Transketolase Regulates the Metabolic Switch to Control Breast Cancer Cell Metastasis Via the Alpha-Ketoglutarate Signaling Pathway

Author Manuscript Published OnlineFirst on March 29, 2018; DOI: 10.1158/0008-5472.CAN-17-2906 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Transketolase Regulates the Metabolic Switch to Control Breast Cancer Cell Metastasis via the Alpha-ketoglutarate Signaling Pathway

Chien-Wei Tseng1,2, Wen-Hong Kuo3, Shih-Hsuan Chan1,2,4, Hong-Lin Chan5, King-Jen Chang6, Lu-Hai Wang*1,2

1Graduate Institute of Integrated Medicine, China Medical University, Taichung, 404, Taiwan 2Institute of Molecular and Genomic Medicine, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli Country 350, Taiwan 3Department of Surgery, National Taiwan University Hospital, Taipei 100, Taiwan 4Institute of Molecular Medicine, National Tsing Hua University, Hsinchu 300, Taiwan 5Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu 300, Taiwan 6 Department of Surgery, Taiwan Adventist Hospital, Taipei 105, Taiwan

*Corresponding author

Running title: TKT regulates breast cancer metastasis via α-KG signaling

Statement of significance: Findings uncover the clinical significance of TKT in breast cancer progression and metastasis and demonstrate effective combined therapies against TKT and α-KG.

Abbreviations list: transketolase, TKT; alpha-ketoglutarate, α-KG; succinate

dehydrogenase, SDH; fumarate hydratase, FH; HIF prolyl hydroxylase 2, PHD2;

triple negative breast cancer, TNBC; aldolase A, ALDOA; triose phosphate isomerase,

TPIS; α-enolase, ENOA; pyruvate dehydrogenase E1, ODPB; pentose phosphate

pathway, PPP; differential gel electrophoresis, DIGE; enhanced chemiluminescence,

ECL; Institutional Animal Care and Use Committee, IACUC; Oxygen consumption

rate, OCR; extracellular acidification rate, ECAR; carbonyl 1

Downloaded from cancerres.aacrjournals.org on September 29, 2021. © 2018 American Association for Cancer Research. Author Manuscript Published OnlineFirst on March 29, 2018; DOI: 10.1158/0008-5472.CAN-17-2906 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

cyanide-p-trifluoromethoxyphenylhydrazone, FCCP; 2-deoxy glucose, 2-DG;

bioluminescence imaging, BLI; vascular endothelial growth factor, VEGF;

G-protein-coupled receptor, GPCR; L-2HG dehydrogenase, L2HGDH; D-2HG

dehydrogenase, D2HGDH; JmjC domain-containing histone demethylase, KDMs;

glucose-6-phosphate, G6P; hepatocellular carcinoma, HCC; reactive oxygen species,

ROS; pyruvate kinase M2, PKM2; 5-methylcytosine, 5mC; 5-carboxylcytosine, 5caC;

docetaxel, Doc; doxorubicin, Dox; oxythiamine, OT

Address Correspondence to: Lu-Hai Wang, China Medical University, No. 91,

Hsueh-Shih Road, Taichung, 40402, Taiwan, Phone: 886-4-22057153, Fax:

886-4-22060248, E-mail: [email protected] or [email protected] .

Conflict of interest statement

The authors declare no potential conflicts of interest.

Abstract

Although metabolic reprogramming is recognized as a hallmark of tumorigenesis and

progression, little is known about metabolic enzymes and oncometabolites that

regulate breast cancer metastasis, and very few metabolic molecules have been

identified as potential therapeutic targets. In this study, the transketolase (TKT)

expression correlated with tumor size in the 4T1/BALB/c syngeneic model. In

addition, TKT expression was higher in lymph node metastases compared with

primary tumor or normal tissues of patients, and high TKT levels were associated

with poor survival. Depletion of TKT or addition of alpha-ketoglutarate (α-KG)

enhanced the levels of tumor suppressors succinate dehydrogenase (SDH) and

fumarate hydratase (FH), decreasing oncometabolites succinate and fumarate and

further stabilizing HIF prolyl hydroxylase 2 (PHD2) and decreasing HIF-1α,

ultimately suppressing breast cancer metastasis. Reduced TKT or addition of α-KG

mediated a dynamic switch of glucose metabolism from glycolysis to oxidative

phosphorylation. Various combinations of the TKT inhibitor oxythiamine, docetaxel,

and doxorubicin enhanced cell death in triple-negative breast cancer (TNBC) cells.

Furthermore, oxythiamine treatment led to increased levels of α-KG in TNBC cells.

Together, our study has identified a novel TKT-mediated α-KG signaling pathway that

regulates breast cancer oncogenesis and can be exploited as a modality for improving

therapy.

Keywords: TNBC, TKT, α-KG, metabolism, metastasis

Introduction

Breast cancer patients have five-year survival rate over 90%; however, for patients

with distant metastasis, their survival rate decreases to only about 25% because of the

lack of effective strategies against breast cancer metastasis and recurrence (1). Tumor

cells with altered metabolic program have high requirements of glucose metabolism

for rapid proliferation. Despite some studies aiming at elucidating the correlation

between aberrant metabolic behavior and tumor progression, how metabolic processes

regulate breast cancer cells growth and metastasis is not fully understood.

A number of studies show that oncogenic signaling in cancers drives metabolic

reprogramming to generate large amounts of biomass during rapid tumor growth (2).

For example, HIF-1α elevates the expression of glycolytic enzymes including aldolase

A, phosphoglycerate kinase 1, and pyruvate kinase (3). In addition, a number of

studies revealed that genetic defects in TCA cycle enzymes, such as SDH and FH,

were also associated with tumor progression (4,5).

In this study, we used proteomic approach to identify certain differentially

expressed metabolic enzymes involved in tumor progression such as aldolase A

(ALDOA), triose phosphate isomerase (TPIS), α-enolase (ENOA), transketolase

(TKT) and pyruvate dehydrogenase E1 (ODPB). Among them, TKT is a metabolic

enzyme involved in the non-oxidative branch of the pentose phosphate pathway (PPP)

and connects PPP with glycolysis. Previous studies revealed that TKT was associated

with metastasis of ovarian (6) and esophageal (7) cancers, as well as poor patient

survival (6,7). To date, no study has reported the effect of TKT-regulated metabolic

signaling on tumor metastasis in breast cancer.

In this study, we reveal clinical significance and regulatory mechanism of TKT in

progression and metastasis of breast cancer via α-KG signaling. TKT plays important

roles in regulating dynamic switch of glucose metabolism. The combined therapy

based on the new targets TKT or α-KG could be developed as an improved

therapeutic approach for TNBC.

Materials and Methods

Cell culture and transfection

The human breast cancer MDA-MB-231, Hs578T and MCF-7 cells and mouse

breast cancer 4T1 cells were from ATCC (Manassas, VA). The 4T1 is a highly

tumorigenic and invasive cell line capable of metastasizing from the primary

mammary gland tumor to liver, lung, lymph nodes and brain. The highly metastatic

cell line MDA-MB-231-IV2-3 was previously established and described (8). All cell

lines were cultured in DMEM (Invitrogen) supplemented with 10% fetal bovine

o serum (Biological Industries, Israel) at 37 C with 5% CO2. Cell lines were clear of

mycoplasma as determined by the Venor GeM kit (MB Minerva biolabs) and were

further authenticated in 2017 by Taiwan Bioresource Collection and Research Centre

(BCRC) using a short tandem repeat method. For transfection assay, cells were

transfected with 20 μM siTKT or 20 μM siRNA control or TKT/pCMV plasmid (1

μg/μL) using Lipofectamine RNAiMAX transfection reagent (Thermo Fisher

Scientific, Waltham, MA).

