Three-year follow-up analysis of axicabtagene ciloleucel in relapsed/refractory indolent non-Hodgkin lymphoma (ZUMA-5).

ABSTRACT: Axicabtagene ciloleucel (axi-cel) is an autologous anti-CD19 chimeric antigen receptor (CAR) T-cell therapy approved for relapsed/refractory (R/R) follicular lymphoma (FL). Approval was supported by the phase 2, multicenter, single-arm ZUMA-5 study of axi-cel for patients with R/R indolent non-Hodgkin lymphoma (iNHL; N = 104), including FL and marginal zone lymphoma (MZL). In the primary analysis (median follow-up, 17.5 months), the overall response rate (ORR) was 92% (complete response rate, 74%). Here, we report long-term outcomes from ZUMA-5. Eligible patients with R/R iNHL after ≥2 lines of therapy underwent leukapheresis, followed by lymphodepleting chemotherapy and axi-cel infusion (2 × 106 CAR T cells per kg). The primary end point was ORR, assessed in this analysis by investigators in all enrolled patients (intent-to-treat). After median follow-up of 41.7 months in FL (n = 127) and 31.8 months in MZL (n = 31), ORR was comparable with that of the primary analysis (FL, 94%; MZL, 77%). Median progression-free survival was 40.2 months in FL and not reached in MZL. Medians of overall survival were not reached in either disease type. Grade ≥3 adverse events of interest that occurred after the prior analyses were largely in recently treated patients. Clinical and pharmacokinetic outcomes correlated negatively with recent exposure to bendamustine and high metabolic tumor volume. After 3 years of follow-up in ZUMA-5, axi-cel demonstrated continued durable responses, with very few relapses beyond 2 years, and manageable safety in patients with R/R iNHL. The ZUMA-5 study was registered at www.clinicaltrials.gov as #NCT03105336.

Clinical metabolomics for inborn errors of metabolism.

Metabolism is a highly regulated process that provides nutrients to cells and essential building blocks for the synthesis of protein, DNA and other macromolecules. In healthy biological systems, metabolism maintains a steady state in which the concentrations of metabolites are relatively constant yet are subject to metabolic demands and environmental stimuli. Rare genetic disorders, such as inborn errors of metabolism (IEM), cause defects in regulatory enzymes or proteins leading to metabolic pathway disruption and metabolite accumulation or deficiency. Traditionally, the laboratory diagnosis of IEMs has been limited to analytical methods that target specific metabolites such as amino acids and acyl carnitines. This approach is effective as a screening method for the most common IEM disorders but lacks the comprehensive coverage of metabolites that is necessary to identify rare disorders that present with nonspecific clinical symptoms. Fortunately, advancements in technology and data analytics has introduced a new field of study called metabolomics which has allowed scientists to perform comprehensive metabolite profiling of biological systems to provide insight into mechanism of action and gene function. Since metabolomics seeks to measure all small molecule metabolites in a biological specimen, it provides an innovative approach to evaluating disease in patients with rare genetic disorders. In this review we provide insight into the appropriate application of metabolomics in clinical settings. We discuss the advantages and limitations of the method and provide details related to the technology, data analytics and statistical modeling required for metabolomic profiling of patients with IEMs.

BESPOKE study protocol: a multicentre, prospective observational study to evaluate the impact of circulating tumour DNA guided therapy on patients with colorectal cancer.

