Biomedical Research (Tokyo) 38 (1) 29–40, 2017

Metabolic profiling of gemcitabine- and paclitaxel-treated immortalized human pancreatic cell lines with K-RASG12D

Akiko TODAKA1, 6, 7, Rina UMEHARA

2, Keiko SASAKI3, Masakuni SERIZAWA

2, Kenichi URAKAMI4, Masatoshi

KUSUHARA2, 5, Ken YAMAGUCHI

6, and Hirofumi YASUI1

1 Division of Gastrointestinal Oncology, Shizuoka Cancer Center, 1007 Shimonagakubo, Nagaizumi-cho, Sunto-gun, Shizuoka 411-8777, Japan; 2 Drug Discovery and Development Division, Shizuoka Cancer Center Research Institute, 1007 Shimonagakubo, Nagaizumi-cho, Sunto-gun, Shizuoka 411-8777, Japan; 3 Division of Pathology, Shizuoka Cancer Centre, 1007 Shimonagakubo, Nagaizumi-cho, Sunto-gun, Shizuoka 411-8777, Japan; 4 Cancer Diagnostics Research Division, Shizuoka Cancer Center Research Institute, 1007 Shimonagakubo, Nagaizumi-cho, Sunto-gun, Shizuoka 411-8777, Japan; 5 Regional Resources Division, Shizuoka Cancer Center Research Institute, 1007 Shimonagakubo, Nagaizumi-cho, Sunto-gun, Shizuoka 411-8777, Japan; 6 Shizuoka Cancer Center, 1007 Shimonagakubo, Nagaizumi-cho, Sunto-gun, Shizuoka 411-8777, Japan; and 7 Department of Surgery, Keio University Graduate School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan

(Received 28 October 2016; and accepted 7 November 2016)

ABSTRACTThe mechanisms of action of gemcitabine (GEM) and paclitaxel (PTX) have been well investigat-ed, and shown to be the inhibition of DNA polymerase and polymerization of tubulin, respective-ly. Meanwhile, genomic research has revealed that mutations in the K-RAS oncogene occur in over 90% of pancreatic cancer. Oncogenic alteration rewires alternative metabolic pathways to sat-isfy the demands of growth. The K-RAS oncogene also has been shown to upregulate glycolysis and glutaminolysis. However, it is still unclear whether K-RAS independently plays a central role in controlling tumor metabolism. Here, we conducted a metabolomic analysis of a simple onco-genic K-RAS cell line model constructed using human telomerase catalytic subunit-immortalized human pancreatic epithelial nestin-expressing cell lines with and without K-RASG12D. We also in-vestigated the effect of GEM and PTX on these cells. As a result, it was shown in the cell with K-RASG12D that the level of lactate was increased and glutamic acid, glutamine, and aspartic acid levels were decreased. In the nucleotide metabolism, GEM-treated cells showed metabolic chang-es, whereas these phenomena were not observed in PTX-treated cells. In conclusion, it was sug-gested that K-RASG12D independently modified tumor metabolism and the difference between GEM and PTX in the nucleotide metabolism was revealed.

Pancreatic cancer is the fourth-leading cause of can-cer-related death in Japan and the USA (25). Surgical resection is the only potentially curative treatment, but the majority of patients have unresectable dis-ease at the time of diagnosis due to distant metasta-

sis or vascular involvement. Gemcitabine (GEM: 4-amino-1-[3,3-difluoro-4-hydroxy-5-(hydroxymeth-yl) tetrahydrofuran-2-yl]-1H-pyrimidin-2-one) mono-therapy became a standard treatment for unresectable pancreatic cancer after it demonstrated superior clin-ical benefit over 5-fluorouracil (3). Median overall survival for patients who received GEM monothera-py for advanced pancreatic cancer in key trials has ranged from 5.7–6.2 months (3, 22). Numerous phase III trials of other treatments have failed to meaningfully improve overall survival over GEM monotherapy (18, 14, 28). Although GEM plus erlo-

Address correspondence to: Dr. Hirofumi YasuiDivision of Gastrointestinal Oncology, Shizuoka Cancer Center, 1007 Shimonagakubo, Nagaizumi-cho, Sunto- gun, Shizuoka 411-8777, JapanTel: +81-55-989-5222, Fax: +81-55-989-6085E-mail: h.yasui@scchr.jp

