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Supporting InformationDaemen et al. 10.1073/pnas.1501605112SI TextSupplementary Note on Metabolic, Transcriptional, and Drug ResponseHeterogeneity Within the Glycolytic and Lipogenic Subtypes.Althoughwe successfully identified three metabolic subtypes for PDACthrough metabolite profiling, we observed heterogeneity withineach subtype. We investigated whether this heterogeneity wasdue to the existence of additional subtypes (or additional subtypecomplexity) that we did not capture with our three metabolicsubtypes. NMF clustering resulted in the identification of threesubtypes (Fig. S1A). The metabolite data did not justify four subtypes,with stability and robustness substantially less in case of foursubtypes compared with three (Fig. S1B). This robustness ar-gues that—at least at the metabolite level —there is no well-defined set of lines with a phenotype partially shared betweenmultiple subtypes.Analysis by ranking of the glycolytic and lipogenic cell lines

from low to high intensity per metabolite further suggested thatadditional intermediate groups do not likely exist (Fig. S4). Forglycolytic metabolites (Fig. 1C and Fig. S1E), cell lines with in-termediate intensity levels (either low expressing glycolytic orhigh expressing lipogenic lines) are not shared between PEP,glyceraldehyde 3-phosphate (GAP), serine, and lactate. For re-dox potential metabolites (Fig. 1C and Fig. S1F), there is con-sistency in intensity levels for interconvertible metabolites GSSGand GSH and NADPH and NADP, but less so for other pairs ofredox metabolites. Although palmitic acid, oleic acid, and pal-mitoleic acid show consistent levels for lipogenic cell lines, thereis no correlation among these fatty acids within the set of gly-colytic cell lines (Fig. 1D). Mitochondrial metabolites coenzymeQ9 and Q10 represent similar readouts of the electron transportchain, whereas there is minimal consistency for aspartate andglutamate (Fig. S1G). Although no individual metabolite canperfectly separate one subtype from the other subtypes, a panelof multiple metabolite measurements robustly separates glyco-lytic from lipogenic cell lines (Fig. S5A).We also investigated the degree to which transcription profiles,

MIDA experiments, and sensitivity to metabolic inhibitors alignwith the metabolic dependency derived from metabolomics data.Although no perfect separation of glycolytic from lipogenic linescould be achieved based on any one individual measurement, theanalyses and experiments showed good concordance when takingall measurements per analysis/experiment into account and sup-port the existence of the stable and reproducible glycolytic andlipogenic subtypes (Fig. S5A). We also ranked the glycolytic andlipogenic cell lines by metabolic dependency based on each datatype separately and compared these rankings. The flux experi-ments on the use of glucose and glutamine are in highest con-cordance with the glycolytic and lipogenic metabolic subtypes.Within the lipogenic subtype, limited heterogeneity was presentat transcriptional level and in drug sensitivity. At the transcrip-tional level, the lipogenic cell line PANC-1 showed a glycolytictranscription profile. In terms of sensitivity profiles, lipogeniccell lines PANC-1, SU.86.86, and PK-59 showed a glycolyticsensitivity profile. Correlation between any of the four rankingsshown in Fig. S5A ranges from 0.61 (metabolite vs. transcriptionscore) to 0.93 (metabolite vs. flux score). Overall, the metab-olomics data, transcription profiles, flux experiments, and drugsensitivity confirm robust differences between glycolytic andlipogenic cell lines.Our metabolite profiling classification succeeded in enriching

for drug sensitivity. The class of glycolytic cell lines respondsconsistently to inhibitors of aerobic glycolysis, glutaminolysis, and

ROS (Fig. S5B). All glycolytic cell lines responded to BPTES, allbut two cell lines responded to the LDHA inhibitor and ox-amate, and all but two cell lines responded to S-4-CPG. Al-though the lipogenic cell lines showed more drug responseheterogeneity, there was good concordance in drug sensitivitybetween all of the lipid inhibitors (SCD inhibitor, FASN in-hibitor, cerulenin, and olristat) (Fig. S5C). In addition, the fivelipogenic lines that were resistant to all lipid synthesis inhibitorswere also resistant to LDHA inhibitor, BPTES, and oxamatetreatment.

SI Materials and MethodsMetabolite Profiling. All cell lines were grown in RPMI (withoutglucose, without glutamine) media (US Biological #R9011) sup-plemented with 6 mM glucose (Teknova # G9005), 2 mM gluta-mine (Gibco #25030–081), 5% (vol/vol) FBS (Sigma F4135), 100μg/mL penicillin, and 100 U/mL streptomycin (Gibco 15140–122).Briefly, cells were plated on LUMOX gas permeable mem-

branes (Sarstedt no. 94.6077.331) in six-well plates at a con-centration range of 5–7.5 × 105 per well. After 48 h, LUMOXmembranes containing the cells were removed from the well witha scalpel. Samples subjected to MxP broad profiling (44) werewashed two times in 0.9% NaCl. Samples subjected to MxPenergy profiling were washed two times in 0.9% NaCl and 4.5 g/Lglucose. Membranes were then immediately transferred to anEppendorf tube containing quenching solution (45% dichloro-methane, 55% ethanol) on dry ice and transferred to liquidnitrogen.Sample preparation and metabolite profiling of intracellular metabolites(MxP broad profiling) (44). Samples were prepared and subjected toLC-MS/MS and GC-MS analysis as described below. Severalgroups of metabolites were analyzed semiquantitatively orquantitatively including amino acids, carbohydrates, fatty acids,mono, di-, and triglycerides, other lipids, organic acids, co-enzymes, vitamins, and secondary metabolites.Adherent cells were cultured on LUMOX plates (Sarstedt no.

94.6077.331). Once the incubation time was completed, themembranes were cut out of the LUMOX plates and were washedin 5 mL isotonic NaCl (0.9%) at 37 °C. This washing step wasrepeated a second time in a fresh solution of the same compo-sition. Cells were then quenched by placing the membranes in apolypropylene tube and the addition of 600 mL of a dichloro-methane/ethanol 9:11 (vol/vol) solution at −80 °C. After additionof water, the samples were extracted using a ball mill (Retsch),filtered through a centrifuge filter (Millipore; mesh size, 0.2 mm),and fractionated into an aqueous, polar phase and an organic,lipophilic phase.For the transmethanolysis of the lipid extracts (lipophilic

phase), a mixture of 140 μL chloroform, 37 μL hydrochloric acid(37% by weight HCl in water), 320 μL methanol, and 20 μLtoluene was added to the evaporated extract. The vessel wassealed tightly and heated for 2 h at 100 °C, with shaking. Thesolution was subsequently evaporated to dryness. The residuewas dried completely.The methoximation of the carbonyl groups was carried out by

reaction with methoxyamine hydrochloride (20 mg/mL in pyri-dine, 100 μL for 1.5 h at 60 °C) in a tightly sealed vessel. Twentymicroliters of a solution of odd-numbered, straight-chain fattyacids [solution of each 0.3 mg/mL of fatty acids from 7 to 25carbon atoms and each 0.6 mg/mL of fatty acids with 27, 29, and31 carbon atoms in 3/7 (vol/vol) pyridine/toluene] was addedas time standards. Finally, the derivatization with 100 μL of

