Tumor Metabolome Research Articles

Abstract 3870: The addition of CB-839 to temozolomide significantly reduces glioma aspartate and glutamate in an IDH mutated patient derived glioma xenograft model

Abstract Background: IDH mutated gliomas are critically dependent on glutaminase for glutamate biosynthesis. CB-839 is a novel glutaminase-1 inhibitor which has demonstrated efficacy in both genetically engineered and patient derived IDH mutated glioma cells, especially in combination with radiation. These preclinical studies evaluate the pharmacodynamic and pharmacokinetic impact of combining CB-839 with TMZ in patient derived xenograft (PDX) glioma models. Methods: GBM164 is a PDX model derived from an IDH1 mutant, 1p/19q non-codeleted glioma. D- and L-2HG levels in untreated GBM164 tumors were compared to GBM6 tumors (IDH wildtype glioma PDX) by GC/MS. Athymic nude mice bearing GBM164 flank tumors were treated with CB-839 (200 mg/kg PO BID x 9 doses) and/or temozolomide (50 mg/kg PO daily x 5 doses) and sacrificed four hours after the last dose to harvest plasma, normal brain and flank tumor. Tumor metabolomics were assessed by GC/MS. DNA damage signaling in tumors was assessed by Western blotting for KAP1 / Chk1 / Chk2 phosphorylation and H2AX / Rad51. Tumor, plasma and brain pharmacokinetics were assessed by LC/MS/MS. Results: Total 2HG levels in GBM164 were 18-fold higher than GBM6. CB-839 monotherapy reduced tumor glutamate (18% reduction, p = 0.15) and aspartate (30% reduction, p = 0.06) when compared to vehicle, however these changes did not reach statistical significance. The combination of CB-839 and TMZ more significantly reduced both tumor glutamate (30% reduction, p = 0.03) and aspartate (34% reduction, p < 0.001) when compared to TMZ monotherapy. CB-839 did not significantly increase DNA damage compared to vehicle, and the combination of CB-839 and TMZ did not significantly increase DNA damage compared to TMZ monotherapy. The mean tumor: plasma ratios of CB-839 concentrations were 0.68 and 0.58 in mice treated with CB-839 monotherapy and CB-839/TMZ, respectively. The mean brain: plasma ratios of CB-839 concentrations were 0.07 and 0.12 in mice treated with CB-839 monotherapy and CB-839/TMZ, respectively. Conclusion: The addition of CB-839 to TMZ significantly reduces glioma aspartate and glutamate in an IDH1 mutant PDX glioma model, without any impact on DNA damage. Survival studies are in progress to assess the efficacy of CB-839 when used in combination with RT and/or TMZ. Citation Format: Sani Haider Kizilbash, Danielle M. Burgenske, Samuel McBrayer, Sandhya Devarajan, Shiv K. Gupta, Taro Hitosugi, Lihong He, Mark A. Schroeder, Brett L. Carlson, Marina Gelman, Charles A. Kunos, Joel M. Reid, Alex A. Adjei, Jann N. Sarkaria. The addition of CB-839 to temozolomide significantly reduces glioma aspartate and glutamate in an IDH mutated patient derived glioma xenograft model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3870.

Abstract 2449: Unsupervised machine learning methods reveal metabolomic based clusters in breast cancer patients