A. Todaka et al.30

　Research based on genomic analyses by next-gen-eration sequencing has revealed that pancreatic can-cer is fundamentally a genetic disease. The early acquisition of activating mutations in the K-RAS on-cogene, which occurs in > 90% of cases, is critical for invasion and metastasis (1, 16). The predominant substitution in K-RAS is an amino acid substitution at position 12 in K-RAS, from a glycine (G) to an aspartic acid (D) (G12D) (7). Constitutive K-RASG12D signaling promotes cell immortality and enhances the survival of tumor cells via the activation of its down-stream signaling pathways, the MAPK pathway (34). To cultivate cell growth, the tumor cells require both sufficient energy and biosynthetic precursors for cel-lular building blocks (2).　Oncogenic alteration rewires alternative metabolic pathways to satisfy the demands of growth and pro-liferation. The characteristic metabolic rewiring of tumor cells is the Warburg effect, which switches cells to preferential glycolysis and lactate fermenta-tion and away from oxidative phosphorylation even in the presence of sufficient oxygen. The regulation of glycolysis and lactate fermentation by various on-cogenes and suppressor gene, for example c-MYC, HIF-1, and p53, has been reported (37). Recently the K-RAS oncogene also has been shown to upreg-ulate glycolysis and glutaminolysis (11, 26, 38).　It is not known whether K-RAS plays a central role in controlling tumor metabolism, or whether it independently alters cell metabolism. Further, no metabolomic evaluation of the effect of widely used cancer drugs such as GEM or PTX in the presence of K-RAS has appeared. Recently, various experi-mental systems involving K-RAS-driven genetically engineered mouse models of pancreatic cancer have been established for metabolomics applications (13, 38). Although such models are valuable tools in an-alyzing the role of K-RAS-mediated oncogenesis, whether they accurately reproduce and reflect the common forms of pancreatic cancer remains contro-versial.　Cell culture models remain a feasible tool for evaluating the effect of a simple factor. Until now, these have been exclusively cell line models derived from tumor tissue, which has limited their experi-mental versatility. The reasons include redundant on-cogenic mutation, heterogeneity of gene alteration, and chromosomal instability (12, 13). Here, we con-ducted a metabolomic study of K-RASG12D using a human telomerase catalytic subunit (hTERT)-immor-talized human pancreatic epithelial nestin-expressing (HPNE) cell line, which was genetically modified by introduction of HPV E6/E7, SV40 small t anti-

tinib, which is an oral epidermal growth factor re-ceptor tyrosine kinase inhibitor, showed significant improvement in overall survival, the benefit was modest (22).　GEM is transported into cells by nucleoside trans-porters. GEM is phosphorylated into gemcitabine monophosphate (dFdCMP) by deoxycytidine kinase (DCK), and dFdCMP is subsequently phosphorylated to gemcitabine diphosphate (dFdCDP) and gemcit-abine triphosphate (dFdCTP) by nucleoside mono-phosphate and diphosphate kinase. dFdCTP competes with deoxycytidine triphosphate (dCTP) as an inhib-itor of DNA polymerase. dFdCDP is a potent inhibi-tor of ribonucleoside reductase, resulting in decline of the deoxyribonucleotide pools necessary for DNA synthesis (21, 29).　Paclitaxel (PTX) inhibits cell proliferation by dif-ferent mechanism. PTX promotes microtubule as-sembly by enhancing the action of tubulin dimers, stabilizing existing microtubules, and inhibiting their disassembly, interfering with the late G2 mitotic phase, and inhibiting cell replication. In addition, the drug can distort mitotic spindles, resulting in the breakage of chromosomes. The newly available data demonstrate that intratumoral concentrations of PTX cause cell death due to chromosome missegregation on multipolar spindles (39).　Recently, two promising drug combinations, FOL-FIRINOX (oxaliplatin, irinotecan, 5-FU, and leucovo-rin) and nanoparticle albumin-bound (nab)-paclitaxel (PTX) plus GEM, have become available, and have demonstrated an improvement in overall survival compared with GEM monotherapy (35). Nab-PTX is the first nanoparticle-based drug approved for use by the US Food and Drug Administration. Nab-PTX is composed of a drug delivery vehicle attached to PTX. It has shown antitumor activity as a single agent and synergistic activity in combination with GEM in murine models of pancreatic cancer (36). This form of the drug can achieve a 33% higher tu-mor uptake relative to solvent-based paclitaxel and does not induce hypersensitivity (allergic) reactions. Chen et al. reported that albumin containing PTX enhanced penetration within tumors through binding to gp60 albumin receptor and caveolae-mediated albumin transcytosis (5). Further, Desai et al. demon-strated that nab-PTX may associate with the albu-min-binding protein SPARC (secreted protein, acidic and rich in cysteine) and promote intratumoral accu-mulation of the drug (8). Recent data also suggest that nab-PTX may enhance the intratumoral concen-tration of GEM by inhibiting cytidine deaminase, the enzyme that degrades gemcitabine (10).