INTRODUCTION: Colorectal cancer (CRC) is a highly prevalent disease, wherein, ~30%-40% of patients with CRC relapse postresection. In some patients with CRC, adjuvant chemotherapy can help delay recurrence or be curative. However, current biomarkers show limited clinical utility in determining if/when chemotherapy should be administered, to provide benefit. Circulating tumour DNA (ctDNA) can measure molecular residual disease (MRD) and relapse with high specificity and sensitivity. This study protocol investigates the clinical utility of ctDNA for optimal use of adjuvant chemotherapy in patients with surgically resected CRC and to detect early disease progression in the surveillance setting. METHODS AND ANALYSIS: This is a multicentre prospective, observational cohort study. A total of 2000 stage I-IV patients will be enrolled in up to 200 US sites, and patients will be followed for up to 2 years with serial ctDNA analysis, timed with the standard-of-care visits. The primary endpoints are to observe the impact of bespoke ctDNA testing on adjuvant treatment decisions and to measure CRC recurrence rates while asymptomatic and without imaging correlate. The secondary endpoints are MRD clearance rate (MRD+ to MRD-) during or after adjuvant chemotherapy, percentage of patients that undergo surgery for oligometastatic recurrence, survival of MRD-negative patients treated with adjuvant chemotherapy versus no adjuvant chemotherapy (active surveillance), overall survival, examine the number of stage I CRC that have recurrent disease detected postsurgery, and patient-reported outcomes. ETHICS AND DISSEMINATION: This study has received ethical approval from the Advarra Institutional Review Board (IRB) protocol: Natera-20-041-NCP/3766.01, BESPOKE Study of ctDNA Guided Therapy in Colorectal Cancer (BESPOKE CRC) (Pro00041473) on 10 June 2021. Data protection and privacy regulations will be strictly observed in the capturing, forwarding, processing and storing of patients' data. Publication of any study results will be approved by Natera in accordance with the site-specific contract. TRIAL REGISTRATION NUMBER: NCT04264702.

Global biochemical analysis of plasma, serum and whole blood collected using various anticoagulant additives.

INTRODUCTION: Analysis of blood for the evaluation of clinically relevant biomarkers requires precise collection and sample handling by phlebotomists and laboratory staff. An important consideration for the clinical application of metabolomics are the different anticoagulants utilized for sample collection. Most studies that have characterized differences in metabolite levels in various blood collection tubes have focused on single analytes. We define analyte levels on a global metabolomics platform following blood sampling using five different, but commonly used, clinical laboratory blood collection tubes (i.e., plasma anticoagulated with either EDTA, lithium heparin or sodium citrate, along with no additive (serum), and EDTA anticoagulated whole blood). METHODS: Using an untargeted metabolomics platform we analyzed five sample types after all had been collected and stored at -80°C. The biochemical composition was determined and differences between the samples established using matched-pair t-tests. RESULTS: We identified 1,117 biochemicals across all samples and detected a mean of 1,036 in the sample groups. Compared to the levels of metabolites in EDTA plasma, the number of biochemicals present at statistically significant different levels (p<0.05) ranged from 452 (serum) to 917 (whole blood). Several metabolites linked to screening assays for rare diseases including acylcarnitines, bilirubin and heme metabolites, nucleosides, and redox balance metabolites varied significantly across the sample collection types. CONCLUSIONS: Our study highlights the widespread effects and importance of using consistent additives for assessing small molecule levels in clinical metabolomics. The biochemistry that occurs during the blood collection process creates a reproducible signal that can identify specimens collected with different anticoagulants in metabolomic studies. IMPACT STATEMENT: In this manuscript, normal/healthy donors had peripheral blood collected using multiple anticoagulants as well as serum during a fasted blood draw. Global metabolomics is a new technology being utilized to draw clinical conclusions and we interrogated the effects of different anticoagulants on the levels of biochemicals from each of the donors. Characterizing the effects of the anticoagulants on biochemical levels will help researchers leverage the information using global metabolomics in order to make conclusions regarding important disease biomarkers.

Precision of a Clinical Metabolomics Profiling Platform for Use in the Identification of Inborn Errors of Metabolism.

BACKGROUND: The application of whole-exome sequencing for the diagnosis of genetic disease has paved the way for systems-based approaches in the clinical laboratory. Here, we describe a clinical metabolomics method for the screening of metabolic diseases through the analysis of a multi-pronged mass spectrometry platform. By simultaneously measuring hundreds of metabolites in a single sample, clinical metabolomics offers a comprehensive approach to identify metabolic perturbations across multiple biochemical pathways. METHODS: We conducted a single- and multi-day precision study on hundreds of metabolites in human plasma on 4, multi-arm, high-throughput metabolomics platforms. RESULTS: The average laboratory coefficient of variation (CV) on the 4 platforms was between 9.3 and 11.5% (median, 6.5-8.4%), average inter-assay CV on the 4 platforms ranged from 9.9 to 12.6% (median, 7.0-8.3%) and average intra-assay CV on the 4 platforms ranged from 5.7 to 6.9% (median, 3.5-4.4%). In relation to patient sample testing, the precision of multiple biomarkers associated with IEM disorders showed CVs that ranged from 0.2 to 11.0% across 4 analytical batches. CONCLUSIONS: This evaluation describes single and multi-day precision across 4 identical metabolomics platforms, comprised each of 4 independent method arms, and reproducibility of the method for the measurement of key IEM metabolites in patient samples across multiple analytical batches, providing evidence that the method is robust and reproducible for the screening of patients with inborn errors of metabolism.