Abstract Background Transcriptomic have led to the now widely used sub-type based classification of breast cancer first described by Perou in 2000. Yet there persists heterogeneity in biological behaviors within breast cancer subtypes, underlining the need to refine the taxonomy of breast cancer. Metabolomics is a rapidly expanding field dedicated to the study of metabolism which integrates the impact of the environment on cell biology. The aim of this study was to identify new biological breast cancer clusters using different unsupervised machine learning (ML) methods based on metabolomic features. Those methods, in which no a priori class label information is given to guide the algorithm, seem suitable to address this type of problem. Methods 52 patients with breast cancer and an indication for adjuvant chemotherapy between 2013 and 2016, were retrospectively included. Tumor resection specimens were analyzed. 1300 metabolomic were extracted by combined liquid chromatography-mass spectroscopy and processed using MZmine software and the “Human Metabolome” database. 5 unsupervised ML methods were used: PCA-Kmeans, Sparcl, SIMLR, Spectral clustering and K-sparse. Clinical differences between clusters and variations for every metabolite of interest were analyzed for each clustering method. Cluster separability and homogeneity was evaluated using the silhouettes method and t-sne visual evaluation. Results Among the 5 clustering methods, with a partitioning optimum parameter k=3, only K-sparse and SIMLR methods generated 3 clusters with significant clinical differences, unmatched to traditional subtypes. These differences concerned: tumor stage, axillary lymph node invasion, histological grade, ki-67 proliferation index, and tumor phenotype. With a silhouette average of 0.84 and 0.85 for K-sparse and SIMLR methods respectively, those 2 methods gave the best score in terms of silhouette average and they showed a better gradient for tumor aggressiveness compared to the 3 other methods. Among them, 42 and 55 metabolites were selected for the construction of tumor metabolome profiles for K-sparse and SIMLR, respectively. Among selected metabolites we found a significant increase of L-methionine, L-phenylalanine, L-isoleucine and L-proline along with a significant decrease in glutathione (also characteristic of oxidative stress) and glutamate in the cluster associated with poorer histopronostic factors. This high concentration of proteinogenic amino-acid and low concentration of amino-acid precursors could be correlated to poorer prognosis. Conclusion Unsupervised ML methods generate heterogeneous results when applied to metabolomics data extracted from breast cancer patients. K-sparse and SIMLR were able to identify three different groups based on tumor metabolome. Tumors with the worst histopronostic factors seemed to present higher concentrations of protienogenic amino-acids. Note: This abstract was not presented at the meeting. Citation Format: Jocelyn Gal, Caroline Bailleux, David Chardin, Thierry Pourcher, Lun Jing, Jean-Marie Guignonis, Jean-Marc Ferrero, Renaud Schiappa, Emmanuel Chamorey, Olivier Humbert. Unsupervised machine learning methods reveal metabolomic based clusters in breast cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2449.

BRAFv600e mutated gliomas: From biology to radiology and treatments.

e13552 Background: BRAF V600E mutation is encountered in brain tumor, mostly low grade pediatric diffuse glioma (LGG) and epileptogenic glioneuronal tumor such as gangliogliomas (GG) or pleomorphic xanthoastrocytomas (PXA). Less frequently this mutation is present in high grade glial or glioneuronal tumors such as pleomorphic xanthoastrocytomas with anaplasia, anaplastic ganglioglioma, anaplastic diffuse astrocytomas or glioblastomas. Recently, few publications were highlighting differently the impact of BRAF mutation and CDKN2A deletion, as independent prognostic factors linked to a worst outcome in low grade forms. Methods: We studied retrospectively a monocentric cohort of 12 LGGs and 9 HGG with BRAFv600e positivity. The patients were aged from 1 to 47 years. Most of the LGG were under 25 years and only 3 patients with HGGs had less than 18 years old. We focused on extended biology assessment by Next generation sequencing of the tumors and their relapses, tumor metabolomics analyses, radiology comprising MRI, PET-scanning and spectroscopy and correlate them to tumor’s evolution and its treatment. Results: Among the LGGs, we had 9 GG and 3 pilocytic astrocytomas and only one had a CDKN2A deletion and one a gain on chromosome 5. 6 had a complete surgical resection, 2 had a minimal residue and 4 had chemotherapeutic treatment after partial surgery and underwent relapses. All HGGs had a surgical resection followed by chemotherapy (mainly Stupp protocol) and radiotherapy. 5 relapsed rapidly, benefit from targeted therapy with vemurafenib and are still in long term remission. In this HGG group, we had two subgroups: 4 patients with “de novo” tumors and 5 patients with a past history of LGG tumors in the same brain region. Both were responding well to targeted treatments and all had an additional CDKN2A deletion. Specific radiological and spectroscopic signs were linked to those two groups and seem to be associated to a specific metabolomic profile in each group. Currently, we are going further in the correlation between MAPK signaling pathway and metabolomic profile to be able to predict in LGG their potential evolution. Conclusions: BRAF mutated gliomas seem to have specific radiological and metabolomic correlations associated to their biology

Integrated omics-based pathway analyses uncover CYP epoxygenase-associated networks as theranostic targets for metastatic triple negative breast cancer