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N-methyl-N-(trimethylsilyl)-2,2,2-trifluoroacetamide (MSTFA)was carried out for 30 min at 60 °C, again in the tightly sealedvessel. The final volume before injection into the GC was 200 μL.For the dried polar phase, the derivatization was performed in

the following way. The methoximation of the carbonyl groups wascarried out by reactionwithmethoxyamine hydrochloride (20mg/mLin pyridine, 50 μL for 1.5 h at 60 °C) in a tightly sealed vessel.Ten microliters of a solution of odd-numbered, straight-chainfatty acids [solution of each 0.3 mg/mL of fatty acids from 7 to 25carbon atoms and each 0.6 mg/mL of fatty acids with 27, 29 and31 carbon atoms in 3/7 (vol/vol) pyridine/toluene] was added astime standards. Finally, the derivatization with 50 μL MSTFA wascarried out for 30 min at 60 °C, again in the tightly sealed vessel.The final volume before injection into the GC was 100 μL.The GC-MS systems consist of an Agilent 6890 GC coupled

to an Agilent 5973 MSD (Agilent), and autosamplers wereCompiPal or GCPal from CTC.In LC-MS analysis, both fractions were reconstituted in ap-

propriate solvent mixtures. HPLC was performed by gradientelution on reversed phase separation columns. MS detection,which allows target and high-sensitivity multiple reaction moni-toring (MRM) profiling in parallel to a full screen analysis, wasperformed as previously outlined in Patent WO2003073464 onmass spectrometry methods for analyzing mixtures of substances.The HPLC instruments were Agilent 1100 (Agilent), and the MSinstruments were API4000 from SCIEX (AB SCIEX).The data were normalized to the protein content, determined

as the median of protein contents of three parallel cultures pertreatment group.Sample preparation and measurement of intracellular energy metabolites(MxP energy profiling). Phosphorylated or carboxylated metaboliteslike ATP, NADH, intermediates of glycolysis, mevalonate pathway,purine and pyrimidine metabolism, pentose phosphate cycle, andKrebs cycle were extracted and analyzed semiquantitatively with aspecial ultra performance liquid chromatography (UPLC)-MS/MSmethod. Adherent cells were cultured on LUMOX plates (Sarstedtno. 94.6077.331). Once the incubation time was completed, themembranes were cut out of the lumox plates and were washed with5 mL isotonic NaCl (0.9%) solution containing 4.5 g/L glucose,preconditioned at 37 °C. This washing step was repeated a secondtime using a fresh solution of the same composition. Cells were thenquenched by placing the membranes in a polypropylene tube con-taining metal beads (Peqlab tubes) and 900 mL of a dichloro-methane/ethanol 2:1 (vol/vol) solution at −80 °C.Ammonium acetate 1.5 M (100 mL) and a uniformly 13C la-

beled internal standard (50 mL) were added to the membranesfor extraction. The internal standard consisted of an aqueous cellextract from yeast grown on U13C glucose substrate. The sampleswere homogenized using a high-speed benchtop homogenizer for30 s at 6.5 m/s (FastPrep-24; MP Biomedicals). The sampleswere centrifuged for 2 min at 4 °C (20,000 × g), and 100-mLportions from the aqueous phases were transferred to a cen-trifugal filter (Millipore; mesh size, 0.2 mm). A second portion ofammonium acetate 1.5 M (150 mL) was added in the poly-propylene tubes for a second extraction. The samples were againhomogenized using the high-speed homogenizer and centri-fuged as above. A 200-mL fraction from the aqueous phase wastransferred to the centrifugal filter and combined with the firstaqueous phase fraction that was collected. The samples werecentrifuged for 5 min (20,000 × g) at 4 °C. The filters were rinsedwith water portions (200 mL), which were combined with thesamples and transferred to high recovery glass vials. The sampleswere freeze dried for 20 h.Chromatographic separation of polar metabolites was achieved

with an ultra-high pressure ion pairing liquid chromatography(IP-UPLC) system (Acquity; Waters). A chromatographic gradientbetween a solvent A (deionized water) and a solvent B [50% ace-tonitrile, 50%water (vol/vol)] was used, with a flow rate of 0.4mL/min

and a column oven temperature of 45 °C. Tributylamine was addedas ion pairing agent to both eluents. The lyophilized samples weredissolved in 100 μL water. Injection volume was 5 μL.Negative mode electrospray tandem MS (−ESI-MS/MS) was

used to assess the polar metabolites separated by UPLC. Thetandem MS/MS (API 5500; AB SCIEX) was operated in MRMmode. Isotopically labeled and nonlabeled forms of individualmetabolites were distinguished by different mass traces.All metabolite signals were normalized to their isotopically

labeled counterpart. Subsequently, the data were normalized tothe protein content, determined as themedian of protein contentsof three parallel cultures per treatment group.

Metabolite Data Processing. With five technical replicates per cellline, outliers were defined as replicates with a correlation

reproducible variation. Metabolites are scaled across all celllines, centered to mean = 0 and scaled to SD = 1.

RNAseq Expression Data Processing. For 36 of 38 pancreatic cell lines(not available for lines PK-1 and Panc 10.05) and for the pan-cancerpanel of cell lines, RNA libraries were made with the TruSeq RNASample Preparation kit (Illumina) according to the manufacturer’sprotocol. The libraries were sequenced on an Illumina HiSEq.2000, using one to four lanes per cell line. We generated a medianof 61 million reads per sample, of which we were able to map amedian of 49 million reads uniquely to the human genome andconcordant with established gene models. Reads were trimmed to75 bp and filtered for quality and rRNA contamination. Genomicalignment was performed using GSNAP (50, 51). RPKM (reads perkilobase per million mapped reads) values on log2 scale were usedin all analyses. RNA sequencing data are available at the EuropeanGenome-phenome Archive (www.ebi.ac.uk/ega/), under accessionno. EGAS00001000610 (51).To find metabolism-associated genes differentially expressed

between the glycolytic and lipogenic cell lines shown in Fig. 1E andDataset S6, we started from a comprehensive list of 2,581 metab-olism genes (52). The DESeq2 R package for R version 3.0.0 wasused for differential expression analysis.Before differential expression and gene set enrichment anal-

ysis, RNAseq data for 2,581 metabolism genes were investigatedin 40 pancreatic cell lines and 194 nonpancreatic cell lines withresponse to oxamate and/or BPTES. We excluded 588 poorlyexpressed metabolism genes, with average RPKM and 90thpercentile of RPKM across all 234 cell lines below 1. Theremaining 1,993 metabolism genes were included in subsequentanalyses.