Validation of a Metabolite Panel for a More Accurate Estimation of Glomerular Filtration Rate Using Quantitative LC-MS/MS.

BACKGROUND: Clinical practice guidelines recommend estimation of glomerular filtration rate (eGFR) using validated equations based on serum creatinine (eGFRcr), cystatin C (eGFRcys), or both (eGFRcr-cys). However, when compared with the measured GFR (mGFR), only eGFRcr-cys meets recommended performance standards. Our goal was to develop a more accurate eGFR method using a panel of metabolites without creatinine, cystatin C, or demographic variables. METHODS: An ultra-performance liquid chromatography-tandem mass spectrometry assay for acetylthreonine, phenylacetylglutamine, pseudouridine, and tryptophan was developed, and a 20-day, multiinstrument analytical validation was conducted. The assay was tested in 2424 participants with mGFR data from 4 independent research studies. A new GFR equation (eGFRmet) was developed in a random subset (n = 1615) and evaluated in the remaining participants (n = 809). Performance was assessed as the frequency of large errors [estimates that differed from mGFR by at least 30% (1 - P30); goal <10%]. RESULTS: The assay had a mean imprecision (≤10% intraassay, ≤6.9% interassay), linearity over the quantitative range (r 2 > 0.98), and analyte recovery (98.5%-113%). There was no carryover, no interferences observed, and analyte stability was established. In addition, 1 - P30 in the validation set for eGFRmet (10.0%) was more accurate than eGFRcr (13.1%) and eGFRcys (12.0%) but not eGFRcr-cys (8.7%). Combining metabolites, creatinine, cystatin C, and demographics led to the most accurate equation (7.0%). Neither equation had substantial variation among population subgroups. CONCLUSIONS: The new eGFRmet equation could serve as a confirmatory test for GFR estimation.

Analyses of SLC13A5-epilepsy patients reveal perturbations of TCA cycle.

OBJECTIVE: To interrogate the metabolic profile of five subjects from three families with rare, nonsense and missense mutations in SLC13A5 and Early Infantile Epileptic Encephalopathies (EIEE) characterized by severe, neonatal onset seizures, psychomotor retardation and global developmental delay. METHODS: Mass spectrometry of plasma, CSF and urine was used to identify consistently dysregulated analytes in our subjects. RESULTS: Distinctive elevations of citrate and dysregulation of citric acid cycle intermediates, supporting the hypothesis that loss of SLC13A5 function alters tricarboxylic acid cycle (TCA) metabolism and may disrupt metabolic compartmentation in the brain. SIGNIFICANCE: Our results indicate that analysis of plasma citrate and other TCA analytes in SLC13A5 deficient patients define a diagnostic metabolic signature that can aid in diagnosing children with this disease.

Elucidation of the complex metabolic profile of cerebrospinal fluid using an untargeted biochemical profiling assay.

We sought to determine the molecular composition of human cerebrospinal fluid (CSF) and identify the biochemical pathways represented in CSF to understand the potential for untargeted screening of inborn errors of metabolism (IEMs). Biochemical profiles for each sample were obtained using an integrated metabolomics workflow comprised of four chromatographic techniques followed by mass spectrometry. Secondarily, we wanted to compare the biochemical profile of CSF with those of plasma and urine within the integrated mass spectrometric-based metabolomic workflow. Three sample types, CSF (N=30), urine (N=40) and EDTA plasma (N=31), were analyzed from retrospectively collected pediatric cohorts of equivalent age and gender characteristics. We identified 435 biochemicals in CSF representing numerous biological and chemical/structural families. Sixty-three percent (273 of 435) of the biochemicals detected in CSF also were detected in urine and plasma, another 32% (140 of 435) were detected in either plasma or urine, and 5% (22 of 435) were detected only in CSF. Analyses of several metabolites showed agreement between clinically useful assays and the metabolomics approach. An additional set of CSF and plasma samples collected from the same patient revealed correlation between several biochemicals detected in paired samples. Finally, analysis of CSF from a pediatric case with dihydropteridine reductase (DHPR) deficiency demonstrated the utility of untargeted global metabolic phenotyping as a broad assessment to screen samples from patients with undifferentiated phenotypes. The results indicate a single CSF sample processed with an integrated metabolomics workflow can be used to identify a large breadth of biochemicals that could be useful for identifying disrupted metabolic patterns associated with IEMs.