BackgroundCurrent prognostic tools and targeted therapeutic approaches have limited value for metastatic triple negative breast cancer (TNBC). Building upon current knowledge, we hypothesized that epoxyeicosatrienoic acids (EETs) and related CYP450 epoxygenases may have differential roles in breast cancer signaling, and better understanding of which may uncover potential directions for molecular stratification and personalized therapy for TNBC patients.MethodsWe analyzed the oxylipin metabolome of paired tumors and adjacent normal mammary tissues from patients with pathologically confirmed breast cancer (N = 62). We used multivariate statistical analysis to identify important metabolite contributors and to determine the predictive power of tumor tissue metabolite clustering. In vitro functional assays using a panel of breast cancer cell lines were carried out to further confirm the crucial roles of endogenous and exogenous EETs in the metastasis transformation of TNBC cells. Deregulation of associated downstream signaling networks associated with EETs/CYPs was established using transcriptomics datasets from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). Comparative TNBC proteomics using the same tissue specimens subjected to oxylipin metabolomics analysis was used as validation set.ResultsMetabolite-by-metabolite comparison, tumor immunoreactivity, and gene expression analyses showed that CYP epoxygenases and arachidonic acid-epoxygenation products, EET metabolites, are strongly associated with TNBC metastasis. Notably, all the 4 EET isomers (5,6-, 8,9-, 11,12-, and 14,15-EET) was observed to profoundly drive the metastasis transformation of mesenchymal-like TNBC cells among the TNBC (basal- and mesenchymal-like), HER2-overexpressing and luminal breast cancer cell lines examined. Our pathway analysis revealed that, in hormone-positive breast cancer subtype, CYP epoxygenase overexpression is more related to immune cell-associated signaling, while EET-mediated Myc, Ras, MAPK, EGFR, HIF-1α, and NOD1/2 signaling are the molecular vulnerabilities of metastatic CYP epoxygenase-overexpressing TNBC tumors.ConclusionsThis study suggests that categorizing breast tumors according to their EET metabolite ratio classifiers and CYP epoxygenase profiles may be useful for prognostic and therapeutic assessment. Modulation of CYP epoxygenase and EET-mediated signaling networks may offer an effective approach for personalized treatment of breast cancer, and may be an effective intervention option for metastatic TNBC patients.

Impact of post-surgical freezing delay on brain tumor metabolomics.

Translational cancer research has seen an increasing interest in metabolomic profiling to decipher tumor phenotypes. However, the impact of post-surgical freezing delays on mass spectrometric metabolomic measurements of the cancer tissue remains elusive. To evaluate the impact of post-surgical freezing delays on cancer tissue metabolomics and to investigate changes per metabolite and per metabolic pathway. We performed untargeted metabolomics on three cortically located and bulk-resected glioblastoma tissues that were sequentially frozen as duplicates at up to six different time delays (0-180min, 34 samples). Statistical modelling revealed that 10% of the metabolome (59 of 597 metabolites) changed significantly after a 3h delay. While carbohydrates and energy metabolites decreased, peptides and lipids increased. After a 2h delay, these metabolites had changed by as much as 50-100%. We present the first list of metabolites in glioblastoma tissues that are sensitive to post-surgical freezing delays and offer the opportunity to define individualized fold change thresholds for future comparative metabolomic studies. More researchers should take these pre-analytical factors into consideration when analyzing metabolomic data. We present a strategy for how to work with metabolites that are sensitive to freezing delays.

Abstract 1297: Studying tumor metabolic reprogramming through integration of metabolomics and transcriptomics

Abstract Metabolic reprogramming is a cancer hallmark important to tumor initiation, progression and drug response. However, our knowledge of the tumor metabolome is limited since only a small number of metabolites can be reliably measured at a time. To enhance our understanding of tumor metabolism, we integrate metabolomics with transcriptomics to reveal potential metabolism-related transcriptional drivers. Our novel method, METRI (for MEtabolomic-TRanscriptomic Integration), identifies (1) metabolic alterations between two conditions, before and after treatment in our case and (2) interactions between signaling pathways and metabolic alterations. First, to identify metabolic alterations, METRI builds a matrix of gene and metabolite expressions for each metabolic pathway obtained from Kyoto Encyclopedia of Genes and Genomes. METRI computes a distance before and after treatment, weighted by the pathway coherence, where coherence is defined as the average absolute correlations across the genes and metabolites, resulting in a score for pathway ranking. Bootstrap is employed to obtain a p-value to assess each pathway's ranking significance. Second, to build an interaction network between metabolic alterations and signaling pathways, METRI identifies signaling genes correlated with each metabolic pathway and obtains enriched signaling pathways of each altered metabolic pathway. We applied METRI to two public non-small cell lung cancer datasets (GSE49644 and GSE17708/ST000010) to study the metabolic alterations associated with the Epithelial-Mesenchymal Transition (EMT), a phenotypic change related to cancer invasion and drug resistance. Both studies generated metabolomic and transcriptomic data and induced EMT with TGF-β treatment on cell lines (A549, HCC827 and NCI-H358) with three replicates for each condition. Using METRI, we found glycerophospholipid metabolism (p-value=0.03), purine metabolism (p-value=0.03), pyrimidine metabolism (p-value=0.04), aminosugars metabolism (p-value=0.04) and sphingolipid metabolism (p-value=0.04) were consistently altered after EMT induction in both datasets. In particular, we identified that several genes of the hexosamine biosynthesis pathway, a major pathway producing aminosugars for protein glycosylation, were upregulated in EMT. In addition, we identified that MAPK (p-value=0.004) and TGF-β (p-value=0.01) signaling pathways were associated with aminosugars metabolism. We also identified p53 signaling (p-value=0.002) was associated with sphingolipid metabolism, which plays a critical role in cancer cell adhesion and migration. In summary, METRI is a new method for data integration to study metabolic alterations and signaling-metabolism interactions and has the potential to reveal new insights to cancer biology and suggest novel targets for therapeutic intervention. Citation Format: Weiruo Zhang, Sylvia Plevritis. Studying tumor metabolic reprogramming through integration of metabolomics and transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1297.

