Role of adenosine deaminase in prostate cancer progression.

Prostate cancer (PCa) is the second most common cancer and constitutes about 14.7% of total cancer cases. PCa is highly prevalent and more aggressive in African-American (AA) men than in European-American (EA) men. PCa tends to be highly heterogeneous, and its complex biology is not fully understood. We use metabolomics to better understand the mechanisms behind PCa progression and disparities in its clinical outcome. Adenosine deaminase (ADA) is a key enzyme in the purine metabolic pathway; it was found to be upregulated in PCa and is associated with higher-grade PCa and poor disease-free survival. The inosine-to-adenosine ratio, which is a surrogate for ADA activity was high in PCa patient urine and higher in AA PCa compared to EA PCa. To understand the significance of high ADA in PCa, we established ADA overexpression models and performed various in vitro and in vivo studies. Our studies have revealed that an acute increase in ADA expression during later stages of tumor development enhances in vivo growth in multiple pre-clinical models. Further analysis revealed that mTOR signaling activation could be associated with this tumor growth. Chronic ADA overexpression shows alterations in the cells' adhesion machinery and a decrease in cells' ability to adhere to the extracellular matrix in vitro. Losing cell-matrix interaction is critical for metastatic dissemination which suggests that ADA could potentially be involved in promoting metastasis. This is supported by the association of higher ADA expression with higher-grade tumors and poor patient survival. Overall, our findings suggest that increased ADA expression may promote PCa progression, specifically tumor growth and metastatic dissemination.

Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review.

Through the developments of Omics technologies and dissemination of large-scale datasets, such as those from The Cancer Genome Atlas, Alzheimer's Disease Neuroimaging Initiative, and Genotype-Tissue Expression, it is becoming increasingly possible to study complex biological processes and disease mechanisms more holistically. However, to obtain a comprehensive view of these complex systems, it is crucial to integrate data across various Omics modalities, and also leverage external knowledge available in biological databases. This review aims to provide an overview of multi-Omics data integration methods with different statistical approaches, focusing on unsupervised learning tasks, including disease onset prediction, biomarker discovery, disease subtyping, module discovery, and network/pathway analysis. We also briefly review feature selection methods, multi-Omics data sets, and resources/tools that constitute critical components for carrying out the integration.

A generalized likelihood-based Bayesian approach for scalable joint regression and covariance selection in high dimensions.

The paper addresses joint sparsity selection in the regression coefficient matrix and the error precision (inverse covariance) matrix for high-dimensional multivariate regression models in the Bayesian paradigm. The selected sparsity patterns are crucial to help understand the network of relationships between the predictor and response variables, as well as the conditional relationships among the latter. While Bayesian methods have the advantage of providing natural uncertainty quantification through posterior inclusion probabilities and credible intervals, current Bayesian approaches either restrict to specific sub-classes of sparsity patterns and/or are not scalable to settings with hundreds of responses and predictors. Bayesian approaches which only focus on estimating the posterior mode are scalable, but do not generate samples from the posterior distribution for uncertainty quantification. Using a bi-convex regression based generalized likelihood and spike-and-slab priors, we develop an algorithm called Joint Regression Network Selector (JRNS) for joint regression and covariance selection which (a) can accommodate general sparsity patterns, (b) provides posterior samples for uncertainty quantification, and (c) is scalable and orders of magnitude faster than the state-of-the-art Bayesian approaches providing uncertainty quantification. We demonstrate the statistical and computational efficacy of the proposed approach on synthetic data and through the analysis of selected cancer data sets. We also establish high-dimensional posterior consistency for one of the developed algorithms.

County-level longitudinal clustering of COVID-19 mortality to incidence ratio in the United States.

