respiratory capacity and reserve predict cell ......based on markers that predict mechanism-based...

Cancer Biology and Translational Studies

Respiratory Capacity and Reserve Predict CellSensitivity toMitochondria Inhibitors:Mechanism-Based Markers to Identify Metformin-ResponsiveCancersJing Tsong Teh1,Wan Long Zhu1, Christopher B. Newgard2,3,4, Patrick J. Casey1,4,and Mei Wang1,5

Abstract

Metformin has been extensively studied for its impact oncancer cell metabolism and anticancer potential. Despiteevidence of significant reduction in cancer occurrence indiabetic patients taking metformin, phase II cancer trials ofthe agent have been disappointing, quite possibly because ofthe lack of molecular mechanism-based patient stratification.In an effort to identify cancers that are responsive to metfor-min,wediscovered thatmitochondria respiratory capacity andrespiratory reserve, which vary widely among cancer cells,

correlate strongly to metformin sensitivity in both the in vitroand in vivo settings. A causal relationship between respiratoryfunction andmetformin sensitivity is demonstrated in studiesin which we lowered respiratory capacity by either geneticknockdown or pharmacologic suppression of electron trans-port chain components, rendering cancer cells more vulner-able to metformin. These findings led us to predict, andexperimentally validate, that metformin and AMP kinaseinhibition synergistically suppress cancer cell proliferation.

IntroductionMost cancer cells have adapted to a high rate of glycolysis

independent of hypoxia—a phenomenon known as aerobicglycolysis or the Warburg effect (1, 2). Despite the early postu-lation that mitochondrial dysfunction accounts for aerobic gly-colysis, studies revealed that most cancer cells retained a consid-erable level of mitochondrial respiration. Like normal cells andtissues, the magnitude of respiration in cancers varies betweentumors (3–5). Aerobic glycolysis prevents complete oxidation ofmetabolic fuels, allowing the accumulation of intermediatesimportant for cancer growth and proliferation (6, 7). Equallyimportant for cancer cells is the generation of high-energy mole-cules such as ATP that are needed for biosynthesis, cell growth anddivision, and cell movement, all of which are highly upregulatedin malignant cells.

There are several key measurements of mitochondrial respi-ration, including basal respiration and maximum respiration/

respiratory capacity (RC), which can be assessed by oximetryanalysis. Respiratory reserve (RR), the difference between therespiratory capacity and basal respiration, is the "spare" respi-ratory capacity that is important for cellular responses tostress (8). Under normal/unstressed conditions, the cell oper-ates at a basal respiration level that is only a fraction of itsmitochondrial respiratory capacity; hence, there is RR. Undersituations of increasing demand, basal respiration rises at theexpense of RR to meet energy need. Therefore, the level of RR ispostulated to be proportional to the capacity of cells to surviveperiods of high-energy expenditure, such as when during celldivision/proliferation and cell migration. Indeed, past studieshave demonstrated not only that inhibiting electron transportchain function induces cell death (9), but also that increasingRR enables cells to resist cell death (8). When cell respirationneeds exceed the maximum capacity, i.e., RR reaches zero,mitochondrial energy production fails to meet the minimalneeds of the cell, cell growth and proliferation are inhibited,and cell death ultimately ensues.

There are a number of reasons why cancer cells likely havelower RR than their normal counterparts. First, a high consti-tutive level of energy-consuming activities, such as proliferationand migration, dictates that cancer cells have higher basalrespiration. Indeed, a recent study demonstrated that, at leastin some epithelial cancers, mitochondria numbers and activ-ities are elevated compared with surrounding normal cells (10).Second, cancer cells amass metabolic intermediates by elevat-ing glycolysis at the expense of oxidative respiration. This leadsto the hypothesis that cancer cells are more vulnerable meta-bolically, in comparison with their normal counterparts, toinhibition of mitochondria respiration. In this regard, severaldrugs targeting the mitochondrial electron transport chain havebeen evaluated as anticancer agents (11, 12). Among these,

1Program inCancer andStemCell Biology, Duke-NUSMedical School, Singapore.2Sarah W. Stedman Nutrition and Metabolism Center and Duke MolecularPhysiology Institute, Durham, North Carolina. 3Department of Medicine, Divisionof Endocrinology, Duke University Medical Center, Durham, North Carolina.4Department of Pharmacology and Cancer Biology, Duke University MedicalCenter, Durham, North Carolina. 5Department of Biochemistry, National Uni-versity of Singapore, Singapore.

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

Corresponding Author: Mei Wang, Duke-NUS Medical School, 8 College Road,Singapore 169857. Phone: 65-6516-8608; E-mail: [email protected]

doi: 10.1158/1535-7163.MCT-18-0766

�2019 American Association for Cancer Research.

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extensive attention has been paid to the widely prescribedantidiabetic drug metformin. Although the molecular mecha-nism(s) behind metformin's anticancer effect need to be furtherclarified, recent studies have provided evidence that metforminreduces tumorigenesis via its action to inhibit mitochondrialcomplex I activity (13–16).

Repurposing metformin for cancer treatment has attractedmuch attention, leading to extensive evaluation of data in treateddiabetic patients (17–19). These studies have shown convincingassociations between diabetes treatment with metformin and areduced risk of developing cancer, in comparison with othertreatment regimens (20–22). Indeed, two meta-analyses con-ducted in 2010 and 2014 showed that cancer incidence andcancer mortality in patients taking metformin were significantlyreduced by 31% and 34%, respectively (23, 24). In addition,many in vitro studies have demonstrated the inhibitory effects ofmetformin on cancer cells (25, 26).

Based on these findings, a number of prospective cancer trialshave been initiated, and some of these trials have completed.To date, the outcomes have not allowed a clear conclusion.Looking for clues to account for the discrepancy betweenpopulation studies and clinical trials of metformin treatment,we examined patient selection criteria in the completedNIH-NCI phase II trials. The cancer-related inclusion criteriaconsisted mostly of surgery status and disease stage; the onlymolecular identifier was HER2 status in one of the trials. Thisapproach is different from that of recent cancer trials fortargeted therapy, which require patient stratification strategiesbased on markers that predict mechanism-based vulnerabilityto the trial compound. Hence, stratification of cancers based onmolecular markers that predict sensitivity to metformin couldenhance outcomes in drug-treated patients.