Metabolomic Profiling of Human Urine as a Screen for Multiple Inborn Errors of Metabolism.

AIMS: We wished to determine the efficacy of using urine as an analyte to screen for a broad range of metabolic products associated with multiple different types of inborn errors of metabolism (IEMs), using an automated mass spectrometry-based assay. Urine was compared with plasma samples from a similar cohort analyzed using the same assay. Specimens were analyzed using two different commonly utilized urine normalization methods based on creatinine and osmolality, respectively. METHODS: Biochemical profiles for each sample (from both affected and unaffected subjects) were obtained using a mass spectrometry-based platform and population-based statistical analyses. RESULTS: We identified over 1200 biochemicals from among 100 clinical urine samples and identified clear biochemical signatures for 16 of 18 IEM diseases tested. The two diseases that did not result in clear signatures, X-linked creatine transporter deficiency and ornithine transcarbamylase deficiency, were from individuals under treatment, which masked biomarker signatures. Overall the process variability and coefficient of variation for isolating and identifying biochemicals by running technical replicates of each urine sample was 10%. CONCLUSIONS: A single urine sample analyzed with our integrated metabolomic platform can identify signatures of IEMs that are traditionally identified using many different assays and multiple sample types. Creatinine and osmolality-normalized data were robust to the detection of the disorders and samples tested here.

A Translational Pharmacology Approach to Predicting Outcomes of Preexposure Prophylaxis Against HIV in Men and Women Using Tenofovir Disoproxil Fumarate With or Without Emtricitabine.

BACKGROUND: A novel translational pharmacology investigation was conducted by combining an in vitro efficacy target with mucosal tissue pharmacokinetic (PK) data and mathematical modeling to determine the number of doses required for effective human immunodeficiency virus (HIV) preexposure prophylaxis (PrEP). METHODS: A PK/pharmacodynamic (PD) model was developed by measuring mucosal tissue concentrations of tenofovir, emtricitabine, their active metabolites (tenofovir diphosphate [TFVdp] and emtricitabine triphosphate [FTCtp], respectively), and competing endogenous nucleotides (dATP and dCTP) in 47 healthy women. TZM-bl and CD4(+) T cells were used to identify 90% effective concentration (EC90) ratios of TFVdp to dATP and FTCtp to dCTP (alone and in combination) for protection against HIV. Monte-Carlo simulations were then performed to identify minimally effective dosing strategies to protect lower female genital tract and colorectal tissues. RESULTS: The colorectal TFVdp concentration was 10 times higher than that in the lower female genital tract, whereas concentrations of endogenous nucleotides were 7-11 times lower. Our model predicted that ≥98% of the population achieved protective mucosal tissue exposure by the third daily dose of tenofovir disoproxil fumarate plus emtricitabine. However, a minimum adherence to 6 of 7 doses/week (85%) was required to protect lower female genital tract tissue from HIV, while adherence to 2 of 7 doses/week (28%) was required to protect colorectal tissue. CONCLUSIONS: This model is predictive of recent PrEP trial results in which 2-3 doses/week was 75%-90% effective in men but ineffective in women. These data provide a novel approach for future PrEP investigations that can optimize clinical trial dosing strategies.

Plasma metabolomic profiles enhance precision medicine for volunteers of normal health.

