MET Inhibition Elicits PGC1α-Dependent Metabolic Reprogramming in Glioblastoma

The kinase c-MET has evolved as a therapeutic target for cancer therapy of some glioblastomas (GBM), the most common malignant primary brain tumors, displaying high levels of c-MET expression through several mechanisms, including MET amplification (1–4). When c-MET is activated through its cognate ligand, HGF, a signal cascade is activated, leading to activation of the AKT and ERK signaling pathway, which are commonly dysregulated in cancer, including GBM. Several c-MET inhibitors have been designed, such as crizotinib, which has been tested in GBM.

Akin to other monotherapies, primary or secondary resistance prevails and therefore drug combination therapies are warranted to address this problem. In this context, we targeted tumor cell metabolism following acute and chronic c-MET inhibition. By utilizing proteomic and transcriptomic analysis coupled with untargeted polar and nonpolar metabolite analysis by LC/MS, we identified a specific metabolic program elicited by c-MET inhibition. Interference with c-MET drives oxidative metabolism by increasing fatty acid oxidation (FAO) and glucose anaplerosis, which was orchestrated by the master-regulator, PGC1α. Based on a drug screen (5), we further found that the mitochondrial matrix chaperone inhibitor, gamitrinib, along with c-MET inhibition causes synergistic cell death, which was mechanistically related to the ability of gamitrinib to suppress oxidative metabolism. In alignment with these findings, FAO inhibitor, etomoxir, enhanced the antiproliferative effects of c-MET inhibition as well. Both combination therapies were active in vivo, suggesting two novel potential combination therapies, involving c-MET inhibitors.

Crizotinib, etomoxir, foretinib, metformin, and oligomycin were purchased from Sellekchem. Gamitrinib-TPP (GTPP) was kindly provided by Dr. Dario Altieri (Wistar Institute, Philadelphia, PA). The compounds were dissolved in DMSO.

U87MG, LN229, and A172 human GBM cell lines were obtained from the ATCC. The respective cell line depository authenticated the cells. Cells were maintained in DMEM with 10% FBS, 4.5 g/L glucose, 4 mmol/L l-glutamine, 1 mmol/L pyruvate, and primocin. Patient-derived xenograft (PDX), GBM12 and GBM14 cells were obtained from Dr. Jann Sarkaria (Mayo Clinic, Rochester, MN). All cell lines were obtained between 2015 and 2017. The U87CrizoR, A172CrizoR, GBM14CrizoR, and U87ForeR cells have been exposed to 1 μmol/L crizotinib/foretinib for 14 days.

Viability assays were performed as previously described (6). For synergism analysis, the CompuSyn software (ComboSyn, Inc.) was used for the determination of the combination index (CI).

Annexin V Apoptosis Detection Kit (BD Pharmingen), Mitochondrial Membrane Potential (Cell Signaling Technology), MitoTracker (Thermo Fisher), and CellRox (Thermo Fisher) stainings were performed according to the manufacturer's instructions and analyzed on a LSRII flow cytometer (BD Pharmingen). The data were analyzed with the FlowJo software (version 8.7.1).

Transfections were performed with Lipofectamine RNAiMaX (Invitrogen) according to the manufacturers' instructions.

c-MET shRNAs were purchased from Santa Cruz Biotechnology. The CRISPR/Cas9 MET knockout lentivirus was purchased from ABMGOOD. Cells were infected in the presence of 8 μg/mL polybrene and were selected with puromycin.

Specific protein expression in cell lines was determined by Western blot analysis or capillary electrophoresis as described before (6). The following antibodies were applied: p-MET [Tyr1234/1235; Cell Signaling Technology (CST) 3077S; 1:250], MET (CST 8198S;1:1,000), Mcl-1 (CST 94296S; 1:500), Bcl-2 (CST 15071S; 1:500), BIM (CST 2933S; 1:500), Bcl-xL (CST 2764; 1:500), Noxa (Millipore OP180; 1:500), and β-actin (Sigma Aldrich A1978, clone AC15; 1:2,000). The horseradish peroxidase–linked secondary antibodies were from Santa Cruz Biotechnology Inc. Western blot signals were detected by using a CCD – camera system (Azure C300 imager). For capillary electrophoresis (Wes instrument, Proteinsimple), the following antibodies were used: p-MET (Tyr1234/1235; CST 3077S, 1:25), MET (CST 8198, 1:25), p-mTOR (Ser2448; CST 5536S, 1:25), p-ERK (Thr202/Tyr204; CST 4370S, 1:25), ERK (CST 4695, 1:25), mTOR (CST 2983, 1:25), p-CREB (Ser133; CST 9198S, 1:25), cAMP response elements binding protein (CREB) (CST 9197S, 1:25), PGC1α (CST2178, 1:25), and Vinculin (Abcam ab129002, 1:200).