To this end, we have studied a large panel of cancer cells andfound that their sensitivities to metformin strongly correlate withtheirmitochondrial RC and RR, independent of tissue origin. Thisfinding supports the notion that metformin inhibition of ETCfunction is central to its anticancer effect (13, 16). Further inves-tigation demonstrates a causal relationship between RC/RR ofcancer cells and tumor tissues and their responsiveness to met-formin, in both in vitro and in vivo studies. This newunderstandingof metformin mechanism of action led us to identify an effectivecombination of an ETC inhibitor with an AMP-activated proteinkinase (AMPK) inhibitor that exhibited synergistic therapeuticefficacy.

Materials and MethodsHuman cell lines and culture

Cancer cell lines H1299, H460, PC9, H522, HPAFII, PANC1,MiaPaca2, MCF7, MDAMB231, MDAMB436, Colo205,HCT116, HCT15, HT29, HepG2, Huh7, SK-Mel28, SK-Mel2,UACC62, M14, Sn12C, and OVCAR-8 were obtained fromATCC. Benign human prostate epithelial cell lines RWPE-1 waspurchased from ATCC. hTERT immortalized BJ fibroblast cellline was a gift from Dr. P. Mathijs Voorhoeve (27). Cell lineswere evaluated for Mycoplasma at the time of acquisition. Celllines were frozen and stored as low passage store in multiplealiquots in liquid nitrogen. Experiments were performed within8 passages with each subsequent thaw. Cells were cultured inRPMI or DMEM (Sigma-Aldrich) supplemented with 10% fetalbovine serum (FBS; HyClone), 50 units/mL penicillin and

50 mg/mL streptomycin (Gibco), at 37�C with 5% CO2. How-ever, in all treatments, RPMI or DMEM used was supplementedwith 5% FBS instead.

Immunoblotting analysisCell proteins were extracted using RIPA buffer (Cell Signaling)

supplemented with protease inhibitor and phosphatase inhibitor(Sigma-Aldrich). Around 5 to 10 mg protein was loaded onto eachlane of polyacrylamide (Bio-Rad) gel for SDS-PAGE. Proteinswere transferred to PVDFmembranes (Bio-Rad), which were thenblocked with 5% nonfat milk in TBS-T at room temperature for1 hour, and incubated with primary antibodies at 4�C overnight.Antibodies for phospho-AMPKa (Thr172), phospho-ACC(Ser79), and GAPDH were all from Cell Signaling Technologyand used at 1: 1,000 dilution. Membranes were washed for 5minutes, 4 times with TBS-T before and after secondary antibody(1: 5,000 dilution) blotting for 1 hour at room temperature.Protein bandswere visualizedwith enhanced chemiluminescence(Thermo Scientific).

Long-term two-dimensional proliferation assayBJ cells were seeded at low density (6,000 cells/well) in a

24-well plate. Cells were allowed to grow for 1 week with thedrug—metformin (SantaCruz) or antimycin (Sigma-Aldrich)—atthe concentrations indicated in the appropriate figure legend;media were changed every 2 to 3 days. Cell viability was thenassessed using MTS CellTiter 96 Aqueous One Solution cellproliferation assay (Promega).

Soft-agar clonogenic assayA bottom layer of 0.5% noble agar (Sigma-Aldrich) in RPMI

or DMEM supplemented with 5% FBS and different concen-trations of drugs was placed in each well of a 24-well cultureplate. Cancer cells were suspended in mixture of 0.25% nobleagar in DMEM supplemented with 5% FBS and the concentra-tions of drug(s) as stated in the respective figure legends. Eachcell line was seeded at a different density, which was based onthe intrinsic colony growth rate of the cell lines. These mixtureswere placed on top of the bottom noble agar layers. Then, toeach well, DMEM with 5% FBS media was added, whichcontained drugs including metformin (Santa Cruz), antimycinA (PubChem CID: 14957, Sigma-Aldrich), rotenone (Pub-Chem CID: 6758, Sigma-Aldrich) or compound C (PubChemCID: 49761481, Santa Cruz) as stated in the respective figurelegends. The drug-containing culture media were changedevery 3 days. The colonies formed were stained by adding50 mL of 5 mg/mL MTT (Sigma-Aldrich) into each well at weeks3–4 after seeding. OpenCFU software (28) was used for colonyquantitation.

Quantitative real-time PCRTotal RNA was extracted from cells using the RNeasy Mini Kit

(Qiagen), followed by cDNA synthesis by reverse transcriptionreaction using Superscript First-strand Synthesis System (Invi-trogen). Quantitative real-time PCR was then carried out withSybr Green (Thermo Scientific) using the iCyler iQ5 Real-timeDetection (Bio-Rad). The primers for UQCRFS1 were synthe-sized based on a previous publication (29); forward primer: 50-GGAAATTGAGCAGGAAGCTG-30, and reverse primer: 50-GGCAAGGGCAGTAATAACCA-30. Primers targeting the othergenes were designed in-house; they were NDUFAF7 forward:

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50-AGCAGAAGCCTTCATACAACATGAC-30, NDUFAF7 reverse:50-GTCGCAAAACCCTCTGAA GGTATCT-30; KRAS forward: 50-GCAAGAGTGCCTTGACGATAC-3, KRAS reverse: 50-TCC AA-GAGACAGGTTTCTCCA-30; p53 forward: 50-GAGGTTGGCTCT-GACTGTACC-30, p53 reverse: 50-TCCGTCCCAGTAGATTAC-CAC-30. Quantitative analysis was done by normalizing mRNAlevels of these genes to that of 18S ribosome. Sequence of 18Sforward primer was 50-AAGTTCGACCGTCTTCTCAGC-30 andthe reverse primer sequence was 50-GTTGATTAA GTCCCTGC-CCTTTG-30.

