKT or aKG couldbe developed as an improved therapeutic approach for triple-negative breast cancer (TNBC).
Materials and MethodsCell culture and transfection
The human breast cancer MDA-MB-231, Hs578T and MCF-7cells, and mouse breast cancer 4T1 cells were obtained from
1Graduate Institute of Integrated Medicine, China Medical University, Taichung,Taiwan. 2Institute of Molecular and Genomic Medicine, National Health ResearchInstitutes, Zhunan, Miaoli County, Taiwan. 3Department of Surgery, NationalTaiwan University Hospital, Taipei, Taiwan. 4Institute of Molecular Medicine,National Tsing Hua University, Hsinchu, Taiwan. 5Institute of Bioinformatics andStructural Biology, National Tsing HuaUniversity, Hsinchu, Taiwan. 6Departmentof Surgery, Taiwan Adventist Hospital, Taipei, Taiwan.
Note: Supplementary data for this article are available at Cancer ResearchOnline (http://cancerres.aacrjournals.org/).
ATCC. The 4T1 is a highly tumorigenic and invasive cell linecapable of metastasizing from the primary mammary glandtumor to liver, lung, lymph nodes, and brain. The highly meta-static cell lineMDA-MB-231-IV2-3waspreviously established anddescribed (8). All cell lines were cultured in DMEM (Invitrogen)supplemented with 10% FBS (Biological Industries) at 37�Cwith5%CO2. Cell lines were clear ofMycoplasma as determined by theVenor GeM kit (Minerva Biolabs) and were further authenticatedin 2017 by Taiwan Bioresource Collection and Research Centre(BCRC) using a short tandem repeat method. For transfectionassay, cells were transfected with 20 mmol/L siTKT or 20 mmol/LsiRNA control or TKT/pCMV plasmid (1 mg/mL) using Lipofecta-mine RNAiMAX transfection reagent (Thermo Fisher Scientific).
Protein extractionCell samples were lysed in lysis buffer containing 7mol/L urea,
2 mol/L thiourea, 4% w/v CHAPS, 10 mmol/L Tris-HCl pH 8.3,and 1 mmol/L EDTA. Protein lysates were extracted, sonicated,and centrifuged and the protein concentration was determinedusing Coomassie Protein Assay Reagent (Bio-Rad).
2-D DIGE gel image analysis and protein identification byMALDI-TOF-MS
The protein profiles of tumor tissues with 0.5, 1, and 2 cm insize were analyzed using 2D differential gel electrophoresis(DIGE). Protein samples were labeled with cyanine dyes Cy2,Cy3, and Cy5, and all procedures have been described previously(9, 10). The Cy-Dye–labeled 2-DE gels were visualized accordingto the previous report (10). For protein identification, the peptidemixture was loaded onto a MALDI plate and samples wereanalyzed using an Autoflex III mass spectrometer (Bruker Dal-tonics) and parameters were described according to the previousreport (10).
Western blottingCells were lysed in the lysis buffer containing 7 mol/L urea,
2 mol/L thiourea, 4%w/v CHAPS, 10mmol/L Tris-HCl (pH 8.3),1 mmol/L EDTA, and phosphatase and protease inhibitors(Roche). Protein lysates were sonicated and centrifuged and theprotein concentration was determined using protein assay kit(Thermo Fisher Scientific). The defined amount of final lysateswas resolved in 8%–12% SDS-polyacrylamide gels, transferredonto polyvinylidene difluoride membrane and probed withappropriate antibodies. Antibodies include rabbit polyclonalanti-LDHA (GTX101416, Genetex), rabbit polyclonal anti–aKGdehydrogenase (clone C2C3, GTX105124, Genetex), rabbit poly-clonal anti-SDH (GTX113833, Genetex), rabbit polyclonal anti-FH (clone N2C2, GTX110128, Genetex), rabbit polyclonal anti-MDM2 (GTX100531, Genetex), mouse monoclonal anti-TKT(clone 7H1AA1, ab112997, Abcam), and mouse monoclonalanti-PHD2 (clone 366G/76/3, Thermo Fisher Scientific). Mousemonoclonal anti-b-actin (clone SPM161, Santa Cruz Biotechnol-ogy) was used as the internal control, and protein expressionlevels were visualized with the Enhanced ChemiluminescenceDetection Kit (Pierce Boston Technology) and exposed to X-rayfilm. All experiments were repeated three times.
IHCParaffin-embedded matched normal, primary tumor, and
lymph node metastatic tissue sections of breast cancer specimens(n ¼ 11) were provided by Dr. Wen-Hung Kuo, National Taiwan
University Hospital (Taipei, Taiwan). Other samples were fromcommercial tissue arrays (US Biomax; SuperBioChips), including19 normal, 90 tumors, and 50 lymph nodemetastatic tissues. Theslides were stained with mouse monoclonal anti-TKT antibody(clone 7H1AA1, ab112997; Abcam) using an automatic slidestainer BenchMark XT (Ventana Medical Systems). The stainingintensities were evaluated and quantified by one pathologist(Pathology Core Lab, National Health Research Institutes) andtwo independent investigators. The IHC scores of TKT for eachspecimen were graded as follows: no expression, weak (þ);moderate (þþ); and strong (þþþ).The expression levels of TKTin tumor cells were quantified as a percentage. Paraffin-embeddedsections of tumor cells with TKTL1 overexpression (Origene,RG205218) were stained with mouse monoclonal anti-TKT anti-body (1 mg/mL, 1:75 dilution; clone 7H1AA1, ab112997;Abcam) or rabbit polyclonal anti-TKTL1 antibody (1 mg/mL,1:75 dilution; clone N1C1, GTX109459; Genetex). We first usedtheD'Agostino and Pearson omnibus normality test to reveal thatthe quantitative results of IHC TKT expression were not Gaussiandistribution (P ¼ 0.0015). Thus, we used nonparametric Mann–Whitney test to analyze the quantitative results.
Proliferation assayCell proliferation was detected using CellTiter 96 AqueousOne
Solution Cell Proliferation Assay (Promega). Assay was per-formed according to manufacturer's protocol. A total of 1.4 �104 cells were cultured in a 24-well plate and incubated fordifferent times. CellTiter 96 Aqueous One Solution reagent wasadded and incubated for 1hour at 37�C. Thequantity of formazanproduct, proportional to living cell numbers,wasmeasured at 490nmusing 96-well plate reader. Each experiment was performed intriplicate and the shown data were mean � SD.
Cell invasion and migration assaysMDA-MB-231 and Hs578T cells were treated with 20 mmol/L
siTKT or 1 mmol/L aKG, or TKT/pCMV plasmid (1 mg/mL). After48 hours, these cells (1 � 105 cells) were seeded on Boydenchamber, incubated for 8 hours, and then stained with 0.5%crystal violet dye. Cell invasion and migration were assayed in8-mm Falcon Cell Culture Inserts with or without Matrigel (BDBiosciences), respectively. All experiments were performed intriplicate.
Soft agar colony formation assayMDA-MB-231 or MCF-7 cells at densities of 1 � 105 cells were
seeded in 6-well plate containing top layer of 0.4% agarose andbottom layer of 0.6% agarose medium. The treatment group wastransfected with 20 mmol/L of siTKT for 48 hours. After onemonth, colonies were stained with p-Iodonitrotetrazolium vio-let (1 mg/mL) for 48 hours and then counted. Data representmean � SD and the experiment was performed in triplicate.