Transcriptome and gene set enrichment analysis (GSEA) were performed as previously described (6). The experiment was deposited online ID: U87:GSE113961, GBM14:GSE134676.

GBM cell lysates were diluted and arrayed on nitrocellulose-coated slides for the generation of sample spots. Sample spots were exposed to antibodies, involving a tyramide-based signal amplification approach. For detection, a colorimetric system with DAB was used. Stained slides were digitalized by the Huron TissueScope scanner, resulting in a 16-bit tiff image. The Array-Pro Analyzer was used to localize and quantify the digitalized antibody spots. The relative protein levels are determined though interpolation of the different dilution curves. The standard curve is obtained by a script. The relative protein levels are then provided as log₂ values and normalized to protein loading, after which, they were transformed to linear values.

RNA was extracted with the miRNAeasy Mini Kit (QIAGEN). mRNA was reverse transcribed (cDNA Synthesis Kit; Origene). For qPCR sample preparation, the SYBR Green RT-PCR Kit (Applied Biosystems) was used. The reactions were run on a qPCR cycler (Quantabio). Primer sequences are in Supplementary Table S1.

Analysis and extraction were performed as described before (7).

The Seahorse XFe24 instrument (Agilent Inc.) was used for the assessment of the oxygen consumption rate (OCR) and extracellular acidification rate (ECAR; ref. 8). Note that 3 × 10⁴ GBM cells were seeded. The assay media were Seahorse XF base medium supplemented with 10 mmol/L glucose, 2 mmol/L glutamine, and 1 mmol/L pyruvate. The following compounds were injected in a sequential order: 2 μmol/L oligomycin, 2 μmol/L FCCP, and 0.5 μmol/L rotenone/antimycin. For the evaluation of glycolysis, GBM cells were exposed to Seahorse XF base medium supplemented with 1 mmol/L l-glutamine in a CO₂-free incubator for 1 hour preceding the assay. During the course of the assay, 10 mmol/L glucose, 1 μmol/L oligomycin, and 50 mmol/L 2-DG were added sequentially.

Cells were incubated with DMEM without glucose, glutamine, and phenol red (Thermo Fisher) for 1 hour. Thereafter, the cells were exposed to media, containing either 25 mmol/L D-glucose (U-¹³C6; Cambridge Isotope Laboratories, Inc.), 4 mmol/L l-glutamine (U-¹³C5), or 100 μmol/L (U-¹³C16) palmitic acid (Cambridge Isotope Laboratories, Inc.) for 24 hours in the presence of 10% dialyzed FBS (Thermo Fisher). Analysis and extraction were performed as described before (7).

A solvent mixture consisting of Cremophor EL (Sigma), Ethanol (Pharmco-Aaper), and PBS was used to solubilize the drugs. Cells were implanted subcutaneously into the flanks of 6- to 8-week-old Nu/Nu mice. A caliper is used to measure the tumor sizes based on the formula: (length × width²) × 0.5. Treatments were given 3 times a week (gamitrinib, 5 mg/kg; crizotinib, 50 mg/kg; etomoxir, 20 mg/kg).

Note that 300,000 U87 GBM cells were implanted into the right striatum of Nu/Nu mice (2 mm lateral of the bregma). Starting on day 5, animals were randomly assigned to four treatment groups, and a total of nine treatments were given (crizotinib, 30–50 mg/kg; etomoxir, 20 mg/kg; between days 5 and 20). Survival was the primary endpoint and was defined as death of an animal, a moribund state or the appearance of neurological symptoms in accordance with the Institutional Animal Care and Use Committee (IACUC) guidelines.