Glycolytic stress test assaysCells were seeded, cultured, and treated with drugs in XF24

cell culture plates as detailed in the respective figure legends.One hour before performing the extracellular acidification rate(ECAR) assays, media were replaced by XF assay medium(Seahorse Bioscience) and incubated at 37�C in a CO2-freeenvironment. The subsequent glycolytic stress test assay(ECAR) was performed per XF24 analyzer standard protocol(Seahorse Bioscience). Glucose and oligomycin were purchasedfrom Sigma, although 2-deoxy-D-glucose were from SantaCruz. ECAR were measured using the XF24 analyzer (SeahorseBioscience).

Oximetry for cell linesOne or two million cells were assayed in 2 mL MiR05 buffer

(3 mmol/L MgCl2, 0.5 mmol/L EGTA, 20 mmol/L taurine, 10mmol/L KH2PO4, 60 mmol/L K-lactobionate, 110 mmol/Lsucrose, 20 mmol/L HEPES, and 1 g/L bovine serum albumin)using the Oroboros Oxygraph-2k respirometer (Oroboros Instru-ments). Respiratory oxygen consumption was assessed in realtime as pmol of O2 per second per million cells. Routine cellularrespiration rate was first measured before the addition of any ETCstimulator or inhibitor. Oligomycin (2.5 mmol/L) was theninjected and the drop in respiration was measured as ATP pro-duction rate of the cells. Maximal respiratory capacity wasachieved by FCCP stimulation. Lastly, antimycin A and rotenonewere added to the final concentration of 1 mmol/L for eachcompound to shut down the ETC to get the non–ETC-contributedoxygen consumption. To measure mitochondrial complexes'activities, digitonin was first added to permeabilize the cells.Subsequently, activities of the complexes were measured accord-ing to the protocol described in our previous study (30). ComplexI, II, and III activities were measured as respiratory rate stimulatedby pyruvate/malate, succinate, and duroquinol (prereduced bysodium borohydride), respectively, and normalized to the resid-ual respiration after the addition of their specific inhibitors—rotenone, malonate, and antimycin. All chemicals were fromSigma-Aldrich.

shRNA constructs and lentiviral transductionSilencing of human UQCRFS1, NDUFAF7, AMPKa1, and

AMPKa2 was achieved using lentiviral transduction of H1299 orMiaPaca2 cells with pLL3.7 vector expressing the correspondingshRNAs. shRNA targeting NDUFAF7 and UQCRFS1 were modi-fied referring to papers of Rendon and Tormos, respective-ly (31, 32): sh-UQCRFS1 #1: 50-GTACCCATTGCAAATGCAG-30,sh-UQCRFS1 #2: 50- GGTAACTGGAGTAACTACT-30, sh-NDUFAF7#1: 50-GTGGACTTCAGTTATTTGC-30, sh-NDUFAF7 #2: 50-GA-GACTTCAAGGTG GAAGA-30. Sequences targeting AMPKa1 anda2 subunits were synthesized based on a previous report (33):

sh-AMPKa1: 50-GAGGAGAGCTA TTTGATTA-30, sh-AMPKa2:50-GCTGTTTGGTGTAGGTAAA-30. In order to achieve desiredknockdown efficiency, transduction of H1299 or MiaPaca2 cellswas conducted 1 to 3 times or with different titers of lentivirusproduced in HEK293T packaging cells.

Metformin treatment in xenograft-harboring miceAll mouse studies were done in accordance with the insti-

tutional IACUC guidelines. H1299 (7 � 106) and MiaPaca2(10� 106) cells were mixed with Matrigel (BD Biosciences) to afinal concentration of 40%Matrigel in volume of 200 mL. Thesecell preparations were then injected subcutaneously into theleft and right flanks of 6–8-week-old female SCID mice. Whenthe average tumor size reached 300 mm3, the mice wererandomized into control and treatment groups (5 mice in eachgroup). For treatment, each mouse was dosed with metformindissolved in sterilized water at 100 mg/kg by once daily oralgavage. Sterilized water was used as placebo in the controlgroup. Notably, the dose ranges of metformin in mouse studieshave been well established, ranging from 50 to 400 mg/kg(34, 35). Tumor volume was calculated as (length � width2 /2)and measured every 2 days using calipers. Treatment and tumorsize determinations were continued for 20 and 32 days forH1299 and MiaPaca2, respectively, before all the mice had tobe sacrificed per protocol.

Oximetry for tumor tissueTo obtain Colo205, H1299, HepG2, HT29, OVCAR8, and

MiaPaca2 tumors, 10 � 106 cells of each cell line were mixedwith Matrigel (BD Biosciences) to a final concentration of 40%Matrigel in volume and injected subcutaneously into the leftand right flanks of 6–8-week-old SCID mice. The mice weresacrificed when the tumors reached size of around 1,500 mm3,whereupon tumors were harvested and dissected away from thesurrounding connective tissues. The tumor tissues were thenminced with dissecting blade. Approximately 15 mg of mincedtissues were weighted out and suspended in MiRO5 buffer forhomogenization using PBI-shredder (OROBOROS). Respirato-ry functions of the homogenized tissues were then measuredby Oroboros Oxygraph-2k respirometer in MiRO5 buffersupplemented with 5 mmol/L pyruvate, 2 mmol/L malate, and5 mmol/L succinate. Maximal respiratory capacity was mea-sured by stepwise stimulation of FCCP to reach the highestOCR, followed by antimycin (2.5 mmol/L) and rotenone(1 mmol/L) inhibition as detailed above.

Quantification and statistical analysisGraphPad Prism software (GraphPad Software) was used in

graph plotting and IC50 calculation for soft-agar clonogenicassay, long-term two-dimensional assay, and short-term cellviability assay. For each correlation curve, Pearson correlationcoefficient of determination (R2 value) was derived usingMicrosoft Excel software to assess the correlation strength. Toassess statistical significance of the correlation, P value wasdetermined using Student t distribution model. To test statis-tical significance of difference between two correlation coeffi-cients, Fisher z-transformation (http://vassarstats.net/rdiff.html) was performed, which gives a P value. For all studies,P values less than 0.05 were considered statistically significant.For drug combination studies, interactions of drugs were ana-lyzed based on the Loewe additivity model and illustrated

Respiration Properties Predict ETC Inhibitor Sensitivity

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using isobologram (36–39), wherein the diagonal line joiningIC50 points of single drug treatments is the additivity line andIC50 points of combination drug treatments below this diag-onal line correspond to a synergistic effect.