As of November 12, 2020, the mortality to incidence ratio (MIR) of COVID-19 was 5.8% in the US. A longitudinal model-based clustering system on the disease trajectories over time was used to identify "vulnerable" clusters of counties that would benefit from allocating additional resources by federal, state and county policymakers. County-level COVID-19 cases and deaths, together with a set of potential risk factors were collected for 3050 U.S. counties during the 1st wave of COVID-19 (Mar25-Jun3, 2020), followed by similar data for 1344 counties (in the "sunbelt" region of the country) during the 2nd wave (Jun4-Sep2, 2020), and finally for 1055 counties located broadly in the great plains region of the country during the 3rd wave (Sep3-Nov12, 2020). We used growth mixture models to identify clusters of counties exhibiting similar COVID-19 MIR growth trajectories and risk-factors over time. The analysis identifies "more vulnerable" clusters during the 1st, 2nd and 3rd waves of COVID-19. Further, tuberculosis (OR 1.3-2.1-3.2), drug use disorder (OR 1.1), hepatitis (OR 13.1), HIV/AIDS (OR 2.3), cardiomyopathy and myocarditis (OR 1.3), diabetes (OR 1.2), mesothelioma (OR 9.3) were significantly associated with increased odds of being in a more vulnerable cluster. Heart complications and cancer were the main risk factors increasing the COVID-19 MIR (range 0.08-0.52% MIR↑). We identified "more vulnerable" county-clusters exhibiting the highest COVID-19 MIR trajectories, indicating that enhancing the capacity and access to healthcare resources would be key to successfully manage COVID-19 in these clusters. These findings provide insights for public health policymakers on the groups of people and locations they need to pay particular attention while managing the COVID-19 epidemic.

Lipid Alterations in African American Men with Prostate Cancer.

African-American (AA) men are more than twice as likely to die of prostate cancer (PCa) than European American (EA) men. Previous in silico analysis revealed enrichment of altered lipid metabolic pathways in pan-cancer AA tumors. Here, we performed global unbiased lipidomics profiling on 48 matched localized PCa and benign adjacent tissues (30 AA, 24 ancestry-verified, and 18 EA, 8 ancestry verified) and quantified 429 lipids belonging to 14 lipid classes. Significant alterations in long chain polyunsaturated lipids were observed between PCa and benign adjacent tissues, low and high Gleason tumors, as well as associated with early biochemical recurrence, both in the entire cohort, and within AA patients. Alterations in cholesteryl esters, and phosphatidyl inositol classes of lipids delineated AA and EA PCa, while the levels of lipids belonging to triglycerides, phosphatidyl glycerol, phosphatidyl choline, phosphatidic acid, and cholesteryl esters distinguished AA and EA PCa patients with biochemical recurrence. These first-in-field results implicate lipid alterations as biological factors for prostate cancer disparities.

Methionine-Homocysteine Pathway in African-American Prostate Cancer.

African American (AA) men have a 60% higher incidence and two times greater risk of dying of prostate cancer (PCa) than European American men, yet there is limited insight into the molecular mechanisms driving this difference. To our knowledge, metabolic alterations, a cancer-associated hallmark, have not been reported in AA PCa, despite their importance in tumor biology. Therefore, we measured 190 metabolites across ancestry-verified AA PCa/benign adjacent tissue pairs (n = 33 each) and identified alterations in the methionine-homocysteine pathway utilizing two-sided statistical tests for all comparisons. Consistent with this finding, methionine and homocysteine were elevated in plasma from AA PCa patients using case-control (AA PCa vs AA control, methionine: P = .0007 and homocysteine: P < .0001), biopsy cohorts (AA biopsy positive vs AA biopsy negative, methionine: P = .0002 and homocysteine: P < .0001), and race assignments based on either self-report (AA PCa vs European American PCa, methionine: P = .001, homocysteine: P < .0001) or West African ancestry (upper tertile vs middle tertile, homocysteine: P < .0001; upper tertile vs low tertile, homocysteine: P = .002). These findings demonstrate reprogrammed metabolism in AA PCa patients and provide a potential biological basis for PCa disparities.

ERR1 and PGC1α associated mitochondrial alterations correlate with pan-cancer disparity in African Americans.

BACKGROUND: African American (AA) patients have higher cancer mortality rates and shorter survival times compared to European American (EA) patients. Despite a significant focus on socioeconomic factors, recent findings strongly argue the existence of biological factors driving this disparity. Most of these factors have been described in a cancer-type specific context rather than a pan-cancer setting. METHODS: A novel in silico approach based on Gene Set Enrichment Analysis (GSEA) coupled to Transcription Factor enrichment was carried out to identify common biological drivers of pan-cancer racial disparity using The Cancer Genome Atlas (TCGA) dataset. Mitochondrial content in patient tissues was examined using a multi-cancer tissue microarray approach (TMA). RESULTS: Mitochondrial oxidative phosphorylation was uniquely enriched in AA tumors compared to EA tumors across various cancer types. AA tumors also showed strong enrichment for the ERR1-PGC1α-mediated transcriptional program, which has been implicated in mitochondrial biogenesis. TMA analysis revealed that AA cancers harbor significantly more mitochondria compared to their EA counterparts. CONCLUSIONS: These findings highlight changes in mitochondria as a common distinguishing feature between AA and EA tumors in a pan-cancer setting, and provide the rationale for the repurposing of mitochondrial inhibitors to treat AA cancers.