The paraffin-embedded sections were dewaxed, rehydrated, and incubated in proteinase K (Agilent DAKO) for 5 minutes at 37°C. Following rinsing, the slides were exposed to terminal deoxynucleotidyl transferase–mediated dUTP nick-end labeling (TUNEL) reaction mixture (1:10 in label solution) for 1 hour at 37°C. Following termination of the reaction converter, (Anti-Fluorescein-POD, Fab fragments) (Sigma) solution was added for 30 minutes at 37°C. TUNEL staining was highlighted with diaminobenzidine, and hematoxylin was used for nonspecific nuclear staining. The sections were rehydrated by using different concentration of alcohol. For Ki67 staining, the antigen retrieval was performed using citrate buffer and heating. The Ki67 (Dako, Cat# GA626) was incubated for 90 minutes at room temperature (RT). The slides were incubated with horse anti mouse IgG (1:200) for 30 minutes at RT. Then, the slides were incubated in ABC-Peroxidase Solution (1:50) for 30 minutes at RT.

Statistical analysis involved the two-tailed Student t test or ANOVA (for multiple comparisons) using Prism version 8.00 (GraphPad). Statistical outliers were removed by applying the Grubbs test. A P ≤ 0.05 was set as the level of statistical significance (*, P < 0.05; **, P < 0.01; and ***/****, P < 0.001). For drug synergism analysis, the CompuSyn software (ComboSyn, Inc.) was used to compute the CI (CI < 1 synergistic, CI = 1 additive, and CI > 1 as antagonistic).

All procedures were in accordance with Animal Welfare Regulations and approved by the IACUC at the Columbia University Medical Center.

MET kinase signaling is important in GBM and other malignancies. To demonstrate a survival impact, we interrogated The Cancer Genome Atlas (TCGA) database and found that high levels of MET mRNA correlate with an unfavorable survival in patients (Fig. 1A). Similar results were seen in the low-grade glioma database (Supplementary Fig. S1A). We confirmed that crizotinib and another c-MET inhibitor, foretinib, interfere with c-MET signaling in our applied model systems, including established GBM and PDX cells (Supplementary Fig. S1B and S1C). In this context, Western blot analysis showed a strong expression of phosphorylated c-MET in GBM cells, which was abrogated by the FDA-approved c-MET inhibitor, crizotinib, and foretinib (Supplementary Fig. S1B and S1C). We also generated CrizoR cells that were chronically exposed to crizotinib to mimic repeated dosing of the relevant compound, which also resulted in a slight resistance toward crizotinib (Supplementary Fig. S1D–S1G). These cells were bigger and displayed a more complex cytosolic architecture (Supplementary Fig. S1H–S1J). We initiated our studies by performing a combined screen, involving transcriptome, proteome, and metabolomic analyses (polar and nonpolar) in the context of chronic c-MET inhibition with crizotinib (Fig. 1B–H; Supplementary Fig. S1D–S1G). Our metabolite analysis demonstrated an accumulation of carnitine-related metabolites. Enrichment pathway analysis revealed an increase in oxidative metabolism pathways, such as the tricarboxylic acid cycle (TCA) cycle and pyruvate metabolism (Fig. 1B), which was associated with upregulation of enzymes related to oxidative metabolism based on a proteomic reverse phase protein array (RPPA) screen, including COX-IV, SDHA, and SOD2 (Fig. 1C). Aside from oxidative metabolism, there was an enrichment of carbohydrate metabolism, involving glycolysis and the pentose phosphate pathway (PPP; Fig. 1B). Given the pronounced changes in carnitine derivatives and upregulation of genes and proteins related to fatty acid metabolism/oxidation, including fatty acid synthase and stearoyl-CoA desaturase (SCD) (Fig. 1C and H), we extended our analyses to nonpolar metabolites, involving a broad-spectrum lipidomic screen. The main finding arising from this screen was a substantial increase in acyl-carnitines and triglycerides, suggesting an increase in β-oxidation (Fig. 1D, E, and G; Supplementary Fig. S2A–S2C). Within the group of acyl-carnitines, we detected an increase in palmitoyl-carnitine (C16-carbon) and stearoyl-carnitine (C18-carbon), which are considered as major substrates for β-oxidation (Fig. 1G). Interestingly, several polyunsaturated fatty acids were substantially increased (Fig. 1E), which may be related to the observation that SCD is upregulated by crizotinib. We also noted an increase in lysophospholipids (Fig. 1D), a phenomenon often observed in settings of enhanced FAO. Real-time PCR analysis of chronically crizotinib exposed cells demonstrated an increase in transcripts related to fatty acid transporters and slightly elevated levels of enzyme related to FAO (Fig. 1H). With regard to the general oxidative phenotype is the upregulation of mtDNA, SOD2, and TXNIP, which also includes the A172 GBM cells (Fig. 1H and E; Supplementary Fig. S2D–S2G). Coupled with this oxidative signature, electron microscopy demonstrated an increase in mitochondria that revealed a tubulated phenotype in U87 cells chronically exposed to crizotinib (Fig. 1I). Consistently, crizotinib-treated cells showed an enhancement in reactive oxygen species (ROS) production and an increase in mitochondrial mass as assessed by flow cytometric analysis (Fig. 1J; Supplementary Fig. S2H and S2I).