ResultsCancer cells exhibit a wide range of sensitivity to metformin

Phase II cancer trials for metformin have failed to show clearefficacy so far, despite a significant association of metformintherapy with reduction of cancer incidence and mortality indiabetes patients. Identification of mechanism-based predic-tors/markers for responsiveness, and application of such markersin patient selection, are clearly needed for further development ofmetformin as an anticancer agent. To this end, we have developedthemethod to assess cancer cell sensitivities tometformin by soft-agar colony formation assay. Using this approach, we compared apair of lung cancer cell lines H1299 and H522, and a pair ofpancreatic cancer cell lines HPAFII and MiaPaca2, for their sen-sitivities to metformin. We found that H1299 cells are moreresistant to metformin than H522 cells (Supplementary Fig.S1A), and HPAFII cells are more resistant than MiaPaCa2 cells(Supplementary Fig. S1B). We then expanded the study to 22cancer cell lines of diverse tissue origins to include lung, pancreas,colon, liver, breast, skin, and ovarian cancer cells. Inhibitioncurves for these cell lines were plotted (Supplementary Fig. S2)to generate IC50 values for metformin. The cell lines exhibited awide range of sensitivities (35-fold) to metformin inhibition ofproliferation, and their sensitivity appeared to be independent oftissue origin.

The growth-inhibitory concentration of metformin for a cancercell line, as measured by IC50, positively correlates with cancercell RC and RR

Inhibition of mitochondrial complex I function has beensuggested to account for the anticancer effect of metformin.Hence, we postulated that the intrinsic respiratory properties ofa cancer cell line might determine its responses/sensitivities tometformin treatment. To evaluate this hypothesis, we profiledmitochondrial respiratory properties of various cancer cell lines,including basal respiration, RC and the calculated RR. As withtheir responses tometformin,we found that cancer cell lines of thesame tissue origin can have diverse respiratory profiles (Table 1),and further that a cell's basal respiration had no correlation withits RC (Table 1). For instance, HPAFII and MiaPaca2 cells havesimilar basal oxygen consumption rates of 19 and 17 pmol O2/s/million cells, respectively, but very different RC and RR. Con-comitantly, they also exhibited very different sensitivities tometformin treatment (Table 1), in that MiaPaCa2 cells, whichhad lower RC and RR, were more sensitive to metformin inhibi-tion of proliferation than HPAFII cells. We observed the sameassociation between RC/RR and sensitivity to metformin in thelung cancer cell pair H1299 andH522 (Table 1). Further, complexI, II, and III respiration capacities of HPAFII were 2.5-fold higherthan basal respiration levels when stimulated with respectivesubstrates or electron donors, indicating an unutilized respiratorypotential under normal condition, whereas MiaPaca2 displayedhardly any increase in respiration in response to the same stimuli(Supplementary Fig. S3A).

To further analyze the relationship between cancer cell vulner-ability to metformin treatment and its respiratory properties, we

Table 1. Mitochondrial basal respiration,maximal respiratory capacity, respiratory reserve, and the IC50 formetformin for each of the 22 cancer cell lines of 7 differenttissue origins

IC50 of metformin Oxygen consumption rate (pmol O2/s/million cells)(mmol/L) Basal mito. resp. Max. resp. capacity Resp. reserve

Cancer cell lines Mean SD Mean SD Mean SD Mean SD

LungH1299 6.0 0.6 47.3 13.5 97.3 15.0 50.0 3.1PC9 1.1 0.7 38.0 10.5 66.9 14.4 28.9 2.6H460 3.9 1.2 24.9 7.4 44.3 5.6 22.2 3.6H522 1.5 0.9 11.5 4.4 19.5 6.2 8.0 3.9

PancreasHPAFII 3.3 1.2 19.1 3.4 56.8 5.7 37.6 5.0PANC1 1.2 0.8 36.1 6.3 60.1 0.9 20.8 3.4Miapaca 0.4 0.1 17.0 4.3 25.2 8.6 8.2 6.0

BreastMCF7 5.5 0.8 30.4 5.3 88.2 13.4 57.8 10.0MDAMB436 2.3 0.2 20.7 7.8 62.3 14.7 41.6 8.1MDAMB231 1.8 1.7 7.9 3.5 37.8 5.4 29.9 2.9

ColonColo205 13.0 2.3 33.7 5.8 109.0 6.4 80.4 3.4HT29 3.2 1.2 9.9 4.3 38.1 1.4 28.2 3.6HCT15 4.8 0.8 18.8 6.4 42.6 7.4 23.8 4.0HCT116 2.7 0.9 29.7 6.8 56.6 4.6 20.9 5.2

LiverHepG2 7.0 0.9 52.7 0.7 114.0 7.9 61.2 7.2Huh7 0.6 0.6 33.2 1.8 57.7 0.5 24.5 2.3

MelanomaSK-Me128 7.6 0.2 44.7 6.2 152.4 19.2 107.3 14.1UACC62 8.3 2.1 36.6 0.7 120.4 15.7 83.8 15.3SK-Mel2 7.8 0.7 31.7 2.9 79.0 4.1 48.1 2.2M14 6.0 0.4 27.3 3.6 72.7 10.6 45.5 7.1

OvarySn12C 2.0 0.9 14.8 2.0 54.1 7.1 39.7 4.4OVCAR-8 1.5 0.7 10.2 3.9 25.5 8.9 15.3 8.5

Teh et al.




expanded respiratory profiling to the 22 cell lines for which wehad metformin sensitivity data. Rank ordering of the cancer celllines by either RC or RR showed very high correlation to their IC50for metformin inhibition of proliferation, with R2 of 0.601 and0.669, respectively (Fig. 1A and B). In contrast, there was littlecorrelation between basal mitochondria respiration and IC50 formetformin, with R2 at 0.216 (Fig. 1C). Basal respiration level isthat of the cell under a specific condition, and hence does notrepresent its capacity to response to stress/challenges. RC and RR,on the other hand, are the properties that convey the ability of thecell towithstand energy stress. By this logic, cells that have RCnearthe basal respiration level, i.e., with little reserve, are expected tobe sensitive to the inhibition of ETC function. Indeed, the correla-tions suggest that cells with higher RC and RR are better able towithstand the inhibitory effect of metformin, which is consistentwith the mechanism of metformin to inhibit ETC function. Theseresults also suggest that the lower the remaining mitochondriacapacity, themore limited is the capacity for cell proliferation andsurvival. We also analyzed the basal glycolytic rate and glycolyticcapacity of the pancreatic and lung cancer cell line panels. Inter-estingly, we observed an inverse relationship between RC/RR andthe rate of glycolysis of these cancer cells, i.e., the lower a cell's RCand RR, the higher the rate of glycolysis (Supplementary Fig. S3Band S3C). These data support the notion that cancer cells upre-gulate glycolysis at the expense of oxidative phosphorylation, andthat different cancer cells have different balances between glycol-ysis and oxidative phosphorylation.