Metabolic Ontology Enrichment Analysis. For metabolite-basedenrichment analysis, we started from established biological cat-egories from ref. 25, listed in Dataset S1 as super- and sub-pathways, and derived ontology groups for enrichment analysis.We merged small, related superpathways and divided large su-perpathways into informative ontology groups. We combinedsuperpathways amino acids and amino acids related into theontology group amino acids. We distinguished fatty acids, cho-lines, sphingolipids, and other complex lipids in the broadersuperpathway complex lipids, fatty acids and related, after apreliminary observation that complex lipids and fatty acids ofdistinct type show different associations with the metabolicsubtypes. Similarly, vitamins, cofactors and related was dividedinto REDOX (mitochondria-related) and other vitamins andcofactors. We left other medium-sized superpathways intact.Each of these ontology groups in Dataset S1 was tested for en-richment of metabolites with high or low intensity in oneparticular subtype compared with other subtypes. All 256 me-tabolites were included, regardless of variation reproducibility.Ranked lists of metabolites based on the T-statistic were ob-tained from all representative lines of each subtype using thelimma package in R version 3.0.0 (46). Permutation-basedP values for ontology enrichment were derived from 10,000permutations of the cell line subtype labels, using the GSEAlmpackage in R. For each ontology, a JG statistic was calculatedproportional to the sum of T-statistics across that ontology’smetabolite set and rescaled to the square root of the ontology setsize (47). Ontology information for all 256 metabolites is avail-able in Dataset S1.RNAseq data were used for enrichment analysis of specific

gene sets (Dataset S5) that cover the established metabolic on-tologies for which we have metabolite intensity data. These genesets were chosen before data analysis. We used publicly availablemetabolism-associated gene sets where appropriate: branchedchain amino acid catabolism (amino acids) from Reactome,regulation via peroxisome proliferator-activated receptor alpha

(PPARA) from BioCarta, genes involved in oxidative phos-phorylation from ref. 53 (oxidative phosphorylation), and a gly-colysis signature from ref. 54. The metabolic ontology energyincludes both glycolytic and pentose phosphate metabolites(Dataset S1). We therefore extended the glycolysis signaturewith an in-house set of 16 pentose phosphate genes (glycolysis/PPP). To cover lipogenesis, we used an in-house set of 22 lipid-associated genes (lipids). All gene sets except for the glycolysisset and two in-house sets were downloaded from the MolecularSignature Database (MSigDB), v4.0 (55). After exclusion ofconsistently low-expressed metabolism genes, ranked lists ofgenes differential expressed between glycolytic subtype vs. lipo-genic subtype cell lines or between cell lines sensitive vs. resistantto the glycolytic inhibitors oxamate and BPTES were based onthe Wald statistic and obtained with the DESeq2 R packageusing R version 3.0.2. The GSEAlm R package was used tocalculate JG statistics and P values based on 1,000 permutationsfor each gene set. As reference, the list of metabolism genesobtained from ref. 52 was included as gene set.

Metabolic Subtype Characterization and Dependence on ProliferationRate. We investigated the degree to which transcription profiles,flux experiments, and sensitivity to metabolic inhibitors align withthe metabolic dependency derived from metabolomics data. Weranked the glycolytic and lipogenic cell lines based on each datatype separately and compared these rankings. To rank cell lines,we defined a metabolic dependency score for each data type asthe difference in profile between the glycolytic and lipogenicsubtype cell lines. Fifty-three metabolites in the metabolomicsdata were significantly different between glycolytic and lipogeniccell lines (t test, adjusted P < 0.05; Dataset S4). The metabolitescore was defined as the difference in average z-score intensity of22 metabolites high in glycolytic lines and 31 metabolites high inlipogenic lines. Based on RNAseq data, 129 metabolism genesfulfilled the differential expression criteria (t test, adjusted P <0.05; Dataset S6). The transcription score was defined as thedifference in average z-score expression of 42 genes high inglycolytic lines and 87 genes high in lipogenic lines. The fluxscore was defined as the difference in average z-score flux of fourmetabolites from the [U-13C6]glucose labeled experiment andfour metabolites from the [U-13C5]glutamine-labeled experi-ment (Dataset S7). Finally, the sensitivity score was defined asthe difference in average z-score sensitivity to five glycolytic in-hibitors (oxamate, BPTES, AOA, BSO, and S-4-CPG) and fourlipid synthesis inhibitors (SCD inhibitor, FASN inhibitor, cer-ulenin, and olristat).A multivariate logistic regression model for the prediction of

subtype entity (glycolytic vs. lipogenic subtype) was fitted to doublingtime and metabolite intensity, for 89 metabolites that were sig-nificantly different between the glycolytic and lipogenic subtypeaccording to a t test with adjusted P < 0.05. The glm function fromthe stats package in R was used for logistic regression modeling.Fifty-seven metabolites were predictive of subtype entity whentaking into account doubling time, with a multivariate P < 0.05. Anadditional 22 metabolites tended to be predictive when keepingdoubling time fixed, with a multivariate P < 0.1, totaling 79 of 89metabolites.

PDAC Signature Validation. Of three clinical subtypes of PDACidentified by molecular profiling (classical, QM-PDA, and exo-crine-like), two subtypes are represented in cell lines (22). Theoriginal PDAssigner signature for PDAC subtyping was thereforereduced from 62 genes to 42 genes, retaining genes representativeof the QM-PDA and classical subtypes and excluding genes forthe exocrine-like subtype (22). Class prediction was calculated asthe mean expression value of the Z-scores of genes characteristicof the QM-PDA subtype minus the mean expression value ofthe Z-scores of genes characteristic of the classical subtype. A


http://www.ebi.ac.uk/ega/www.pnas.org/cgi/content/short/1501605112

positive score indicates the QM-PDA subtype and a negativescore indicates the classical subtype. Z-scores were derived fromlog2 RPKM data of 36 cell lines.

Pan-Cancer EMT Signature. We associated sensitivity to the LDHAinhibitor, oxamate, BPTES, and ROS-inducing agents [BSO and(S)-4-CPG] with mesenchymal status, represented by a pan-cancer EMT signature (33) (Fig. 4E) and vimentin (Fig. S3A).The pan-cancer EMT signature we used contains ten genes thatare mostly associated with EMT and that were independentlyobtained in six datasets from three cancer types (ovary, colon,and breast) (33, 56). Those 10 genes are COL5A2, VCAN,SPARC, THBS2, FBN1, COL1A2, COL5A1, FAP, AEBP1, andCTSK. The EMT score in non-PDAC cell lines was calculated asthe average expression value (log2 RPKM) of those 10 genes.