Metabolic profiling of pancreatic cell lines 31

Ltd., Osaka, Japan) ranging between 0.00005 and 500 nmol/L and PTX (Wako Pure Chemical Indus-tries, Ltd.) ranging between 0.00001 and 100 nmol/L, respectively. Following drug exposure for three days, cell viability was measured by the viability kit (CCK-8 kit, Dojindo). The experiment was performed in n = 6 at each concentration.

Metabolomic analysis. For detection of the change of metabolites in a few hours after drug exposure, we previously confirmed appropriate concentration of GEM and PTX for metabolome experiments was 50 nM and 10 nM respectively in pilot study. Next, the exposure time for drugs was decided to 4 h from the experiment which the cells incubate in 2, 4 and 8 h with 50 nM GEM. Fig. 3 showed typical alter-ation of nucleotide metabolism which is main phar-macological effect of GEM.　Cells were seeded in 10-cm culture dishes and in-cubated in growth medium until they reached 80% confluence, then were exposed to GEM or PTX at concentrations of 50 nM and 10 nM, respectively. Sample collection for metabolite extraction, analytic conditions for metabolomic analysis and data visual-ization were performed according to previously de-scribed methods (31). Briefly, at 0, 2, 4, and 8 h, the medium was removed, 1.0 mL of methanol was add-ed, and the cells were collected using a cell scraper.　Cationic and anionic metabolite analysis was per-formed with the HMT Metabolomics Solution Package (Human Metabolome Technologies Inc., Yamagata, Japan) according to a published method (31). A total of 102 standard metabolites (a kind gift from Hu-man Metabolome Technologies, Inc.) were analyzed as references. The quantities and identities of cation-ic and anionic metabolites present in the samples were determined by capillary electrophoresis-mass spectrometry (CE-MS/6224 TOFMS; Agilent Tech-nologies, Inc., Santa Clara, CA, USA). Quantities and identities of the 102 standard metabolites were determined according to their m/z ratios and their migration times using special software (MasterHands metabolomics analysis software, Keio University). All target metabolites were identified by comparing their m/z values and migration times to those of the standard compounds. For each sample, the measured metabolite concentrations were normalized by cell number to obtain the amount of metabolite con-tained per million cells (nmol/M cells).　Because there are no references and no informa-tion about migration times for GEM, dFdCMP, dFdCDP and dFdCTP, non-targeted CE-TOF-MS me-tabolomic analysis was performed. GEM metabolites

gen and K-RASG12D (CRL-4039), as well as its par-ent cell line without K-RASG12D (CRL-4037) (4).

MATERIALS AND METHODS

Cell culture. CRL-4037 and CRL-4039 were pur-chased from the American Type Culture Collection (Manassas, VA USA). Cells were subcultured in 75-cm2 tissue culture flasks (Corning Glass Works, Corning NY, USA) and maintained in growth medi-um: 25% Medium M3 Base (INCELL Corp, San Antonio, Texas) /75% DMEM without glucose (Sig-ma-Aldrich, St. Louis, MO) supplemented with 5% fetal calf serum (FCS), 10 ng/mL human recombi-nant epidermal growth factor (Sigma), 1 g/L D-glu-cose (Sigma), and 750 ng/mL puromycin (Sigma) at 37°C in water-saturated air with 5% CO2.

Whole exome sequencing analysis. Whole exome se-quencing analysis was performed according to previ-ously described methods (23). Briefly, DNA was extracted from 106 cells using a kit (Blood & Cell Culture DNA Midi Kit; QIAGEN, Hilden, Germany). The exome library used for whole exome sequencing (WES) was prepared using an Ion Torrent AmpliSeq RDY Exome Kit (Thermo Fisher Scientific) in ac-cordance with the manufacturer’s protocol. Libraries were quantified using the quantitative polymerase chain reaction (qPCR), and DNA (8 pM) was se-quenced using a semiconductor DNA sequencer (Ion Torrent Proton Sequencer, Thermo Fisher Scientific) according to the manufacturer’s protocol. Specific variants of CRL-4037 or CRL-4039 were identified using commercial software (Ion Reporter ver. 4.4 software, Thermo Fisher Scientific) (15) after base calling, quality trimming, and mapping to the hg19/GRCh37 reference genome using commercial soft-ware (Torrent Suite software ver. 4.4, Thermo Fish-er Scientific) (27).

Cell growth assay. A total of 1.5 × 103 cells per well were suspended in growth medium and incubated at 37°C in an atmosphere containing 5% CO2. At Days 1, 2, 3, and 4, the number of viable cells was deter-mined using a kit (Cell Counting Kit-8, CCK-8; Dojindo Laboratories, Gaithersburg MD, USA).