Protein extraction

Cell samples were lysed in lysis buffer containing 7M urea, 2M thiourea, 4% w/v

CHAPS, 10 mM Tris-HCl pH 8.3 and 1 mM EDTA. Protein lysates were extracted,

sonicated and centrifuged and the protein concentration was determined using

Coomassie Protein Assay Reagent (BioRad).

2-D DIGE, gel image analysis and protein identification by MALDI-TOF-MS

The protein profiles of tumor tissues with 0.5, 1 and 2 cm in size were analyzed

using 2-D 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 according to the previous

report (10). For protein identification, the peptide mixture was loaded onto a MALDI

plate and samples were analyzed using an Autoflex III mass spectrometer (Bruker

Daltonics) and parameters were described according to the previous report (10).

Western blotting

Cells were lysed in the lysis buffer containing 7M urea, 2M thiourea, 4% w/v

CHAPS, 10 mM Tris-HCl pH 8.3, 1 mM EDTA, phosphate and protease inhibitors

(Roche). Protein lysates were sonicated and centrifuged and the protein concentration

was determined using protein assay kit (Thermo). The defined amount of final lysates

was resolved in 8-12% SDS-polyacrylamide gels, transferred onto PVDF membrane

and probed with appropriate antibodies. Antibodies include rabbit polyclonal

anti-LDHA (GTX101416, Genetex), rabbit polyclonal anti-α-KG dehydrogenase

(clone C2C3, GTX105124, Genetex), rabbit polyclonal 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), mouse monoclonal anti-PHD2 (clone 366G/76/3,

ThermoFisher). Mouse monoclonal anti-β-actin (clone SPM161, Santa Cruz

Biotechnology) was used as the internal control and protein expression levels were

visualized with the enhanced chemiluminescence (ECL) detection kit (Pierce, Boston

Technology, Woburn, MA) and exposed to X-ray film. All experiments were repeated

three times.

Immunohistochemistry

Paraffin-embedded matched normal, primary tumor and lymph node metastatic

tissue sections of breast cancer specimens (n = 11) were provided by Dr. Wen-Hung

Kuo, National Taiwan University Hospital. Other samples were from commercial

tissue arrays (US Biomax, MD; SuperBioChips, Korea), including 19 normal, 90

tumors and 50 lymph node metastatic tissues. The slides were stained with mouse

monoclonal anti-TKT antibody (clone 7H1AA1, ab112997. abcam) using an

automatic slide stainer BenchMark XT (Ventana Medical Systems). The staining

intensities were evaluated and quantified by one pathologist (Pathology Core Lab.,

National Health Research Institutes) and 2 independent investigators. The IHC scores

of TKT for each specimen were graded as follows: no expression, weak (+); moderate

(++); strong (+++).The expression levels of TKT in tumor cells were quantified as

percentage. Paraffin-embedded sections of tumor cells with TKTL1 overexpression

(Origene, RG205218) were stained with mouse monoclonal anti-TKT antibody (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 used D’Agostino and Pearson omnibus normality test to reveal that the

quantitative results of IHC TKT expression were not Gaussian distribution (P =

0.0015). Thus, we used non-parametric Mann-Whitney test to analyze the quantitative

results.

Proliferation assay

Cell proliferation was detected using CellTiter 96 Aqueous One Solution cell

proliferation assay (Promega). Assay was performed according to manufacturer’s

protocol. 1.4 x 104 cells were cultured in a 24-well plate and incubated for different

times. CellTiter 96 Aqueous One Solution reagent was added and incubated for 1h at

37 oC. The quantity of formazan product, proportional to living cell numbers, was

measured at 490 nm using 96-well plate reader. Each experiment was performed in

triplicate and the shown data were mean ± S.D.

Cell invasion and migration assays

MDA-MB-231 and Hs578T cells were treated with 20 μM siTKT or 1 mM α-KG.

or TKT/pCMV plasmid (1 μg/μL). After 48h, these cells (1 x 105 cells) were seeded

on Boyden chamber, incubated for 8h and then stained with 0.5% crystal violet dye.

Cell invasion and migration were assayed in 8 μm Falcon Cell Culture Inserts with or

without Matrigel (BD Biosciences), respectively. All experiments were performed in

triplicate.

Soft agar colony formation assay

MDA-MB-231 or MCF-7 cells at densities of 1 x 105 cells were seeded in 6-well

plate containing top layer of 0.4% agarose and bottom layer of 0.4% agarose medium.

Treatment group was transfected with 20 μM of siTKT for 48h. After one month,

colonies were stained with p-Iodonitrotetrazolium violet (1 mg/ml) for 48h and then

counted. Data represent mean ± S.D and experiment was performed in triplicate.

Tail vein injection and orthotopic metastasis assays in mouse models (8)

To study the effects of α-KG on tumor progression, MDA-MB-231 cells (1 x 106)

re-suspended in 100 μL PBS were implanted orthotopically in 4th mammary fat pads

of eight-week-old female CB17-SCID mice. After implantation of MDA-MB-231

cells for 24h, α-KG reagent was intraperitoneally injected three times a week until 3

months. α-KG dissolved in PBS was used for injection of 10 mg/kg each time. Tumor

volume was calculated by the formula: tumor volume [cm3] = [length (cm) × width

(cm)2 × 0.5]. To study the effects of TKT knockdown on tumor metastasis,

MDA-MB-231-IV2-3 cells were treated with 20 μM siTKT. After 48h,

MDA-MB-231-IV2-3 cells (1 x 106) re-suspended in 100 μL PBS were injected per

mouse intravenously via tail veins into six to eight-week-old female CB17-SCID mice

(BioLASCO, Taiwan). Tumor growth and metastasis to individual organs were

observed using live animal BLI (Caliper IVIS system, PerkinElmer). Tumor volume

and weight were also measured at the end point. Cell metastases were quantified by

BLI signals of each mouse at the end point. Animal experiments were approved by the

Institutional Animal Care and Use Committee (IACUC).

Orthotopic injection of stable TKT knockdown cells in mouse model

MDA-MB-231 cells were respectively transfected with two independent

GFP-TKT/pCMV plasmid (Origene, NM001064, Derwood, MD, USA). After 48h,

these cells were selected by flow cytometry and transfection efficiency was confirmed

by Western blot. Stable shTKT cells were orthotopically injected at 1 x 106 cells per

mouse into 4th mammary fat pads of CB17-SCID mice (n = 7) and tumor volumes

were recorded once a week during the 70 days period. Tumor volume = 4/3∏R3, R =

[length (cm) + width (cm)]/2. Animal experiments were approved by IACUC.

TKT activity

MDA-MB-231 cells were transfected with 20 μM siTKT. After 48h, these cells

were lysed with 0.1 M Tris-HC1 buffer (pH 7.6), centrifuged and the supernatant was

collected (34). 50 μL supernatant was mixed with 200 μL reaction mixture including

14.4 mM/L ribose-5-phosphate, 190 μM/L NADH, 380 μM/L TP, > 250U/L

glycerol-3-phosphate dehydrogenase and > 6500 U/L triose phosphate isomerase (11).

Enzyme activity was detected at 340 nm. One unit of enzyme activity indicates the

amount of enzyme catalyzing the oxidation of 1 μmol o NADH per min

Metabolic assay

Oxygen consumption rate (OCR) is an indicator of mitochondrial oxidation and

extracellular acidification rate (ECAR) is an indicator of lactate production which is

equated to the glycolytic rate. OCR and ECAR were detected by XFe24 extracellular

flux analyzer (Seahorse Bioscience). MDA-MB-231 cells (7 x 103 cells) were cultured

in X24 culture plate (Seahorse Bioscience). OCR and ECAR were measured in XF

base medium (Seahorse Bioscience). OCR was analyzed over time following injection

of 1 μM oligomycin, 2 μM carbonyl cyanide-p-trifluoromethoxyphenylhydrazone

(FCCP) and 0.5 μM rotenone/antimycin. ECAR was measured over time following

injection of 10 mM glucose, 1 μM oligomycin and 50 mM 2-deoxy glucose (2-DG).