Plasma TuM2-PK correlates with tumor size, CRP and CA 15-3 in metastatic breast carcinomas; short versus long term follow up study of the Egyptian breast cancer patients.

The key regulator of tumor metabolome is the glycolytic isoenzyme M2-PK which favors the generation of nucleic acid via glutaminolysis as hypoxic adaptive mechanism in the tumor cells. The study aimed to evaluate the prognostic role of M2-PK, CRP, and CA 15-3 in preoperative and metastatic breast carcinomas. The study included 70females; 15 controls, 33 preoperative primary breast carcinomas clinically metastasis free, and 22 clinically diagnosed metastatic breast carcinomas. M2-PK and CA 15-3 were detected by ELISA. CRP was quantified using the CRP LATEX kit. TuM2-PK significantly increased in metastatic and preoperative groups when compared to controls (p= 0.049, p= 0.001); respectively. Both CRP and CA 15-3 were significantly increased in metastatic than the preoperative group (p= 0.002). CA 15-3 was significantly increased in both groups when compared to controls (p= 0.016; p< 0.001; respectively). TuM2-PK level significantly related to tumor size in metastatic group (p= 0.006) and with menstruation status (p= 0.039), and liver metastasis (p= 0.036) in preoperative group. TuM2-PK significantly correlated with CRP (r= 0.793, p= 0.004), and CA 15-3 (r= 0.568, p= 0.006) in the metastatic group.Metastatic group with TuM2-PK ⩽ 15U/ml had significantly higher survival rate than those with > 15U/ml (χ2= 13.841, p< 0.001) within 3.3-4.2 but not after 10-20years follow up period. Metastasis to bone and lymph nodes significantly increased in the metastatic than the preoperative group (p= 0.002, p= 0.013; respectively). Within 3.3-4.2 years, CA15.3 has the highest prognostic performance in metastatic group while both TuM2-PK and CRP have same specificity. On the other hand, TuM2-PK has the highest prognostic performance in preoperative group. After 20years follow up period, there was neither significant difference in the performance of the three markers in predicting mortality in metastatic and preoperative groups nor in predicting metastasis in preoperative group. Current results document for the first time, a cross-talk between TuM2-PK and each of CRP and CA 15-3 in metastatic breast cancer.

Metabolomic profling in gastric cancer tissues using time-of-flight mass spectrometry.

66 Background: To explore carcinogenic and prognostic biomarkers for gastric cancer, genomics, transcriptomics and proteomics approaches have been extensively applied;however, little has been investigated regarding the role of metabolomics profiles on progression and prognosis of gastric cancer. Therefore, in order to elucidate the role of metabolome on prognosis of gastric cancer, we investigated the metabolic profiles of gastric cancer tissue using time-of-flight mass spectrometry (TOFMS) Methods: A total of 162 patients with gastric cancer underwent gastrectomy from February 2010 to March 2013 were enrolled in this study. Cancer tissues (CA) and adjacent non-cancerous tissues (NC) were obtained from surgically resected sample and were snap-frozen in liquid nitrogen. The samples were then homogenized and then applied to capillary electrophoresis TOFMS (CE-TOFMS) The metabolomics dates were analyzed using principal component analysis (PCA) and hierachical clustering analysis(HCA) in order to compare the metabolic profiles of NC and CA. Metabolites date were further assessed according by the non-parametric Mann–Whitney U-test on the presence or absence of recurrence . Results: A total 96 metabolites were detected and quantified. PCA of the date well-distinguished CA from NC. In CA, lactate / pyruvate ratio was significantly higher, while adenylate energy charge was significantly lower than NC, which reaffirms the Warburug effect of cancer. Total glutathione and reducted glutathione/oxidized glutathione ratio in CA were significantly higher than in NC which possibly contributes to the homeostasis of redox status in CA. Intriguingly, in patients with recurrence, tumor concentrations of β-Ala, Asp, GDP and Gly were significantly lower than in those without recurrence. Conclusions: Metabolomic profiling clearly differentiated CA from NC. Considerably high lactate and amino acids levels expectedly highlighted the metabolome of tumors. Certain metabolite will be a candidate for biomarker in gastric cancer.