Cell growth inhibition assay. Cells in the logarith-mic growth phase were plated at a density of 3 × 102 cells/well in 96-well plates. Following overnight ad-herence, the complete medium was replaced with growth medium containing 12 different concentra-tions of GEM (Wako Pure Chemical Industries,

A. Todaka et al.32

were identified by subtracting the metabolomic data of non-treated CRL-4037 from those of GEM-treat-ed CRL-4037 cells.　The results were mapped to metabolic pathways using special software (Visualization and Analysis of Networks containing Experimental Data [VANTED] visualization software) (19). Three or more indepen-dent experiments were conducted.

RESULTS AND DISCUSSION

In this study, we first confirmed the characteristics of the CRL-4037 and CRL-4039 cell lines. Whole exome sequencing for CRL-4039 and CRL-4037 was performed using a next-generation DNA se-quencer. By comparing both sequences (Table 1), 37 single nucleotide variants (SNVs) were detected ei-ther in CRL-4039 or CRL-4037. Generally, the chromosomes of cultured cells are unstable and karyotypes change with successive subculture (30). However, few studies have compared SNVs in suc-cessive subcultured cells. In isogenic cells also, for example parent cells and their daughter cells, little is known about difference in SNVs (24). Compari-son of SNVs in the present genetically engineered cell lines is interesting. Among the 37 SNVs, six are registered in the Catalogue of Somatic Mutations In Cancer (COSMIC) database, which records more than 6 million non-coding mutations found in hu-man cancer (9). These six SNVs did not include driver gene mutations, as defined by Vogelstein et al. (33). A typical tumor contains driver gene muta- Fig. 1　Growth curve of CRL-4037 and CRL4039.

tions that activate proto-oncogenes, leading to a se-lective growth advantage, as well as mutations that conversely inactivate the function of tumor suppres-sor genes. These SNVs profile showed no evidence of the driver gene that affects the tumor signaling pathway. The genetic profile of CRL-4037 was iden-tical to that of CRL-4039 except that, as expected, CRL-4039 expressed extrinsic K-RASG12D.　In terms of cell growth (Fig. 1), CRL-4039 with K-RASG12D grew faster than CRL-4037; growth sig-nals from K-RASG12D may explain this. A growth in-hibition assay of GEM and PTX performed using a colorimetric assay (Fig. 2) revealed that the IC50

Fig. 2　Cell growth inhibition assay.

Metabolic profiling of pancreatic cell lines 33

Tab

le 1

　D

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s in

exo

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ces

betw

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CR

L-40

37 a

nd C

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thC

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9 1

chr1

3190

6973

GA

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INC

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2m

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aGc/

aAc:

S377

N,S

432N

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6N,S

441N

0.0

14.3

2321

2ch

r157

4768

29C

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nal t

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298

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A. Todaka et al.34

glutamic acid (Glu), glutamine (Gln), and aspartic acid (Asp) levels in CRL-4039 were decreased. The K-RAS mutation in CRL-4039 led to increased glu-cose uptake coupled to enhanced lactate production through the Warburg effect (32). This was not af-fected by GEM or PTX. Oncogenic K-RAS is known to control the reprogramming of tumor cells by sev-eral mechanisms, including enhanced glycolysis and glutaminolysis (2). In an inducible K-RASG12D-driv-en pancreatic ductal adenocarcinoma (PDAC) mouse model, K-RASG12D extinction led to decreased glu-cose uptake and lactate production (38). Further-more, reprogramming of glutamine metabolism in human pancreatic ductal adenocarcinoma has been reported (26). Glutamate dehydrogenase in the mito-chondria converts glutamine to oxaloacetic acid by aspartate transaminase (GOT1). Thereafter, oxalo-acetic acid is converted to malic acid and pyruvic acid. Accompanying a high NADPH/NADP + ratio to maintain the redox state is a distinctive pathway on which the PDAC cells with the K-RAS mutation are strongly dependent. Therefore, this study has demonstrated that K-RASG12D can reprogram hTERT-

value for GEM was 0.03 nM for CRL-4039 and 0.06 nM for CRL-4037. The IC50 value for PTX was 0.0004 nM for CRL-4039 and 0.06 nM for CRL-4037. The large difference in IC50 values for PTX between CRL-4039 and CRL-4037, in other words the high sensitivity of CRL-4039 with K-RASG12D to PTX, can be explained by the mechanism of action of PTX, which is to inhibit cell division. These characteristics make CRL-4039 and CRL-4037 ap-propriate cells to explore the distinct role of K-RAS.　To examine the metabolic role of K-RASG12D, tar-geted CE-TOF-MS metabolomic analysis was per-formed to comprehensively characterize metabolic changes. Metabolomic analysis of cultured cells de-tected 102 metabolites, of which 95 were identified according to m/z values of standards (Table 2). These molecules were mapped to the metabolic pathways shown in Figs. 4–6. Distinct metabolic changes be-tween CRL-4037 and CRL-4039 with K-RASG12D were observed in glucose metabolism (Fig. 4) and amino acid metabolism (Fig. 6). There were no dif-ferences in pentose phosphate metabolism (Fig. 5). Levels of lactate in CRL-4039 were increased and