Metformin is transported in and out of the cells primarily bythe OCT1 and MATE1 transporters, respectively (40, 41).Hence, we also evaluated the relationship between metforminsensitivity and the expression levels of OCT1 and MATE1. We

analyzed the expression levels of OCT1 and MATE1 transpor-ters in 12 cancer cell lines, covering a broad range of respiratorycapacity and metformin sensitivity. We found that there existedno correlation between IC50 for metformin and the expressionlevel of OCT1 or MATE1 (Supplementary Fig. S4), in contrast tothe high correlation level of IC50 to respiration capacityand reserve. This result suggests that the level of metformininflux and efflux is not the limiting factor in its inhibition of cellproliferation and survival. These data provide further supportthe utility of using respiratory capacity as a reliable predictorfor a cancer cell's sensitivity to metformin and other ETCinhibitors.

Fresh tumor sample can be used to assess respiratory capacity,which predicts the in vivo response of the tumor to metformintreatment

Having established a strong correlation between the RC/RR of acancer cell and its response tometformin in vitro, it is important todetermine whether our findings apply to samples obtained froman in vivomodel. To this end, we compared RCs determined fromcancer cells grown in vitro with those from isolated tumor tissuesderived from the same cell lines (Fig. 2A; see Materials andMethods). Six cancer cell lines covering a range of metforminsensitivities that consistently form xenograft tumors were selectedfor the study; these cell lines are Colo205, H1299, HepG2, HT29,OVCAR-8, and MiaPaCa2 (Table 1). This analysis showed highlevels of correlation between the RC values obtained from tumortissues and the RC and RR values from the corresponding cancercells grown in tissue culture conditions, with R2 values of 0.844and0.942, respectively (Fig. 2B andC).Notably, theprocedures ofsample preparation for the measurement of respiration for

Figure 1.

Cancer cell respiratory capacity and reserve inversely correlate with its sensitivity to metformin. A–C, Correlation curves of IC50 of metformin against maximalrespiratory capacity (A), respiratory reserve (B), or basal mitochondrial respiration (C), with the square of Pearson correlation coefficient (R2) values equal to0.601, 0.669, and 0.216, respectively. IC50 values were derived from colony formation assays, and respiratory capacity and reserve were obtained by oximetryanalysis of 22 cancer cell lines as stated. Respiratory functions were measured using OROBOROS oximetry. All data are represented as mean� SD derived fromat least three biological repeats. IC50 for metformin was assessed by soft-agar clonogenic assay, and then calculated using GraphPad Prism software. Thesignificance of correlation was tested using Student t distribution, with degree of freedom (n� 2)¼ 20; P values derived for the three curves were all less than0.05. Differences between the correlations were accessed using Fisher z-transformation. There was no significant difference between curves A and B, but bothcurves A and B are significantly different from curve C with P value of less than 0.05.





cultured cells and tumor samples are different. For the tumorsamples, tissue homogenization disrupts the plasma membraneintegrity and exposes the mitochondria to the assay buffer, mak-ing basal respiration measurement unreliable. However, therespiratory capacity obtained from in vivo and in vitro samples ofthe same cancer cell lines is highly consistent, suggesting that

substrate utilization does not contribute to this respiratoryproperty.

We then tested the predictive value of RC for in vivo efficacy ofmetformin in the xenograft mouse model. H1299 andMiaPaCa2were selected for the study because of their comparable prolifer-ation rates in vivo, their significantly different in vitro response to

Figure 2.

Cancer cell respiratory properties and in vivo responsiveness to metformin treatment can be determined from xenograft tumor samples.A, Diagram showingworkflow of respirometry analysis of xenograft tumor tissue for maximal respiratory capacities. The analysis was performed on the xenograft tumors derivedfrom the Colo205, H1299, HepG2, HT29, OVCAR8, and MiaPaCa2 cancer cells. B and C, Correlation between maximal respiratory capacity of xenograft tumorsand that of the same cells grown in tissue culture condition (B), and between respiratory reserves of cells from xenograft and grown in tissue culture condition(C). The squared values of Pearson correlation coefficients (R2) are 0.844 and 0.942 for (B) and (C), respectively. The significance of correlations was testedusing Student t distribution, with degree of freedom (n� 2)¼ 4, and P values derived for both curves were

metformin treatment, and importantly, their differences in RCvaluesmeasured in either cultured cells or tumor tissue.We foundthat, while metformin treatment significantly inhibited thegrowth of MiaPaCa2 xenografts, the same treatment had noimpact on tumors derived from H1299 cells (Fig. 2D and E).Taken together, these studies indicate that in vivo efficacy ofmetformin treatment for a particular cancer can be predictedfrom the RC assessed from the tumor tissue before initiatingtreatment, supporting the potential use of RC measurements inpatient selection in a clinical setting.

Suppressing the expression of essential respiratory proteinsinhibits the cancer cell proliferation similar to metformintreatment.

