Integrative rare disease biomedical profile based network supporting drug repurposing or repositioning, a case study of glioblastoma.

BACKGROUND: Glioblastoma (GBM) is the most aggressive and common malignant primary brain tumor; however, treatment remains a significant challenge. This study aims to identify drug repurposing or repositioning candidates for GBM by developing an integrative rare disease profile network containing heterogeneous types of biomedical data. METHODS: We developed a Glioblastoma-based Biomedical Profile Network (GBPN) by extracting and integrating biomedical information pertinent to GBM-related diseases from the NCATS GARD Knowledge Graph (NGKG). We further clustered the GBPN based on modularity classes which resulted in multiple focused subgraphs, named mc_GBPN. We then identified high-influence nodes by performing network analysis over the mc_GBPN and validated those nodes that could be potential drug repurposing or repositioning candidates for GBM. RESULTS: We developed the GBPN with 1,466 nodes and 107,423 edges and consequently the mc_GBPN with forty-one modularity classes. A list of the ten most influential nodes were identified from the mc_GBPN. These notably include Riluzole, stem cell therapy, cannabidiol, and VK-0214, with proven evidence for treating GBM. CONCLUSION: Our GBM-targeted network analysis allowed us to effectively identify potential candidates for drug repurposing or repositioning. Further validation will be conducted by using other different types of biomedical and clinical data and biological experiments. The findings could lead to less invasive treatments for glioblastoma while significantly reducing research costs by shortening the drug development timeline. Furthermore, this workflow can be extended to other disease areas.

A Pilot Cross-Sectional Study of Immunological and Microbiome Profiling Reveals Distinct Inflammatory Profiles for Smokers, Electronic Cigarette Users, and Never-Smokers.

Smokers (SM) have increased lung immune cell counts and inflammatory gene expression compared to electronic cigarette (EC) users and never-smokers (NS). The objective of this study is to further assess associations for SM and EC lung microbiomes with immune cell subtypes and inflammatory gene expression in samples obtained by bronchoscopy and bronchoalveolar lavage (n = 28). RNASeq with the CIBERSORT computational algorithm were used to determine immune cell subtypes, along with inflammatory gene expression and microbiome metatranscriptomics. Macrophage subtypes revealed a two-fold increase in M0 (undifferentiated) macrophages for SM and EC users relative to NS, with a concordant decrease in M2 (anti-inflammatory) macrophages. There were 68, 19, and 1 significantly differentially expressed inflammatory genes (DEG) between SM/NS, SM/EC users, and EC users/NS, respectively. CSF-1 and GATA3 expression correlated positively and inversely with M0 and M2 macrophages, respectively. Correlation profiling for DEG showed distinct lung profiles for each participant group. There were three bacteria genera-DEG correlations and three bacteria genera-macrophage subtype correlations. In this pilot study, SM and EC use were associated with an increase in undifferentiated M0 macrophages, but SM differed from EC users and NS for inflammatory gene expression. The data support the hypothesis that SM and EC have toxic lung effects influencing inflammatory responses, but this may not be via changes in the microbiome.

Preexisting Autoimmunity Is Associated With Increased Severity of Coronavirus Disease 2019: A Retrospective Cohort Study Using Data From the National COVID Cohort Collaborative (N3C).

BACKGROUND: Identifying individuals with a higher risk of developing severe coronavirus disease 2019 (COVID-19) outcomes will inform targeted and more intensive clinical monitoring and management. To date, there is mixed evidence regarding the impact of preexisting autoimmune disease (AID) diagnosis and/or immunosuppressant (IS) exposure on developing severe COVID-19 outcomes. METHODS: A retrospective cohort of adults diagnosed with COVID-19 was created in the National COVID Cohort Collaborative enclave. Two outcomes, life-threatening disease and hospitalization, were evaluated by using logistic regression models with and without adjustment for demographics and comorbidities. RESULTS: Of the 2 453 799 adults diagnosed with COVID-19, 191 520 (7.81%) had a preexisting AID diagnosis and 278 095 (11.33%) had a preexisting IS exposure. Logistic regression models adjusted for demographics and comorbidities demonstrated that individuals with a preexisting AID (odds ratio [OR], 1.13; 95% confidence interval [CI]: 1.09-1.17; P < .001), IS exposure (OR, 1.27; 95% CI: 1.24-1.30; P < .001), or both (OR, 1.35; 95% CI: 1.29-1.40; P < .001) were more likely to have a life-threatening disease. These results were consistent when hospitalization was evaluated. A sensitivity analysis evaluating specific IS revealed that tumor necrosis factor inhibitors were protective against life-threatening disease (OR, 0.80; 95% CI: .66-.96; P = .017) and hospitalization (OR, 0.80; 95% CI: .73-.89; P < .001). CONCLUSIONS: Patients with preexisting AID, IS exposure, or both are more likely to have a life-threatening disease or hospitalization. These patients may thus require tailored monitoring and preventative measures to minimize negative consequences of COVID-19.