For ECAR, glucose (10 mM), oligomycin (1 μM) and 2-DG (50 mM) were used to

estimate glycolytic metabolism. Glucose treatment could increase glycolytic

metabolism in cells. 2-DG, a synthetic glucose analog, acted as a competitor for

glucose and interfered with glucose metabolism. For OCR, oligomycin (1 μM),

carbonyl cyanide-4-(trifluoromethoxy)phenylhydrazone (FCCP) (1μM) and

rotenone/antimycin (0.5 μM) were used to estimate oxidative respiration. For

mitochondrial respiration, oligomycin treatment inhibited ATP synthase in

mitochondria. FCCP, a proton ionophore in mitochondria, transported protons across

cell membranes to disrupt ATP synthesis. Finally, rotenone and antimycin were

inhibitors for electric transport chain in mitochondria.

Statistical analysis

Kaplan-Meier method (logrank test) was used to analyze survival data. Data were

presented as mean ± SD. Student’s t test was used to compare the differences between

two experimental groups and one-way ANOVA was used to compare the differences

between multiple groups using Tukey test in GraphPad. χ2 test was used to analyze

the correlation between TKT levels and clinical factors; *P < 0.05, ** P < 0.01, *** P

< 0.001. OCR and ECAR data were calculated by paired t-test.

Results

Identification of metabolic proteins potentially involved in breast cancer

progression using proteomic analysis

Using proteomic approach and examining tumors of varying sizes, we attempted to

identify differentially expressed proteins associated with breast cancer progression.

We used syngeneic orthotopic implantation of 4T1 cells in BALB/c mice, and tumors

with 0.5, 1 and 2 cm in size were collected for further proteomic analyses. The protein

profiles from the tumor with 0.5 cm in size were compared with those tumors with 1

and 2 cm in size by two dimensional protein gel analysis (Supplementary Fig.

S1A-S1C). After spot detection and quantification from the 2-D gel images, a total of

21 differentially expressed proteins (P < 0.05) with 1.5-fold changes were chosen for

further identification (Supplementary Fig. S1A-S1C) by using MALDI-TOF-MS and

MASCOT database (Supplementary Table S1). Three proteins related to glycolysis

were up-regulated in the bigger tumors; they included ALDOA, TPIS and ENOA.

Other metabolic enzymes included the up-regulated TKT involved in PPP and the

down-regulated ODPB involved in pyruvate oxidation (Supplementary Table S1,

Supplementary Fig. S1D). Tumors with 1 and 2 cm in size had 1.5 and 2 fold,

respectively, increased expression of TKT when compared with the 0.5 cm tumor

(Supplementary Table S1, Supplementary Fig. S1D).

TKT displays higher expression in metastatic lymph node tissues and breast

cancer patients with high TKT expression have poor overall survival

We analyzed TKT expression in normal and tumor tissues according to gene

expression arrays from Oncomine database (Bild data). As compared with normal

tissues, TKT displayed significantly higher expression in tumor tissues (Fig. 1A,

non-parametric Mann-Whitney test, P = 0.03). We also found that the levels of TKT in

TNBC patients were significantly higher than those in non-TNBC patients (Fig. 1B, P

< 0.001). Kaplan-Meier survival curve (logrank test) from Curtis 5-year overall

survival data showed that patients with higher TKT levels had poorer 5-year survival

than those with lower TKT levels (n = 637, Fig. 1C) (P = 0.019, Chi-square = 5.502,

HR = 1.3298). The similar result is also observed in different clinical database (n =

158, Fig. 1D) (P = 0.003, Chi-square =8.7476, HR = 2.3131), suggesting that TKT has

a prognostic potential. TNBC is the breast cancer subtype with the poorest outcome;

however, very few metabolic enzymes as prognostic indicators for TNBC patients are

known. The role of TKT in TNBC patients has not been reported, thus we further

analyzed the correlation between TKT expression levels and TNBC patients’ 5-year

overall survival. Among the 637 cases, there were a total of 106 TNBC patients. Our

analysis showed that TNBC patients with higher TKT levels had poorer 5-year overall

survival than those with lower TKT levels (n = 106, Fig. 1E) (P = 0.0006, Chi-square

=11.7166, HR = 2.3758), showing that TKT might have a prognostic potential in

TNBC patients, and it could play a role in TNBC progression. The clinicopathologic

features of TKT in breast cancer patients from Curtis data showed that TKT levels

were significantly associated with some clinical factors, including stage, age, grade,

type, TNBC and tumor size (Supplementary Table S2). We also analyzed TKT

expression in normal, primary tumor and lymph node metastatic tissues by using

immunohistochemistry. First, we checked whether TKT antibody used in the IHC

staining cross-reacted with TKTL-1. To address this, we used TKTL1/pCMV plasmid

to overexpress TKTL1 in MDA-MB-231 cells. The overexpression efficiency was

verified (Supplementary Fig. S2A). The paraffin-embedded sections of tumor cells

with TKTL1 overexpression were stained with anti-TKT or anti-TKTL1 antibody.

Our results displayed high staining intensity of TKTL1 in tumor cells overexpressing

TKTL1 using anti-TKTL1, whereas staining intensity using TKT antibody was

insignificant (Supplementary Fig. S2B). These results suggest that TKT antibody used

in the IHC staining does not cross-react with TKTL1.

The staining intensity of TKT was evaluated and quantified as no expression to

the highest expression by a pathologist and two independent investigators of our team.

As summarized in Supplementary Fig. S2C, a high percentage of normal tissues

displayed insignificant TKT intensities (60%) or low intensities of TKT (30%) when

compared with those of tumor tissues (P < 0.001). Moreover, metastatic lymph node

tissues displayed a higher percentage of high intensities of TKT (56%) when

compared with primary tumor (25%, P < 0.001). The representative staining

photographs are shown in Fig. 1F. The percentage of TKT expression in tumor cells,

not including stroma cells, from primary tumor and lymph node metastatic tissue

sections were further quantified. Metastatic lymph node tissues displayed a higher

percentage of TKT expression in tumor cells when compared with the primary tumor

(Supplementary Fig. S2D, P < 0.001). These results showed that TKT expression

levels were the highest in lymph node metastases, suggesting that a possible

correlation of TKT levels with progression of metastasis in breast cancer.

Downregulation of TKT suppresses metastatic functions and affects cell cycle

distribution

To further elucidate the functional role of TKT, we manipulated TKT expression by

siRNA depletion of TKT in MDA-MB-231 and Hs578T TNBC cells (Fig. 2A). The

downregulation of TKT in MDA-MB-231 cells resulted in significantly decreased cell

proliferation (Fig. 2B-2D). This phenomenon was also observed in Hs578T cells (Fig.

2E-2G). The inhibition by TKT knockdown in both cell lines was significantly

rescued by TKT/pCMV overexpression (Fig. 2B-2G). Cell migration and invasion

were carried out by transwell Boyden chamber assays. Downregulation of TKT led to

a significant inhibition of invasion (Fig. 2H) and migration (Fig. 2I) of MDA-MB-231

and Hs578T cells, whereas the inhibitory effects were almost completely rescued by

TKT overexpression. MDA-MB-231 cells with the inhibited TKT expression

displayed reduced ability of colony formation (Fig. 2J). TKT knockdown increased

the percentage of cells in the G2/M phase in MDA-MB-231 and Hs578T cells (Fig.