Tail vein injection and orthotopic metastasis assays inmouse models
To study the effects of aKG on tumor progression, MDA-MB-231cells (1 � 106) resuspended in 100-mL PBS were implantedorthotopically in 4th mammary fat pads of 8-week-old femaleCB17-SCID mice (8). After implantation of MDA-MB-231 cellsfor 24 hours, aKG reagent was intraperitoneally injected 3 timesa week until 3 months. aKG dissolved in PBS was used forinjection of 10 mg/kg each time. The tumor volume was
Tseng et al.
Cancer Res; 78(11) June 1, 2018 Cancer Research2800
calculated by the formula: tumor volume (cm3) ¼ [length(cm) � width (cm)2 � 0.5]. To study the effects of TKTknockdown on tumor metastasis, MDA-MB-231-IV2-3 cells weretreatedwith 20mmol/L siTKT.After 48hours,MDA-MB-231-IV2-3cells (1 � 106) resuspended in 100-mL PBS were injected permouse intravenously via tail veins into 6- to 8-week-old femaleCB17-SCID mice (BioLASCO). Tumor growth and metastasisto individual organs were observed using live animal biolumi-nescence imaging (BLI; Caliper IVIS system, PerkinElmer). Tumorvolume and weight were also measured at the end point. Cellmetastaseswere quantified byBLI signals of eachmouse at the endpoint. Animal experiments were approved by the InstitutionalAnimal Care and Use Committee (IACUC).
Orthotopic injection of stable TKT knockdown cells in mousemodel
MDA-MB-231 cells were, respectively, transfected with twoindependent GFP-TKT/pCMV plasmids (Origene, NM001064).After 48 hours, these cells were selected by flow cytometry andtransfection efficiency was confirmed by Western blot analysis.Stable shTKT cells were orthotopically injected at 1� 106 cells permouse into 4th mammary fat pads of CB17-SCID mice (n ¼ 7)and tumor volumeswere recorded once aweek during the 70 daysperiod. Tumor volume ¼ 4/3pR3, R ¼ [length (cm) þ width(cm)]/2. Animal experiments were approved by IACUC. n ¼ 7.
TKT activityMDA-MB-231 cells were transfected with 20 mmol/L siTKT.
After 48 hours, these cells were lysed with 0.1 mol/L Tris-HC1 buffer (pH 7.6), centrifuged, and the supernatant wascollected (11). Supernatant (50 mL) was mixed with 200 mLreaction mixture including 14.4 mmol/L ribose-5-phosphate,190 mmol/L NADH, 380 mmol/L TP, >250 U/L glycerol-3-phosphate dehydrogenase, and >6,500 U/L triose phosphateisomerase (12).
Enzyme activity was detected at 340 nm. One unit of enzymeactivity indicates the amount of enzyme catalyzing the oxidationof 1 mmol of NADH per minute.
Metabolic assayOxygen consumption rate (OCR) is an indicator of mito-
chondrial oxidation and extracellular acidification rate (ECAR)is an indicator of lactate production that is equated to theglycolytic rate. OCR and ECAR were detected by XFe24 extra-cellular flux analyzer (Seahorse Bioscience). MDA-MB-231 cells(7 � 104 cells) were cultured in X24 culture plate (SeahorseBioscience). OCR and ECAR were measured in XF base medium(Seahorse Bioscience). OCR was analyzed over time followinginjection of 1 mmol/L oligomycin, 2 mmol/L carbonyl cyanide-p-trifluoromethoxyphenylhydrazone (FCCP), and 0.5 mmol/Lrotenone/antimycin. ECAR was measured over time follow-ing injection of 10 mmol/L glucose, 1 mmol/L oligomycin, and50 mmol/L 2-deoxyglucose (2-DG). For ECAR, glucose(10 mmol/L), oligomycin (1 mmol/L), and 2-DG (50 mmol/L)were used to estimate glycolytic metabolism. Glucose treat-ment could increase glycolytic metabolism in cells. 2-DG, asynthetic glucose analogue, acted as a competitor for glucoseand interfered with glucose metabolism. For OCR, oligomycin(1 mmol/L), carbonyl cyanide-4-(trifluoromethoxy)phenylhydra-zone (FCCP; 1 mmol/L), and rotenone/antimycin (0.5 mmol/L)were used to estimate oxidative respiration. For mitochondrial
respiration, oligomycin treatment inhibited ATP synthase in mito-chondria. FCCP, a proton ionophore in mitochondria, transportsprotons across cell membranes to disrupt ATP synthesis. Finally,rotenone and antimycin are inhibitors for electric transport chainin mitochondria.
Statistical analysisKaplan–Meier method (log-rank test) was used to analyze
survival data. Data were presented as mean � SD. Student t testwas used to compare the differences between two experimentalgroups and one-way ANOVAwas used to compare the differencesamongmultiple groups using Tukey test in GraphPad. x2 test wasused to analyze the correlation between TKT levels and clinicalfactors; �, P < 0.05; ��, P < 0.01; ���, P < 0.001. OCR and ECARdata were calculated by paired t test.
ResultsIdentification of metabolic proteins potentially involved inbreast cancer progression using proteomic analysis
Using a proteomic approach and examining tumors of vary-ing sizes, we attempted to identify differentially expressedproteins associated with breast cancer progression. We usedsyngeneic orthotopic implantation of 4T1 cells in BALB/c mice,and tumors with 0.5, 1, and 2 cm in size were collected forfurther proteomic analyses. The protein profiles from the tumorwith 0.5 cm in size were compared with those tumors with 1and 2 cm in size by two-dimensional protein gel analysis(Supplementary Fig. S1A–S1C). After spot detection and quan-tification from the two-dimensional gel images, a total of 21differentially expressed proteins (P < 0.05) with 1.5-foldchanges were chosen for further identification (SupplementaryFig. S1A–S1C) by using MALDI-TOF-MS and MASCOT data-base (Supplementary Table S1). Three proteins related toglycolysis were upregulated in the bigger tumors; they includedALDOA, TPIS, and ENOA. Other metabolic enzymes includedthe upregulated TKT involved in PPP and the downregulatedODPB involved in pyruvate oxidation (Supplementary TableS1; Supplementary Fig. S1D). Tumors with 1 and 2 cm in sizehad 1.5- and 2-fold, respectively, increased expression of TKTwhen compared with the 0.5-cm tumor (Supplementary TableS1; Supplementary Fig. S1D).