Shall we Sample Every Pancreatic Mass?

Sampling every pancreatic mass is debated but most observers believe that it should be mandatory for several reasons. Even if the vast majority of pancreatic masses consist of adenocarcinoma, 15% of them are of other origin and require their own specific therapy. Moreover, the progressive implementation of personalized medicine will allow offering the best therapeutic approach to any patient. Indeed, sampling any pancreatic mass can help determine the whole nature of the tumor (genomics, proteomics, and metabolomics) and thus permit to give the appropriate therapy only to patients that will certainly benefit from it.

Abstract LB-200: Integrating dynamic 18F-FDG PET-CT, tumor metabolomics and functional genomics to understand metformin's pharmacodynamic effects in breast cancer: results of a phase 0 clinical trial

Abstract There is growing interest in the anticancer effect of the diabetes drug, metformin, and over 100 clinical trials are underway worldwide. However it is still not determined as to whether metformin has significant direct effects on cancer cells or solely indirect effects via modulation of host metabolism. We recruited 41 non-diabetic patients with primary breast cancer to a neoadjuvant window trial. Patients received an escalating dose of metformin (500mg up to 1500mg) for 2 weeks with pre- and post-metformin pharmacodynamic assessments including, dynamic 18F-FDG PET-CT scans, serum metabolic markers, and tumour biopsies for whole transcriptome RNASeq, tumour metabolomics and immunohistochemistry. Assessment of tumour FDG kinetics using a classic 2-tissue compartment model with three rate constants showed a 1.3 fold change (FC) post-metformin in the composite 18F-FDG flux constant, Kflux (p = 0.041, on paired t-test). There was a non-statistically significant 1.1 fold increase in the variable, metabolic rate of glucose utilization (MRglu) (p = 0.141). Mass spectrometry analysis revealed a decrease in intra-tumoral levels of the short-chain acyl-carnitines, propionylcarnitine (FC -0.50, p = 0.039) and acetylycarnitine (FC -0.40, p = 0.046) consistent with inhibition of fatty acid oxidation (wilcoxon rank test). This effect on fatty acid oxidation was validated in pre-clinical in vitro and in vivo breast cancer cell line models. There was a statistically significant positive correlation between the actual difference in Kflux and intratumoral levels of acetylcarnitine (p = 0.012, Spearman's rank test). Small but consistent falls in the levels of serum glucose (p = 0.032), c-peptide (p = 0.001), insulin (p = 0.005), IGF1 (p = 0.048) and IGF2 (p = 0.040) were observed following metformin treatment (paired t-test) but there was no correlation with change in Kflux or short chain acyl-carnitine levels. Pathway annotation analysis using GeneCodis revealed several pathways associated with mitochondrial and fatty acid metabolism that were upregulated. Immunohistochemical intratumoral nuclear expression of pAMPK increased 1.5 fold (p = 0.037, paired t-test) but this did not correlate with change in Kflux or levels of short-chain acyl-carnitines. Peak serum metformin levels correlated with intratumoral metformin levels (p = 0.012, Spearman's rank test) but did not correlate with change in Kflux or short-chain acyl-carnitine levels. Our data shows that metformin treatment alters FDG kinetics, inhibits fatty acid oxidation and leads to altered gene expression in the mitochondrial (nuclear encoded) transcriptome but that these effects do not correlate with changes in host metabolism. This data provides strong evidence that metformin has a direct effect on breast cancer metabolism at clinical doses. Citation Format: Simon Lord, Daniel Liu, Syed Haider, Edoardo Gaude, Eugene Teoh, Patel Neel, Qifeng Zhang, Fergus Gleeson, Michael Wakelam, Christian Frezza, Francesca Buffa, Fenwick John, Adrian Harris. Integrating dynamic 18F-FDG PET-CT, tumor metabolomics and functional genomics to understand metformin's pharmacodynamic effects in breast cancer: results of a phase 0 clinical trial. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-200.