Precision medicine, taking account of human individuality in genes, environment, and lifestyle for early disease diagnosis and individualized therapy, has shown great promise to transform medical care. Nontargeted metabolomics, with the ability to detect broad classes of biochemicals, can provide a comprehensive functional phenotype integrating clinical phenotypes with genetic and nongenetic factors. To test the application of metabolomics in individual diagnosis, we conducted a metabolomics analysis on plasma samples collected from 80 volunteers of normal health with complete medical records and three-generation pedigrees. Using a broad-spectrum metabolomics platform consisting of liquid chromatography and GC coupled with MS, we profiled nearly 600 metabolites covering 72 biochemical pathways in all major branches of biosynthesis, catabolism, gut microbiome activities, and xenobiotics. Statistical analysis revealed a considerable range of variation and potential metabolic abnormalities across the individuals in this cohort. Examination of the convergence of metabolomics profiles with whole-exon sequences (WESs) provided an effective approach to assess and interpret clinical significance of genetic mutations, as shown in a number of cases, including fructose intolerance, xanthinuria, and carnitine deficiency. Metabolic abnormalities consistent with early indications of diabetes, liver dysfunction, and disruption of gut microbiome homeostasis were identified in several volunteers. Additionally, diverse metabolic responses to medications among the volunteers may assist to identify therapeutic effects and sensitivity to toxicity. The results of this study demonstrate that metabolomics could be an effective approach to complement next generation sequencing (NGS) for disease risk analysis, disease monitoring, and drug management in our goal toward precision care.

Untargeted metabolomic analysis for the clinical screening of inborn errors of metabolism.

Global metabolic profiling currently achievable by untargeted mass spectrometry-based metabolomic platforms has great potential to advance our understanding of human disease states, including potential utility in the detection of novel and known inborn errors of metabolism (IEMs). There are few studies of the technical reproducibility, data analysis methods, and overall diagnostic capabilities when this technology is applied to clinical specimens for the diagnosis of IEMs. We explored the clinical utility of a metabolomic workflow capable of routinely generating semi-quantitative z-score values for ~900 unique compounds, including ~500 named human analytes, in a single analysis of human plasma. We tested the technical reproducibility of this platform and applied it to the retrospective diagnosis of 190 individual plasma samples, 120 of which were collected from patients with a confirmed IEM. Our results demonstrate high intra-assay precision and linear detection for the majority compounds tested. Individual metabolomic profiles provided excellent sensitivity and specificity for the detection of a wide range of metabolic disorders and identified novel biomarkers for some diseases. With this platform, it is possible to use one test to screen for dozens of IEMs that might otherwise require ordering multiple unique biochemical tests. However, this test may yield false negative results for certain disorders that would be detected by a more well-established quantitative test and in its current state should be considered a supplementary test. Our findings describe a novel approach to metabolomic analysis of clinical specimens and demonstrate the clinical utility of this technology for prospective screening of IEMs.

A novel test for IGT utilizing metabolite markers of glucose tolerance.

The oral glucose tolerance test (OGTT) is the only method to diagnose patients having impaired glucose tolerance (IGT), but its use has diminished considerably in recent years. Metabolomic profiling studies have identified a number of metabolites whose fasting levels are associated with dysglycemia and type 2 diabetes. These metabolites may serve as the basis of an alternative test for IGT. Using the stable isotope dilution technique, quantitative assays were developed for 23 candidate biomarker metabolites. These metabolites were measured in fasting plasma samples taken just prior to an OGTT from 1623 nondiabetic subjects: 955 from the Relationship between Insulin Sensitivity and Cardiovascular Disease Study (RISC Study; 11.7% IGT) and 668 subjects from the Diabetes Mellitus and Vascular Health Initiative (DMVhi) cohort from the DEXLIFE project (11.8% IGT). The associations between metabolites, anthropometric, and metabolic parameters and 2hPG values were assessed by Pearson correlation coefficients and Random Forest classification analysis to rank variables for their ability to distinguish IGT from normal glucose tolerance (NGT). Multivariate logistic regression models for estimating risk of IGT were developed and evaluated using AUCs calculated from the corresponding ROC curves. A model based on the fasting plasma levels of glucose, α-hydroxybutyric acid, ß-hydroxybutyric acid, 4-methyl-2-oxopentanoic acid, linoleoylglycerophosphocholine, oleic acid, serine and vitamin B5 was optimized in the RISC cohort (AUC = 0.82) and validated in the DMVhi cohort (AUC = 0.83). A novel, all-metabolite-based test is shown to be a discriminate marker of IGT. It requires only a single fasted blood draw and may serve as a more convenient surrogate for the OGTT or as a means of identifying subjects likely to be IGT.

Serum metabolic signatures of primary biliary cirrhosis and primary sclerosing cholangitis.