In Vitro [U-13C5]Glutamine and [U-13C6]Glucose Experiments. Cells were

cultured for ∼18 h in RPMI with 10% dialyzed FBS supplementedwith either 3 mM D[U-13C]glucose or 1 mM L[U-13C]glutamine.The final concentrations of D-glucose and L-glutamine were ad-justed to 6 and 2 mM, respectively, using unlabeled D-glucose andL-glutamine. Metabolic activity was quenched with a solutioncontaining 4.5:4.5:1 ratio of methanol, acetonitrile, and water(−20 °C). The cells were incubated with constant shaking for15 min at 4 °C and then collected in tubes by scraping the wells.The extracts were sonicated for 30 s at 4 °C and centrifuged at16,000 × g for 10 min at 4 °C. The supernatant was collected andsubjected to LC-MS analysis. Metabolic fluxes and the contribu-tion of tracers to TCA metabolites were calculated with theMultiQuant software.

Analysis of Total Fatty Acid Synthesis. Cells were grown in RPMImedium 1640 supplemented with 10% FBS, 100 U/mL penicillin,0.1 mg/mL streptomycin, and 2 mM glutamine. For lipid synthesisexperiments, cells were seeded overnight in 12-well plates inRPMI medium 1640 containing 2% FBS, 2 mM glutamine, and2.5 mM glucose. D[U-14C] glucose (Perkin-Elmer) was added tothe media at a final concentration of 1 μCi/mL. After 6 h ofincubation, the cells were washed twice with ice cold PBS, andthen lipids were extracted from plates two times with 500 μLhexane:isoproponal (3:2). Phase separation was achieved byaddition of 300 μL PBS, and the nonpolar phase was collected.An additional 300 μL PBS was added to the hexane fraction towash off the remaining polar radioactivity. The hexane fractionwas dried under nitrogen gas, and radioactivity was quantified byliquid scintillation counting.

Fatty Acid Uptake Assays. Lipid uptake was measured using afluorescent free fatty acid uptake assay (Abcam; ab176768),following the manufacturer’s instructions. Briefly, cells wereseeded at a density of 50,000 cells per well in 96-well plate, andthe following day, cells were deprived of serum for 1 h. Cellswere then incubated with labeled C12 fatty acid at room tem-perature, and fluorescence per well (excitation: 485 nm, emis-sion: 515 nm) was measured in 26-s intervals using a SpectraMaxM5 plate reader (Molecular Devices). Cells were then fixed in4% paraformaldehyde and counterstained with Hoescht 33342(Life Technologies; H3570) to normalize lipid uptake to cellnumber; cells were imaged using an IN Cell Analyzer 2000 (GEHealthcare) under a 10× objective (four fields per well), and thenumber of cells per field was calculated using IN Cell AnalyzerWorkstation v3.7.1 software (GE Healthcare).

In Vitro Drug Treatment Experiments. For short-term viability as-says, cells were plated using optimal seeding densities in 384-wellplates using RPMI (without glucose, without glutamine) media(US Biological #R9011) supplemented with 6 mM Glucose(Teknova #G9005), 2 mM glutamine (Gibco #25030-081), 5%

FBS (Sigma F4135), 100 μg/mL penicillin, and 100 U/mLstreptomycin (Gibco 15140–122). Optimal seeding densities wereestablished for each cell line to reach 75–80% confluence at theend of the assay. The following day, cells were treated withvarious small molecule inhibitors using a 6-pt dose titrationscheme. After 72 h, cell viability was assessed using the CellTiter-Glo Luminescence Cell Viability assay. Absolute inhibitory con-centration (IC) values were calculated using four-parameterlogistic curve fitting and are averages from a minimum of twoindependent experiments. In cases where an IC50 could not bereached with the maximum concentration of drug, the IC50equivalent to the maximum drug concentration was used forstatistical analysis.For long-term growth assays, glycolytic subtype cell lines (MIA

Paca-2, SW 1990, PSN1, and HUP-T3) and lipid subtype cell lines(PA-TU-8902, PK-8, KP-3L, and SUIT-2) were seeded in a six-well dish at 3,000 cells per well overnight (RPMI, 5% serum,2 mM glutamine) and then treated in media with indicatedconcentrations of oxamate, SCD inhibitor, or DMSO for 12 d at37 °C and 5% CO2. Media (±drug) were changed every 3 d.After 12 d, cells were washed with PBS and stained with 0.5%crystal violet for 30 min, followed by washes with water untilbackground was removed, and then imaged.

Oxygen Consumption (Seahorse) Assays.Cells were plated at 20,000cells per well in XF 96-well cell culture microplates (SeahorseBioscience) pretreated with poly-D-lysine and incubated for 24 hat 37 °C in a 5% CO2 incubator. To assay OCR, the growthmedia were replaced with bicarbonate-free, serum-free pre-warmed medium and the plate was loaded into the XF96 Ex-tracellular Flux Analyzer (Seahorse Bioscience). Measurementsare plotted as pmoles of O2 per minute per cell for OCR. Cellnumbers used for normalization were determined by fixing theplate after analysis with 4% paraformaldehyde, staining withHoechst, imaging four quadrants per well on a Molecular De-vices ImageXpress HCS, and counting the average nuclei num-ber per quadrant.

Mitotracker and TMRE Experiments.Cells were plated at 10,000 cellsper well in 384-well cell culture microplates pretreated with poly-D-lysine and incubated for 24 h at 37 °C in a 5% CO2 incubatorovernight. Cells were then stained with 25 μg/mL Hoechst 33342(Life Technologies), 200 nM TMRE (Life Technologies), and200 nM MitoTracker Deep Red (Life Technologies) for 30 min(37 °C, 5% CO2). Images were acquired with a Perkin-ElmerOpera confocal imaging system, using a 20× water immersionobjective (0.6 NA). Mean pixel intensity of the 565- (TMRE) and690-nm (MitoTracker) emission channels within cytoplasmicregions was determined on a per-cell basis using Acapella imageanalysis software (Perkin-Elmer).