TKT displays higher expression in metastatic lymph nodetissues and patients with breast cancer with high TKTexpression have poor overall survival
We analyzed TKT expression in normal and tumor tissuesaccording to gene expression arrays from Oncomine database(Bild data). As compared with normal tissues, TKT displayedsignificantly higher expression in tumor tissues (Fig. 1A, nonpara-metric Mann–Whitney test, P ¼ 0.03). We also found thatthe levels of TKT in TNBC patients were significantly higherthan those in non-TNBC patients (Fig. 1B, P < 0.001). Kaplan–Meier survival curve (log-rank test) from Curtis 5-year overallsurvival data showed that patients with higher TKT levels hadpoorer 5-year survival than those with lower TKT levels (n ¼637, Fig. 1C; P ¼ 0.019, x2¼ 5.502, HR ¼ 1.3298). The similarresult is also observed in different clinical database (n ¼ 158;Fig. 1D;P¼ 0.003,c2¼8.7476, andHR¼ 2.3131), suggesting thatTKT has a prognostic potential. TNBC is the breast cancer subtypewith the poorest outcome; however, very few metabolic enzymes
TKT Regulates Breast Cancer Metastasis via aKG Signaling
www.aacrjournals.org Cancer Res; 78(11) June 1, 2018 2801
as prognostic indicators for patients with TNBC are known. Therole of TKT in patients with TNBC has not been reported; thus,we further analyzed the correlation between TKT expression levelsand patients with TNBC 5-year overall survival. Among the 637cases, there were a total of 106 patients with TNBC. Our analysisshowed that patientswithTNBCwithhigher TKT levels hadpoorer5-year overall survival than those with lower TKT levels (n ¼106; Fig. 1E; P ¼ 0.0006, x2 ¼ 11.7166, HR ¼ 2.3758), showingthat TKTmight have a prognostic potential in patients with TNBC,and it could play a role in TNBC progression. The clinicopatho-logic features of TKT in patients with breast cancer from Curtisdata showed that TKT levels were significantly associated withsome clinical factors, including stage, age, grade, type, TNBC, and
tumor size (Supplementary Table S2). We also analyzed TKTexpression in normal, primary tumor, and lymph nodemetastatictissues by using IHC. First, we checkedwhether TKT antibody usedin the IHC staining cross-reacted with TKTL-1. To address this, weused TKTL1/pCMV plasmid to overexpress TKTL1 in MDA-MB-231 cells. The overexpression efficiency was verified (Supplemen-tary Fig. S2A). The paraffin-embedded sections of tumor cells withTKTL1 overexpression were stained with anti-TKT or anti-TKTL1antibody.Our results displayedhigh staining intensity of TKTL1 intumor cells overexpressing TKTL1 using anti-TKTL1, whereasstaining intensity using TKT antibody was insignificant (Supple-mentary Fig. S2B). These results suggest that TKT antibody used inthe IHC staining does not cross-react with TKTL1.
Figure 1.
Clinical significance of TKT in patientswith TNBC. A, The expression levels ofTKT in tumor (n¼40) andnormal (n¼ 7)tissueswere analyzed according to geneexpression arrays in Oncomine database(nonparametric Mann–Whitney test,P ¼ 0.03). B, The levels of TKT in non-TNBC (n ¼ 1725) and patients withTNBC (n ¼ 250) from Curtis data werecompared (���, P < 0.001). C, Kaplan–Meier curve for TKT expression inassociation with 5-year survival of 637patients with breast cancer. Patientswere divided into high (blue line) andlow (red line) TKT expression groupsbased on the mean þ SD levels amongthe patients analyzed (log-rank test, P¼0.019). D, Kaplan–Meier curve for TKTexpression in association with overallsurvival (n¼ 158). Patients were dividedinto high (blue line) and low (red line)TKT expression groups based on themedian levels among the patientsanalyzed (log-rank test, P ¼ 0.003).E, Kaplan–Meier curve for TKTexpression in association with 5-yearsurvival of 106 patients with TNBCamong the 637 patients with breastcancer. Patients were divided into high(blue line) and low (red line) TKTexpression groups based on the meanþSD levels among the patients analyzed(log-rank test, P ¼ 0.0006). F,Representative pictures of TKT IHC fromnormal, primary tumor and lymph nodemetastatic tissues (scale bar, 1 mm;Supplementary Fig. S2 showsquantitative results).
Tseng et al.
Cancer Res; 78(11) June 1, 2018 Cancer Research2802
The staining intensity of TKT was evaluated and quantifiedranging from no expression to the highest expression by a pathol-ogist and two independent investigators of our team. As summa-rized in Supplementary Fig. S2C, a high percentage of normaltissues displayed insignificant TKT intensities (60%) or low inten-sities of TKT (30%) when compared with those of tumor tissues(P < 0.001). Moreover, metastatic lymph node tissues displayeda higher percentage of high intensities of TKT (56%) whencompared with primary tumor (25%, P < 0.001). The represen-tative staining photographs are shown in Fig. 1F. The percentageof TKT expression in tumor cells, not including stroma cells, fromprimary tumor and lymph node metastatic tissue sections wasfurther quantified. Metastatic lymph node tissues displayed ahigher percentage of TKT expression in tumor cells when com-pared with the primary tumor (Supplementary Fig. S2D; P <0.001). These results showed that TKT expression levels werethe highest in lymph node metastases, suggesting that a possiblecorrelation of TKT levels with progression of metastasis inbreast cancer.
Downregulation of TKT suppresses metastatic functionsand affects cell-cycle distribution
To further elucidate the functional role of TKT, we manip-ulated TKT expression by siRNA depletion of TKT in MDA-MB-231 and Hs578T TNBC cells (Fig. 2A). The downregulation ofTKT in MDA-MB-231 cells resulted in significantly decreasedcell proliferation (Fig. 2B–D). This phenomenon was alsoobserved in Hs578T cells (Fig. 2E–G). The inhibition byTKT knockdown in both cell lines was significantly rescued byTKT/pCMV overexpression (Fig. 2B–G). Cell migration andinvasion were carried out by transwell Boyden chamber assays.Downregulation of TKT led to a significant inhibition of inva-sion (Fig. 2H) and migration (Fig. 2I) of MDA-MB-231 andHs578T cells, whereas the inhibitory effects were almostcompletely rescued by TKT overexpression. MDA-MB-231 cellswith the inhibited TKT expression displayed reduced ability ofcolony formation (Fig. 2J). TKT knockdown increased thepercentage of cells in the G2–M phase in MDA-MB-231 andHs578T cells (Fig. 2K). Taken together, these data suggestedthat the depletion of TKT impaired tumor cell growth andmetastasis-related abilities.
Knockdown of TKT suppresses lung metastasis of breastcancer cells
To evaluate whether the depletion of TKT suppressedcancer cell metastasis in vivo, we used tail vein injection ofthe highly invasive MDA-MB-231-IV2-3 cells (1 � 106 cells)in CB17-SCID mice (n ¼ 8). The highly metastatic MDA-MB-231-IV2-3 sublines derived from the MDA-MB-231 parentalline were established and described previously (8). TheMDA-MB-231-IV2-3 cells exhibited dramatically higher inva-siveness than the MDA-MB-231 parental cells in vitro andthey also exhibited more aggressive lung and lymph nodemetastasis in vivo (8). The data from tail vein injection modelshowed that knockdown of TKT resulted in greatly decreasedlung metastasis of the MDA-MB-231-IV2-3 cells [Fig. 3A(P ¼ 0.005) and B (P ¼ 0.002)] by BLI as also reflected inhematoxylin and eosin staining (Fig. 3C). These findingsindicated that knockdown of TKT inhibited lung metastasisof the highly invasive breast cancer cells (SupplementaryFig. S3A–S3F).
To further assess whether decreased lung metastasis by thedepletion of TKT resulted from decreased targeting of the tumorcells to lung, the cells transfected with the control or TKT siRNAwere injected into CB17-SCIDmice through tail vein (n¼ 8) andafter 24 hours, the lung was perfused with PBS to flush outintravascular tumor cells and subsequently the expressionlevels of human GAPDH reflecting the injected cells in lungtissues were measured. BLI analysis exhibited about equivalentsignals in the lungs of siCon or siTKT-transfected cells 30minutesafter injection (Fig. 3D, P ¼ 0.294). The qPCR data confirmedthe result (Fig. 3E, P¼ 0.222). To confirm the inhibitory effects oftransient TKT knockdown on tumor growth, two different knock-down stable lines, MDA-MB-231-shTKT1 and MDA-MB-231-shTKT2, as well as MDA-MB-231-shNC line, were established,and each (1 � 106 cells) were implanted orthotopically intothe 4th mammary fat pad of CB17-SCID mouse (n ¼ 7). Theknockdown efficiency of shTKT was verified (Fig. 3F) and theresult showed that tumor sizes in both TKT knockdown groupswere significantly smaller than those in the control group (Fig. 3Gand H; Supplementary Fig. S3G–S3I). These findings indicatedthat knockdown of TKT did not inhibit lung targeting (Supple-mentary Fig. S4A–S4D), but inhibited the subsequent lung col-onization ability of the tumor cells.