Integrative Rare Disease Biomedical Profile based Network Supporting Drug Repurposing, a case study of Glioblastoma.

Background Glioblastoma (GBM) is the most aggressive and common malignant primary brain tumor; however, treatment remains a significant challenge. This study aims to identify drug repurposing candidates for GBM by developing an integrative rare disease profile network containing heterogeneous types of biomedical data. Methods We developed a Glioblastoma-based Biomedical Profile Network (GBPN) by extracting and integrating biomedical information pertinent to GBM-related diseases from the NCATS GARD Knowledge Graph (NGKG). We further clustered the GBPN based on modularity classes which resulted in multiple focused subgraphs, named mc_GBPN. We then identified high-influence nodes by performing network analysis over the mc_GBPN and validated those nodes that could be potential drug repositioning candidates for GBM. Results We developed the GBPN with 1,466 nodes and 107,423 edges and consequently the mc_GBPN with forty-one modularity classes. A list of the ten most influential nodes were identified from the mc_GBPN. These notably include Riluzole, stem cell therapy, cannabidiol, and VK-0214, with proven evidence for treating GBM. Conclusion Our GBM-targeted network analysis allowed us to effectively identify potential candidates for drug repurposing. This could lead to less invasive treatments for glioblastoma while significantly reducing research costs by shortening the drug development timeline. Furthermore, this workflow can be extended to other disease areas.

Pre-existing autoimmunity is associated with increased severity of COVID-19: A retrospective cohort study using data from the National COVID Cohort Collaborative (N3C).

Importance: Identifying individuals with a higher risk of developing severe COVID-19 outcomes will inform targeted or more intensive clinical monitoring and management. Objective: To examine, using data from the National COVID Cohort Collaborative (N3C), whether patients with pre-existing autoimmune disease (AID) diagnosis and/or immunosuppressant (IS) exposure are at a higher risk of developing severe COVID-19 outcomes. Design setting and participants: A retrospective cohort of 2,453,799 individuals diagnosed with COVID-19 between January 1 st , 2020, and June 30 th , 2022, was created from the N3C data enclave, which comprises data of 15,231,849 patients from 75 USA data partners. Patients were stratified as those with/without a pre-existing diagnosis of AID and/or those with/without exposure to IS prior to COVID-19. Main outcomes and measures: Two outcomes of COVID-19 severity, derived from the World Health Organization severity score, were defined, namely life-threatening disease and hospitalization. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using logistic regression models with and without adjustment for demographics (age, BMI, gender, race, ethnicity, smoking status), and comorbidities (cardiovascular disease, dementia, pulmonary disease, liver disease, type 2 diabetes mellitus, kidney disease, cancer, and HIV infection). Results: In total, 2,453,799 (16.11% of the N3C cohort) adults (age> 18 years) were diagnosed with COVID-19, of which 191,520 (7.81%) had a prior AID diagnosis, and 278,095 (11.33%) had a prior IS exposure. Logistic regression models adjusted for demographic factors and comorbidities demonstrated that individuals with a prior AID (OR = 1.13, 95% CI 1.09 - 1.17; p =2.43E-13), prior exposure to IS (OR= 1.27, 95% CI 1.24 - 1.30; p =3.66E-74), or both (OR= 1.35, 95% CI 1.29 - 1.40; p =7.50E-49) were more likely to have a life-threatening COVID-19 disease. These results were confirmed after adjusting for exposure to antivirals and vaccination in a cohort subset with COVID-19 diagnosis dates after December 2021 (AID OR = 1.18, 95% CI 1.02 - 1.36; p =2.46E-02; IS OR= 1.60, 95% CI 1.41 - 1.80; p =5.11E-14; AID+IS OR= 1.93, 95% CI 1.62 - 2.30; p =1.68E-13). These results were consistent when evaluating hospitalization as the outcome and also when stratifying by race and sex. Finally, a sensitivity analysis evaluating specific IS revealed that TNF inhibitors were protective against life-threatening disease (OR = 0.80, 95% CI 0.66-0.96; p =1.66E-2) and hospitalization (OR = 0.80, 95% CI 0.73 - 0.89; p =1.06E-05). Conclusions and Relevance: Patients with pre-existing AID, exposure to IS, or both are more likely to have a life-threatening disease or hospitalization. These patients may thus require tailored monitoring and preventative measures to minimize negative consequences of COVID-19.

Dietary Energy Intake and Presence of Aberrant Crypt Foci Are Associated with Phospholipid, Purine, and Taurine Metabolite Abundances in C57BL/6N Mouse Colon.