From our polar LC/MS analysis, we noted an increase in metabolites of the TCA cycle (Fig. 2A; Supplementary Fig. S3A–S3C). To address the questions about how chronically MET inhibitor–treated GBM cells alter their fuel utilization and how the increases in TCA cycle metabolites are mediated, we conducted glucose, glutamine, and palmitic acid tracing experiments (Fig. 2B–M). Glucose carbons (U-¹³C-Glucose) are metabolized in glycolysis to pyruvate. Pyruvate can be converted either to acetyl-CoA via pyruvate dehydrogenase (PDH) reaction or to oxaloacetate via pyruvate carboxylase (PC). This results in either m+2 citrate (PDH reaction) or m+3 citrate (PC reaction; Fig. 2M). In crizotinib-treated cells, we noted a substantial enhancement of carbon labeling throughout all the major metabolites of the TCA cycle (Fig. 2B–F). However, it is noteworthy that we detected a reduction of m+2 citrate, whereas the m+3, m+4, m+5, and m+6 isotopologs of citrate were all increased, suggesting a reduction of PDH and an increase in PC activity, which is consistent with enhanced anaplerosis to fuel the TCA cycle (Fig. 2B). Similar observations were made with regard to the other TCA cycle metabolites (Fig. 2C–F). In addition to the TCA cycle metabolites, we detected increased labeling of glutamate, aspartate, and glutathione from glucose carbons (Supplementary Fig. S3D–S3F).

Aside from glucose, glutamine is known to have similar functions. Through glutaminase and glutamate dehydrogenase, glutamine is converted to α-ketoglutarate (m+5). In turn, α-ketoglutarate is oxidized to citric acid (m+4) via succinate, fumarate, malate, and oxaloacetate (Fig. 2M). However, via reductive carboxylation, α-ketoglutarate may be converted to citrate (m+5). In the setting of chronic MET inhibitor–treated cells, we noted an overall decrease in carbon labeling of TCA metabolites originating from glutamine (Fig. 2G–J; Supplementary Fig. S3G–S3K). Consistently, both glutamine oxidation and reductive carboxylation were significantly reduced. Consequently, glutamine-driven carbon labeling of aspartate and glutathione was reduced as well (Supplementary Fig. S3G–S3I). In contrast, oxidation of palmitic acid was increased with enhanced labeling of citric acid, α-ketoglutarate, l-acetylcarnitine, succinate, and malate by palmitic acid–derived carbons (Fig. 2K and L; Supplementary Fig. S3L–S3N). These findings highlight an overall dependence of the TCA cycle on glucose carbons in the context of chronic MET inhibition. A summary of the metabolite pathways described above is shown in Fig. 2M. Moreover, given the enhancement of glucose anaplerosis, we noted an increase in phosphorylation of PDH at 24 hours after crizotinib treatment, which indicates inhibition of PDH (Supplementary Fig. S3O).

Having noticed an increase in oxidative metabolism, we then assessed the role of glucose metabolism in the context of c-MET inhibition because our pathway analysis suggested a dysregulation of glycolysis and the PPP (Supplementary Fig. S3A–S3C). To this end, we utilized extracellular flux analysis on the seahorse analyzer. We found that chronic crizotinib-treated cells revealed enhanced ECAR glycolysis with lactate production accompanied by a reduced coupling efficiency (Supplementary Fig. S4A–S4C). In support of the functional extracellular flux analysis, we noted a significant increase in glycolytic metabolites (Supplementary Fig. S3A), in keeping with an activation of this pathway. Aside from glycolysis, the PPP is pivotal for cancer cells to maintain their metabolic requirements. In the context of c-MET inhibition, our screening analysis suggested a potential involvement of this pathway. In line with this notion, we found that crizotinib-treated cells exhibit an upregulation of key metabolites of the PPP, including 6-phospho-gluconolactone and others (Supplementary Fig. S3A–S3C). Accordingly, this leads to an increase in reduction equivalents in the form of NADPH₂ that in turn may be used for ROS scavenging due to a global increase in oxidative metabolism. These findings position glucose metabolism as a potential liability of crizotinib-treated cells that may be exploited for combination therapies.