Abstract A24: Integration of tumor metabolomics, cancer genome sequencing and dynamic functional imaging to assess the metabolic effects of metformin in breast cancer

Abstract There is great interest in the effects on cancer metabolism of the diabetes drug, metformin and its potential as a cancer treatment. Despite intense investigation it is still not clear as to its metabolic effects on primary human cancer in vivo. 41 patients with primary breast cancer were recruited to a ‘window of opportunity study’ prior to neoadjuvant chemotherapy. Before and after 2 weeks of metformin, dynamic 18F-FDG PET-CT scans were carried out, breast core biopsies of the primary tumour taken for RNASeq, metabolomics and immunohistochemistry analysis and host serum metabolic markers assessed. Metabolomic mass spectrometry analysis revealed an increase in the ADP/ATP and AMP/ATP ratio (fold change 8.6 and 24.9, respectively, p = 0.07 and p = 0.14) after metformin treatment. RNA sequencing of the first 21 paired samples showed upregulation of many genes encoding components of complex 1, and the TCA cycle, glutaminolysis, and glycolysis pathways. Several genes encoding for glucose and glutamine transport were amongst those most consistently upregulated, including GLUT1, SLC38A1 and SLC7A5 (all p&amp;lt;0.0001). Dynamic PET-CT analysis of the first 17 paired scans demonstrated a trend toward a decrease in the variable K2 post-metformin (an estimate of 18F-FDG efflux from tumour intracellular pool back to blood), but with much variability between paired scans (0.70±0.11 vs 0.51±0.05 (p=0.14)). There was a decrease in serum C-peptide (0.56±0.04nmol/L vs 0.48±0.02nmol/L, pre- and post-metformin, p=0.003) consistent with reduced host insulin secretion. This is the first study in the clinical setting to show that metformin can cause an energy stress in cancer cells. The effect on mRNA expression was compatible with direct mitochondrial effects on the cancer cell leading to upregulation of the glutaminolysis and glycolysis pathways. The decrease in efflux of radiolabelled glucose may indicate retention of glucose possibly secondary to changes in blood glucose availability. We expect this study will help define biomarker development and future treatment strategies for metformin, in particular potential combination therapy. Note: This abstract was not presented at the conference. Citation Format: Simon R. Lord, Nilay Patel, D. Liu, J. Fenwick, C. Frezza, S. Henriques da Costa, E. Gaude, Q. Zhang, M. Wakelam, F. Gleeson, S. Haider, F. Buffa, A. L. Harris. Integration of tumor metabolomics, cancer genome sequencing and dynamic functional imaging to assess the metabolic effects of metformin in breast cancer [abstract]. In: Proceedings of the Thirteenth Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2014 Sep 27-Oct 1; New Orleans, LA. Philadelphia (PA): AACR; Can Prev Res 2015;8(10 Suppl): Abstract nr A24.

Highlights in tumor metabolome research: Choline metabolism influences integrin expression and supports cell attachment.

Highlights in tumor metabolome research: Choline metabolism influences integrin expression and supports cell attachment.

Mouse to Human Blood-Based Cancer Biomarker Discovery Strategies

There is an urgent need for noninvasive molecular tests to assist in the detection of cancers. There is additionally a need for prognostic and predictive markers and for monitoring for disease recurrence. The improved understanding of molecular features of common cancers and the availability of genetically engineered mouse models (GEMMs) of cancer have resulted in increased interest in the application of mouse models to the discovery of cancer biomarkers relevant to humans. Unlike humans, mouse models allow sampling of tumor and host tissues and biological fluids at defined time points in the course of tumor development and progression. Interrogation of the genome, transcriptome, proteome, and metabolome of tumors and biological fluids from mouse models engineered to recapitulate human tumors makes it possible to apply a systems approach to define biomarker signatures from the earliest stages of tumor development to advanced stages and metastasis and signatures reflective of driver genes and pathways.

Liquid chromatography/mass spectrometry methods for measuring dipeptide abundance in non‐small‐cell lung cancer

Metabolomic profiling is a promising methodology of identifying candidate biomarkers for disease detection and monitoring. Although lung cancer is among the leading causes of cancer-related mortality worldwide, the lung tumor metabolome has not been fully characterized. We utilized a targeted metabolomic approach to analyze discrete groups of related metabolites. We adopted a dansyl [5-(dimethylamino)-1-naphthalene sulfonamide] derivatization with liquid chromatography/mass spectrometry (LC/MS) to analyze changes of metabolites from paired tumor and normal lung tissues. Identification of dansylated dipeptides was confirmed with synthetic standards. A systematic analysis of retention times was required to reliably identify isobaric dipeptides. We validated our findings in a separate sample cohort. We produced a database of the LC retention times and MS/MS spectra of 361 dansyl dipeptides. Interpretation of the spectra is presented. Using this standard data, we identified a total of 279 dipeptides in lung tumor tissue. The abundance of 90 dipeptides was selectively increased in lung tumor tissue compared to normal tissue. In a second set of validation tissues, 12 dipeptides were selectively increased. A systematic evaluation of certain metabolite classes in lung tumors may identify promising disease-specific metabolites. Our database of all possible dipeptides will facilitate ongoing translational applications of metabolomic profiling as it relates to lung cancer.