Identification of TKT-regulated metabolites in breastcancer cells
Recent reports indicated the involvement of Warburg effect intumor metastasis and suggested that the molecules participatingin metabolic modulation were potential targets for antimetas-tasis therapy (13). To address TKT-regulated metabolic path-ways in breast cancer cells, we manipulated TKT expression bysiRNA treatment of MDA-MB-231 cells for 48 hours and thencell lysates were harvested for identifying altered metabolites byLC-MS/MS (Waters Corporation). The differentially expressedmetabolites in siTKT-treated cells were identified when compar-ing with the siRNA control cells. Knockdown of TKT increasedsome TCA-cycle intermediates including aKG (Fig. 4A) andmalate, while decreased succinate and fumurate (P < 0.05).Reports indicated that the alternation of metabolites in the TCAcycle was associated with tumor formation (4). For example,succinate and fumarate accumulated in the mitochondria leakedout to the cytosol because of inactivation of the tumor sup-pressors SDH and FH, resulting in promoting cancer formation(14). Currently, the potential role of aKG and the relationshipbetween TKT and aKG in TNBC are still unclear. Our findingsthat TKT might play an important role in metastasis and itsknockdown led to increased aKG prompted us to furtherinvestigate the potential effect of aKG in oncogenic behaviorof cancer cells.
aKG suppresses tumor cell growth, migration, and invasionWe further found that TKT overexpression attenuated aKG
levels (Fig. 4B, P < 0.001), which was consistent with the resultfrom TKT knockdown. The physiologic concentration of aKG inhealthy brain tissues ranges from 1 to 3 mmol/L, whereas itsconcentration is decreased to 100 to 300 mmol/L in gliomas(15). IDH1-mutated tumor cells exhibited decreased aKG,leading to increased HIF1a levels (16). The similar results wereobserved in aKG derivatives treatments in IDH1-mutated gli-omas (17) or SDH-deficient tumor cells (18). Despite its tumorsuppressor role of artificial aKG derivative in cancers, many
TKT Regulates Breast Cancer Metastasis via aKG Signaling
www.aacrjournals.org Cancer Res; 78(11) June 1, 2018 2803
Downregulation of TKT suppresses growth, invasion/migration and colony formation, and affects cell-cycle distribution of breast cancer cells. A,Twenty mmol/L siTKT reduced TKT expression in MDA-MB-231 and Hs578T cells, whereas its inhibitory effects were rescued by TKT/pCMV overexpression(1 mg/mL). The effects of TKT expression on cell proliferation in MDA-MB-231 (B–D) and Hs578T cells (E–G) were measured after siTKT or siTKT, and TKT/pCMVcotreatment for 24, 48, and 72 hours (� , P < 0.05; �� , P < 0.01; and ��� , P < 0.001). For invasion (H) and migration (I) assays, MDA-MB-231 and Hs578Tcells were treated with siTKT or siTKT, and TKT/pCMV cotreatment for 48 hours (��� , P < 0.001) and then incubated on Boyden chamber for 8 hours.J, For colony assay, 1 � 105 cells MDA-MB-231 or MCF-7 (Supplementary Fig. S4E) cells were transfected with siTKT. K, MDA-MB-231 and Hs578T cells weretransfected with siTKT. Forty-eight hours later, tumor cells were harvested for analysis of cell-cycle distribution after propidium iodide staining. Thepercentage of cells was quantified by FlowJo 7.6 (� , P < 0.05; ���, P < 0.001).
Tseng et al.
Cancer Res; 78(11) June 1, 2018 Cancer Research2804
studies revealed that non-aKG derivative could attenuate cellproliferation of colon cancer (19) and reduces the levels ofVEGF and erythropoietin through decreasing HIF1a, thereby,inhibiting angiogenesis ability of the Hep3B hepatoma cells(20). These findings suggested the potential tumor suppressingrole of aKG. Furthermore, G-protein–coupled receptor GPR99was reported to function as a receptor for the TCA cycleintermediate aKG (21). Although, previous studies indicatedthat aKG–dependent dioxygenases signaling pathways func-tioned as tumor suppressors (22), the regulatory role of aKG inbreast cancer is unclear. Treatment of aKG resulted in signif-icantly decreased MDA-MB-231 cell growth when compared
with the control (Fig. 4C). Furthermore, treatment of aKG ledto a significant inhibition of cell invasion (Fig. 4D) and migra-tion (Fig. 4E). MCF-7 cells with the inhibited TKT expressiondisplayed reduced ability of colony formation (SupplementaryFig. S4E). TKT overexpression promoted cell proliferation inMDA-MB-231 (Fig. 4F–H) and Hs578T (Supplementary Fig.S4F–S4H) cells, whereas its effect was substantially reversed byaKG treatment. These findings indicate that aKG can impairmetastatic-related abilities of breast cancer cells. We furtherverified that the promotion of TKT on invasion (Fig. 4I) andmigration (Fig. 4J) in MDA-MB-231 and Hs578T cells wassubstantially reversed by aKG treatment, suggesting that TKT
Figure 3.
Knockdown of TKT does not inhibitearly targeting to lung but suppresseslung metastasis of breast cancer cells.A, MDA-MB-231-IV2-3 cells (1 � 106
cells) were transfected with 20 mmol/Lof siCon or siTKT. After 48 hours, 1� 106
cells per mouse were injectedintravenously into CB17-SCID mice viatail veins (n ¼ 8). Lung metastases asreflected by amount of cancer cells inlung in vivo (A) and ex vivo (B) werequantified using BLI signal (n ¼ 8).C, Images show hematoxylin and eosinstaining of lung metastases. Moredetailed data are shown inSupplementary Fig. S3A–S3F.Scale bar, 1 mm. T, tumor cells inthe lung. D, MDA-MB-231-IV2-3 cells(1 � 106 cells) transiently transfectedwith siCon or siTKT were injected at1 � 106 cells per mouse into CB17-SCIDmice via tail veins (n ¼ 8). BLI imagesshowed lung metastasis of tumor cellsin siCon and siTKT-treated mice30 mins after injection (P ¼ 0.294).E, Twenty-four hours after injection, themice were perfused with PBS to rid ofblood and lung tissues were harvested.Specific qPCR primers for humanGAPDH were used to detect injectedcells in lung tissues and mouse actinmRNA was used as the internal control(P ¼ 0.222). F, Knockdown efficiencyof shTKT in the two independentstable lines, MDA-MB-231-shTKT-1 andMDA-MB-231-shTKT-2 were confirmedwhen compared with the control group(stable MDA-MB-231-shNC cells). G andH, Stable shTKT cells wereorthotopically injected at 1 � 106 cellspermouse into 4thmammary fat padsofCB17-SCID mice (n ¼ 7; G) and tumorvolumes (H) were recorded once aweek during the 70 days period(��� , P < 0.001).