To assess oxidative metabolism, we performed extracellular flux analysis in chronically exposed crizotinib cells and found that they had an elevated OCR with an increased spare respiratory capacity and maximal respiration (Fig. 3A). Similarly, we observed the same findings in A172 CrizoR cells (Fig. 3B; Supplementary Fig. S4D–S4F). Given that the TCA cycle intermediates displayed an increase in labeling from glucose carbons, we asked whether or not glucose contributes to enhanced OCR, acknowledging that glucose in itself is not oxidized, but through anaplerosis contributes to the oxidation of other substrates, e.g., fatty acid (Fig. 3C).

Because crizotinib is a pharmacologic inhibitor, which may elicit off-target effects, we silenced the expression of c-MET in both U87 and GBM14 cells, using shRNA or alternatively with CRISPR/Cas9. Next, we conducted extracellular flux analysis to determine the oxygen consumption in the setting of silenced MET. Genetic interference enhanced oxidative metabolism in both established as well as PDX cell cultures (Fig. 3D and E; Supplementary Figs. S4G–S4H and S5A–S5L). We also measured the ECAR and found that in like manner it was increased in cells with silenced MET (Supplementary Fig. S5D–S5G).

Given the findings of chronic c-MET inhibition on energy metabolism, we also interrogated early events upon acute MET inhibition. U87 GBM cells were treated with crizotinib for 24 hours and thereafter we determined the OCR and related parameters. We noted an increase in OCR, especially the maximal respiration and spare respiratory capacity (Fig. 3F). We validated these results in PDX cells, GBM14. Akin to the established cell culture, crizotinib mediated an increase in OCR (Supplementary Fig. S4G) and an increase in ECAR (Supplementary Fig. S4H). An increased OCR was observed following chronic foretinib inhibition as well (Supplementary Fig. S4I).

To further corroborate the importance of oxidative phosphorylation in the context of MET inhibition, we utilized two inhibitors, Metformin, an FDA-approved drug that blocks complex I of the respiratory chain, and Oligomycin, an ATP-synthase inhibitor (complex V). Combining crizotinib with either metformin or oligomycin leads to synergistic reduction in proliferation of tumor cells (Fig. 4A and B; Supplementary Fig. S6A–S6G), suggesting that oxidative phosphorylation operates as a prosurvival pathway in the context of MET inhibition. Given our earlier observation of an increase in polar and nonpolar metabolites related to FAO and an upregulation of transporters related to fatty acid transport, we hypothesized that MET regulates FAO and that MET inhibition will facilitate β-oxidation, which in turn will increase the OCR. To this end, we took advantage of measuring oxidation of palmitic acid through extracellular flux analysis. Our findings revealed that MET inhibition enhanced the oxidation of palmitic acid (Fig. 4C), confirming our observation from the metabolite and transcriptome analysis and further suggesting that fatty acids are a major fuel for the observed enhanced OCR.

Our transcriptome analysis demonstrated that MET inhibition led to an increase of a master-regulator of oxidative metabolism, PGC1α, which was confirmed by real-time PCR analysis as well as on the protein level in several GBM model systems (Fig. 4D–I; Supplementary Fig. S6C and S6G). In agreement, GSEA of chronically crizotinib-treated cells revealed an increase for the PPAR signaling pathway, which was accompanied by an activation of ERK and CREB signaling, which are known pathways to be upstream of PGC1α, suggesting their potential involvement in the transcriptional regulation of PGC1α through MET inhibition (Fig. 4E).

We hypothesized that the increase of PGC1α is a key event in the metabolic reprogramming elicited by MET inhibition. To this purpose, we silenced the expression of PGC1α to assess its impact on oxidative metabolism by extracellular flux analysis (Fig. 4H and I). Whereas nontargeting GBM cells responded with an increase in OCR upon crizotinib treatment, we found an almost complete abrogation of this effect when PGC1α is silenced, positioning PGC1α as a major regulator of metabolism in the context of MET inhibition. Because CREB is the transcription factor that binds to the proximal promoter region of PGC1α to drive its transcription, we hypothesized that CREB may be involved in the regulation of PGC1α expression in the context of MET inhibition. We even noted an increase in phosphorylation of CREB after chronic exposure to crizotinib and foretinib in U87 cells (Fig. 4J). To assess the role of CREB on the OCR in chronically crizotinib-exposed cells, we silenced the expression of CREB by shRNA and analyzed these cells on the seahorse analyzer (Fig. 4K and L). We detected a substantial suppression of OCR in CREB-silenced cells compared with nontargeting transduced cells. Moreover, silencing of CREB suppressed the expression levels of PGC1α in chronically crizotinib-treated cells (Fig. 4K). Overall, these findings support the hypothesis that CREB is implicated in the regulation of oxidative phosphorylation in chronically crizotinib-treated cells, and this occurs dependent on the transcription. To demonstrate that there is enhanced binding of CREB to the promoter region of PGC1α, we conducted chromatin immunoprecipitation (CHIP)-qPCR. We observed an increased binding of CREB to the promoter region of PGC1α, in keeping with an upregulation of PGC1α (Fig. 4M).