Fig. 3　Pathway map of metabolites in nucleotide metabolism of GEM-treated cells. White, light grey, dark grey and black boxes represent metabolite concentrations (nmol/million cells) at 0, 2, 4, and 8 h in CRL-4037, respectively. Upper and low-er chart show the GEM treated and untreated cells, respectively. Charts for dCTP and dATP in boxes (broken line) show a decrease when GEM was added. Especially dATP did not change when more than 4 h. All error bars represent S.D. (n = 3) except at 2 and 8 h (n = 1).

Metabolic profiling of pancreatic cell lines 35

Metabolite (anion) m/zGlyoxylic acid 72.9931Glycolic acid 75.0088Pyruvic acid 87.0088Lactic acid 89.0244Fumaric acid 115.00372-Oxoisovaleric acid 115.0401Succinic acid 117.0193Malic acid 133.01422-Oxoglutaric acid 145.0142Phosphoenolpyruvic acid 166.9751Dihydroxyacetone phosphate 168.9908Glycerol 3-phosphate 171.0064cis-Aconitic acid 173.00923-Phosphoglyceric acid 184.9857Citric acid 191.0197Isocitric acid 191.0197Gluconic acid 195.0510Erythrose 4-phosphate 199.0013Ribose 5-phosphate 229.0119Ribulose 5-phosphate 229.0119Fructose 6-phosphate 259.0224Glucose 6-phosphate 259.0224Glucose 1-phosphate 259.02246-Phosphogluconic acid 275.0174Sedoheptulose 7-phosphate 289.0330dTMP 321.0493CMP 322.0446cAMP 328.0452Fructose 1,6-diphosphate 338.9888cGMP 344.0402AMP 346.0558IMP 347.0398GMP 362.0507NADPH 371.5383CoA 382.5503PRPP 388.9445FAD 391.5713dTDP 401.0157CDP 402.0109Acetyl CoA 403.5556Malonyl CoA 425.5505ADP 426.0221Succinyl CoA 432.5584GDP 442.0171dCTP 465.9823dTTP 480.9820CTP 481.9772UTP 482.9613dATP 489.9936ATP 505.9885GTP 521.9834NAD+ 662.1019NADH 664.1175NADP+ 742.0682

Table 2 　The list of standard metabolies and their exact mass

Metabolite (cation) m/zGly 76.0390Putrescine 89.1070β-Ala 90.0550Ala 90.0550γ-Aminobutyric acid 104.0710Ser 106.0500Cytosine 112.0510Uracil 113.0350Creatinine 114.0660Pro 116.0710Val 118.0860Homoserine 120.0660Thr 120.0660Cys 122.0270Hydroxyproline 132.0660Creatine 132.0770Ile 132.1020Leu 132.1020Asn 133.0610Ornithine 133.0970Asp 134.0450Adenine 136.0620Hypoxanthine 137.0460Anthranilic acid 138.0550Tyramine 138.0910Spermidine 146.1650Gln 147.0760Lys 147.1130Glu 148.0600Met 150.0580Guanine 152.0570His 156.0770Phe 166.0860Arg 175.1190Citrulline 176.1030Tyr 182.0810DOPA 198.0760Spermine 203.2230Trp 205.0970Carnosine 227.1140Cytidine 244.0930Uridine 245.0770Adenosine 268.1040Inosine 269.0880Guanosine 284.0990Glutathione (GSSG) divalent 307.0830Glutathione (GSH) 308.0910S-Adenosylmethionine 399.1450

A. Todaka et al.36

not change when exposed to GEM and PTX. In contrast, metabolism of the nucleotide deoxyadenos-ine triphosphate (dATP) and deoxycytidine triphos-phate (dCTP) was decreased in GEM-treated, but not

HPNE cells.　Comparison of GEM and PTX in glycolysis, the citrate cycle (Fig. 4), and the pentose phosphate pathway (Fig. 5) showed that most metabolites did

Fig. 4　Pathway map of metabolites in glycogenesis, glycolysis, the citrate cycle and amino acids in GEM-treated and PTX-treated cells. White, grey and black boxes represent metabolite concentrations (nmol/million cells) in untreated, GEM treated, and PTX treated cells, respectively. Upper and lower charts show metabolite concentrations for CRL4039 and CRL-4037, respectively. Charts in boxes (broken line) show lactic acid was significantly higher in CRL-4039 than CRL 4037. Likewise, Asp, Glu and Gln were lower in CRL-4039 than CRL-4037. All error bars represent S.D. (n = 3).