In Vivo Experiments. All mice were housed and treated in accor-dance with protocols approved by the Institutional Animal Careand Use Committee at Genentech. MIA Paca-2 and HPACshLDHA human pancreatic cell lines were generated by stablytransducing with inducible shRNA constructs targeting LDHAusing the lentivirus pHush–shRNA system (57) and shLDHAsequence GGCAAAGACTATAATGTAA.To determine in vivo knockdown efficiency of LDHA, tumors

were allowed to establish to between 125 and 250 mm3 and thentreated with 5% sucrose (control) or doxycycline (1 mg/mL in5% sucrose) to induce knockdown of LDHA. After 8 d, tumorswere collected and subjected to immunoblot analysis usinghuman-specific anti-LDHA (Cell Signaling; CS3582), and tubulin(Sigma; T6074).The MIA Paca-2 shLDHA model was used to evaluate the

effects of LDHA knockdown vs. therapeutic response to SCDinhibition. Briefly, 5 × 106 MIA Paca-2 shLDHA cells were



implanted s.c. in Matrigel in the right flank of NCR nude (nu/nu)mice, and tumors were allowed to establish to between 125 and250 mm3. Animals were grouped out to ensure even distributionof tumor sizes, and treatment was initiated. For shRNA exper-iments, control (5% sucrose) or doxycycline (1 mg/mL in 5%sucrose) was administered in the animals’ drinking water ad li-bidum. For SCD inhibition, animals were treated with vehicle(6.5% DMSO, 43.5% PEG-400) or SCD inhibitor G01523403 at75 mg/kg by oral gavage, twice a day (BID) for 21 d. Tumorvolumes were measured in two dimensions (length and width)using Ultra Cal-IV calipers (model 54-10-111; Fred V. FowlerCo.) and analyzed using Excel, version 14.2.5 (Microsoft Cor-poration). The tumor volume was calculated with the followingformula:

tumor size�mm3

�=�longer measurement

× shorter measurement2�× 0.5.

Reductions in tumor size were tracked as partial responses (PRs)(>50% decrease from the initial tumor volume) or completeresponses (CRs) (100% decrease in tumor volume).Animal body weights were measured using an Adventura Pro

AV812 scale (Ohaus Corporation). Percent weight change wascalculated using the following formula:

Body weight changeð%Þ= f½½ðcurrent body weight=initial body weightÞ−1�× 100�g.

To appropriately analyze the repeated measurement of tumorvolumes from the same animals over time, a mixed modelingapproach was used (58). This approach addresses both repeatedmeasurements and modest dropouts due to any non–treatment-related death of animals before end of study.Cubic regression splines were used to fit a nonlinear profile to

the time courses of log2 tumor volume at each dose level. Thesenonlinear profiles were then related to dose within the mixedmodel. Tumor growth inhibition as a percentage of vehicle (%TGI)was calculated as the percentage of the area under the fitted curve(AUC) for the respective dose group per day in relation to thevehicle, using the following formula:

%TGI= 100× ð1−AUCdose=AUCvehÞ.

To calculate uncertainty intervals (UIs) for %TGI, the fitted curveand the fitted covariance matrix were used to generate a randomsample as an approximation to the distribution of %TGI. The ran-dom sample is composed of 1,000 simulated realizations of thefitted-mixed model, where the %TGI has been recalculated foreach realization. Our reported UI is the values for which 95% ofthe time, the recalculated values of %TGI will fall in this regiongiven the fitted model. The 2.5 and 97.5 percentiles of the sim-ulated distribution were used as the upper and lower UIs.

Plotting was performed and generated using R version 2.8.1and Excel, version 14.2.5 (Microsoft). Data were analyzed using Rversion 2.8.1 (46), and the mixed models were fit within R usingthe nlme package, version 3.1−89 (58). Efficacy at 50% maximalefficacy observed (ED50) and 90% maximal efficacy observed(ED90) were calculated using PRISM based on nonlinearregression.

Fatty Acid Desaturation Studies of Xenograft Tissues. To analyzefatty acid desaturation inMIA Paca-2 shLDHA tumor tissues andmouse plasma and liver, samples were collected at the end of theefficacy study (2 h after the last dose) and snap frozen. Fatty acidprofiling was performed by Microbial ID using a standard samplepreparation method for saponification and methylation. The fattyacid methyl esters were extracted and analyzed by gas chroma-tography. Desaturation index was expressed as the ratio of oleicon stearic methyl ester acids or palmitoleic on palmitic methylester acids.

Cell Line Authentication/Quality Control.Short tandem repeat profiling. Short tandem repeat (STR) profileswere determined for each line using the Promega PowerPlex 16System. STR profiling was performed once and compared withexternal STR profiles of cell lines (when available) to determinecell line ancestry. The loci analyzed were as follows: detection of16 loci (15 STR loci and Amelogenin for sex identification),including D3S1358, TH01, D21S11, D18S51, Penta E, D5S818,D13S317, D7S820, D16S539, CSF1PO, Penta D, AMEL, vWA,D8S1179, and TPOX (Dataset S9).SNP fingerprinting. SNP profiles were performed each time newstocks were expanded for cryopreservation. Cell line identity wasverified by high-throughput SNP profiling using Fluidigm mul-tiplexed assays. SNPs were selected based on minor allele fre-quency and presence on commercial genotyping platforms. SNPprofiles were compared with SNP calls from available internal andexternal data (when available) to determine or confirm ancestry.In cases where data were unavailable or cell line ancestry wasquestionable, DNA or cell lines were repurchased to performprofiling to confirm cell line ancestry. The SNPs analyzed wereas follows: rs11746396, rs16928965, rs2172614, rs10050093,rs10828176, rs16888998, rs16999576, rs1912640, rs2355988,rs3125842, rs10018359, rs10410468, rs10834627, rs11083145,rs11100847, rs11638893, rs12537, rs1956898, rs2069492, rs10740186,rs12486048, rs13032222, rs1635191, rs17174920, rs2590442,rs2714679, rs2928432, rs2999156, rs10461909, rs11180435,rs1784232, rs3783412, rs10885378, rs1726254, rs2391691, rs3739422,rs10108245, rs1425916, rs1325922, rs1709795, rs1934395, rs2280916,rs2563263, rs10755578, rs1529192, rs2927899, rs2848745, andrs10977980.Mycoplasma testing. All stocks were tested for mycoplasma beforeand after cells were cryopreserved. Two methods were used toavoid false-positive/negative results: Lonza Mycoalert and Stra-tagene Mycosensor. Cell growth rates and morphology were alsomonitored for any batch-to-batch changes.