TKT Regulates Breast Cancer Metastasis via aKG Signaling
www.aacrjournals.org Cancer Res; 78(11) June 1, 2018 2805
aKG inhibits growth, lymph node, and lung metastases of breast cancer cells in CB17-SCID mice. MDA-MB-231 cells were treated with siTKT (A) or TKT/pCMV(B). After 48 hours, their effects on aKG levels were measured by LC-MS. C, MDA-MB-231 cells were treated with or without 100 or 1000 mmol/LaKG for 18, 24, and 48 hours and cell growth was measured using MTS assay. � , P < 0.05; �� , P < 0.01; ��� , P < 0.001. For invasion (D) and migration (E) assays,MDA-MB-231 cells were treated with 1 mmol/L aKG (treatment) for 48 hours and then incubated on Boyden chamber for 8 hours. Each experimentwas repeated three times. TKT overexpression promoted cell proliferation of MDA-MB-231 (F–H) and Hs578T (Supplementary Fig. S4F–S4H). A total of1 mmol/L aKG treatment decreased the phenomenon. TKT overexpression promoted cell invasion (I) and migration (J), whereas its effects were decreased byaKG treatment. � , P < 0.05; �� , P < 0.01; ��� , P < 0.001. MDA-MB-231 cells were orthotopically injected with 1 � 106 cells per mouse into 4th mammary fatpads of CB17-SCID mice. The next day, the mice were intraperitoneally injected with aKG (10 mg/kg) or PBS control three times per week. The images of tumorcells in tumors (K) and various organs, including spleen, lung, liver, and lymph node (N) from individual mice (n ¼ 10) were monitored by BLIsignal. Representative BL1 images are shown after 3 months of continuous treatment with PBS or aKG. Tumor weight (L) and tumor volume (M)quantifications in aKG or PBS control were measured. � , P < 0.05; �� , P < 0.01; ��� , P < 0.001.
Tseng et al.
Cancer Res; 78(11) June 1, 2018 Cancer Research2806
regulated invasion and migration of tumor cells via aKGsignaling. In this study, we observed the cellular levels ofaKG were increased after the treatment of aKG (SupplementaryFig. S5A and S5B).
aKG suppresses lung metastasis of breast cancer cellsWe next assessed the effect of this metabolic pathway on
tumor growth and metastasis using a mouse model. A total of1 � 106 MDA-MB-213 cells were implanted orthotopicallyinto mammary fat pads of CB17-SCID mice (n ¼ 10). Oneday after implantation, intraperitoneal aKG (10 mg/kg)administration was started three times a week for 3 months.BLI data revealed that aKG treatment led to a significantreduction of primary tumor growth (Fig. 4K, P < 0.001). Therewere significant differences in the weights (Fig. 4L, P ¼ 0.024)and sizes (Fig. 4M, P ¼ 0.004) of primary tumors betweencontrol and the aKG–treated groups after 3 months. Individualorgan metastases were also examined, and we found that aKGtreatment significantly diminished lung and lymph nodemetastases (Fig. 4N, P < 0.05). Overall, our data for the firsttime demonstrated that TKT-mediated aKG signaling sup-pressed growth and metastases of breast cancer.
TKT regulates breast cancer metastasis via the aKGsignaling pathway
To further explore TKT-regulated downstream pathways inbreast cancer metastasis, the effects of TKT on the aKG andTCA-cycle enzymes were examined. Previous studies indicatedthat accumulation of aKG enhanced the activity of PHD andsubsequent destabilization of its downstream target HIF1a(23). To assess the relationship between TKT and HIF1a inMDA-MB-231 cells, the impact of TKT on PHD2 was investi-gated. Results revealed that downregulation of TKT enhancedPHD2 expression (Fig. 5A) and this phenomenon was alsoobserved in the aKG–treated cells (Fig. 5B). Moreover, knock-down of TKT reduced HIF1a expression (Fig. 5A), suggestingthat TKT affected HIF1a expression via the PHD2 signalingpathway. HIF1a has been reported to be associated with tumormetastasis (24) and is known to be a transcription factorregulating the expression of LDHA (25). Other studies revealedthat knockdown of LDHA inhibited breast cancer metastasis(26). Currently, the relationship between TKT and LDHA is notknown; thus, we further assess the effect of TKT on LDHAexpression. Our data showed that knockdown of TKT inhibitedLDHA expression (Fig. 5A) and this phenomenon was alsoobserved in aKG–treated cells (Fig. 5B). These results suggestedthat TKT decreased LDHA expression and promoted HIF1adegradation through the aKG signaling pathway, leading tothe inhibition of breast cancer metastasis. Our data suggest thata regulatory network of those metabolites and their corre-sponding catalyzing enzymes are involved in the regulation ofbreast cancer metastasis.
Previous study indicates that L-2HG dehydrogenase(L2HGDH) and D-2HG dehydrogenase (D2HGDH) preventoncometabolites L-2HG and D-2HG from accumulating in nor-mal cells, respectively, by converting them back to aKG (22).We have found that TKT depletion enhanced the levels ofL2HGDH and D2HGDH (Fig. 5A). Overall, these results indicatethat TKT depletion enhances L2HGDH and D2HGDH levels,resulting in the increase of aKG and PHD2 levels and therebypromoting HIF1a degradation.
TKT regulates tumor suppressors SDH and FH signalingpathways
Our data showed that knockdown of TKT decreased theexpression levels of metabolites succinate and fumarate(Fig. 5C). Previous studies indicated that the inactivationmutations in SDH and FH led to abnormal accumulation ofmetabolites succinate and fumarate in TCA cycle, which in turninhibited PHD and increased HIF1a in tumors (4, 5). Thecorrelation between TKT, SDH, and FH in breast cancer is stillunclear; thus, we investigated the effects of TKT knockdown onthe expression levels of SDH and FH. We found that knock-down of TKT increased the levels of SDH and FH (Fig. 5A),leading to decreased levels of succinate and fumarate and thusstabilizing the PHD2-regulated signaling pathway.
Previous studies reported aKG–dependent dioxygenases sig-naling pathways functioning as tumor suppressors (22). Inaddition, SDH and FH have been reported to be targets ofaKG–dependent dioxygenases, including JmjC domain-containing histone demethylase (KDMs) and DNA demethy-lases (27). These studies suggest that TKT may control tran-scriptional regulation of SDH and FH via aKG–dependentdioxygenases. To elucidate the potential underlying mecha-nism, we detected the effects of TKT depletion or aKG treat-ment on RNA levels of SDH and FH. Our results showed thatTKT depletion (Fig. 5D, P < 0.001) or aKG treatment (Fig. 5E,P < 0.01) indeed increased RNA levels of SDH and FH, suggest-ing regulation at the transcriptional level. Overall, the likelyregulatory mechanism of TKT via aKG signaling in breast cancermetastasis is depicted in Fig. 5F.
Tumor cells predominantly metabolize glucose through gly-colysis instead of oxidative phosphorylation in TCA cycle torapidly produce ATPs and nucleic acid building stones forsupporting their high rate of growth (28). The effect of TKTon metabolic activities in cancers was unclear; thus, we exam-ined the relationship among glycolysis, mitochondrial meta-bolism, and TKT signaling. The knockdown efficiency of TKT inMDA-MB-231 cells was initially estimated (Fig. 6A). TKTknockdown (Fig. 6B, P < 0.001) or aKG treatment (Fig. 6C,P < 0.001) exhibited decreased ECAR. TKT knockdown (Fig. 6D,P < 0.001) or aKG treatment (Fig. 6E, P < 0.001) elevated OCR.These results demonstrated that reduced TKT led to switch ofglucose metabolism from glycolysis to mitochondrial respira-tion via the aKG signaling pathway.
To further confirm whether knockdown of TKT drove theswitch of glucose metabolism from glycolysis to TCA cycle, weused mass spectrometry to measure expression levels of meta-bolites in glycolysis and TCA cycle. Reduction of TKT dimin-ished the levels of glycolytic metabolites including glucose-6-phosphate (G6P), pyruvate, and lactic acid, while increased theTCA-cycle metabolites including aKG and malate (Supplemen-tary Fig. S5A). We treated the cancer cells with aKG andobserved a similar result like TKT knockdown (SupplementaryFig. S5B), suggesting that reduction of TKT drove the switch ofglucose metabolism from glycolysis to mitochondrial metab-olism at least in part through the aKG signaling pathway.