Metabolic profiling of pancreatic cell lines 37

Fig. 5　Pathway map of metabolites in the pentose phosphate pathway in GEM-treated and PTX-treated cells. White, grey and black boxes represent metabolite concentrations (nmol/million cells) in untreated, GEM treated, and PTX treated cells, respectively. Upper and lower charts show metabolite concentrations in CRL4039 and CRL-4037, respectively. Almost no change was seen in any metabolite. All error bars represent S.D. (n = 3).

in PTX-treated, CRL-4037 and CRL-4039 (Fig. 6). One of the cytotoxic effects of GEM is attributed to GEM diphosphate (dFdCDP), which inhibits ribonu-cleotide reductase (21). This enzyme is responsible for catalyzing the reactions that generate deoxynu-cleoside triphosphates for DNA synthesis. Inhibition of this enzyme by the diphosphate nucleoside caus-es a reduction in deoxynucleotide concentrations, in-cluding dCTP and dATP. Fig. 7 shows a schematic chart for the effect on cellular metabolism, and mechanisms and self-potentiation of GEM. “Self-po-tentiation” means that the reduction in the intracel-lular concentration of dCTP by GEM enhances the incorporation of GEM triphosphate into DNA in competition with dCTP. The relative concentrations of GEM, dFdCMP, dFdCDP, and dFdCTP were cal-culated by non-targeted CE-TOF-MS metabolomic analysis. The typical signature for GEM was ob-served, namely a rapid disappearance of GEM, de-crease in dCTP, and accumulation of dFdCDP and dFdCTP.　Remarkably, the metabolite chart (Fig. 6) indicates

that there was no considerable difference between GEM-treated CRL-4037 and GEM-treated CRL-4039 K-RASG12D samples except for guanosine and ade-nosine, which were reduced in GEM-treated CRL-4037 but not in GEM-treated CRL-4039. A previous study reported that subgroup patients with K-RAS mutations showed an adverse response and shorter survival compared to those with wild-type K-RAS (17). Though the relationship between GEM and the K-RASG12D mutation is controversial, the difference between CRL-4037 and CRL-4039 in adenosine and guanosine highlights the significance of K-RASG12D.　Interestingly, despite the strong growth inhibitory effect, both CRL-4037 and CRL-4039 treated with PTX showed no changes in metabolite levels through any pathway. This was expected since the mecha-nism of action of PTX is to bind microtubules and prevent their breakdown. Recently, a clinical study showed that nab-paclitaxel in combination with GEM was a promising regimen (35); while another study found that PTX does not affect the pharmacokinet-ics of GEM, and that GEM does not affect the phar-

A. Todaka et al.38

Fig. 6　Pathway of metabolites in nucleotide metabolism in GEM-treated and PTX-treated cells. White, grey and black box-es represent metabolite concentrations (nmol/million cells) in untreated, GEM treated, and PTX treated cells, respectively. Upper and lower charts show metabolite concentrations for CRL4039 and CRL-4037, respectively. Charts in boxes (broken line) indicate that dCTP and dATP were strongly decreased in GEM-treatd cells. Likewise, adenosine and guanosine are lower in GEM-treated CRL-4037 than GEM-treated CRL-4039 cells. All error bars represent S.D. (n = 3).

Fig. 7　Cellular metabolism, mechanisms and self-potentiation of gemcitabine. White, light grey, dark grey and black boxes represent metabolite concentrations (nmol/million cells) at 0, 2, 4, and 8 h in CRL-4037, respectively. Estimating the con-centration of dFdCDP at 0 h is about 1, the relative concentration of GEM, dFdCMP, dFCTP were calculated. Upper and lower chart show the GEM treated and untreated cells, respectively. Solid and broken line represented metabolic flow and inhibition respectively. All error bars represent S.D. (n = 3) except at 2 and 8 h (n = 1).

Metabolic profiling of pancreatic cell lines 39

macokinetics of PTX (20). These data indicate that GEM and PTX may have different effects on cellu-lar metabolism and cell cycle distribution, and po-tentially different additive effects.　Here, we demonstrated the effects of GEM and PTX on a K-RAS cell line model with and without K-RASG12D. This is the first report of a comprehen-sive analysis of metabolites in hTERT immortalized cells with K-RASG12D in the presence or absence of chemotherapeutic agents.

REFERENCES 1. Biankin AV, Waddell N, Kassahn KS, Gingras MC,

Muthuswamy LB, et al. (2012) Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes. Nature 491, 399–405.