Therapeutic Lifestyle Changes Improve HDL Function by Inhibiting Myeloperoxidase-Mediated Oxidation in Patients With Metabolic Syndrome.

OBJECTIVE: Phagocyte-derived myeloperoxidase (MPO) and proinflammatory HDL are associated with metabolic syndrome (MetS) and increased cardiovascular disease risk. Therapeutic lifestyle changes (TLCs), such as a Mediterranean diet and exercise, decrease this risk. However, the link among TLCs, HDL, and MPO-mediated oxidative stress remains unclear. RESEARCH DESIGN AND METHODS: In this study, we characterized changes in cholesterol efflux capacity (CEC), a metric of HDL function; MPO-mediated oxidation; and the HDL proteomic profile in 25 patients with MetS who underwent 12 weeks of TLCs. RESULTS: After 12 weeks, before significant changes to HDL levels, most MetS components improved as a result of the TLCs. CEC was significantly increased, and HDL MPO oxidation products, 3-chlorotyrosine and 3-nitrotyrosine, were decreased with TLCs. The changes in CEC were inversely related to the unit changes in 3-chlorotyrosine after we controlled for changes in the other MetS components. TLCs did not remodel the HDL proteome. CONCLUSIONS: In summary, TLCs improved HDL function by inhibiting MPO-mediated oxidative stress even before appreciable changes in HDL levels.

Distinct Lipidomic Landscapes Associated with Clinical Stages of Urothelial Cancer of the Bladder.

BACKGROUND: The first global lipidomic profiles associated with urothelial cancer of the bladder (UCB) and its clinical stages associated with progression were identified. OBJECTIVE: To identify lipidomic signatures associated with survival and different clinical stages of UCB. DESIGN, SETTING, AND PARTICIPANTS: Pathologically confirmed 165 bladder-derived tissues (126 UCB, 39 benign adjacent or normal bladder tissues). UCB tissues included Ta (n=16), T1 (n=30), T2 (n=43), T3 (n=27), and T4 (n=9); lymphovascular invasion (LVI) positive (n=52) and negative (n=69); and lymph node status N0 (n=28), N1 (n=11), N2 (n=9), N3 (n=3), and Nx (n=75). RESULTS AND LIMITATIONS: UCB tissues have higher levels of phospholipids and fatty acids, and reduced levels of triglycerides compared with benign tissues. A total of 59 genes associated with altered lipids in UCB strongly correlate with patient survival in an UCB public dataset. Within UCB, there was a progressive decrease in the levels of phosphatidylserine (PS), phosphatidylethanolamines (PEs), and phosphocholines, whereas an increase in the levels of diacylglycerols (DGs) with tumor stage. Transcript and protein expression of phosphatidylserine synthase 1, which converts DGs to PSs, decreased progressively with tumor stage. Levels of DGs and lyso-PEs were significantly elevated in tumors with LVI and lymph node involvement, respectively. Lack of carcinoma in situ and treatment information is the limitation of our study. CONCLUSIONS: To date, this is the first study describing the global lipidomic profiles associated with UCB and identifies lipids associated with tumor stages, LVI, and lymph node status. Our data suggest that triglycerides serve as the primary energy source in UCB, while phospholipid alterations could affect membrane structure and/or signaling associated with tumor progression. PATIENT SUMMARY: Lipidomic alterations identified in this study set the stage for characterization of pathways associated with these altered lipids that, in turn, could inform the development of first-of-its-kind lipid-based noninvasive biomarkers and novel therapeutic targets for aggressive urothelial cancer of the bladder.

Influence of the neural microenvironment on prostate cancer.