Metabolomic discrimination of intestinal- and diffuse-type gastric cancer tissues using capillary electrophoresis time-of-flight mass spectrometry.

e14635 Background: Clinicopathological characteristics of gastric cancer patients strongly depend on histological type. In contrast with gene and protein expressions, metabolic properties of intestinal- and diffuse-type gastric cancer have been largely unknown. Here, we conducted metabolome analysis of paired non-tumor and tumor gastric tissues by using capillary electrophoresis and liquid chromatography combined with time-of-flight mass spectrometry (CE- and LC-TOFMS, respectively) in order to metabolomically characterize non-tumors (NTs), intestinal-type tumors (ITs), and diffuse-type tumors (DTs). Methods: Tumor and surrounding non-tumor tissues were surgically excised pair-wise from 27 gastric cancer patients (18 ITs and 9 DTs) who underwent gastrectomy at our institution between February and May 2011. Following tissue homogenization and metabolite extraction, we measured 254 and 138 metabolites, respectively, by CE-TOFMS and LC-TOFMS. Results: Metabolomic profiles of tumor tissues, especially ITs, were well-distinguished from those of NTs: Lactate and most glycolytic intermediate levels in ITs were significantly higher than those in NTs, which reaffirms the Warburg effect of cancer, but the significance was lesser in DTs. Levels of all the measured amino acids were significantly higher in ITs and relatively higher in DTs than in NTs, showing high capacities of cancer cells for protein synthesis. Although levels of ATP, GTP, and energy charge in ITs and DTs were lower than those in NTs, purine contents were rather higher in the tumors than in NTs, which may support their high demand for DNA replication. Moreover, reduced glutathione in DTs were the lowest among others, implying their potential vulnerability against oxidative stress. Conclusions: Metabolomic profiles of NTs, ITs, and DTs were discriminated by CE- and LC-TOFMS analyses: Considerably high lactate, amino acid, and purine levels highlighted the metabolome of tumors, especially of ITs. Relatively low energy and redox statuses of DTs, however, could be targeted for developing more effective cancer therapeutics.

Choline-releasing glycerophosphodiesterase EDI3 drives tumor cell migration and metastasis

Metastasis from primary tumors remains a major problem for tumor therapy. In the search for markers of metastasis and more effective therapies, the tumor metabolome is relevant because of its importance to the malignant phenotype and metastatic capacity of tumor cells. Altered choline metabolism is a hallmark of cancer. More specifically, a decreased glycerophosphocholine (GPC) to phosphocholine (PC) ratio was reported in breast, ovarian, and prostate cancers. Improved strategies to exploit this altered choline metabolism are therefore required. However, the critical enzyme cleaving GPC to produce choline, the initial step in the pathway controlling the GPC/PC ratio, remained unknown. In the present work, we have identified the enzyme, here named EDI3 (endometrial differential 3). Purified recombinant EDI3 protein cleaves GPC to form glycerol-3-phosphate and choline. Silencing EDI3 in MCF-7 cells decreased this enzymatic activity, increased the intracellular GPC/PC ratio, and decreased downstream lipid metabolites. Downregulating EDI3 activity inhibited cell migration via disruption of the PKCα signaling pathway, with stable overexpression of EDI3 showing the opposite effect. EDI3 was originally identified in our screening study comparing mRNA levels in metastasizing and nonmetastasizing endometrial carcinomas. Both Kaplan-Meier and multivariate analyses revealed a negative association between high EDI3 expression and relapse-free survival time in both endometrial (P < 0.001) and ovarian (P = 0.029) cancers. Overall, we have identified EDI3, a key enzyme controlling GPC and choline metabolism. Because inhibition of EDI3 activity corrects the GPC/PC ratio and decreases the migration capacity of tumor cells, it represents a possible target for therapeutic intervention.