2. Bryant KL, Mancias JD, Kimmelman AC and Der CJ (2014) KRAS: feeding pancreatic cancer proliferation. Trends Bio-chem Sci 39, 91–100.

3. Burris HA 3rd, Moore MJ, Andersen J, Green MR, Rothenberg ML, et al. (1997) Improvements in survival and clinical ben-efit with gemcitabine as first-line therapy for patients with advanced pancreas cancer: a randomized trial. J Clin Oncol 15, 2403–2413.

4. Campbell PM, Groehler AL, Lee KM, Ouellette MM, Khazak V and Der CJ (2007) K-Ras promotes growth transformation and invasion of immortalized human pancreatic cells by Raf and phosphatidylinositol 3-kinase signaling. Cancer Res 67, 2098–2106.

5. Chen N, Brachmann C, Liu X, Pierce DW, Dey J, Kerwin WS, Li Y, Zhou S, Hou S, Carleton M, Klinghoffer RA, Palmisano M and Chopra R (2015) Albumin-bound nanopar-ticle (nab) paclitaxel exhibits enhanced paclitaxel tissue dis-tribution and tumor penetration. Cancer Chemother Pharmacol 76, 699–712.

6. Conroy T, Desseigne F, Ychou M, Bouché O and Guimbaud R (2011) Groupe Tumeurs Digestives of Unicancer; PRODIGE Intergroup. FOLFIRINOX versus gemcitabine for metastatic pancreatic cancer. N Engl J Med 364, 1817–1825.

7. Cox AD and Der CJ (2010) Ras history: The saga continues. Small GTPases 1, 2–27.

8. Desai N, Trieu V, Damascelli B and Soon-Shiong P (2009) SPARC expression correlates with tumor response to albu-min-bound paclitaxel in head and neck cancer patients. Transl Oncol 2, 59–64.

9. Forbes SA, Beare D, Gunasekaran P, Leung K, Bindal N, Boutselakis H, Ding M, Bamford S, Cole C, Ward S, Kok CY, Jia M, De T, Teague JW, Stratton MR, McDermott U and Campbell PJ (2015) COSMIC: exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res 43, D805-D811.

10. Frese KK, Neesse A, Cook N, Bapiro TE, Lolkema MP, Jodrell DI and Tuveson DA (2012) nab-Paclitaxel potentiates gemcitabine activity by reducing cytidine deaminase levels in a mouse model of pancreatic cancer. Cancer Discov 2, 260–269.

11. Gaglio D, Metallo CM, Gameiro PA, Hiller K, Danna LS, Balestrieri C, Alberghina L, Stephanopoulos G and Chiaradonna F (2011) Oncogenic K-Ras decouples glucose and glutamine metabolism to support cancer cell growth. Mol Syst Biol 7, 523.

12. Gillet JP, Calcagno AM, Varma S, Marino M and Gottesman MM (2011) Redefining the relevance of established cancer cell lines to the study of mechanisms of clinical anti-cancer drug resistance. Proc Natl Acad Sci USA 108, 18708–18713.

13. Gillet JP, Varma S and Gottesman MM (2013) The clinical relevance of cancer cell lines. J Natl Cancer Inst 105, 452–458.

14. Gonçalves A, Gilabert M, François E, Dahan L, Perrier H, et al. (2012) BAYPAN study: a double-blind phase III random-ized trial comparing gemcitabine plus sorafenib and gemcit-abine plus placebo in patients with advanced pancreatic cancer. Ann Oncol 23, 2799–2805.

15. Ion Reporter Software User Guide: Tumor-Normal pair work-flow (2016) https://ionreporter.thermofisher.com/ionreporter/help/GUID-76DAA3C5-573B-4446-B467-A7185DF28C6D.html

16. Jones S, Zhang X, Parsons DW, Lin JC, Leary RJ, et al. (2008) Core signaling pathways in human pancreatic cancers re-vealed by global genomic analyses. Science 321, 1801–1806.

17. Kim ST, Lim DH, Jang KT, Lim T, Lee J, Choi YL, Jang HL, Yi JH, Baek KK, Park SH, Park YS, Lim HY, Kang WK and Park JO (2011) Impact of KRAS mutations on clinical outcomes in pancreatic cancer patients treated with first-line gemcitabine-based chemotherapy. Mol Cancer Ther 10, 1993–1999.

18. Kindler HL, Niedzwiecki D, Hollis D, Sutherland S, Schrag D, et al. (2010) Gemcitabine plus bevacizumab compared with gemcitabine plus placebo in patients with advanced pancreat-ic cancer: phase III trial of the Cancer and Leukemia Group B (CALGB 80303). J Clin Oncol 28, 3617–3622.