C

Cholines

Other complex lipids

Fatty acids

Sphingolipids

Glycolysis / PPP

OXPHOS /mitochondria related REDOX

Carbohydrates

Amino acids

JG score

15 10 5 0 5

Up in Glycolytic/Lipogenic

subtype

Up in Slow prolife-rating subtype

JG score

Slow

Proli

ferati

ng

Glyc

olytic

Lipog

enic

20

30

40

50

60

70

Dou

blin

gTi

me

(Hrs

) *** * D E

Glyc

olytic

Lipog

enic

-2.0

-1.0

0.0

1.0

2.0

3.0

log2

Asp

arta

teR

U

Glyc

olytic

Lipog

enic

-2.0

-1.0

0.0

1.0

2.0

log2

Glu

tam

ate

RU

Glyc

olytic

Lipog

enic

-2.0

-1.0

0.0

1.0

2.0

log2

Coe

nzy m

e Q

10R

U

Glyc

olytic

Lipog

enic

-3.0

-2.0

-1.0

0.0

1.0

2.0

log2

Coe

nzym

e Q

9R

U

* **

* ***

G

AsP

C−

1Y A

PC

CF

PAC

−1

KP

−3

TC

C−

PAN

2H

s 76

6TC

apan

−2

Pan

c 02

.03

Pan

c 04

.03

Pan

c 03

.27

Pan

c 05

.04

KLM

−1

Pan

c 10

.05

MIA

Pac

a−2

KP

−2

PS

N1

HU

P−

T3

PK

−45

HP

L45

PA−

TU

−89

88T

KP

4P

K−

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SW

199

0PA

−T

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8902

HPA

CK

CI−

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H1

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C−

1S

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−2

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−89

88S

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6.86

PK

−8

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−1

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UP

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xPC

−3

PK

−59

KP

−3L

KP−3LPK−59BxPC−3HUP−T4DAN−GPK−1PK−8SU.86.86HPAF−IIPA−TU−8988SSUIT−2PANC−1KCI−MOH1HPACPA−TU−8902SW 1990PK−45PKP4PA−TU−8988TPL45PK−45HHUP−T3PSN1KP−2MIA Paca−2Panc 10.05KLM−1Panc 05.04Panc 03.27Panc 04.03Panc 02.03Capan−2Hs 766TTCC−PAN2KP−3CFPAC−1YAPCAsPC−1

A

Glyco

lytic

Lipog

enic

4

6

8

10lo

g2La

ctat

eRU

Glyc

olytic

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enic

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-2.0

0.0

2.0

4.0

log2

Serin

e(R

U) *

Glyc

olytic

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enic

-4.0

-3.0

-2.0

-1.0

log2

NA

DH

(RU

)

Glyc

olytic

Lipog

enic

0.0

0.5

1.0

1.5

2.0

2.5

log2

NA

DPH

(RU

) *

Glyc

olytic

Lipog

enic

-3.0

-2.0

-1.0

0.0

1.0

log2

NA

DP

(RU

) *

Glyc

olytic

Lipog

enic

-2.0

-1.0

0.0

1.0

log2

FAD

(RU

)

*

Glyc

olytic

Lipog

enic

0.0

0.5

1.0

1.5

2.0

2.5

log2

GSH

(RU

)

F

0.90

0.92

0.94

0.96

0.98

1.00Cophenetic coefficient

Factorization rank2 3 4 5 6 7

0.75

0.80

0.85

0.90

0.95

1.00Dispersion coefficient

Factorization rank2 3 4 5 6 7

B

Fig. S1. (Continued)



Glyc

olytic

Lipog

enic

5

6

7

8

9

log2

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CR

24(R

PKM

+1)

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olytic

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enic

2

3

4

5

6

log2

DG

AT

(RPK

M+1

)

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olytic

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enic

1

2

3

4

5

log2

SCD

5DL

(RPK

M+1

)

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olytic

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enic

4

6

8

10

log2

FDFT

1(R

PKM

+ 1)

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olytic

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enic

4

6

8

log2

HM

GC

S1(R

PKM

+1)

Glyc

olytic

Lipog

enic

3

4

5

6

7

log2

MVD

(RPK

M+1

) ** * **

* ** *

Glycolysis / PPP

Amino acids

PPARA

OXPHOS

Lipids

JG score

4 2 0 2

Up in Lipogenic subtype

Up in Glycolytic

subtype

JG score

H

Glyc

olytic

Lipog

enic

3

4

5

6

log2

PSPH

(RPK

M+1

)

Glyc

olytic

Lipog

enic

1

2

3

4

log2

PDK

1(R

PKM

+1)

* * I

Glyc

olytic

Lipog

enic

8

9

10

11

log2

ENO

1(R

PKM

+ 1)

Glyc

olytic

Lipog

enic

0

1

2

3

4

log2

ENO

2(R

PKM

+1)

J

subtype

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olytic

Lipo

8

Glyc

olytic

Lipog

enic

0.0

1.0

2.0

3.0

MC

T1(2

%FB

S)R

U

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olytic

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enic

0.0

1.0

2.0

3.0

MC

T1(1

0%FB

S)R

U ** *

l

K

Fig. S1. Related to Fig. 1. (A) Cell line-by-cell line consensus heatmap shows the clustering consensus obtained with nonnegative matrix factorization (NMF)based on 200 runs (21); yellow color indicates similar metabolic profiles and blue indicates dissimilar. The three identified subtypes are colored on top: slowproliferating subtype in gray, glycolytic subtype in purple, lipogenic subtype in cyan. (B) Cophenetic coefficient (measure of subtype stability) and dispersioncoefficient (measure of subtype robustness) in function of the number of subtypes ranging from 2 to 7. The cophenetic coefficient equals 1 for a perfectconsensus matrix with entries 0 and 1 and decreases when entries become scattered between 0 and 1. (C) Relative enrichment of the eight metabolic ontologyclasses in the slow proliferating subtype vs. the glycolytic/lipogenic subtypes, represented by JG score (47). Positive scores represent ontologies enriched formetabolites with high intensities in the slow proliferating subtype. Negative scores represent ontologies characteristic of the glycolytic/lipogenic subtypes. SeeDataset S1 for a list of metabolites per ontology and Dataset S4 for the list of differentially expressed metabolites. (D) Doubling time for all cell lines groupedby subtype, with a lower proliferation rate for cell lines in the slow proliferating subtype. Proliferation was measured using CyQUANT Cell Proliferation Assays.Data from Dataset S7. (E) Normalized metabolite intensity level for lactate involved in glycolysis. RU stands for relative unit, similar to Fig. 1C. (F) Normalized

Legend continued on following page



metabolite intensity levels for metabolites involved in redox pathways that were differentially expressed between glycolytic and lipogenic lines. RU stands forrelative unit, similar to Fig. 1C. (G) Normalized metabolite intensity levels for metabolites involved in the electron transport chain and aspartate/malate shuttle thatwere differentially expressed between glycolytic and lipogenic subtype lines. RU stands for relative unit, similar to Fig. 1C. (H) Relative enrichment of the fivecurated metabolism gene sets in the glycolytic and lipogenic subtypes, represented by JG score. Positive scores represent gene sets enriched in the glycolyticsubtype. Negative scores represent gene sets characteristic of the lipogenic subtype. The transcriptomic profile of the glycolytic subtype is enriched with genesinvolved in glycolysis and pentose phosphate. Cell lines from the lipogenic subtype show higher expression of lipid synthesis genes. Dataset S5 lists genes per geneset, and Dataset S6 lists differentially expressed genes. (I) Expression of several of the glycolysis genes that were differentially expressed between glycolytic andlipogenic lines (Dataset S5 and Fig. 1E). (J) Enolase homologs ENO1 and ENO3 show no differential expression between glycolytic and lipogenic lines. The expressionprofile for ENO2 is shown in Fig. 1F. (K) Western blots and quantification of Mct1 protein in glycolytic and lipogenic lines (quantification normalized to HSP90).(L) Expression of several of the fatty acid synthesis genes (cholesterol and lipids) that were differentially expressed between glycolytic and lipogenic lines (DatasetS5 and Fig. 1F). Asterisks denote a statistically significant difference by t test (*P < 0.05, **P < 0.01, ***P < 0.001).