2K). Taken together, these data suggested that the depletion of TKT impaired tumor

cell growth and metastasis-related abilities.

Knockdown of TKT suppresses lung metastasis of breast cancer cells

To evaluate whether depletion of TKT suppressed cancer cell metastasis in vivo, we

used tail vein injection of the highly invasive MDA-MB-231-IV2-3 cells (1x106 cells)

in CB17-SCID mice (n=8). The highly metastatic MDA-MB-231-IV2-3 sublines

derived from the MDA-MB-231 parental line was established and described

previously (8). The MDA-MB-231-IV2-3 cells exhibited dramatically higher

invasiveness than the MDA-MB-231 parental cells in vitro and they also exhibited

more aggressive lung and lymph node metastasis in vivo (8). The data from tail vein

injection model showed that knockdown of TKT resulted in greatly decreased lung

metastasis of the MDA-MB-231-IV2-3 cells (Fig. 3A, P = 0.005, Fig. 3B, P = 0.002)

by bioluminescence imaging (BLI) as also reflected in H&E staining (Fig. 3C). These

findings indicated that knockdown of TKT inhibited lung metastasis of the highly

invasive breast cancer cells (Supplementary Fig. S3A-S3F).

To further assess whether decreased lung metastasis by the depletion of TKT

resulted from decreased targeting of the tumor cells to lung, the cells transfected with

the control or TKT siRNA were injected into CB17-SCID mice through tail vein (n=8)

and after 24h, the lung was perfused with PBS to flush out intravascular tumor cells

and subsequently the expression levels of human GAPDH reflecting the injected cells

in lung tissues were measured. BLI analysis exhibited about equivalent signals in the

lungs of siCon or siTKT transfected cells 30 mins after injection (Fig. 3D, P = 0.294).

The qPCR data confirmed the result (Fig. 3E, P = 0.222). To confirm the inhibitory

effects of transient TKT knockdown on tumor growth, two different knockdown

stable lines, MDA-MB-231-shTKT1 and MDA-MB-231-shTKT2, as well as

MDA-MB-231-shNC line were established, and each (1 x 106 cells) were implanted

orthotopically into the 4th mammary fat pad of CB17-SCID mouse (n = 7). The

knockdown efficiency of shTKT was verified (Fig. 3F) and the result showed that

tumor sizes in both TKT knockdown groups were significantly smaller than those in

the control group (Fig. 3G-3H, Supplementary Fig. S3G-S3I). These findings

indicated that knockdown of TKT did not inhibit lung targeting (Supplementary Fig.

S4A-S4D), but inhibited the subsequent lung colonization ability of the tumor cells.

Identification of TKT-regulated metabolites in breast cancer cells

Recent reports indicated the involvement of Warburg effect in tumor metastasis and

suggested that molecules participating in metabolic modulation were potential targets

for anti-metastasis therapy (12). To address TKT-regulated metabolic pathways in

breast cancer cells, we manipulated TKT expression by siRNA treatment of

MDA-MB-231 cells for 48h and then cell lysates were harvested for identifying

altered metabolites by LC/MS-MS (Waters, Massachusetts, USA). The differentially

expressed metabolites in siTKT-treated cells were identified when comparing with the

siRNA control cells. Knockdown of TKT increased some TCA cycle intermediates

including α-KG (Fig. 4A) and malate, while decreased succinate and fumurate (P <

0.05). Reports indicated that the alternation of metabolites in the TCA cycle was

associated with tumor formation (4). For example, succinate and fumarate

accumulated in the mitochondria leaked out to the cytosol because of inactivation of

the tumor suppressors SDH and FH resulting in promoting cancer formation (13). At

present, the potential role of α-KG and the relationship between TKT and α-KG in

triple negative breast cancer are still unclear. Our findings that TKT might play an

important role in metastasis and its knockdown led to increased α-kG prompted us to

further investigate the potential effect of α-KG in oncogenic behavior of cancer cells.

α-KG suppresses tumor cell growth, migration and invasion

We further found that TKT overexpression attenuated α-KG levels (Fig. 4B, P <

0.001), which was consistent with the result from TKT knockdown. The physiological

concentration of α-KG in healthy brain tissues ranges from 1 to 3 mM, whereas its

concentration is decreased to 100 to 300 μM in gliomas (14). IDH1 mutated tumor

cells exhibited decreased α-KG, leading to increased HIF-1α levels (15). The similar

results were observed in α-KG derivatives treatments in IDH1 mutated gliomas (16)

or SDH-deficient tumor cells (17). Despite its tumor suppressor role of artificial

α–KG derivative in cancers, many studies revealed that non-α-KG derivative could

attenuate cell proliferation of colon cancer (18) and reduce the levels of vascular

endothelial growth factor (VEGF) and erythropoietin through decreasing HIF-1α,

thereby inhibiting angiogenesis ability of the Hep3B hepatoma cells (19). These

findings suggested the potential tumor suppressing role of α-KG. Furthermore,

G-protein-coupled receptor (GPCR) GPR99 was reported to function as a receptor for

the TCA cycle intermediate α-KG (20). Although previous studies indicated that

α-KG-dependent dioxygenases signaling pathways functioned as tumor suppressors

(21), the regulatory role of α-KG in breast cancer is unclear. Treatment of α-KG

resulted in significantly decreased MDA-MB-231 cell growth when compared with

the control (Fig. 4C). Furthermore, treatment of α-KG led to a significant inhibition of

cell invasion (Fig. 4D) and migration (Fig. 4E). MCF-7 cells with the inhibited TKT

expression displayed reduced ability of colony formation (Supplementary Fig. S4E).

TKT overexpression promoted cell proliferation in MDA-MB-231 (Fig. 4F-4H) and

Hs578T (Supplementary Fig. S4F-S4H) cells, whereas its effect was substantially

reversed by α-KG treatment. These findings indicate that α-KG can impair

metastatic-related abilities of breast cancer cells. We further verified that the

promotion of TKT on invasion (Fig. 4I) and migration (Fig. 4J) in MDA-MB-231 and

Hs578T cells were substantially reversed by α-KG treatment, suggesting that TKT

regulated invasion and migration of tumor cells via α-KG signaling. In our study, we

observed the cellular levels of α-KG were increased after the treatment of α-KG

(Supplementary Fig. S5A-S5B).

α-KG suppresses lung metastasis of breast cancer cells

We next assessed the effect of this metabolic pathway on tumor growth and

metastasis using a mouse model. 1 x 106 MDA-MB-213 cells were implanted

orthotopically into mammary fat pads of CB17-SCID mice (n=10). One day after

implantation, intraperitoneal α-KG (10 mg/kg) administration was started three times

a week for 3 months. BLI data revealed that α-KG treatment led to a significant

reduction of primary tumor growth (Fig. 4K, P < 0.001). There were significant

differences in the weights (Fig. 4L, P = 0.024) and sizes (Fig. 4M, P = 0.004) of

primary tumors between control and the α-KG treated groups after 3 months.

Individual organ metastases were also examined, and we found that α-KG treatment

significantly diminished lung and lymph node metastases (Fig. 4N, P < 0.05). Overall,

our data for the first time demonstrated that TKT-mediated α-KG signaling suppressed

growth and metastases of breast cancer.