19. Klukas C and Schreiber F (2010) Integration of -omics data and networks for biomedical research with VANTED. J Inte-gr Bioinform 7, 112–117.

20. Kroep JR, Giaccone G, Voorn DA, Smit EF, Beijnen JH, Rosing H, van Moorsel CJ, van Groeningen CJ, Postmus PE, Pinedo HM and Peters GJ (1999) Gemcitabine and pacli-taxel: pharmacokinetic and pharmacodynamic interactions in patients with non-small-cell lung cancer. J Clin Oncol 17, 2190–2197.

21. Mini E, Nobili S, Caciagli B, Landini I and Mazzei T (2006) Cellular pharmacology of gemcitabine. Ann Oncol 17, v7–12

22. Moore MJ, Goldstein D, Hamm J, Figer A, Hecht JR, et al. (2007) Erlotinib plus gemcitabine compared with gemcit-abine alone in patients with advanced pancreatic cancer: a phase III trial of the National Cancer Institute of Canada Clinical Trials Group. J Clin Oncol 25, 1960–1966.

23. Nagashima T, Shimoda Y, Tanabe T, Naruoka A, Saito J, et al. (2016) Optimizing an ion semiconductor sequencing data analysis method to identify somatic mutations in the genomes of cancer cells in clinical tissue samples. Biomed Res (Tokyo) 37, 359–366.

24. Quentmeier H, Pommerenke C, Ammerpohl O, Geffers R, Hauer V, MacLeod RA, Nagel S, Romani J, Rosati E, Rosén A, Uphoff CC, Zaborski M and Drexler HG (2016) Sub-clones in B-lymphoma cell lines: isogenic models for the study of gene regulation. Oncotarget 23 doi: 10.18632/onco-target.11524.

25. Siegel RL, Miller KD and Jemal A (2015) Cancer statistics, 2015. CA Cancer J Clin 65, 5–29.

26. Son J, Lyssiotis CA, Ying H, Wang X and Kimmelman AC (2013) Glutamine supports pancreatic cancer growth through a KRAS-regulated metabolic pathway. Nature 496, 101–105.

27. Torrent Suite v4.4.3 User and Admin Guide (2015) https://ioncommunity.thermofisher.com/docs/DOC-9305

A. Todaka et al.40

28. Ueno H, Ioka T, Ikeda M, Ohkawa S, Yanagimoto H, et al. (2013) Randomized phase III study of gemcitabine plus S-1, S-1 alone, or gemcitabine alone in patients with locally ad-vanced and metastatic pancreatic cancer in Japan and Tai-wan: GEST Study. J Clin Oncol 31, 1640–1648.

29. Ueno H, Kiyosawa K and Kaniwa N (2007) Pharmacog-enomics of gemcitabine: can genetic studies lead to tai-lor-made therapy? Br J Cancer 97, 145–151.

30. UKCCCR guidelines for the use of cell lines in cancer re-search (2000) Br J Cancer 82, 1495–1509.

31. Urakami K, Zangiacomi V, Yamaguchi K and Kusuhara M (2013) Impact of 2-deoxy-D-glucose on the target metabo-lome profile of a human endometrial cancer cell line. Biomed Res (Tokyo) 34, 221–229.

32. Vander Heiden MG, Cantley LC and Thompson CB (2009) Understanding the Warburg effect: the metabolic require-ments of cell proliferation. Science 324, 1029–1033.

33. Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA Jr and Kinzler KW (2013) Cancer genome landscapes.

Science 339, 1546–1558.34. Vojtek AB and Der CJ (1998) Increasing complexity of the

Ras signaling pathway. J Biol Chem 273, 19925–19928.35. Von Hoff DD, Ervin T, Arena FP, Chiorean EG, Infante J, et

al. (2013) Increased survival in pancreatic cancer with nab- paclitaxel plus gemcitabine. N Engl J Med 369, 1691–1703.

36. Von Hoff DD, Ramanathan RK, Borad MJ, Laheru DA, Smith LS, et al. (2011) Gemcitabine plus nab-paclitaxel is an active regimen in patients with advanced pancreatic cancer: a phase I/II trial. J Clin Oncol 29, 4548–4554.

37. Yeung SJ, Pan J and Lee MH (2008) Roles of p53, MYC and HIF-1 in regulating glycolysis – the seventh hallmark of cancer. Cell Mol Life Sci 65, 3981–3999.

38. Ying H, Kimmelman AC, Lyssiotis CA, Hua S, Chu GC, et al. (2012) Oncogenic Kras maintains pancreatic tumors through regulation of anabolic glucosemetabolism. Cell 149, 656–670.

39. Weaver BA (2014) How Taxol/paclitaxel kills cancer cells. Mol Biol Cell 25, 2677–2681.