Metabolomic profiling for biomarker discovery in pancreatic cancer

Pancreatic cancer (PC) is the fourth leading cause of cancer death in the United States, with 4% survival, 5 years after diagnosis. Patients with pancreatic cancer are usually diagnosed at late stages, when the disease is incurable. Sensitive and more specific markers are critical for supporting new prevention, diagnostic or therapeutic strategies. Here, we report mass spectrometry-based metabolomic profiling of human pancreas matched tumor and normal tissue. Multivariate data analysis using two independent methods shows significant alterations in the profiles of the tumor metabolome as compared to the normal tissue. These findings offer an information-rich matrix for discovering novel candidate biomarkers with diagnostic or prognostic potential.

Emerging molecular biomarkers—blood-based strategies to detect and monitor cancer

There is an urgent need for blood-based, noninvasive molecular tests to assist in the detection and diagnosis of cancers in a cost-effective manner at an early stage, when curative interventions are still possible. Additionally, blood-based diagnostics can classify tumors into distinct molecular subtypes and monitor disease relapse and response to treatment. Increasingly, biomarker strategies are becoming critical to identify a specific patient subpopulation that is likely to respond to a new therapeutic agent. The improved understanding of the underlying molecular features of common cancers and the availability of a multitude of recently developed technologies to interrogate the genome, transcriptome, proteome and metabolome of tumors and biological fluids have made it possible to develop clinically applicable and cost-effective tests for many common cancers. Overall, the paradigm shift towards personalized and individualized medicine relies heavily on the increased use of diagnostic biomarkers and classifiers to improve diagnosis, management and treatment. International collaborations, involving both the private and public sector will be required to facilitate the development of clinical applications of biomarkers, using rigorous standardized assays. Here, we review the recent technological and scientific advances in this field.

Pyruvate kinase type M2: a crossroad in the tumor metabolome

Cell proliferation is a process that consumes large amounts of energy. A reduction in the nutrient supply can lead to cell death by ATP depletion, if cell proliferation is not limited. A key sensor for this regulation is the glycolytic enzyme pyruvate kinase, which determines whether glucose carbons are channelled to synthetic processes or used for glycolytic energy production. In unicellular organisms pyruvate kinase is regulated by ATP, ADP and AMP, by ribose 5-P, the precursor of the nucleic acid synthesis, and by the glycolytic intermediate fructose 1,6-P2 (FBP), thereby adapting cell proliferation to nutrient supply. The mammalian pyruvate kinase isoenzyme type M2 (M2-PK) displays the same kinetic properties as the pyruvate kinase enzyme from unicellular organisms. The mammalian M2-PK isoenzyme can switch between a less active dimeric form and a highly active tetrameric form which regulates the channeling of glucose carbons either to synthetic processes (dimeric form) or to glycolytic energy production (tetrameric form). Tumor cells are usually characterized by a high amount of the dimeric form leading to a strong accumulation of all glycolytic phosphometabolites above pyruvate kinase. The tetramer-dimer ratio is regulated by ATP, FBP and serine and by direct interactions with different oncoproteins (pp60v-src, HPV-16 E7). In solid tumors with sufficient oxygen supply pyruvate is supplied by glutaminolysis. Pyruvate produced in glycolysis and glutaminolysis is used for the synthesis of lactate, glutamate and fatty acids thereby releasing the hydrogen produced in the glycolytic glyceraldehyde 3-phosphate dehydrogenase reaction.

Metabolic cooperation between different oncogenes during cell transformation: interaction between activated ras and HPV-16 E7.

The metabolism of tumor cells (tumor metabolome) is characterized by a high concentration of glycolytic enzymes including pyruvate kinase isoenzyme type M2 (M2-PK), a high glutaminolytic capacity, high fructose 1,6-bisphosphate (FBP) levels and a low (ATP+GTP):(CTP+UTP) ratio. The sequence of events required for the establishment of the tumor metabolome is presently unknown. In non-transformed rat kidney (NRK) cells we observed a high glutaminolytic flux rate and a low (ATP+GTP):(CTP+UTP) ratio, whereas FBP levels and M2-PK activity are still extremely low. After stable expression of oncogenic ras in NRK cells a strong upregulation of FBP levels and of M2-PK activity was observed. Elevated FBP levels induce a tetramerization of M2-PK and its migration into the glycolytic enzyme complex. AMP levels increase whereas UTP and CTP levels strongly decrease. Thus, ras expression completes the glycolytic part of tumor metabolism leading to the inhibition of nucleic acid synthesis and cell proliferation. The HPV-16 E7 oncoprotein, which cooperates with ras in cell transformation, directly binds to M2-PK, induces its dimerization and restores nucleic acid synthesis as well as cell proliferation. Apparently, the combination of the different metabolic effects of ras and E7 constructs the perfect tumor metabolome as generally found in tumor cells.