HPAC (Lipid Subtype) ( p yp )

0 5 10 15 200

500

1000

1500

2000

2500

Day

Tum

or V

olum

e (m

m3 )

9% TGI

shLDHA - 5% SucroseshLDHA - Dox (1 mg/mL)

E

B

LDHA

Tubulin

- DOX + DOX

AA

DGl

ycoly

ticLip

id

20.0

40.0

60.0

80.0

100.0

Rel

ativ

e G

row

th in

0.2

5X L

ipid

* C

Glyc

olytic

Lipid

0.0

0.1

0.2

0.3

FA U

ptak

e (R

FU/c

ell) *

Glyc

olytic

Lipog

enic

0

20

40

60

80

100

Oxa

mat

e IC

50 (m

M)

Glyc

olytic

Lipog

enic

0

5

10

15

20

Cer

ulen

in IC

50 (u

M)

Glyc

olytic

Lipog

enic

0

20

40

60

80

100

Olri

stat

IC50

(uM

)

* A

Fig. S2. Related to Fig. 3. (A) Comparison of IC50 values of lipid synthesis inhibitors cerulenin and orlistat between representative glycolytic and lipogenic celllines in short-term (3 d) viability assays. The mean and SD between cell lines belonging to the glycolytic vs. lipogenic subtype is plotted where each cell line isshown as one dot, representing the mean of three replicates. Asterisks denote a statistically significant difference by Mann–Whitney test (*P < 0.05, **P < 0.01,***P < 0.001). Data from Dataset S7. (B) Comparison in baseline fatty acid (FA) uptake between representative glycolytic and lipogenic cell lines. Asterisksdenote a statistically significant difference by t test (*P < 0.05, **P < 0.01, ***P < 0.001). (C) Comparison in percent growth in 3.75% delipidated serum:1.25%FBS (relative to 5% FBS) between representative glycolytic and lipogenic cell lines. Asterisks denote a statistically significant difference by Mann–Whitney test(*P < 0.05, **P < 0.01, ***P < 0.001). (D) Western blots showing 98% in vivo knockdown of LDHA levels in HPAC xenografts administered with doxycycline(1 mg/mL) for 8 d vs. 5% sucrose. (E) In vivo knockdown of LDHA results in 9% TGI in the HPAC shLDHA model of a lipogenic subtype tumor.



Fig. S3. Related to Fig. 4. (A) Relative enrichment of the five curated metabolism gene sets in cell lines that are sensitive (positive JG score) or resistant(negative JG score) to oxamate in a pan-cancer panel of 133 nonpancreatic cell lines after exclusion of cell lines with intermediate response. See Dataset S5 fora list of genes per gene set. (B) Ratio of ENO2 expression to average lipid gene expression associates with sensitivity to LDHA inhibitor, oxamate, and BPTESacross a variety of tumor types. Saturated values correspond to cell lines where an IC50 was not reached at the maximum drug concentration. Low is defined byRPKM values < lower quartile; high = RPKM values > third quartile. (C) High expression of a pan-cancer EMT signature (EMT) and mesenchymal marker vi-mentin (Vim) associates with sensitivity to LDHA inhibitor and (S)-4-CPG across a variety of tumor types. EMT and Vim low are defined by RPKM values < lowerquartile, EMT and Vim high = RPKM values > third quartile. Asterisks denote a statistically significant difference by Mann–Whitney test (*P < 0.05, **P < 0.01,***P < 0.001, ****P < 0.0001). (D) Metabolic dependency preference in the panel of 36 PDAC cell lines is based on the ratio of ENO2 expression to the averageexpression of five lipid genes. Shown are expression (log2 RPKM + 1) of glycolytic gene ENO2, five lipid genes DGAT1, DHCR7, FDFT1, HMGCS1, and MVD,

Legend continued on following page



average expression of the five lipid genes (Lipid Ave), and the ratio of ENO2 to average lipid expression (ENO2/Lipid Ave), as per Fig. 4C. Slow proliferating linesare labeled in gray, glycolytic lines in purple, and lipogenic in cyan. Six Slow proliferating lines favor the glycolytic phenotype, and six favor the lipogenicphenotype. (E) Epithelial/mesenchymal score for all PDAC lines based on a 42-gene set characteristic of the classical and QM-PDA subtypes (22). The score isdefined as the difference in average expression of QM-PDA vs. classical genes, with a positive score indicative of QM-PDA and a negative score of classical. Celllines are colored by metabolic subtype, with slow proliferating lines in gray, glycolytic lines in purple, and lipogenic lines in cyan. Six slow proliferating lines arestrongly epithelial (of which four are more lipogenic based on expression profiling in D), and six are more mesenchymal (of which four are more glycolyticbased on expression profiling).



PEP

4 2 0 1 2

0.54 0.62

5 6 7 8

78

910

1112

0.025

42

01

2

GAP 0.44 0.098

Serine

32

10

12

0.071

7 8 9 10 11 12

56

78

3 2 1 0 1 2

Lactate

Glycolytic metabolitesA

NADPH

3.0 1.5 0.0

0.85 0.34

1 2 3 4 5

0.44 0.50

1.5 0.0

0.0

1.0

2.0

0.62

3.0

1.5

0.0

NADP 0.56 0.62 0.72 0.71

NADH 0.46 0.52

3.5

2.0

0.34

12

34

5

GSSG 0.78 0.67

GSH

0.5

1.5

0.58

0.0 1.0 2.01.

50.