In connection with earlier findings that crizotinib-treated cells displayed an enhanced oxidation of fatty acids, we hypothesized that inhibition of FAO by etomoxir and gamitrinib will have an impact on OCR, mitochondrial size, ROS production, and survival of tumor cells in the context of MET inhibition (Fig. 5A–G). To this purpose, GBM cells were treated with crizotinib in the presence or absence of etomoxir, a clinically validated drug compound inhibiting CPT1A. CPT1A is one of the key transport proteins that facilitates the translocation of fatty acid into the mitochondria. We found that the combination treatment of crizotinib and etomoxir synergistically reduced the proliferation rate of established and PDX cell cultures, suggesting that FAO exerts a prosurvival function in the context of MET inhibition and that crizotinib and etomoxir might be a potential drug combination therapy (Fig. 5H and I; Supplementary Fig. S7A). Consistent with this effect on proliferation, we also confirmed that etomoxir reverses the increase in OCR in the context of MET inhibition (Fig. 5A, H, and I).

Based on an earlier drug screen, involving the TRAP1 inhibitor, gamitrinib (GTPP; ref. 5), we found that MET inhibition is synthetically lethal with gamitrinib. Therefore, we confirmed that crizotinib/foretinib and gamitrinib reduce cellular proliferation in a synergistic manner, and this is mediated by enhanced apoptosis in U87, LN229, GBM14, and GBM12 cells (Fig. 5J and K; Supplementary Figs. S7B–S7I, S8A–S8C, S9A–S9F, and S10A). Similar observations were made when foretinib was employed in lieu of crizotinib, demonstrating synergistic reduction cell growth following the combination treatment with gamitrinib/and or etomoxir in several model systems tested (Supplementary Fig. S7D–S7I). Following treatment with the drug combination of gamitrinib and crizotinib, we noted a loss of mitochondrial membrane potential (Supplementary Fig. S9E and S9F) and cleavage of initiator and effector caspases as well as cleavage of PARP (Supplementary Fig. S8A), consistent with a cell death with apoptotic features. Because of the engagement of apoptosis, we assessed the protein levels of downstream regulators of apoptosis, Bcl-2, Bcl-xL, Mcl-1, BIM, and Noxa. The results of this analysis showed a mixed regulation with Mcl-1 being the most consistent member to be downregulated (Supplementary Fig. S8B). To specifically confirm the role of c-MET, we evaluated the efficacy of gamitrinib in the context of silenced c-MET and found an enhancement of gamitrinib-mediated growth reduction (Supplementary Fig. S8C).

Related to this finding, we hypothesized that this synergistic interaction is based on the fact that TRAP1 regulates oxidative phosphorylation at the level of succinate dehydrogenase (complex II of the respiratory chain). To this purpose, we conducted extracellular flux analysis in U87 GBM cells and found that crizotinib increases OCR and ATP production, which is almost completely abrogated in the presence of gamitrinib (Fig. 5B–D). Similar data were observed in GBM14 PDX cell line (Fig. 5A). Earlier research has demonstrated that gamitrinib interferes with aerobic glycolysis by interference with hexokinase-II (9). Therefore, we tested whether or not crizotinib-driven glycolysis is attenuated in the presence of gamitrinib. In keeping with our hypothesis, we found that suboptimal dosages of gamitrinib suppress MET inhibitor–driven glycolysis (Fig. 5E). Consistently, we noted a suppression of crizotinib-mediated ROS production, a decrease in mitochondrial mass, and ATP production by gamitrinib (Fig. 5F and G; Supplementary Fig. S10B–S10D).