To further verify this, the effect of decreased TKT on theexpression levels of metabolic enzymes in TCA cycle wasevaluated. We found that the depletion of TKT resulted in
TKT Regulates Breast Cancer Metastasis via aKG Signaling
www.aacrjournals.org Cancer Res; 78(11) June 1, 2018 2807
TKT and aKG reversely regulate glucose metabolic enzymes. Knockdown of TKT (A) or aKG treatment (B) significantly altered the expression ofTCA-cycle enzymes. C, LC-MS data showed reduction of TKT decreased the levels of succinate and fumarate. The effects of TKT knockdown (D) or aKGtreatment (E) on RNA levels of SDH and FH were measured by qPCR. GAPDH served as the internal control. ��, P < 0.01; ��� , P < 0.001. F, Model ofbreast cancer cell metastasis suppressed by downregulation of TKT via aKG and SDH and FH commonly mediated signaling pathways.
Tseng et al.
Cancer Res; 78(11) June 1, 2018 Cancer Research2808
increased expression levels of metabolic enzymes in TCA cycleincluding aconitase, aKG dehydrogenase, SDH, FH, and malatedehydrogenase (Supplementary Fig. S6A). The similar resultwas obtained in aKG–treated cells (Supplementary Fig. S6B). Incontrast, the depletion of TKT resulted in decreased levels ofglycolytic enzymes including PKM2, HK, and PFK (Supplemen-tary Fig. S6C), and the similar results were also observed inaKG–treated cells (Supplementary Fig. S6C). Taken together,these results indicate that reduced TKT leads to the alteration ofglucose metabolism by switching it from glycolytic to mito-chondrial metabolism via the elevation of metabolic enzymesin TCA cycle through the aKG signaling pathway. As tumor cellsdepend on glycolysis for their rapid growth, inhibition of TKTor addition of aKG could be used as a modality for developingcancer therapeutics not only for breast cancer including triple-negative breast cancer as shown in this study, but for othertypes of cancer as well.
Oxythiamine in combination with docetaxel and/ordoxorubicin enhances inhibitory effects of TNBC cells
Docetaxel and doxorubicin are commonly used drugs forTNBC, but their efficiencies are limited as a result of thedevelopment of drug resistance. Oxythiamine inhibits TKTand thus could lead to downregulation of glycolysis, nottargeted by the above two drugs. Thus combinatory treatmentof oxythiamine together with the two drugs may enhance thekilling effect of cancer cells. Oxythiamine, an antimetabolitethiamine analogue, induces cell apoptosis and suppressestumor cell growth in cancers by targeting TKT (29, 30).Although some studies indicate that oxythiamine can suppresstumor progression, the effects of oxythiamine in breast cancer
are unclear. In this study, we first assessed the effect ofoxythiamine on TKT activity according to previous study(12). Tumor cells were treated with 5 mmol/L oxythiaminefor 48 hours. Our results revealed that TKT activity wassignificantly reduced by oxythiamine treatment (Fig. 7A, P <0.01). In addition, we found oxythiamine treatment elevatedthe levels of aKG in MDA-MB-231 (Fig. 7B, P < 0.001) andHs578T (Fig. 7C, P < 0.001) cells as expected, suggesting thatoxythiamine suppressed tumor growth could in part throughthe aKG signaling pathway. Then, we analyzed whether oxy-thiamine treatment affected growth of breast normal cells. Theresults showed that cell viabilities of nontumorigenic humanbreast epithelial cell line H184 for 24 (P ¼ 0.16), 48 (P ¼0.08), and 72 hours (P ¼ 0.07) were not significantlydecreased by 5 mmol/L oxythiamine treatment when com-pared with those without oxythiamine treatment (Fig. 7D),indicating there was no significant side effects of oxythiaminein human breast normal cells. We observed that docetaxel ordoxorubicin treatment increased aKG levels (Fig. 7E, P <0.001). Moreover, previous studies reported that docetaxel ordoxorubicin treatment attenuated HIF1a levels (31, 32),further supporting our findings that TKT affects HIF1a expres-sion via aKG signaling. Thus, we tested the inhibitory effects ofoxythiamine in combination with docetaxel and/or doxorubi-cin on cell proliferation. We treated TNBC cell lines MDA-MB-231 (Fig. 7F–H) and Hs578T (Fig. 7I–K) with 5 mmol/Loxythiamine, 1 mmol/L docetaxel, 1 mmol/L doxorubicin andoxythiamine in combination with docetaxel and/or doxorubi-cin for 24, 48, and 72 hours. Treatment of oxythiamine hadsignificant inhibitory effects for 24 (Fig. 7F and I) and 48 hours(Fig. 7G and J) in both cell lines. Although treatment of
Figure 6.
Knockdown of TKT or aKG additionaffects glucose metabolism andmitochondrial oxygen consumption.A, Knockdown efficiency of siTKT inMDA-MB-231 cells was confirmed.Reduced TKT or aKG additiondecreased glycolytic metabolism(ECAR; P < 0.001; B and C) while itincreased OCR (D and E; P < 0.001).The ECAR and OCR values werenormalized with 7 � 104 MDA-MB-231cells per well.
TKT Regulates Breast Cancer Metastasis via aKG Signaling
www.aacrjournals.org Cancer Res; 78(11) June 1, 2018 2809
docetaxel or doxorubicin had inhibitory effects of TNBC cells,the killing effects of oxythiamine combining with docetaxel ordoxorubicin could be strengthened in TNBC cells, In addition,combining of the three drugs had maximum killing effects(>90% decrease) for 72 hours in both TNBC cell lines (Fig. 7Hand K). These findings indicate that oxythiamine couldenhance the two-drug sensitivities of TNBC cells.
DiscussionIncreasing evidence suggests that some pivotal genes, includ-
ing HIF1a, are able to regulate certain enzymes to inducemetabolic reprogramming in cancers. HIF1 has been reportedto induce glycolytic enzymes, including aldolase A, phospho-glycerate kinase 1, and pyruvate kinase (3). HIF1a regulates
Figure 7.
Oxythiamine in combination with docetaxel and/or doxorubicin enhances inhibitory effects on TNBC cell viability. A, MDA-MB-231 cells weretreated with 5 mmol/L oxythiamine (OT). After 48 hours, the effect of oxythiamine on TKT activity was measured. The effects of 5 mmol/Loxythiamine treatment on the levels of aKG for 24 hours in MDA-MB-231 (B) and Hs578T (C) cells. D, The effects of OT treatment on viabilitiesof nontumorigenic human normal breast cell line H184 for 24 hours (P ¼ 0.16), 48 (P ¼ 0.08,) and 72 hours (P ¼ 0.07) were assessed. E, Theeffects of docetaxel (Doc) or doxorubicin (Dox) on the levels of aKG were measured by LC-MS (��� , P < 0.001). The effects of oxythiamine incombination with docetaxel and/or doxorubicin on cell viabilities of MDA-MB-231 (F–H) and Hs578T (I–K) were assessed. Cell viabilities for 24 (F and I),48 (G and J), and 72 hours (H and K) were measured.
Tseng et al.