Having established the correlation between RC/RR andcancer cell sensitivity to metformin, we next investigated ifthe levels of RC/RR determine or control the levels of respon-siveness of cancer cells to ETC inhibitors such as metformin.To this end, we genetically downregulated RC and RR ofH1299 cells to study the changes of sensitivity to metformin.H1299 cells were chosen because they exhibit high RC and RRand relative resistance to metformin. To manipulate RC andRR in these cells, we knocked down Ubiquinol-Cytochrome CReductase Rieske Iron-Sulfur Polypeptide 1, UQCRFS1, anessential subunit of mitochondrial complex III, using twoindependent shRNAs (Fig. 3A). Both shRNAs targetingUQCRFS1 led to decreased levels of RC and RR in H1299cells in a dose-dependent manner in comparison with controlshRNA (Fig. 3B). More importantly, the ability of H1299 cellsto form colonies in soft agar was reduced by UQCRFS1knockdown in a titer-dependent manner for both targetingshRNAs (Fig. 3C). From several experiments using the twotargeting shRNAs at different titers, we were able to demon-strate a strong correlation between RR reduction and colony-forming capacity, with an R2 value of 0.771 (Fig. 3D). We alsoreduced RC and RR by knocking down NADH: UbiquinoneOxidoreductase Complex Assembly Factor 7 (NDUFAF7), theassembly factor of mitochondrial complex I, using two target-ing shRNA sequences, in H1299 cells (Fig. 3E). As observed inUQCRFS1 studies, NDUFAF7 knockdown significantlyreduced RC and RR in H1299 cells (Fig. 3F), resulting inreduction of colony-forming abilities (Fig. 3G). These datastrongly support the existence of a causal and predictiverelationship between RC and RR and the sensitivity of a cancercell to ETC inhibition.

Reduction of RC and RR sensitizes cancers cells to metformintreatment

The findings of an inverse relationship between a cancer cell'ssensitivity to ETC inhibition and its RC and RR suggested thatlowering the RC/RR of a metformin-resistant cell should render itmore sensitive to the drug. Such a demonstration would strengththe relationship between cell-intrinsic respiratory properties andsensitivity to ETC inhibitors. To this end, we again used theapproach of knocking down the expression of ETC proteins.Complex I protein NDUFAF7 expression was suppressed inH1299 cells by 40% and 60%, respectively, using low viral titersof the afore-mentioned shRNAs to achieve moderate knockdownand reduction of RCandRR (Fig. 4A). Indeed, reducingNDUFAF7expression rendered H1299 cells more sensitive to metformininhibition of cell proliferation, as assessed by colony formation

assay (Fig. 4B). We also performed similar studies by knockingdown the complex III protein UQCRFS1. Consistently, knock-down of UQCRFS1 also rendered the cells more sensitive tometformin (Fig. 4C and D).

In addition to genetic knockdown of ETC essential proteins,we also used a pharmacologic approach to reduce RC/RR, bytreating H1299 cells with rotenone, a known ETC inhibitor.Rotenone-treated H1299 cells were rendered more sensitive tometformin and, vice versa, metformin-treated cells were moresensitive to rotenone (Fig. 4E). The interaction of metforminwith rotenone was analyzed with isobologram, a method toidentify drug synergy (42). The concentrations of the two drugsin combination to inhibit 50% colony formation were belowthe diagonal lines joining the IC50 values for metformin androtenone as single agents for H1299 cells (Fig. 4F), which ischaracteristic for synergistic interaction between two drugs.Similar experiments were performed on another metformin-resistant cell line HPAFII, and a similar interplay betweenmetformin and rotenone sensitivity was observed (Supplemen-tary Fig. S5A and S5B). Together with the knockdown studies,these data provide compelling evidence for the direct relation-ship between the cell respiratory properties and cell vulnera-bility to ETC inhibitors. Further, these studies support thenotion that anticancer effect of metformin is mediated throughthe inhibition of mitochondrial respiration.

Malignant transformation reduces RCandRR and increases cellsensitivity to ETC inhibition

Knowledge has been growing steadily about metabolic dif-ferences between cancer cells and benign cells. Although themolecular mechanisms for the metabolic adaptation are mul-tifaceted and likely variable in different cancer cells, severalcommon tumor suppressors or oncogenes, such as p53 andRAS, have been linked to the regulation of glycolysis andmitochondrial activity (43–46). We postulated that cancer cellshave lower RC/RC than their normal counterparts as a directconsequence of the transformation process. To test this hypoth-esis, we transformed immortalized benign human fibroblast BJcells using an established method of knocking down the tumorsuppressor p53 and introducing an oncogenic mutant ofKRAS (47). Specifically, retroviruses encoding shRNA targetingp53 shRNA and KRAS (G12V) were introduced sequentiallyinto BJ cells immortalized by stable expression of the telome-rase reverse transcriptase. Analysis of mRNA levels of p53 andmutant KRAS confirmed the identity of the cells (Fig. 5A).Comparing the transformed isogenic BJ cells with parental BJcells, the former were found to have significant reductions inRC and RR (Fig. 5B). Consistent with the above findings, thetransformed cells were more sensitive to metformin treatmentthan control cells, with IC50 values of 1.7 mmol/L and 13.3mmol/L, respectively (Fig. 5C). This transformation model,generated using two of the most frequently-mutated genes inhuman cancers, supports the hypothesis that malignant trans-formation programs cells to have lower mitochondria RC andRR, thereby becoming more responsive to ETC inhibition thantheir normal counterparts.

ETC inhibition by metformin and AMPK inhibition aresynergistic in inhibiting cancer cell proliferation

AMPK is an important regulatory protein in cell metabolismthat is activated by nutrient and energy depletion. Indeed, we





found that cells treated with concentrations of metformin thatsuppressed respiration exhibited increased phosphorylation ofAMPK (Thr172) and acetyl-coA carboxylase (ACC, a directsubstrate of AMPK; Fig. 6A), suggesting AMPK signaling isactivated in response to energy depletion elicited by metfor-min. Activation of AMPK has been described as a double-edgedsword in cancer cells that can both promote tumorigenesis

and suppress cancer cell proliferation in a context-dependentmanner (48, 49). To clarify the roles of AMPK activationin metformin-induced inhibition of cancer cell growth andproliferation, we knocked down AMPKa1 and a2 subunits inMiaPaCa2 cells using shRNA-expressing lentiviruses (Fig. 6B).We found that knockdown of AMPK sensitized MiaPaCa2to metformin treatment, shifting the dose–response curve

Figure 3.