In addition, it is noteworthy that the combination treatment of gamitrinib and crizotinib was more efficient to elicit synergistic apoptosis induction than various combination treatments, involving kinase inhibitors of the MEK, PI3K, and mTOR pathway (Supplementary Fig. S10E–S10J).

Finally, we tested whether or not our findings bear translational relevance. To assess this, we used two GBM PDX models, GBM12 and GBM14. We initiated our studies with GBM12. After cell injection and tumor formation, mice were randomly divided in the indicated groups (control, crizotinib, etomoxir, and the combination of etomoxir and crizotinib). Although vehicle, crizotinib, and etomoxir treatment groups did not differ in tumor growth, we noted a substantial and statistical significant suppression in tumor growth in animals that received the combination treatment of crizotinib and etomoxir (Fig. 6A and B). Next, we tested the drug combination of gamitrinib and crizotinib in vivo. To this end, we established GBM12 tumors in mice, formed treatment groups, and treated them with the indicated drug compounds. We noted a statistical significant reduction of tumor growth in the combination treatment with single treatments and controls (Fig. 6C and D). The same experiments as performed in the GBM12 model were subsequently conducted in GBM14. Akin to the GBM12 model, we found that both combination treatments (crizotinib and gamitrinib; crizotinib and etomoxir) led to a more pronounced reduction in tumor growth than each single treatment or control (Supplementary Fig. S11A and S11B).

To confirm how the combination treatment (crizotinib and gamitrinib) reduced tumor growth, we performed histologic analysis of tumors with hematoxylin and eosin, TUNEL, and Ki67 staining (Fig. 6E–H). Through TUNEL staining, we found that overall there is more cell death in tumor slides from the combination treatment. The proliferation index (Ki67) was reduced in the combination treatment as well. These findings suggest both a reduced proliferation of tumor cells and an enhanced induction of cell death, which provides an explanation for the reduced size of the tumors in the combination treatment. Similarly, a reduction of cellularity and a reduced proliferation index were found in the combination treatment of crizotinib and etomoxir (Supplementary Fig. S11C–S11G). Regarding the toxicity of the combination treatment, we assessed the integrity of organs (brain, heart, intestine, kidney, liver, lung, pancreas, and spleen) and did not find histologic evidence of organ toxicity following the combination treatment (Supplementary Fig. S11H).

Finally, we validated whether the combination treatment of c-MET and FAO inhibition is effective in GBM cells engrafted into the brain of nude mice. To this purpose, U87 cells were utilized because these cells bear highest levels of c-Met and have been previously utilized by others to assess impact of c-MET inhibition (10). Following engraftment and after completion of a treatment regimen of either vehicle, single (crizotinib, etomoxir), or the combination treatment, we noted a small, but significant survival extension of animals receiving the combination treatment, suggesting that this treatment concept may have potential following optimization of delivery or inhibitor modification (Fig. 6I).

The integration of tumor cell metabolism as an opportunity to design novel therapies is receiving considerable attention in the cancer research community (11–15). First, with the wealth of novel technical opportunities to measure metabolomic changes, it has become increasingly easier to characterize tumor cell metabolism (16). Second, sophisticated high-throughput genetic screening methods and large data sets obtained from deep sequencing have permitted the community to identify key genetic alterations in metabolism. Examples include the mutation of IDH1, amplifications in the PHGDH gene, or loss of succinate dehydrogenase (17, 18). Third, through advancement in drug discovery and synthesis, tumor cell metabolism becomes a more suitable target. In this vein, PHGDH inhibitors have been recently developed (11, 13, 15, 19–21).

Targeting aberrant active kinases is a central dogma in cancer therapy with certainly some, but limited success (22). In this work, we have focused on the MET kinase pathway, which is active in human GBM and has been identified as a therapeutic target before by others (10, 23). Consequently, several drugs have been designed, including crizotinib. Because c-MET inhibition displays limited antitumor effects, we intended to identify means that improve the efficacy of c-MET inhibition in the current work. To accomplish this goal, we utilized several screening methods, involving gene set enrichment and proteome analysis coupled with metabolite screening by LC/MS. This approach has resulted in the identification of substantial metabolic reprogramming elicited by c-MET inhibition that to the best of our knowledge has not been demonstrated before. Short and long exposure leads to activation of oxidative metabolism in established and PDX cells, which was morphologically exemplified by an increase in mitotracker, ROS production, enhanced OCR with related indices (as determined by extracellular flux analysis), and an increase in mitochondria as demonstrated by electron microcopy. These findings were accompanied by upregulation of the master-regulator transcription factor, PGC1α (24–26), which was a major mediator of these effects. To the best of our knowledge, our group is the first describing these metabolic aberrations upon c-MET inhibition. The present findings are in keeping with an earlier report demonstrating that BRAF-kinase inhibition in BRAF V600E–mutated melanomas activates oxidative energy metabolism (27). Similar findings had been seen in the context of CDK4 inhibition in pancreatic tumor cells, which resulted in an increase of both glycolysis and oxidative metabolism (28).