BACKGROUND: Nerves are key factors in prostate cancer (PCa), but the functional role of innervation in prostate cancer is poorly understood. PCa induced neurogenesis and perineural invasion (PNI), are associated with aggressive disease. METHOD: We denervated rodent prostates chemically and physically, before orthotopically implanting cancer cells. We also performed a human neoadjuvant clinical trial using botulinum toxin type A (Botox) and saline in the same patient, before prostatectomy. RESULT: Bilateral denervation resulted in reduced tumor incidence and size in mice. Botox treatment in humans resulted in increased apoptosis of cancer cells in the Botox treated side. A similar denervation gene array profile was identified in tumors arising in denervated rodent prostates, in spinal cord injury patients and in the Botox treated side of patients. Denervation induced exhibited a signature gene profile, indicating translation and bioenergetic shutdown. Nerves also regulate basic cellular functions of non-neoplastic epithelial cells. CONCLUSION: Nerves play a role in the homeostasis of normal epithelial tissues and are involved in prostate cancer tumor survival. This study confirms that interactions between human cancer and nerves are essential to disease progression. This work may make a major impact in general cancer treatment strategies, as nerve/cancer interactions are likely important in other cancers as well. Targeting the neural microenvironment may represent a therapeutic approach for the treatment of human prostate cancer.

Tobacco-Specific Carcinogens Induce Hypermethylation, DNA Adducts, and DNA Damage in Bladder Cancer.

Smoking is a major risk factor for the development of bladder cancer; however, the functional consequences of the carcinogens in tobacco smoke and bladder cancer-associated metabolic alterations remain poorly defined. We assessed the metabolic profiles in bladder cancer smokers and non-smokers and identified the key alterations in their metabolism. LC/MS and bioinformatic analysis were performed to determine the metabolome associated with bladder cancer smokers and were further validated in cell line models. Smokers with bladder cancer were found to have elevated levels of methylated metabolites, polycyclic aromatic hydrocarbons, DNA adducts, and DNA damage. DNA methyltransferase 1 (DNMT1) expression was significantly higher in smokers than non-smokers with bladder cancer. An integromics approach, using multiple patient cohorts, revealed strong associations between smokers and high-grade bladder cancer. In vitro exposure to the tobacco smoke carcinogens, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone and benzo[a]pyrene (BaP) led to increase in levels of methylated metabolites, DNA adducts, and extensive DNA damage in bladder cancer cells. Cotreatment of bladder cancer cells with these carcinogens and the methylation inhibitor 5-aza-2'-deoxycytidine rewired the methylated metabolites, DNA adducts, and DNA damage. These findings were confirmed through the isotopic-labeled metabolic flux analysis. Screens using smoke-associated metabolites and DNA adducts could provide robust biomarkers and improve individual risk prediction in bladder cancer smokers. Noninvasive predictive biomarkers that can stratify the risk of developing bladder cancer in smokers could aid in early detection and treatment. Cancer Prev Res; 10(10); 588-97. ©2017 AACR.

Inhibition of the hexosamine biosynthetic pathway promotes castration-resistant prostate cancer.

The precise molecular alterations driving castration-resistant prostate cancer (CRPC) are not clearly understood. Using a novel network-based integrative approach, here, we show distinct alterations in the hexosamine biosynthetic pathway (HBP) to be critical for CRPC. Expression of HBP enzyme glucosamine-phosphate N-acetyltransferase 1 (GNPNAT1) is found to be significantly decreased in CRPC compared with localized prostate cancer (PCa). Genetic loss-of-function of GNPNAT1 in CRPC-like cells increases proliferation and aggressiveness, in vitro and in vivo. This is mediated by either activation of the PI3K-AKT pathway in cells expressing full-length androgen receptor (AR) or by specific protein 1 (SP1)-regulated expression of carbohydrate response element-binding protein (ChREBP) in cells containing AR-V7 variant. Strikingly, addition of the HBP metabolite UDP-N-acetylglucosamine (UDP-GlcNAc) to CRPC-like cells significantly decreases cell proliferation, both in-vitro and in animal studies, while also demonstrates additive efficacy when combined with enzalutamide in-vitro. These observations demonstrate the therapeutic value of targeting HBP in CRPC.

Integrative Pathway Analysis of Metabolic Signature in Bladder Cancer: A Linkage to The Cancer Genome Atlas Project and Prediction of Survival.