Cancer Res; 78(11) June 1, 2018 Cancer Research2810
dynamic switch from oxidative to glycolytic metabolism byactivating glucose transporters and glycolytic enzymes (33).Certain metabolic enzymes involved in glucose transport, gly-colysis and lipid metabolism are targets of HIF1a (34). In ourstudy, we found that TKT depletion promoted HIF1a degra-dation via aKG signaling. These results suggest that TKT-medi-ated signaling pathways may collaborate to regulate dynamicswitch of glucose metabolism. Xu and colleagues (11) reportedthat TKT reduced oxidative stress and played important roles inglycolysis and glutathione synthesis in hepatocellular carcino-ma (HCC) cells. TKT knockdown attenuated NADPH produc-tion and led to the increase of reactive oxygen species (ROS;ref. 11). TKT knockdown decreased glucose flux, and purinemetabolites including AMP, ADP, ATP, and GTP (11). Together,these results provide evidence that TKT may play an importantrole in metabolic reprogramming in tumors.
The emerging evidence demonstrates that several TCA cycleenzymes are tumor suppressors, such as SDH and FH, and theirgenetic defects are associated with tumorigenesis. The inacti-vation mutations in SDH and FH leads to abnormal accumu-lation of metabolites succinate and fumarate in TCA cycle, andthe subsequent inhibition of PHD and enhancement of HIF1apathways in tumors (4, 5). Here, we have demonstrated thatreduction of TKT augments levels of SDH, FH, and PHD2, butdecreased levels of HIF1a. In addition, levels of oncometabo-lites succinate and fumarate are significantly reduced by TKTknockdown, which is likely due to increased levels of SDH andFH, which in turn affects PHD2 stabilization and HIF1adegradation. HIF1a is a transcription factor regulating theexpression of LDHA (25) and its knockdown inhibits breastcancer metastasis (26). We have also noticed that knockdownof TKT decreases levels of LDHA, suggesting that reduction ofTKT resulted in decreased HIF1a and LDHA via elevated levelsof SDH and FH, leading to the inhibition of tumor metastasis.
Previous reports indicate that a glycolytic enzyme pyruvatekinase M2 (PKM2) is a transcriptional coactivator for HIF1,amplifying HIF1 activity via a positive feedback regulation, andthereby promoting cancer progression (35). To date, the under-lying mechanism of TKT-mediated regulation of PKM2 via aKGsignaling is unclear. We found that TKT depletion or aKGtreatment reduced PKM2 levels (Supplementary Fig. S6C) andpromoted HIF1a degradation. A significant positive correlationexisted between TKT and PKM2 (r ¼ 0.4635, P < 0.0001,Supplementary Fig. S6D). Patients with breast cancer (N ¼3951, P < 0.001, Supplementary Fig. S6E) including patientswith TNBC (N ¼ 255, P ¼ 0.045, Supplementary Fig. S6F) withhigher TKT and PKM2 levels had poorer recurrence-free survival(RFS) than those with lower TKT and PKM2. We also observedthat patients with breast cancer (N ¼ 3951, P < 0.001, Sup-plementary Fig. S6G) including patients with TNBC (N ¼ 255,P ¼ 0.0049, Supplementary Fig. S6H) with higher TKT, PKM2,and HIF1a levels had poorer RFS than those lower TKT, PKM2and HIF1a. On the other hand, a study indicated that p53induced tumor suppressor MDM2 E3-ubiquitin-mediated deg-radation of HIF1a (36). To date, the underlying mechanism ofTKT-mediated regulation of MDM2 via aKG signaling is notknown. We found that TKT depletion or aKG treatmentenhanced MDM2 levels (Supplementary Fig. S6I) and promot-ed HIF1a degradation. A significant negative correlation existedbetween TKT and MDM2 (r ¼ �0.2618, P < 0.0001, Supple-mentary Fig. S6J). Patients with breast cancer with higher
MDM2 levels had better RFS than those with lower MDM2(N ¼ 3951, P ¼ 0.0019, Supplementary Fig. S6K). As bothPKM2 and MDM2 could regulate HIF1a stability, our resultssuggest that aside from the TKT/aKG–mediated regulation ofPHD2 and HIF1a degradation, PKM2 and MDM2 could alsoplay a role in TKT-mediated control of HIF1a stability.
aKG functions as a cosubstrate for Fe (II)/aKG–dependentdioxygenases, including KDMs and the TET (ten-eleven trans-location) family of DNA hydroxylases (27). They catalyzehydroxylation in diverse substrates including proteins, alky-lated DNA/RNA and 5-methylcytosine (5mC) of genomic DNA(27). TET family of DNA hydroxylases catalyzes a three-stepoxidation reaction to convert 5mC to 5-carboxylcytosine(5caC) and subsequent decarboxylation of 5caC, leading toDNA demethylation (27). SDH and FH have been reported tobe the targets of aKG–dependent dioxygenases, includingKDMs and DNA demethylases (27). Our results showed thatTKT depletion or aKG treatment increased RNA levels of SDHand FH. Together, these studies suggest that TKT may controltranscription of SDH and FH via aKG–dependent dioxygenasessignaling.
TKT inhibitor oxythiamine had been reported to have antican-cer activity (29, 30). For example, oxythiamine in combinationwith sorafenib had enhanced effects onHCC cell growth by in vivoassay (11). Despite its potential therapeutic development, atpresent, the targeted therapy of TKT against TNBC cells has notbeen reported. Our results showed that the combinations ofoxythiamine with docetaxel and doxorubicin had maximuminhibitory effects in TNBC cells, suggesting combinatory drugtreatment as a novel therapy against TNBC. Our study for the firsttime revealed that oxythiamine treatment elevated the levels ofaKG in TNBC cells, suggesting that oxythiamine suppressedtumor cell growth via aKG signaling pathway. Together, it isfeasible to develop a combinatory drug treatment with the con-ventional therapeutic drugs to improve treatment benefits forTNBC.
Disclosure of Potential Conflicts of InterestNo potential conflicts of interest were disclosed.
Authors' ContributionsConception and design: L.-H. Wang, C.-W. Tseng, H.-L. Chan, andK.-J. ChangDevelopment of methodology: L.-H. Wang, C.-W. Tseng, S.-H. Chan, andH.-L. ChanAcquisition of data (provided animals, acquired and managed pati-ents, provided facilities, etc.): C.-W. Tseng, W.-H. Kuo, S.-H. Chan, andH.-L. ChanAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): L.-H. Wang, C.-W. Tseng, W.-H. Kuo, S.-H. Chan,and H.-L. ChanWriting, review, and/or revision of the manuscript: L.-H. Wang andC.-W. TsengAdministrative, technical, or material support (i.e., reporting or organiz-ing data, constructing databases): L.-H. Wang, C.-W. Tseng, S.-H. Chan,and H.-L. ChanStudy supervision: L.-H. Wang, C.-W. Tseng, and K.-J. Chang
AcknowledgmentsWe thank the Protein Chemistry Core Lab, Pathology Core Lab, and Cell
Sorter Core Lab of the National Health Research Institutes for massspectrometric analysis, H&E and IHC staining, and technical assistance ofcell cycle, respectively. This study has been supported by Ministry of Scienceand Technology (MOST), Taiwan (MOST 104-2320-B-039-055-MY3, MOST
TKT Regulates Breast Cancer Metastasis via aKG Signaling
www.aacrjournals.org Cancer Res; 78(11) June 1, 2018 2811
4. Selak MA, Armour SM, MacKenzie ED, Boulahbel H, Watson DG, Mans-field KD, et al. Succinate links TCA cycle dysfunction to oncogenesis byinhibiting HIF-alpha prolyl hydroxylase. Cancer Cell 2005;7:77–85.