TKT regulates breast cancer metastasis via the α-KG signaling pathway

To further explore TKT-regulated downstream pathways in breast cancer metastasis,

the effects of TKT on the α-KG and TCA cycle enzymes were examined. Previous

studies indicated that accumulation of α-KG enhanced the activity of PHD and

subsequent destabilization of its downstream target HIF-1α (22). To assess the

relationship between TKT and HIF-1α in MDA-MB-231 cells, the impact of TKT on

PHD2 was investigated. Results revealed that downregulation of TKT enhanced

PHD2 expression (Fig. 5A) and this phenomenon was also observed in the

α-KG-treated cells (Fig. 5B). Moreover, knockdown of TKT reduced HIF-1α

expression (Fig. 5A), suggesting that TKT affected HIF-1α expression via the PHD2

signaling pathway. HIF-1α has been reported to be associated with tumor metastasis

(23) and is known to be a transcription factor regulating the expression of LDHA (24).

Other studies revealed that knockdown of LDHA inhibited breast cancer metastasis

(25). At present, the relationship between TKT and LDHA is not known, thus we

further assess the effect of TKT on LDHA expression. Our data showed that

knockdown of TKT inhibited LDHA expression (Fig. 5A) and this phenomenon was

also observed in α-KG-treated cells (Fig. 5B). These results suggested that TKT

decreased LDHA expression and promoted HIF-1α degradation through the α-KG

signaling pathway, leading to the inhibition of breast cancer metastasis. Our data

suggest that a regulatory network of those metabolites and their corresponding

catalyzing enzymes are involved in the regulation of involved in breast cancer

metastasis.

Previous study indicates that L-2HG dehydrogenase (L2HGDH) and D-2HG

dehydrogenase (D2HGDH) prevent oncometabolites L-2HG and D-2HG from

accumulating in normal cells, respectively, by converting them back to α-KG (21). We

have found that TKT depletion enhanced the levels of L2HGDH and D2HGDH (Fig.

5A). Overall, these results indicate that TKT depletion enhances L2HGDH and

D2HGDH levels, resulting in the increase of α-KG and PHD2 levels and thereby

promoting HIF-1α degradation.

TKT regulates tumor suppressors SDH and FH signaling pathway

Our data showed that knockdown of TKT decreased the expression levels of

metabolites succinate and fumarate (Fig. 5C). Previous studies indicated that the

inactivation mutations in SDH and FH led to abnormal accumulation of metabolites

succinate and fumarate in TCA cycle, which in turn inhibited PHD and induced

HIF-1α in tumors (4,5). The correlation between TKT and SDH and FH in breast

cancer is still unclear; thus, we investigated the effects of TKT knockdown on the

expression levels of SDH and FH. We found that knockdown of TKT increased the

levels of SDH and FH (Fig. 5A), leading to decreased levels of succinate and

fumarate and thus stabilizing the PHD2-regulated signaling pathway.

Previous studies report α-KG-dependent dioxygenases signaling pathways

functioned as tumor suppressors (21). Additionally, SDH and FH have been reported

to be targets of α-KG-dependent dioxygenases, including JmjC domain-containing

histone demethylase (KDMs) and DNA demethylases (26). These studies suggest that

TKT may control transcriptional regulation of SDH and FH via α-KG-dependent

dioxygenases. To elucidate the potential underlying mechanism, we detected the

effects of TKT depletion or α-KG treatment on RNA levels of SDH and FH. Our

results showed that TKT depletion (Fig. 5D, P < 0.001) or α-KG treatment (Fig. 5E, P

< 0.01) indeed increased RNA levels of SDH and FH suggesting regulation at the

transcriptional level. Overall, the regulatory mechanism of TKT via α-KG signaling in

breast cancer metastasis is depicted in Fig. 5F.

Reduced TKT or α-KG treatment regulates glucose metabolism and

mitochondrial oxygen consumption

Tumor cells predominantly metabolize glucose through glycolysis instead of

oxidative phosphorylation in TCA cycle to rapidly produce ATPs and nucleic acid

building stones for supporting their high rate of growth (27). The effect of TKT on

metabolic activities in cancers was unclear; thus, we examined the relationship among

glycolysis, mitochondrial metabolism and oncogenic TKT signaling. The knockdown

efficiency of TKT in MDA-MB-231 cells was initially estimated (Fig. 6A). TKT

knockdown (Fig. 6B, P < 0.001) or α-KG treatment (Fig. 6C, P < 0.001) exhibited

decreased ECAR. TKT knockdown (Fig. 6D, P < 0.001) or α-KG treatment (Fig. 6E,

P < 0.001) elevated OCR. These results demonstrated that reduced TKT led to switch

of glucose metabolism from glycolysis to mitochondrial respiration via the α-KG

signaling pathway.

To further confirm whether knockdown of TKT drove the switch of glucose

metabolism from glycolysis to TCA cycle, we used mass spectrometry to measure

expression levels of metabolites in glycolysis and TCA cycle. Reduction of TKT

diminished the levels of glycolytic metabolites including glucose-6-phosphate (G6P),

pyruvate and lactic acid, while increased the TCA cycle metabolites including α-KG

and malate (Supplementary Fig. S5A). We treated the cancer cells with α-KG and

observed a similar result like TKT knockdown (Supplementary Fig. S5B), suggesting

that reduction of TKT drove the switch of glucose metabolism from glycolysis to

mitochondrial metabolism at least in part through the α-KG signaling pathway.

To further verify this, the effect of decreased TKT on the expression levels of

metabolic enzymes in TCA cycle was evaluated. We found that the depletion of TKT

resulted in increased expression levels of metabolic enzymes in TCA cycle including

aconitase, α-KG dehydrogenase, SDH, FH, and malate dehydrogenase

(Supplementary Fig. S6A). The similar result was obtained in α-KG-treated cells

(Supplementary Fig. S6B). By contrast, the depletion of TKT resulted in decreased

levels of glycolytic enzymes including PKM2, HK, and PFK (Supplementary Fig.

S6C) and the similar results were also observed in α-KG-treated cells (Supplementary

Fig. S6C). Taken together, these results indicate that reduced TKT leads to the

alteration of glucose metabolism by switching from glycolytic activity to

mitochondrial metabolism via the elevation of metabolic enzymes in TCA cycle

through the α-KG signaling pathway. Since tumor cells depend on glycolysis for their

rapid growth, inhibition of TKT or addition of α-KG could be used as a modality for

developing cancer therapeutics not only for breast cancer including triple negative

breast cancer as shown in this study, but for other types of cancer as well.

Oxythiamine in combination with docetaxel and/or doxorubicin enhances

inhibitory effects of TNBC cells

Docetaxel and doxorubicin are commonly used drugs for TNBC, but their

efficiencies are limited as a result of the development of drug resistance. Oxythiamine

inhibits TKT and thus could lead to downregulation of glycolysis, not targeted by the

two drugs. Thus combinatory treatment of oxythiamine together with the two drugs

may enhance the killing effect of cancer cells. Oxythiamine, an anti-metabolite

thiamine analogue, induces cell apoptosis and suppresses tumor cell growth in cancers

by targeting TKT (28,29). Although some studies indicate that oxythiamine can

suppress tumor progression, the effects of oxythiamine in breast cancer are unclear. In

this study, we first assessed the effect of oxythiamine on TKT activity according to

previous study (11). Tumor cells were treated with 5 mM oxythiamine for 48h. Our

results revealed that TKT activity was significantly reduced by oxythiamine treatment

(Fig. 7A, P < 0.01). In addition, we found oxythiamine treatment elevated the levels

of α-KG in MDA-MB-231 (Fig. 7B, P < 0.001) and Hs578T (Fig. 7C, P < 0.001) cells

as expected, suggesting that oxythiamine suppressed tumor growth could in part

through the α-KG signaling pathway. Then we analyzed whether oxythiamine

treatment affected growth of breast normal cells. The results showed that cell

viabilities of non-tumorigenic human breast epithelial cell line H184 for 24 (P = 0.16),

48 (P = 0.08) and 72h (P = 0.07) were not significantly decreased by 5 mM

oxythiamine treatment when compared with those without oxythiamine treatment (Fig.