PURPOSE: We used targeted mass spectrometry to study the metabolic fingerprint of urothelial cancer and determine whether the biochemical pathway analysis gene signature would have a predictive value in independent cohorts of patients with bladder cancer. MATERIALS AND METHODS: Pathologically evaluated, bladder derived tissues, including benign adjacent tissue from 14 patients and bladder cancer from 46, were analyzed by liquid chromatography based targeted mass spectrometry. Differential metabolites associated with tumor samples in comparison to benign tissue were identified by adjusting the p values for multiple testing at a false discovery rate threshold of 15%. Enrichment of pathways and processes associated with the metabolic signature were determined using the GO (Gene Ontology) Database and MSigDB (Molecular Signature Database). Integration of metabolite alterations with transcriptome data from TCGA (The Cancer Genome Atlas) was done to identify the molecular signature of 30 metabolic genes. Available outcome data from TCGA portal were used to determine the association with survival. RESULTS: We identified 145 metabolites, of which analysis revealed 31 differential metabolites when comparing benign and tumor tissue samples. Using the KEGG (Kyoto Encyclopedia of Genes and Genomes) Database we identified a total of 174 genes that correlated with the altered metabolic pathways involved. By integrating these genes with the transcriptomic data from the corresponding TCGA data set we identified a metabolic signature consisting of 30 genes. The signature was significant in its prediction of survival in 95 patients with a low signature score vs 282 with a high signature score (p = 0.0458). CONCLUSIONS: Targeted mass spectrometry of bladder cancer is highly sensitive for detecting metabolic alterations. Applying transcriptome data allows for integration into larger data sets and identification of relevant metabolic pathways in bladder cancer progression.

A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data.

MOTIVATION: Recent advances in high-throughput omics technologies have enabled biomedical researchers to collect large-scale genomic data. As a consequence, there has been growing interest in developing methods to integrate such data to obtain deeper insights regarding the underlying biological system. A key challenge for integrative studies is the heterogeneity present in the different omics data sources, which makes it difficult to discern the coordinated signal of interest from source-specific noise or extraneous effects. RESULTS: We introduce a novel method of multi-modal data analysis that is designed for heterogeneous data based on non-negative matrix factorization. We provide an algorithm for jointly decomposing the data matrices involved that also includes a sparsity option for high-dimensional settings. The performance of the proposed method is evaluated on synthetic data and on real DNA methylation, gene expression and miRNA expression data from ovarian cancer samples obtained from The Cancer Genome Atlas. The results show the presence of common modules across patient samples linked to cancer-related pathways, as well as previously established ovarian cancer subtypes. AVAILABILITY AND IMPLEMENTATION: The source code repository is publicly available at https://github.com/yangzi4/iNMF. CONTACT: gmichail@umich.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

EMT-induced metabolite signature identifies poor clinical outcome.

Metabolic reprogramming is a hallmark of cancer. Epithelial-mesenchymal transition (EMT) induces cancer stem cell (CSC) characteristics and promotes tumor invasiveness; however relatively little is known about the metabolic reprogramming in EMT. Here we show that breast epithelial cells undergo metabolic reprogramming following EMT. Relative to control, cell lines expressing EMT transcription factors show ≥1.5-fold accumulation of glutamine, glutamate, beta-alanine and glycylleucine as well as ≥1.5-fold reduction of phosphoenolpyruvate, urate, and deoxycarnitine. Moreover, these metabolic alterations were found to be predictive of overall survival (hazard ratio = 2.3 (95% confidence interval: 1.31-4.2), logrank p-value = 0.03) and define breast cancer molecular subtypes. EMT-associated metabolites are primarily composed of anapleurotic precursors, suggesting that cells undergoing EMT have a shift in energy production. In summary, we describe a unique panel of metabolites associated with EMT and demonstrate that these metabolites have the potential for predicting clinical and biological characteristics associated with patient survival.

CAPER is vital for energy and redox homeostasis by integrating glucose-induced mitochondrial functions via ERR-α-Gabpa and stress-induced adaptive responses via NF-κB-cMYC.