SCOPE: Colon metabolomes associated with high-fat (H) versus energy-restricted (E) diets in early colorectal cancer (CRC) models have never been directly compared. The objectives of this study are to elucidate metabolites associated with diet, aberrant crypt foci (ACF), and diet:ACF interaction, using a lifetime murine model. METHODS AND RESULTS: Three-week-old mice consumed control (C), E, or H initiation diets for 18 weeks. ACF formation is initiated weeks 16-21 with azoxymethane injections, followed by progression diet crossover (to C, E, or H) through week 60. Colon extracts are analyzed using ultra-high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). Metabolites associated with diet, ACF, or diet:ACF are determined using regression models (FDR-adjusted p-value <0.05). No metabolites are significantly associated with initiation diets, but concentrations of acylcarnitines and phospholipids are associated with C, E, and H progression diets. Purines, taurine, and phospholipids are associated with ACF presence. No significant associations between metabolites and diet:ACF interaction are observed. CONCLUSIONS: These results suggest that recent, rather than early-life, diet is more closely associated with the colon metabolome, particularly lipid metabolism. Results from this study also provide candidate biomarkers of early CRC development and provide support for the importance of early diet on influencing pre-CRC risk.

Saliva and Lung Microbiome Associations with Electronic Cigarette Use and Smoking.

The microbiome has increasingly been linked to cancer. Little is known about the lung and oral cavity microbiomes in smokers, and even less for electronic cigarette (EC) users, compared with never-smokers. In a cross-sectional study (n = 28) of smokers, EC users, and never-smokers, bronchoalveolar lavage and saliva samples underwent metatranscriptome profiling to examine associations with lung and oral microbiomes. Pairwise comparisons assessed differentially abundant bacteria species. Total bacterial load was similar between groups, with no differences in bacterial diversity across lung microbiomes. In lungs, 44 bacteria species differed significantly (FDR < 0.1) between smokers/never-smokers, with most decreased in smokers. Twelve species differed between smokers/EC users, all decreased in smokers of which Neisseria sp. KEM232 and Curvibacter sp. AEP1-3 were observed. Among the top five decreased species in both comparisons, Neisseria elongata, Neisseria sicca, and Haemophilus parainfluenzae were observed. In the oral microbiome, 152 species were differentially abundant for smokers/never-smokers, and 17 between smokers/electronic cigarette users, but only 21 species were differentially abundant in both the lung and oral cavity. EC use is not associated with changes in the lung microbiome compared with never-smokers, indicating EC toxicity does not affect microbiota. Statistically different bacteria in smokers compared with EC users and never-smokers were almost all decreased, potentially due to toxic effects of cigarette smoke. The low numbers of overlapping oral and lung microbes suggest that the oral microbiome is not a surrogate for analyzing smoking-related effects in the lung. PREVENTION RELEVANCE: The microbiome affects cancer and other disease risk. The effects of e-cig usage on the lung microbiome are essentially unknown. Given the importance of lung microbiome dysbiosis populated by oral species which have been observed to drive lung cancer progression, it is important to study effects of e-cig use on microbiome.

Identification of transcription factor co-regulators that drive prostate cancer progression.

In prostate cancer (PCa), and many other hormone-dependent cancers, there is clear evidence for distorted transcriptional control as disease driver mechanisms. Defining which transcription factor (TF) and coregulators are altered and combine to become oncogenic drivers remains a challenge, in part because of the multitude of TFs and coregulators and the diverse genomic space on which they function. The current study was undertaken to identify which TFs and coregulators are commonly altered in PCa. We generated unique lists of TFs (n = 2662), coactivators (COA; n = 766); corepressors (COR; n = 599); mixed function coregulators (MIXED; n = 511), and to address the challenge of defining how these genes are altered we tested how expression, copy number alterations and mutation status varied across seven prostate cancer (PCa) cohorts (three of localized and four advanced disease). Testing of significant changes was undertaken by bootstrapping approaches and the most significant changes were identified. For one commonly and significantly altered gene were stably knocked-down expression and undertook cell biology experiments and RNA-Seq to identify differentially altered gene networks and their association with PCa progression risks. COAS, CORS, MIXED and TFs all displayed significant down-regulated expression (q.value < 0.1) and correlated with protein expression (r 0.4-0.55). In localized PCa, stringent expression filtering identified commonly altered TFs and coregulator genes, including well-established (e.g. ERG) and underexplored (e.g. PPARGC1A, encodes PGC1α). Reduced PPARGC1A expression significantly associated with worse disease-free survival in two cohorts of localized PCa. Stable PGC1α knockdown in LNCaP cells increased growth rates and invasiveness and RNA-Seq revealed a profound basal impact on gene expression (~ 2300 genes; FDR < 0.05, logFC > 1.5), but only modestly impacted PPARγ responses. GSEA analyses of the PGC1α transcriptome revealed that it significantly altered the AR-dependent transcriptome, and was enriched for epigenetic modifiers. PGC1α-dependent genes were overlapped with PGC1α-ChIP-Seq genes and significantly associated in TCGA with higher grade tumors and worse disease-free survival. These methods and data demonstrate an approach to identify cancer-driver coregulators in cancer, and that PGC1α expression is clinically significant yet underexplored coregulator in aggressive early stage PCa.