7D), meaning there was no significant side effects of oxythiamine in human breast

normal cells. We observed that docetaxel or doxorubicin treatment increased α-KG

levels (Fig. 7E, P < 0.001). Moreover, previous studies report that docetaxel or

doxorubicin treatment attenuates HIF-1α levels (30,31), further supporting our

findings that TKT affects HIF-1α expression via α-KG signaling. Thus, we tested the

inhibitory effects of oxythiamine in combination with docetaxel and/or doxorubicin

on cell proliferation. We treated TNBC cell lines MDA-MB-231 (Fig. 7F-7H) and

Hs578T (Fig. 7I-7K) with 5 mM oxythiamine, 1 μM docetaxel, 1 μM doxorubicin and

oxythiamine in combination with docetaxel and/or doxorubicin for 24, 48 and 72

hours. Treatment of oxythiamine had significant inhibitory effects for 24 (Fig. 7F, 7I)

and 48h (Fig. 7G, 7J) in both cell lines. Although treatment of docetaxel or

doxorubicin had inhibitory effects of TNBC cells, the killing effects of oxythiamine

combining with docetaxel or doxorubicin could be strengthened in TNBC cells, In

addition, combining of the three drugs had maximum killing effects (> 90% decrease)

for 72h in both TNBC cell lines (Fig. 7H, 7K). These findings indicate that

oxythiamine could enhance drug sensitivities of TNBC cells.

Discussion

Increasing evidence suggests that some pivotal genes, such as HIF-1α, which is

able to regulate certain enzymes to induce metabolic reprogramming in cancers.

HIF-1 has been reported to induce glycolytic enzymes, including aldolase A,

phosphoglycerate kinase 1, and pyruvate kinase (3). HIF-1α regulates dynamic switch

from oxidative to glycolytic metabolism by activating glucose transporters and

glycolytic enzymes (32). Certain metabolic enzymes involved in glucose transport,

glycolysis and lipid metabolism are targets of HIF-1α (33). In our study, we found that

TKT depletion promoted HIF-1α degradation via α-KG signaling. These results

suggest that TKT mediated signaling pathways may collaborate to regulate dynamic

switch of glucose metabolism. Xu et al (34) reported that TKT reduced oxidative

stress and played important roles in glycolysis and glutathione synthesis in

hepatocellular carcinoma (HCC) cells. TKT knockdown attenuated NADPH

production and led to the increase of reactive oxygen species (ROS) (34). TKT

knockdown decreased glucose flux, and purine metabolites including AMP, ADP, ATP

and GTP (34). Together, these results provide evidence that TKT may play an

important role in metabolic reprogramming in tumors.

The emerging evidence demonstrates that several TCA cycle enzymes are tumor

suppressors, such as SDH and FH, and their genetic defects are associated with

tumorigenesis. The inactivation mutations in SDH and FH leads to abnormal

accumulation of metabolites succinate and fumarate in TCA cycle, and the subsequent

inhibition of PHD and enhancement of HIF-1α pathways in tumors (4,5). Here, we

have demonstrated that reduction of TKT augments levels of SDH, FH and PHD2, but

decreased levels of HIF-1α. In addition, levels of oncometabolites succinate and

fumarate are significantly reduced by TKT knockdown, which is likely due to

increased levels of SDH and FH, which in turn affects PHD2 stabilization and HIF-1α

degradation. HIF-1α is a transcription factor regulating the expression of LDHA (24)

and its knockdown inhibits breast cancer metastasis (25). We have also noticed that

knockdown of TKT decreases levels of LDHA, suggesting that reduction of TKT

resulted in decreased HIF-1α and LDHA via elevated levels of SDH and FH, leading

to the inhibition of tumor metastasis.

Previous reports indicate that a glycolytic enzyme pyruvate kinase M2 (PKM2) is a

transcriptional coactivator for HIF-1, amplifying HIF-1 activity via a positive

feedback regulation, and thereby promoting cancer progression (35). To date, the

underlying mechanism of TKT-mediated regulation of PKM2 via α-KG signaling is

unclear. We found that TKT depletion or α-KG treatment reduced PKM2 levels

(Supplementary Fig. S6C) and promoted HIF-1α degradation. A significant positive

correlation existed between TKT and PKM2 (r = 0.4635, P < 0.0001, Supplementary

Fig. S6D). Breast cancer patients (N = 3951, P < 0.001, Supplementary Fig. S6E)

including TNBC patients (N = 255, P = 0.045, Supplementary Fig. S6F) with higher

TKT and PKM2 levels had poorer recurrence-free survival (RFS) than those with

lower TKT and PKM2. We also observed that breast cancer patients (N = 3951, P <

0.001, Supplementary Fig. S6G) including TNBC patients (N = 255, P = 0.0049,

Supplementary Fig. S6H) with higher TKT, PKM2 and HIF-1α levels had poorer RFS

than those lower TKT, PKM2 and HIF-1α. On the other hand, a study indicated that

p53 induced tumor suppressor MDM2 E3-ubiuitin-mediated degradation of HIF-1α

(36). To date, the underlying mechanism of TKT-mediated regulation of MDM2 via

α-KG signaling is not known. We found that TKT depletion or α-KG treatment

enhanced MDM2 levels (Supplementary Fig. S6I) and promoted HIF-1α degradation.

A significant negative correlation existed between TKT and MDM2 (r = -0.2618, P <

0.0001, Supplementary Fig. S6J). Breast cancer patients with higher MDM2 levels

had better RFS than those with lower MDM2 (N = 3951, P = 0.0019, Supplementary

Fig. S6K). Since both PKM2 and MDM2 could regulate HIF-1α stability, our results

suggest aside from the TKT/α-KG–mediated regulation of PHD2 and HIF-1α

degradation, PKM2 and MDM2 could also play a role in TKT-mediate control of

HIF-1α stability.

α-KG functions as a co-substrate for Fe (II)/ α-KG-dependent dioxygenases,

including KDMs and the TET (ten-eleven translocation) family of DNA hydroxylases

(26). They catalyze hydroxylation in diverse substrates including proteins, alkylated

DNA/RNA and 5-methylcytosine (5mC) of genomic DNA (26). TET family of DNA

hydroxylases catalyzes a three-step oxidation reaction to convert 5mC to

5-carboxylcytosine (5caC), and subsequent decarboxylation of. 5caC leading to DNA

demethylation (26). Succinate dehydrogenase (SDH) and fumarate hydratase (FH)

have been reported to be the targets of α-KG-dependent dioxygenases, including

KDMs and DNA demethylases (26). Our results showed that TKT depletion or α-KG

treatment increased RNA levels of SDH and FH. Together, these studies suggest that

TKT may control transcription of SDH and FH via α-KG-dependent dioxygenases

signaling.

TKT inhibitor oxythiamine had been reported to have anti-cancer activity (28,29).

For example, Oxythiamine in combination with Sorafenib had enhanced effects on

HCC cell growth by in vivo assay (34). Despite its potential therapeutic development,

at present, the targeted therapy of TKT against TNBC cells has not been reported. Our

results showed that the combinations of oxythiamine with docetaxel and doxorubicin

had maximum inhibitory effects in TNBC cells, suggesting the combining drugs as a

novel therapy against TNBC. Our study for the first time revealed that oxythiamine

treatment elevated the levels of α-KG in TNBC cells, meaning that oxythiamine

suppressed tumor cell growth via α-KG signaling pathway. Together, it is important to

develop effective targeted therapy in combination with the conventional therapeutic

drugs to maximize therapeutic benefits for TNBC.