Ever since we developed mitochondria to generate ATP, eukaryotes required intimate mito-nuclear communication. In addition, since reactive oxygen species are a cost of mitochondrial oxidative phosphorylation, this demands safeguards as protection from these harmful byproducts. Here we identified a critical transcriptional integrator which eukaryotes share to orchestrate both nutrient-induced mitochondrial energy metabolism and stress-induced nuclear responses, thereby maintaining carbon-nitrogen balance, and preserving life span and reproductive capacity. Inhibition of nutrient-induced expression of CAPER arrests nutrient-dependent cell proliferation and ATP generation and induces autophagy-mediated vacuolization. Nutrient signaling to CAPER induces mitochondrial transcription and glucose-dependent mitochondrial respiration via coactivation of nuclear receptor ERR-α-mediated Gabpa transcription. CAPER is also a coactivator for NF-κB that directly regulates c-Myc to coordinate nuclear transcriptome responses to mitochondrial stress. Finally, CAPER is responsible for anaplerotic carbon flux into TCA cycles from glycolysis, amino acids and fatty acids in order to maintain cellular energy metabolism to counter mitochondrial stress. Collectively, our studies reveal CAPER as an evolutionarily conserved 'master' regulatory mechanism by which eukaryotic cells control vital homeostasis for both ATP and antioxidants via CAPER-dependent coordinated control of nuclear and mitochondrial transcriptomic programs and their metabolisms. These CAPER dependent bioenergetic programs are highly conserved, as we demonstrated that they are essential to preserving life span and reproductive capacity in human cells-and even in C. elegans.

Pathway-centric integrative analysis identifies RRM2 as a prognostic marker in breast cancer associated with poor survival and tamoxifen resistance.

Breast cancer (BCa) molecular subtypes include luminal A, luminal B, normal-like, HER-2-enriched, and basal-like tumors, among which luminal B and basal-like cancers are highly aggressive. Biochemical pathways associated with patient survival or treatment response in these more aggressive subtypes are not well understood. With the limited availability of pathologically verified clinical specimens, cell line models are routinely used for pathway-centric studies. We measured the metabolome of luminal and basal-like BCa cell lines using mass spectrometry, linked metabolites to biochemical pathways using Gene Set Analysis, and developed a novel rank-based method to select pathways on the basis of their enrichment in patient-derived omics data sets and prognostic relevance. Key mediators of the pathway were then characterized for their role in disease progression. Pyrimidine metabolism was altered in luminal versus basal BCa, whereas the combined expression of its associated genes or expression of one key gene, ribonucleotide reductase subunit M2 (RRM2) alone, associated significantly with decreased survival across all BCa subtypes, as well as in luminal patients resistant to tamoxifen. Increased RRM2 expression in tamoxifen-resistant patients was verified using tissue microarrays, whereas the metabolic products of RRM2 were higher in tamoxifen-resistant cells and in xenograft tumors. Both genetic and pharmacological inhibition of this key enzyme in tamoxifen-resistant cells significantly decreased proliferation, reduced expression of cell cycle genes, and sensitized the cells to tamoxifen treatment. Our study suggests for evaluating RRM2-associated metabolites as noninvasive markers for tamoxifen resistance and its pharmacological inhibition as a novel approach to overcome tamoxifen resistance in BCa.

Metabolomic profiling identifies biochemical pathways associated with castration-resistant prostate cancer.

Despite recent developments in treatment strategies, castration-resistant prostate cancer (CRPC) is still the second leading cause of cancer-associated mortality among American men, the biological underpinnings of which are not well understood. To this end, we measured levels of 150 metabolites and examined the rate of utilization of 184 metabolites in metastatic androgen-dependent prostate cancer (AD) and CRPC cell lines using a combination of targeted mass spectrometry and metabolic phenotyping. Metabolic data were used to derive biochemical pathways that were enriched in CRPC, using Oncomine concept maps (OCM). The enriched pathways were then examined in-silico for their association with treatment failure (i.e., prostate specific antigen (PSA) recurrence or biochemical recurrence) using published clinically annotated gene expression data sets. Our results indicate that a total of 19 metabolites were altered in CRPC compared to AD cell lines. These altered metabolites mapped to a highly interconnected network of biochemical pathways that describe UDP glucuronosyltransferase (UGT) activity. We observed an association with time to treatment failure in an analysis employing genes restricted to this pathway in three independent gene expression data sets. In summary, our studies highlight the value of employing metabolomic strategies in cell lines to derive potentially clinically useful predictive tools.