5. Yang M, Soga T, Pollard PJ. Oncometabolites: linking altered metabolismwith cancer. J Clin Invest 2013;123:3652–8.
6. Ricciardelli C, Lokman NA, Cheruvu S, Tan IA, Ween MP, Pyragius CE,et al. Transketolase is upregulated in metastatic peritoneal implants andpromotes ovarian cancer cell proliferation. Clin Exp Metastasis 2015;32:441–55.
7. Chao YK, Peng TL, Chuang WY, Yeh CJ, Li YL, Lu YC, et al. Transke-tolase serves a poor prognosticator in esophageal cancer by promotingcell invasion via epithelial–mesenchymal transition. J Cancer 2016;7:1804–11.
8. Chan SH, Huang WC, Chang JW, Chang KJ, Kuo WH, Wang MY, et al.MicroRNA-149 targets GIT1 to suppress integrin signaling and breastcancer metastasis. Oncogene 2014;33:4496–507.
9. Lai TC, Chou HC, Chen YW, Lee TR, Chan HT, Shen HH, et al. Secretomicand proteomic analysis of potential breast cancer markers by two-dimen-sional differential gel electrophoresis. J Proteome Res 2010;9:1302–22.
10. Chou HC, Lu YC, Cheng CS, Chen YW, Lyu PC, Lin CW, et al. Proteomicand redox-proteomic analysis of berberine-induced cytotoxicity in breastcancer cells. J Proteomics 2012;75:3158–76.
11. Xu IM, Lai RK, Lin SH, Tse AP, Chiu DK, Koh HY, et al. Transketolasecounteracts oxidative stress to drive cancer development. ProcNatl Acad SciU S A 2016;113:E725–34.
12. Zhao Y, Wu Y, Hu H, Cai J, Ning M, Ni X, et al. Downregulation oftransketolase activity is related to inhibition of hippocampal progenitorcell proliferation induced by thiamine deficiency. BioMed Res Int 2014;2014:572915.
13. Doppler H, Storz P. Differences in metabolic programming define the siteof breast cancer cell metastasis. Cell Metab 2015;22:536–7.
14. King A, Selak MA, Gottlieb E. Succinate dehydrogenase and fumaratehydratase: linkingmitochondrial dysfunction and cancer. Oncogene 2006;25:4675–82.
15. Thirstrup K, Christensen S, Moller HA, Ritzen A, Bergstrom AL, SagerTN, et al. Endogenous 2-oxoglutarate levels impact potencies ofcompetitive HIF prolyl hydroxylase inhibitors. Pharmacol Res 2011;64:268–73.
16. Xu W, Yang H, Liu Y, Yang Y, Wang P, Kim SH, et al. Oncometabolite 2-hydroxyglutarate is a competitive inhibitor of alpha-ketoglutarate-dependent dioxygenases. Cancer Cell 2011;19:17–30.
17. Zhao S, Lin Y, Xu W, Jiang W, Zha Z, Wang P, et al. Glioma-derivedmutations in IDH1 dominantly inhibit IDH1 catalytic activity and induceHIF-1alpha. Science 2009;324:261–5.
18. MacKenzie ED, Selak MA, Tennant DA, Payne LJ, Crosby S, FrederiksenCM, et al. Cell-permeating alpha-ketoglutarate derivatives alleviate
pseudohypoxia in succinate dehydrogenase-deficient cells. Mol CellBiol 2007;27:3282–9.
19. Rzeski W, Walczak K, Juszczak M, Langner E, Pozarowski P, Kandefer-SzerszenM, et al. Alpha-ketoglutarate (AKG) inhibits proliferationof colonadenocarcinoma cells in normoxic conditions. Scand J Gastroenterol2012;47:565–71.
20. Matsumoto K, Imagawa S, Obara N, Suzuki N, Takahashi S, Nagasawa T,et al. 2-Oxoglutarate downregulates expression of vascular endothelialgrowth factor and erythropoietin through decreasing hypoxia-induciblefactor-1alpha and inhibits angiogenesis. J Cell Physiol 2006;209:333–40.
21. He W, Miao FJ, Lin DC, Schwandner RT, Wang Z, Gao J, et al. Citric acidcycle intermediates as ligands for orphan G-protein-coupled receptors.Nature 2004;429:188–93.
22. Losman JA, KaelinWG Jr.What a difference a hydroxylmakes:mutant IDH,(R)-2-hydroxyglutarate, and cancer. Genes Dev 2013;27:836–52.
23. Vatrinet R, LeoneG,De LuiseM,Girolimetti G, VidoneM,Gasparre G, et al.The alpha-ketoglutarate dehydrogenase complex in cancer metabolic plas-ticity. Cancer Metab 2017;5:3.
24. Zhao T, ZhuY,MorinibuA,KobayashiM, ShinomiyaK, Itasaka S, et al.HIF-1-mediated metabolic reprogramming reduces ROS levels and facilitatesthe metastatic colonization of cancers in lungs. Sci Rep 2014;4:3793.
25. Miao P, Sheng S, Sun X, Liu J, Huang G. Lactate dehydrogenase A in cancer:a promising target for diagnosis and therapy. IUBMB Life 2013;65:904–10.
26. Rizwan A, Serganova I, Khanin R, Karabeber H, Ni X, Thakur S, et al.Relationships between LDH-A, lactate, and metastases in 4T1 breasttumors. Clin Cancer Res 2013;19:5158–69.
27. Xiao M, Yang H, Xu W, Ma S, Lin H, Zhu H, et al. Inhibition of alpha-KG-dependent histone and DNA demethylases by fumarate and succinate thatare accumulated in mutations of FH and SDH tumor suppressors. GenesDev 2012;26:1326–38.
28. Soga T. Cancer metabolism: key players in metabolic reprogramming.Cancer Sci 2013;104:275–81.
29. Wang J, Zhang X, Ma D, Lee WP, Xiao J, Zhao Y, et al. Inhibition oftransketolase by oxythiamine altered dynamics of protein signals inpancreatic cancer cells. Exp Hematol Oncol 2013;2:18.
30. Zhao F, Mancuso A, Bui TV, Tong X, Gruber JJ, Swider CR, et al. Imatinibresistance associated with BCR-ABL upregulation is dependent on HIF-1alpha-induced metabolic reprograming. Oncogene 2010;29:2962–72.
31. OhET, KimCW,KimSJ, Lee JS,Hong SS, ParkHJ.Docetaxel induced-JNK2/PHD1 signaling pathway increases degradation of HIF-1alpha and causescancer cell death under hypoxia. Sci Rep 2016;6:27382.
32. Blagosklonny MV, An WG, Romanova LY, Trepel J, Fojo T, Neckers L. p53inhibits hypoxia-inducible factor-stimulated transcription. J Biol Chem1998;273:11995–8.
33. SemenzaGL.HIF-1: upstreamanddownstreamof cancermetabolism.CurrOpin Genet Dev 2010;20:51–6.
34. Keith B, Johnson RS, SimonMC. HIF1alpha and HIF2alpha: sibling rivalryin hypoxic tumour growth and progression. Nat Rev Cancer 2011;12:9–22.
35. Luo W, Semenza GL. Pyruvate kinase M2 regulates glucose metabolism byfunctioning as a coactivator for hypoxia-inducible factor 1 in cancer cells.Oncotarget 2011;2:551–6.
36. Ravi R, Mookerjee B, Bhujwalla ZM, Sutter CH, Artemov D, Zeng Q, et al.Regulation of tumor angiogenesis by p53-induced degradation of hypoxia-inducible factor 1alpha. Genes Dev 2000;14:34–44.
Cancer Res; 78(11) June 1, 2018 Cancer Research2812