Reducing cancer cell respiratory capacity and reserve by suppressing expression of essential ETC proteins results in decreased soft-agar colony formation. A–C,Knockdown of the mitochondria complex III subunit UQCRFS1 in H1299 cells using two targeting shRNA sequences, labeled as #1 and #2, respectively; scrambleshRNAwas used as control. "þ" and "þþ" represent two titers of shRNA-expressing lentivirus used to achieve escalating levels of knockdowns. All experimentalassessments were performed within a week after the introduction of shRNAs. Three biological repeats were conducted for these studies with similar outcomes asshown. A,Quantitative PCR analysis for UQCRFS1 knockdown in H1299 cells. 18S ribosome gene expression was used for normalization. B,Oximetry analysis ofmaximal respiratory capacity and respiratory reserve of H1299 cells with and without UQCRFS1 knockdown. For both (B) and (C), error bars represent standarddeviation of technical duplicate. C, Colony formation assay comparing proliferation ability in soft agar of H1299 cells with different extents of UQCRFS1knockdown, as shown inA. D, Correlation curve plotting respiratory reserve against colony-forming unit of H1299 cells at different levels of UQCRFS1 knockdown;the respiratory reserve and colony-forming unit are both presented in percentages relative to those of sh-scramble control of H1299 cells. The analysis compileddata from two biological repeats for each shRNA sequences at two different levels of knockdowns. Correlation coefficient R-squared value was calculated to be0.771. E–G, Similar experiments as in A–Cwere carried out using H1299 cells with the knockdown of mitochondrial complex I assembly factor NDUFAF7. E, qPCRanalysis to validate knockdown efficiency; F, respiratory capacity and reserve analysis at different knockdown efficiencies; and (G) clonogenic analysis. Errorbars, standard deviation of technical duplicate. The entire study was repeated three times with similar results.

Teh et al.




markedly to the left (Fig. 6C and D). Similarly, suppression ofAMPK activation with the inhibitor compound C also sensi-tized the cells to metformin (Fig. 6E); isobologram analysis ofcombination treatment confirmed the synergistic suppressionof cell proliferation in MiaPaCa2 cells by the combination ofmetformin and AMPK inhibition (Fig. 6F). This synergisticinteraction suggests that AMPK activation, which promotes

mitochondrial biogenesis and catabolic processes such asautophagy, is an adaptive response to metformin-inducedenergy deficiency that facilitates short-term cell survival(Fig. 6G). These results extend our understanding of cell sig-naling events downstream of ETC inhibition and identify apotential strategy of combined treatment with ETC and AMPKinhibitors to treat specific cancers.

Figure 4.

Reduction of respiratory capacity and respiratory reserve, by either knockdown ETC proteins or treatment of complex I inhibitor rotenone, sensitizes cancer cellsto metformin inhibition of cell proliferation. A, Impact of knockdown of ETC complex I subunit protein NDUFAF7 on respiratory capacity and respiratory reserveof H1299 cells as assayed by oximetry study. Error bar, standard deviation derived from two technical repeats. B, Left, pictures of colony formation assaycomparing sensitivity of NDUFAF7 knockdown H1299 cells to that of control cells expressing sh-Scramble with treatment of 2.5 mmol/L metformin. Colonieswere grown for 10 days before MTT staining. Right, dose–response curves of H1299 cells with NDUFAF7 knockdown or scramble control under metformintreatment as assayed by colony formation; colony-forming units as a percentage of control are plotted against metformin concentration. Error bar, standarddeviation of technical duplicates. The studies in A and Bwere repeated four times with similar outcomes. C and D, Similar experiments as in A–Bwith theknockdown of complex III subunit protein UQCRFS1. E, Colony formation dose–response curves of H1299 cells cotreated with various concentrations ofmetformin and rotenone; the graphs show colony-forming units as a percentage of untreated controls against rotenone concentration. Each curve representsdata collected at a fixed dose of metformin. Error bars, standard deviation of technical triplicates. F, Isobologram analysis for the synergistic effect of rotenoneandmetformin cotreatment of H1299 cells. The dashed line joins the IC50 values of rotenone andmetformin when used as single agent, although the solid curveconnects all IC50 values of the two drugs in combination at different dosages.





DiscussionIt is well established that cancer cells change their metabolic

programduring oncogenesis. However, the relative importance ofmitochondrial respiration in cancer cells remains a point ofdebate (1–3, 5, 7). Our studies have demonstrated that cancercells are more vulnerable to the inhibition of mitochondrialrespiration than nontransformed cells. This notion makes bio-logical sense and offers new opportunities for therapeutictargeting.

Cancer cells need high-energy molecules for the processes ofbiosynthesis/anabolic activities, such as cell growth, proliferation,and cell migration, which are significantly upregulated comparedwith normal cells. Besides generating ATP, ETC is important for itsessential role in pyrimidine biosynthesis through mitochondrialdihydroorotate dehydrogenase (DHODH), a component of themitochondrial electron transport chain (ETC; refs. 50–52). TheWarburg effect, i.e., elevation of glycolysis at the expense ofmitochondrial respiration, causes remaining mitochondria res-piration to be less dispensable.

Abundant experimental evidence suggests that downregula-tion of mitochondrial respiration capacity is intimately linkedto the process of malignant transformation (43–46). Indeed,we found that the process of transforming BJ fibroblast cells ledto the reduction of RC and RR, providing direct evidenceconnecting malignant transformation to changes in respiratoryproperties, and increased vulnerability to antimitochondriaagents such as metformin. Although more comprehensivelystudies are necessary to evaluate the impact of transformationon cell respiratory capacity, our model of manipulating theexpression of a frequently mutated tumor suppressor and asimilarly common oncogene provides a clear and relevantdemonstration of the principle. This approach also revealeddifferential responses of benign cells and transformed cells toETC inhibitors and provided support for new potential thera-peutic strategies.