Acknowledgments

We thank the Protein Chemistry Core Lab, Pathology Core Lab and Cell Sorter

Core Lab of the National Health Research Institutes for mass spectrometric analysis,

H&E and IHC staining and technical assistance of cell cycle, respectively. All authors

received Ministry of Science and Technology (MOST), Taiwan (MOST

104-2320-B-039-055-MY3, MOST 104-2320-B-039-054-MY3, MOST

106-2811-B-039-004) and National Health Research Institutes (NHRI) (NHRI

06A1-MGPP09-014) grants.

References

1. Ci Y, Qiao J, Han M. Molecular Mechanisms and Metabolomics of Natural

Polyphenols Interfering with Breast Cancer Metastasis. Molecules 2016;21,pii:

E1634.

2. Schulze A, Harris AL. How cancer metabolism is tuned for proliferation and

vulnerable to disruption. Nature 2012;491:364-73.

3. Semenza GL, Roth PH, Fang HM, Wang GL. Transcriptional regulation of

genes encoding glycolytic enzymes by hypoxia-inducible factor 1. J Biol

Chem 1994;269:23757-63.

4. Selak MA, Armour SM, MacKenzie ED, Boulahbel H, Watson DG, Mansfield

KD, et al. Succinate links TCA cycle dysfunction to oncogenesis by inhibiting

HIF-alpha prolyl hydroxylase. Cancer Cell 2005;7:77-85.

5. Yang M, Soga T, Pollard PJ. Oncometabolites: linking altered metabolism

with 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 and promotes

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. Transketolase

Serves a Poor Prognosticator in Esophageal Cancer by Promoting Cell

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 breast cancer

metastasis. Oncogene 2014;33:4496-507.

9. Lai TC, Chou HC, Chen YW, Lee TR, Chan HT, Shen HH, et al. Secretomic

and proteomic analysis of potential breast cancer markers by two-dimensional

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. Proteomic

and redox-proteomic analysis of berberine-induced cytotoxicity in breast

cancer cells. J Proteomics 2012;75:3158-76.

11. Zhao Y, Wu Y, Hu H, Cai J, Ning M, Ni X, et al. Downregulation of

transketolase activity is related to inhibition of hippocampal progenitor cell

proliferation induced by thiamine deficiency. BioMed Res Int

2014;2014:572915.

12. Doppler H, Storz P. Differences in Metabolic Programming Define the Site of

Breast Cancer Cell Metastasis. Cell Metab 2015;22:536-7.

13. King A, Selak MA, Gottlieb E. Succinate dehydrogenase and fumarate

hydratase: linking mitochondrial dysfunction and cancer. Oncogene

2006;25:4675-82.

14. Thirstrup K, Christensen S, Moller HA, Ritzen A, Bergstrom AL, Sager TN, et

al. Endogenous 2-oxoglutarate levels impact potencies of competitive HIF

prolyl hydroxylase inhibitors. Pharmacol Res 2011;64:268-73.

15. 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.

16. Zhao S, Lin Y, Xu W, Jiang W, Zha Z, Wang P, et al. Glioma-derived

mutations in IDH1 dominantly inhibit IDH1 catalytic activity and induce

HIF-1alpha. Science 2009;324:261-5.

17. MacKenzie ED, Selak MA, Tennant DA, Payne LJ, Crosby S, Frederiksen CM,

et al. Cell-permeating alpha-ketoglutarate derivatives alleviate pseudohypoxia

in succinate dehydrogenase-deficient cells. Mol Cell Biol 2007;27:3282-9.

18. Rzeski W, Walczak K, Juszczak M, Langner E, Pozarowski P,

Kandefer-Szerszen M, et al. Alpha-ketoglutarate (AKG) inhibits proliferation

of colon adenocarcinoma cells in normoxic conditions. Scand J Gastroenterol

2012;47:565-71.

19. Matsumoto K, Imagawa S, Obara N, Suzuki N, Takahashi S, Nagasawa T, et

al. 2-Oxoglutarate downregulates expression of vascular endothelial growth

factor and erythropoietin through decreasing hypoxia-inducible factor-1alpha

and inhibits angiogenesis. J Cell Physiol 2006;209:333-40.

20. He W, Miao FJ, Lin DC, Schwandner RT, Wang Z, Gao J, et al. Citric acid

cycle intermediates as ligands for orphan G-protein-coupled receptors. Nature

2004;429:188-93.

21. Losman JA, Kaelin WG, Jr. What a difference a hydroxyl makes: mutant IDH,

(R)-2-hydroxyglutarate, and cancer. Genes Dev 2013;27:836-52.

22. Vatrinet R, Leone G, De Luise M, Girolimetti G, Vidone M, Gasparre G, et al.

The alpha-ketoglutarate dehydrogenase complex in cancer metabolic plasticity.

Cancer Metab 2017;5:3.

23. Zhao T, Zhu Y, Morinibu A, Kobayashi M, Shinomiya K, Itasaka S, et al.

HIF-1-mediated metabolic reprogramming reduces ROS levels and facilitates

the metastatic colonization of cancers in lungs. Sci Rep 2014;4:3793

24. 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.

25. Rizwan A, Serganova I, Khanin R, Karabeber H, Ni X, Thakur S, et al.

Relationships between LDH-A, lactate, and metastases in 4T1 breast tumors.

Clin Cancer Res 2013;19:5158-69.

26. 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 that are accumulated in mutations of FH and SDH tumor suppressors.

Genes Dev 2012;26:1326-38.

27. Soga T. Cancer metabolism: key players in metabolic reprogramming. Cancer

Sci 2013;104:275-81.

28. Wang J, Zhang X, Ma D, Lee WP, Xiao J, Zhao Y, et al. Inhibition of

transketolase by oxythiamine altered dynamics of protein signals in pancreatic

cancer cells. Exp Hematol Oncol 2013;2:18.

29. Zhao F, Mancuso A, Bui TV, Tong X, Gruber JJ, Swider CR, et al. Imatinib

resistance associated with BCR-ABL upregulation is dependent on

HIF-1alpha-induced metabolic reprograming. Oncogene 2010;29:2962-72.

30. Oh ET, Kim CW, Kim SJ, Lee JS, Hong SS, Park HJ. Docetaxel

induced-JNK2/PHD1 signaling pathway increases degradation of HIF-1alpha

and causes cancer cell death under hypoxia. Sci Rep 2016;6:27382.

31. Blagosklonny MV, An WG, Romanova LY, Trepel J, Fojo T, Neckers L. p53

inhibits hypoxia-inducible factor-stimulated transcription. J Biol Chem

1998;273:11995-8.

32. Semenza GL. HIF-1: upstream and downstream of cancer metabolism. Curr

Opin Genet Dev 2010;20:51-6.

33. Keith B, Johnson RS, Simon MC. HIF1alpha and HIF2alpha: sibling rivalry in

hypoxic tumour growth and progression. Nat Rev Cancer 2011;12:9-22.

34. Xu IM, Lai RK, Lin SH, Tse AP, Chiu DK, Koh HY, et al. Transketolase

counteracts oxidative stress to drive cancer development. Proc Natl Acad Sci

USA 2016;113:E725-34.

35. Luo W, Semenza GL. Pyruvate kinase M2 regulates glucose metabolism by

functioning 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.

Figure Legends

Figure 1 Clinical significance of TKT in TNBC patients.