BACKGROUND & AIMS: A greater understanding of cholestatic disease is necessary to advance diagnostic tools and therapeutic options for conditions such as primary biliary cirrhosis (PBC) and primary sclerosing cholangitis (PSC). The purpose of this study was to determine and compare the serum metabolomes of patients with PBC (n = 18) or PSC (n = 21) and healthy controls (n = 10) and to identify metabolites that may differentiate these two cholestatic diseases. METHODS AND RESULTS: Using a mass spectrometry-based, non-targeted biochemical profiling approach, we identified 420 serum metabolites, 101 that differed significantly (P ≤ 0.05) between PBC and control groups, 115 that differed significantly between PSC and control groups, and 56 that differed significantly between PSC and PBC groups. Random forest classification analysis was able to distinguish patients with PBC or PSC with 95% accuracy with selected biochemicals reflective of protein and amino acid metabolism identified as the major contributors. Metabolites related to bile acid metabolism, lipid metabolism, inflammation, and oxidative stress/lipid peroxidation were also identified as differing significantly when comparing the disease groups and controls, with some of these pathways differentially affected in the PBC and PSC groups. CONCLUSION: In this study, we identified novel metabolic changes associated with cholestatic disease that were both consistent and different between PBC and PSC. Validation studies in larger patient cohorts are required to determine the utility of these biochemical markers for diagnosis and therapeutic monitoring of patients with PBC and PSC.

Plasma metabolomic profiles of breast cancer patients after short-term limonene intervention.

Limonene is a lipophilic monoterpene found in high levels in citrus peel. Limonene demonstrates anticancer properties in preclinical models with effects on multiple cellular targets at varying potency. While of interest as a cancer chemopreventive, the biologic activity of limonene in humans is poorly understood. We conducted metabolite profiling in 39 paired (pre/postintervention) plasma samples from early-stage breast cancer patients receiving limonene treatment (2 g QD) before surgical resection of their tumor. Metabolite profiling was conducted using ultra-performance liquid chromatography coupled to a linear trap quadrupole system and gas chromatography-mass spectrometry. Metabolites were identified by comparison of ion features in samples to a standard reference library. Pathway-based interpretation was conducted using the human metabolome database and the MetaCyc database. Of the 397 named metabolites identified, 72 changed significantly with limonene intervention. Class-based changes included significant decreases in adrenal steroids (P < 0.01), and significant increases in bile acids (P ≤ 0.05) and multiple collagen breakdown products (P < 0.001). The pattern of changes also suggested alterations in glucose metabolism. There were 47 metabolites whose change with intervention was significantly correlated to a decrease in cyclin D1, a cell-cycle regulatory protein, in patient tumor tissues (P ≤ 0.05). Here, oral administration of limonene resulted in significant changes in several metabolic pathways. Furthermore, pathway-based changes were related to the change in tissue level cyclin D1 expression. Future controlled clinical trials with limonene are necessary to determine the potential role and mechanisms of limonene in the breast cancer prevention setting.

Bladder cancer biomarker discovery using global metabolomic profiling of urine.

Bladder cancer (BCa) is a common malignancy worldwide and has a high probability of recurrence after initial diagnosis and treatment. As a result, recurrent surveillance, primarily involving repeated cystoscopies, is a critical component of post diagnosis patient management. Since cystoscopy is invasive, expensive and a possible deterrent to patient compliance with regular follow-up screening, new non-invasive technologies to aid in the detection of recurrent and/or primary bladder cancer are strongly needed. In this study, mass spectrometry based metabolomics was employed to identify biochemical signatures in human urine that differentiate bladder cancer from non-cancer controls. Over 1000 distinct compounds were measured including 587 named compounds of known chemical identity. Initial biomarker identification was conducted using a 332 subject sample set of retrospective urine samples (cohort 1), which included 66 BCa positive samples. A set of 25 candidate biomarkers was selected based on statistical significance, fold difference and metabolic pathway coverage. The 25 candidate biomarkers were tested against an independent urine sample set (cohort 2) using random forest analysis, with palmitoyl sphingomyelin, lactate, adenosine and succinate providing the strongest predictive power for differentiating cohort 2 cancer from non-cancer urines. Cohort 2 metabolite profiling revealed additional metabolites, including arachidonate, that were higher in cohort 2 cancer vs. non-cancer controls, but were below quantitation limits in the cohort 1 profiling. Metabolites related to lipid metabolism may be especially interesting biomarkers. The results suggest that urine metabolites may provide a much needed non-invasive adjunct diagnostic to cystoscopy for detection of bladder cancer and recurrent disease management.