There have been significant efforts in developing effectivemitochondria inhibitors for cancer treatment (16, 19, 53), butclinical trials of one mitochondrial inhibitor, metformin, anFDA-approved antidiabetic drug, have yielded disappointingresults (17, 19, 54). The lack of mechanism-based patientstratification strategies could certainly be a major factor forinconclusive outcomes of metformin trials. Recent studies havesuggested that cancer cells harboring certain mutations displayincreased sensitivity to biguanides (55, 56). However, thesestudies have also highlighted that other factors, mostly unde-fined, and the complicated interplay of these factors, contributeto the sensitivity to biguanides. There has been recent effort toanalyze metabolic signatures associated with metformin treat-ment in ovarian cancers using the approach of integratedmetabolomics, which yielded interesting results (57). However,no straightforward predictive metabolite markers have beenclearly defined using this sophisticated approach. In contrast,our model establishes a direct functional link between mea-surable intrinsic respiratory property of cancer cells and theirresponsiveness to metformin. Indeed, in the study on 22 cancercell lines of diverse tissue origin, we found that there is a broadspectrum of cancer cell sensitivities to the antiproliferativeeffect of metformin, and that a cancer cell's sensitivity to ETCinhibition strongly correlates with its RC and RR. The currentstudy also established the correlation between the RCmeasuredin isolated tumor tissue samples and the RC obtained from thesame cells under normal tissue culture conditions, clearing theway for clinical application using small biopsy tumor samples.Practically, the oximetry analysis method can be done efficiently—within an hour of obtaining of fresh tumor sample, whichrequires no expensive equipment and reagents. These findingssuggest that respiration-based stratification could be applied toimprove efficacy of not just metformin, but ETC targetingagents in general, in cancer clinical trials and potentially infuture clinical practice.

Figure 5.

Transformation of BJ fibroblast cells by suppression of p53 expression and introduction of mutant KRAS reduced BJ cell respiratory capacity and respiratoryreserve and sensitized the cells to metformin. ShRNA targeting p53 and KRAS (G12V) coding sequence were introduced into hTERT immortalized BJ fibroblastcells using retroviruses; scrambled shRNA and empty expression vector were used as controls. A,Quantitative PCR assessment of p53 and KRAS expression in BJcells before and after the introduction of the viruses. Error bars, standard deviations of technical triplicate. B, Respirometry analysis of maximal respiratorycapacity and reserve of the immortalized BJ control cells and the transformed BJ cells. Error bars, standard deviations derived from two technical repeats.C, Long-term 2D proliferation assay comparing the responses of the control and transformed BJ cells to metformin treatment. Top, cell viability plotted againstmetformin concentration. Bottom, IC50 values calculated from the data of the top plot using Prism GraphPad software.

Teh et al.




Figure 6.

AMPK inhibition enhances the antiproliferative effect of metformin. A, Immunoblot quantification of pAMPK and pACC in metformin-treated MiaPaCa2 and H522cells; GAPDH served as a loading control. B–D, AMPKa1/2 knockdown studies. Control- and AMPK-targeting shRNAs were introduced into MiaPaCa2 cells bylentiviruses; B, immunoblot analysis of pAMPK and pACC levels is shown; C andD, results of colony formation assay comparing metformin sensitivities betweencontrol shRNA- and AMPKa1/2-targeting shRNA performed aweek after infection. The experiments were repeated three times with similar results. C, Pictures ofcolonies grown under soft-agar assay conditions in specified metformin concentrations. D, Dose–response curves of colony formation of MiaPaCa2 cellsexpressing control shRNA or that targeting AMPKa1/2. E,MiaPaCa2 cell colony formation dose–response curves in response to AMPK inhibitor compound C,expressed in percentage of untreated control; each curve represents assays performed at a fixed dose of metformin, as shown in the inset, and varied doses ofcompound C. F, Isobologram analysis of the combination effect of compound C andmetformin on MiaPaCa2 cells. The dashed line connects the IC50 values ofcompound C andmetformin when applied alone, although solid line connects IC50 under the treatment of the two drugs in different dosages. G, Amodeldepicting the interactions among RC, RR, energy metabolism, and the role of AMPK signaling in metformin-induced energy stress.





The mechanism of action of metformin in the inhibition ofcancer cell proliferation and survival is not without debate,despite recent evidence connecting its role in suppressingcomplex I function with its antitumorigenesis effects (13,16). In this regard, the current study not only demonstratesa strong correlation between the RC and cell sensitivity tometformin, but also establishes the causal relationship betweenRC and sensitivity to metformin. Altering cancer cell sensitivityto metformin through genetic and pharmacologic manipula-tion of ETC and RC provides important evidence for thisconcept. Further support for this conclusion comes from thedemonstration that, under ETC inhibition, simultaneous sup-pression of AMPK activation synergistically inhibits cancer cellproliferation. The advances in understanding the mechanism ofaction, in developing methods to stratify patients with cancer,and in designing effective combination strategies will, hope-fully, facilitate the clinical application of ETC inhibitors in thetreatment of human cancers.

Disclosure of Potential Conflicts of InterestNo potential conflicts of interest were disclosed.

Authors' ContributionsConception and design: J.T. Teh, W.L. Zhu, C.B. Newgard, M. WangDevelopment of methodology: J.T. Teh, W.L. Zhu, M. WangAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): J.T. Teh, W.L. Zhu, M. WangAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): J.T. Teh, W.L. Zhu, M. WangWriting, review, and/or revision of the manuscript: J.T. Teh, C.B. Newgard,P.J. Casey, M. WangAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): W.L. Zhu, M. WangStudy supervision: M. WangOther (applied andprovided grant support for the studies reported):M.Wang

AcknowledgmentsThe funding for the project is provided by the grants awarded toMeiWang by

the Singapore Ministry of Education Tier2 grant (MOE2013-T2-2-170) andSingapore National Medical Research Council (NMRC) Individual Grant(NMRC/CIRG/1486/2018).

The costs of publication of this articlewere defrayed inpart by the payment ofpage charges. This article must therefore be hereby marked advertisement inaccordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received July 11, 2018; revised November 22, 2018; accepted January 14,2019; published first March 1, 2019.

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2019;18:693-705. Mol Cancer Ther Jing Tsong Teh, Wan Long Zhu, Christopher B. Newgard, et al. Metformin-Responsive CancersMitochondria Inhibitors: Mechanism-Based Markers to Identify Respiratory Capacity and Reserve Predict Cell Sensitivity to

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