(A) The expression levels of TKT in tumor (n= 40) and normal (n= 7) tissues were

analyzed according to gene expression arrays in Oncomine database (non-parametric

Mann-Whitney test, P = 0.03). (B) The levels of TKT in non-TNBC (n = 1725) and

TNBC (n = 250) patients from Curtis data were compared (***P < 0.001). (C)

Kaplan-Meier curve for TKT expression in association with 5-year survival of 637

breast cancer patients. Patients were divided into high (blue line) and low (red line)

TKT expression groups based on the mean + SD levels among the patients analyzed

(log-rank test, P = 0.019). (D) Kaplan-Meier curve for TKT expression in association

with overall survival (n = 158). Patients were divided into high (blue line) and low

(red line) TKT expression groups based on the median levels among the patients

analyzed (log-rank test, P = 0.003). (E) Kaplan-Meier curve for TKT expression in

association with 5-year survival of 106 TNBC patients among the 637 breast cancer

patients. Patients were divided into high (blue line) and low (red line) TKT expression

groups based on the mean + SD levels among the patients analyzed (log-rank test, P =

0.0006). (F) Representative pictures of TKT IHC from normal, primary tumor and

lymph node metastatic tissues (scale bar: 1 mm, Supplementary Fig. S2 shows

quantitative results).

Figure 2 Downregulation of TKT suppresses growth, invasion/migration and

colony formation and affects cell cycle distribution of breast cancer cells.

(A) 20 μM siTKT reduced TKT expression in MDA-MB-231 and Hs578T cells,

whereas its inhibitory effects were rescued by TKT/pCMV overexpression (1 μg/μL).

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/pCMV

co-treatment for 24, 48 and 72h (*P < 0.05, **P < 0.01, ***P < 0.001). For invasion

(H) and migration (I) assays, MDA-MB-231 and Hs578T cells were treated with

siTKT or siTKT and TKT/pCMV co-treatment for 48h (***P < 0.001) and then

incubated on Boyden chamber for 8h. (J) For colony assay, 1 x 105 cells

MDA-MB-231 or MCF-7 (Supplementary Fig. S4E) cells were transfected with

siTKT. (K) MDA-MB-231 and Hs578T cells were transfected with siTKT. 48h later,

tumor cells were harvested for analysis of cell cycle distribution after PI staining. The

percentage of cells was quantified by FlowJo 7.6 (*P < 0.05, ***P < 0.001).

Figure 3 Knockdown of TKT does not inhibit early targeting to lung but

suppresses lung metastasis of breast cancer cells.

(A) MDA-MB-231-IV2-3 cells (1 x 106 cells) were transfected with 20 μM of siCon

or siTKT. After 48h, 1 x 106 cells per mouse were injected intravenously into

CB17-SCID mice via tail veins (n = 8). Lung metastases as reflected by amount of

cancer cells in lung in vivo (A) and ex vivo (B) were quantified using BLI signal (n=8).

Supplementary Fig. S3A-S3F. Scale bar: 1mm. T represents tumor cells in the lung.

(D) MDA-MB-231-IV2-3 cells (1 x 106 cells) transiently transfected with siCon or

siTKT were injected at 1 x 106 cells per mouse into CB17-SCID mice via tail veins

(n=8). BLI images showed lung metastasis of tumor cells in siCon and siTKT-treated

mice 30 mins after injection (P = 0.294). (E) 24h after injection, the mice were

perfused with PBS to rid of blood and lung tissues were harvested. Specific qPCR

primers for human GAPDH were used to detect injected cells in lung tissues and

mouse actin mRNA was used as the internal control (P = 0.222). (F) Knockdown

efficiency of shTKT in the two independent stable lines, MDA-MB231-shTKT-1 and

MDA-MB231-shTKT-2 was confirmed when compared with the control group (stable

MDA-MB-231-shNC cells). (G) Stable shTKT cells were orthotopically injected at 1

x 106 cells per mouse into 4th mammary fat pads of CB17-SCID mice (n = 7) and

tumor volumes (H) were recorded once a week during the 70 days period (***P <

0.001).

Figure 4 α-KG 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 48h, their

effects on α-KG levels were measured by LC-MS. (C) MDA-MB-231 cells were

treated with or without 100 or 1000 μM α-KG for 18, 24 and 48h 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 mM α-KG

(treatment) for 48h and then incubated on Boyden chamber for 8h. Each experiment

was repeated three times. TKT overexpression promoted cell proliferation of

MDA-MB-231 (F-H) and Hs578T (Supplementary Fig. S4F-S4H). 1 mM α-KG

treatment decreased the phenomenon. TKT overexpression promoted cell invasion (I)

and migration (J), whereas its effects were decreased by α-KG treatment (*P < 0.05,

**P < 0.01, ***P < 0.001). MDA-MB-231 cells were orthotopically injected at 1 x

106 cells per mouse into 4th mammary fat pads of CB17-SCID mice. Starting the next

day, the mice were intraperitoneal injected with α-KG (10 mg/kg) or PBS control

three times per week. The images of tumor cells in tumors (K) and various organs,

including spleen, lung, liver and lymph node (N) from individual mice (n=10) were

monitored by BLI signal. Representative BL1 images were shown after 3 months of

continuous treatment with PBS or α-KG. Tumor weight (L) and tumor volume (M)

quantification in α-KG or PBS control were measured (*P < 0.05,**P < 0.01, ***P <

0.001).

Figure 5 TKT and α-KG reversely regulate glucose metabolic enzymes.

Knockdown of TKT (A) or α-KG treatment (B) significantly altered the expression of

TCA cycle enzymes. (C) LC-MS data showed reduction of TKT decreased the levels

of succinate and fumarate. The effects of TKT knockdown (D) or α-KG treatment (E)

on RNA levels of SDH and FH were measured by qPCR. GAPDH was served as the

internal control (**P < 0.01, ***P < 0.001). (F) Model of breast cancer cell metastasis

suppressed by downregulation of TKT via α-KG and SDH and FH commonly

mediated signaling pathways.

Figure 6 Knockdown of TKT or α-KG addition affects glucose metabolism and

mitochondrial oxygen consumption.

(A) Knockdown efficiency of siTKT in MDA-MB-231 cells was confirmed. Reduced

TKT or α-KG addition decreased glycolytic metabolism (ECAR) (B, C, P < 0.001)

while increased oxygen consumption rate (OCR) (D, E, P < 0.001). The ECAR and

OCR values were normalized with 7 x 104 MDA-MB-231cells per well.

Figure 7 Oxythiamine in combination with docetaxel and/or doxorubicin

enhances inhibitory effects on TNBC cell viability.

(A) MDA-MB-231 cells were treated with 5 mM oxythiamine. After 48h, the effect of

oxythiamine on TKT activity was measured. The effects of 5 mM oxythiamine (OT)

treatment on the levels of α-KG for 24h in MDA-MB-231 (B) and Hs578T (C) cells.

(D) The effects of OT treatment on viabilities of non-tumorigenic human normal

breast cell line H184 for 24h (P = 0.16), 48h (P = 0.08) and 72h (P = 0.07) were

assessed. (E) The effects of docetaxel (Doc) or doxorubicin (Dox) on the levels of

α-KG were measured by LC-MS (***P < 0.001). The effects of OT in combination

with Doc and/or Dox on cell viabilities of MDA-MB-231 (F-H) and Hs578T (I-K)

were assessed. Cell viabilities for 24h (F, I), 48h (G, J) and 72h (H, K) were measured.

Transketolase regulates the metabolic switch to control breast cancer cell metastasis via the alpha-ketoglutarate signaling pathway

Chien-Wei Tseng, Wen-Hung Kuo, Shih-Hsuan Chan, et al.

Cancer Res Published OnlineFirst March 29, 2018.

Updated version Access the most recent version of this article at: doi:10.1158/0008-5472.CAN-17-2906

Supplementary Access the most recent supplemental material at: Material http://cancerres.aacrjournals.org/content/suppl/2018/03/29/0008-5472.CAN-17-2906.DC1

Author Author manuscripts have been peer reviewed and accepted for publication but have not yet Manuscript been edited.

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