03.5 2.0 0.5 1.5

FAD

Redox potential metabolitesB

Palmitic acid (C16:0)

0.5 0.0 0.5 1.0

0.74 0.64

1.5 0.5 0.5 1.5

0.5

0.0

0.5

1.0

0.79

0.5

0.0

0.5

1.0

Oleic acid (C18:cis[9]1) 0.80 0.69

Palmitoleic acid (C16:cis[9]1)

1.5

0.5

0.5

1.5

0.68

0.5 0.0 0.5 1.0

1.5

0.5

0.5

1.5

1.5 0.5 0.5 1.5

Myristic acid (C14:0)

Lipid metabolitesC

Coenzyme Q9

1.0 0.0 1.0

20

1

0.71

2 1 0 1

1.0

0.5

Coenzyme Q10

Mitochondrial metabolites (electron transport chain)D

Glutamate

1 0 1 2

1.5

0.0

1.5

0.43

1.5 0.0 1.0

11

2

Aspartate

Mitochondrial metabolites (aspartate malate shuttle)E

Fig. S4. Related to SI Text. Overlap in metabolite intensities between the glycolytic and lipogenic subtypes is not indicative of a phenotype that partially reflects the glycolytic and lipogenic subtypes. Associationplots are shown per set of metabolites: A, glycolytic metabolites; B, redox potential metabolites; C, lipid metabolites; D, mitochondrial metabolites important for the electron transport chain; E, mitochondrialmetabolites from the aspartate-malate shuttle. Shown below the diagonal are scatter plots for each pairwise comparison of metabolites, with relative intensity levels on the x and y axes. Each dot represents a cellline, with glycolytic lines in purple (circle) and lipogenic lines in cyan (triangle). Above the diagonal are the respective Spearman correlation coefficients.



Metabolitescore

1 0 1 2

0.61 0.93

2 1 0 1 2

20

12

3

0.69

10

12

Transcriptionscore 0.73 0.74

Fluxscore

21

01

2

0.65

2 0 1 2 3

21

01

2

2 1 0 1 2

Sensitivityscore

A

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20 60 100

0.63 0.56

0 1000 2000

0.60 0.67

0 200 400

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2 5 10 50 5 10 20 50 100

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0

25

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Cerulenin

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0.005 0.050 0.500

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C

Fig. S5. Related to SI Text. (A) The metabolomics data, transcription profiles, flux experiments, and drug sensitivity confirm robust differences between glycolytic and lipogenic subtype cell lines. PDAC-derived lines wereranked by metabolic dependency for each data type separately, defined as the difference in profile between the glycolytic and lipogenic subtype cell lines, and labeled as metabolite score, transcription score, flux score, andsensitivity score. Shown below the diagonal are scatter plots for each pairwise comparison of scores. (B) Concordance in drug sensitivity to inhibitors of aerobic glycolysis, glutaminolysis, and ROS. Shown below the diagonalare scatter plots for each pairwise comparison of compounds, with IC50 values on the x and y axes. Data are from Dataset S7. (C) Concordance in sensitivity to lipid synthesis inhibitors. Shown below the diagonal are scatterplots for each pairwise comparison of compounds, with IC50 values on log10 scale on the x and y axes. Data are from Dataset S7. For A–C, each dot represents a cell line, with glycolytic lines in purple (circle) and lipogenic linesin cyan (triangle). Above the diagonal are the respective Spearman correlation coefficients.



Dataset S1. Overview of 188 measured, identifiable metabolites, with annotation by super- and subpathway obtained from ref. 25,derived ontology classes used for pathway enrichment analysis, and CAS number

Dataset S1

Dataset S2. Metanomics data for 208 metabolites captured with the broad profiling platform, of which 124 are known metabolites

Dataset S2

Data for each metabolite were normalized to a reference pool of samples, log10-transformed, and normalized to the median signal per sample.

Dataset S3. Metanomics data for 64 metabolites captured with the energy platform, of which all are known

Dataset S3

Data for each metabolite were normalized against the corresponding 13C-analyte, log10-transformed, and normalized to the median signal per sample.

Dataset S4. Worksheet A (differential measurement of all metabolites between the glycolytic and lipogenic subtypes and worksheet B(differential measurement of all metabolites between the slow proliferating subtype and glycolytic/lipogenic subtypes)

Dataset S4

Adjusted P values are based on the Benjamini–Hochberg approach (59). Metabolite ontologies refer to those used in the metabolite enrichment analysis(Fig. 1B and Fig. S1C). Metabolites measured on the MxP energy profiling platform are indicated by their name followed by E.

Dataset S5. Metabolism gene sets

Dataset S5

Consistently low-expressed genes were flagged and excluded from gene set enrichment analysis.

Dataset S6. Differential expression of metabolism genes between the glycolytic and lipogenic subtypes

Dataset S6

Columns show log2 fold change, Wald statistic, P value, and adjusted P value. Positive values for fold change and statistic values are indicative of higherexpression in the lipogenic subtype. Metabolism genes were obtained from Dataset S5 and from ref. 52 are labeled as Metabolome.

Dataset S7. List of pancreatic cell lines with subtype label, QM-PDA prediction score (scored only for glycolytic and lipogenic cell lineswith available RNAseq data), proliferation rate, response to a variety of metabolic inhibitors, 13C and 14C metabolic flux data, O2 consumptionrate, mitochondrial content, fatty acid uptake, growth in delipidated serum, KRAS mutation status, histology information, and siteof origin

Dataset S7


http://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1501605112/-/DCSupplemental/pnas.1501605112.sd01.xlshttp://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1501605112/-/DCSupplemental/pnas.1501605112.sd02.xlsxhttp://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1501605112/-/DCSupplemental/pnas.1501605112.sd03.xlsxhttp://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1501605112/-/DCSupplemental/pnas.1501605112.sd04.xlshttp://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1501605112/-/DCSupplemental/pnas.1501605112.sd05.xlshttp://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1501605112/-/DCSupplemental/pnas.1501605112.sd06.xlshttp://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1501605112/-/DCSupplemental/pnas.1501605112.sd07.xlsxwww.pnas.org/cgi/content/short/1501605112

Dataset S8. Panel of 216 nonpancreatic cell lines, with sensitivity to metabolic inhibitors oxamate, BPTES, BSO, and (S)-4-CPG and log2(RPKM+1) values for the pan-cancer EMT signature (33), VIM, ENO2, and lipid genes MVD, HMGCS1, FDFT1, DCHR7, and DGAT

Dataset S8

IC50 = half maximal inhibitory concentration, or compound concentration required for 50% inhibition in vitro. Saturated values (25, 50, 500, 2,000)correspond to cell lines where an IC50 was not reached at the maximum drug concentration. Cell lines included in the gene set enrichment analyses foroxamate and/or BPTES are flagged (Fig. 4B and Fig. S3A). Sensitivity to LDHA inhibitor is available in ref. 26.

Dataset S9. STR profiles for the panel of 38 pancreatic and 216 nonpancreatic cancer cell lines

Dataset S9

http://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1501605112/-/DCSupplemental/pnas.1501605112.sd08.xlsbhttp://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1501605112/-/DCSupplemental/pnas.1501605112.sd09.xlsxwww.pnas.org/cgi/content/short/1501605112

supporting information - pnas...jul 21, 2015 · supporting information daemen et al....

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