Coupled with the enhanced oxidative metabolism is our observation that c-MET inhibition drives FAO, which we validated in extracellular flux analysis because the CPT1a inhibitor, etomoxir, attenuated c-MET inhibition–mediated OCR. Whereas fatty acids were oxidized, glucose carbons appeared to be mainly necessary to provide its carbons for anaplerosis of the TCA cycle. In addition, etomoxir and crizotinib reduced proliferation of GBM cells in a synergistic manner in vitro and in vivo, further corroborating the translational implication of our findings. Enhancement of both oxidative phosphorylation and FAO has been seen in the context of treatment of myeloid leukemia by cytarabine. Similarly, to our findings, etomoxir enhanced the effects of cytarabine (29).

Gamitrinib is a novel drug compound that will enter clinical testing. It elicits antitumor activity primarily by dampening oxidative metabolism through inhibition of mitochondrial matrix chaperones. In connection with an earlier high-throughput drug screen (5), we found that the c-MET inhibitor synergizes with gamitrinib in vitro and in vivo. As predicted, gamitrinib potently attenuated the pro-oxidative effects by c-MET inhibition.

Another recent article that used similar model systems like in our study demonstrated that crizotinib resistance activated several central signaling pathways, such as mTOR, EGFR, FGFR1, STAT3, and COX2 (10). They showed that interference with these pathways enhanced the efficacy of crizotinib in vivo (10). It is tempting to speculate whether due to the poor brain penetration of crizotinib, the in vivo effects in orthotopic models fall below expectations. Although our study made similar observations with regard to mTOR signaling, our focus rested on tumor cell metabolism and how c-MET inhibition–mediated reprogrammed tumor cell metabolism can be targeted for therapy. From a broader perspective, it is possible that c-MET inhibition–driven mTOR and EGFR signaling might unleash a glycolytic and oxidative phenotype in GBM cells because these pathways are inherently linked to drive tumor cell metabolism.

Taken together, our work has unraveled two novel drug combination therapies by integrating transcriptome, proteome, and metabolite analyses.

No potential conflicts of interest were disclosed.

Conception and design: Y. Zhang, T.T.T. Nguyen, M.D. Siegelin

Development of methodology: Y. Zhang, T.T.T. Nguyen, N. Humala, M.D. Siegelin

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): Y. Zhang, T.T.T. Nguyen, E. Shang, A. Mela, A. Mahajan, C. Shu, C. Torrini, M.J. Sanchez-Quintero, G. Kleiner, C.M. Quinzii

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): Y. Zhang, T.T.T. Nguyen, E. Shang, J. Zhao, M.D. Siegelin

Writing, review, and/or revision of the manuscript: Y. Zhang, T.T.T. Nguyen, E. Shang, A. Mela, A. Mahajan, G. Kleiner, M.-A. Westhoff, C.M. Quinzii, G. Karpel-Massler, J.N. Bruce, P. Canoll, M.D. Siegelin

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): T.T.T. Nguyen, A. Mela, A. Mahajan, E. Bianchetti, M.D. Siegelin

Study supervision: M.D. Siegelin

Other (Surgeries): N. Humala

M.D. Siegelin was supported by NIH NINDS R01NS095848, R01NS102366, K08NS083732, Louis V. Gerstner, Jr. Scholars Program (2017–2020), and American Brain Tumor Association Discovery Grant 2017 (DG1700013). T.T.T. Nguyen was supported by American Brain Tumor Association Basic Research Fellowship in Memory of Katie Monson (BRF1900018). Transcriptome analysis was supported by the CTSA grant UL1-TR001430 to the Boston University Microarray and Sequencing Resource Core Facility. These studies used the resources of the Cancer Center Flow Core Facility funded in part through center grants P30CA013696 and S10RR027050. Metabolomics shown in Figs. 1 and 2A, and Supplementary S2A, S2B, S3A–S3C were performed by the Whitehead Institute Metabolite Profiling Facility (Cambridge, MA).

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