Plasma metabolomic biomarker panel to distinguish patients with amyotrophic lateral sclerosis from disease mimics.

Our objective was to identify plasma biomarkers of ALS that can aid in distinguishing patients with ALS from those with disease mimics. In this multi-center study, plasma samples were collected from 172 patients recently diagnosed with ALS, 50 healthy controls, and 73 neurological disease mimics. Samples were analyzed using metabolomics. Using all identified biochemicals detected in > 50% of all samples in the metabolomics analysis, samples were classified as ALS or mimic with 65% sensitivity and 81% specificity by LASSO analysis (AUC of 0.76). A subset panel of 32 candidate biomarkers classified these diagnosis groups with a specificity of 90%/sensitivity 58% (AUC of 0.81). Creatinine was lower in subjects with lower revised ALS Functional Rating Scale (ALSFRS-R) scores. In conclusion, ALS can be distinguished from neurological disease mimics by global biochemical profiling of plasma samples. Our analysis identified ALS versus mimics with relatively high sensitivity. We identified a subset of 32 metabolites that identify patients with ALS with a high specificity. Interestingly, lower creatinine correlates significantly with a lower ALSFRS-R score. Finally, molecules previously reported to be important in disease pathophysiology, such as urate, are included in our metabolite panel.

Impaired metabolic reactivity to oxidative stress in early psychosis patients.

Because increasing evidence point to the convergence of environmental and genetic risk factors to drive redox dysregulation in schizophrenia, we aim to clarify whether the metabolic anomalies associated with early psychosis reflect an adaptation to oxidative stress. Metabolomic profiling was performed to characterize the response to oxidative stress in fibroblasts from control individuals (n = 20) and early psychosis patients (n = 30), and in all, 282 metabolites were identified. In addition to the expected redox/antioxidant response, oxidative stress induced a decrease of lysolipid levels in fibroblasts from healthy controls that were largely muted in fibroblasts from patients. Most notably, fibroblasts from patients showed disrupted extracellular matrix- and arginine-related metabolism after oxidative stress, indicating impairments beyond the redox system. Plasma membrane and extracellular matrix, 2 regulators of neuronal activity and plasticity, appeared as particularly susceptible to oxidative stress and thus provide novel mechanistic insights for pathophysiological understanding of early stages of psychosis. Statistically, antipsychotic medication at the time of biopsy was not accounting for these anomalies in the metabolism of patients' fibroblasts, indicating that they might be intrinsic to the disease. Although these results are preliminary and should be confirmed in a larger group of patients, they nevertheless indicate that the metabolic signature of reactivity to oxidative stress may provide reliable early markers of psychosis. Developing protective measures aimed at normalizing the disrupted pathways should prevent the pathological consequences of environmental stressors.

MYC-driven accumulation of 2-hydroxyglutarate is associated with breast cancer prognosis.

Metabolic profiling of cancer cells has recently been established as a promising tool for the development of therapies and identification of cancer biomarkers. Here we characterized the metabolomic profile of human breast tumors and uncovered intrinsic metabolite signatures in these tumors using an untargeted discovery approach and validation of key metabolites. The oncometabolite 2-hydroxyglutarate (2HG) accumulated at high levels in a subset of tumors and human breast cancer cell lines. We discovered an association between increased 2HG levels and MYC pathway activation in breast cancer, and further corroborated this relationship using MYC overexpression and knockdown in human mammary epithelial and breast cancer cells. Further analyses revealed globally increased DNA methylation in 2HG-high tumors and identified a tumor subtype with high tissue 2HG and a distinct DNA methylation pattern that was associated with poor prognosis and occurred with higher frequency in African-American patients. Tumors of this subtype had a stem cell-like transcriptional signature and tended to overexpress glutaminase, suggestive of a functional relationship between glutamine and 2HG metabolism in breast cancer. Accordingly, 13C-labeled glutamine was incorporated into 2HG in cells with aberrant 2HG accumulation, whereas pharmacologic and siRNA-mediated glutaminase inhibition reduced 2HG levels. Our findings implicate 2HG as a candidate breast cancer oncometabolite associated with MYC activation and poor prognosis.