MDM2-Dependent Rewiring of Metabolomic and Lipidomic Profiles in Dedifferentiated Liposarcoma Models.

Dedifferentiated liposarcoma (DDLPS) is an aggressive mesenchymal cancer marked by amplification of MDM2, an inhibitor of the tumor suppressor TP53. DDLPS patients with higher MDM2 amplification have lower chemotherapy sensitivity and worse outcome than patients with lower MDM2 amplification. We hypothesized that MDM2 amplification levels may be associated with changes in DDLPS metabolism. Six patient-derived DDLPS cell line models were subject to comprehensive metabolomic (Metabolon) and lipidomic (SCIEX 5600 TripleTOF-MS) profiling to assess associations with MDM2 amplification and their responses to metabolic perturbations. Comparing metabolomic profiles between MDM2 higher and lower amplification cells yielded a total of 17 differentially abundant metabolites across both panels (FDR < 0.05, log2 fold change < 0.75), including ceramides, glycosylated ceramides, and sphingomyelins. Disruption of lipid metabolism through statin administration resulted in a chemo-sensitive phenotype in MDM2 lower cell lines only, suggesting that lipid metabolism may be a large contributor to the more aggressive nature of MDM2 higher DDLPS tumors. This study is the first to provide comprehensive metabolomic and lipidomic characterization of DDLPS cell lines and provides evidence for MDM2-dependent differential molecular mechanisms that are critical factors in chemoresistance and could thus affect patient outcome.

Biomarkers of Exposure and Effect in the Lungs of Smokers, Nonsmokers, and Electronic Cigarette Users.

BACKGROUND: Nicotine-containing electronic cigarette (e-cig) use has become widespread. However, understanding the biological impact of e-cigs compared with smoking on the lung is needed. There are major gaps in knowledge for chronic effects and for an etiology to recent acute lung toxicity leading to death among vapers. METHODS: We conducted bronchoscopies in a cross-sectional study of 73 subjects (42 never-smokers, 15 e-cig users, and 16 smokers). Using bronchoalveolar lavage and brushings, we examined lung inflammation by cell counts, cytokines, genome-wide gene expression, and DNA methylation. RESULTS: There were statistically significant differences among never-smokers, e-cig users, and smokers for inflammatory cell counts and cytokines (FDR q < 0.1). The e-cig users had values intermediate between smokers and never-smokers, with levels for most of the biomarkers more similar to never-smokers. For differential gene expression and DNA methylation, e-cig users also more like never-smokers; many of these genes corresponded to smoking-related pathways, including those for xenobiotic metabolism, aryl hydrocarbon receptor signaling, and oxidative stress. Differentially methylated genes were correlated with changes in gene expression, providing evidence for biological effects of the methylation associations. CONCLUSIONS: These data indicate that e-cigs are associated with less toxicity than cigarettes for smoking-related pathways. What is unknown may be unique effects for e-cigs not measured herein, and a comparison of smokers completely switching to e-cigs compared with former smokers. Clinical trials for smokers switching to e-cigs who undergo serial bronchoscopy and larger cross-sectional studies of former smokers with and without e-cig use, and for e-cigs who relapse back to smoking, are needed. IMPACT: These data can be used for product regulation and for informing tobacco users considering or using e-cigs. What is unknown may be unique effects for e-cigs not measured herein, and clinical trials with serial bronchoscopy underway can demonstrate a direct relationship for changes in lung biomarkers.

Effects of Electronic Cigarette Constituents on the Human Lung: A Pilot Clinical Trial.

Electronic cigarette (e-cig) use is continuing to increase, particularly among youth never-smokers, and is used by some smokers to quit. The acute and chronic toxicity of e-cig use is unclear generally in the context of increasing reports of inflammatory-type pneumonia in some e-cig users. To assess lung effects of e-cigs without nicotine or flavors, we conducted a pilot study with serial bronchoscopies over 4 weeks in 30 never-smokers, randomized either to a 4-week intervention with the use of e-cigs containing only 50% propylene glycol (PG) and 50% vegetable glycerine or to a no-use control group. Compliance to the e-cig intervention was assessed by participants sending daily puff counts and by urinary PG. Inflammatory cell counts and cytokines were determined in bronchoalveolar lavage (BAL) fluids. Genome-wide expression, miRNA, and mRNA were determined from bronchial epithelial cells. There were no significant differences in changes of BAL inflammatory cell counts or cytokines between baseline and follow-up, comparing the control and e-cig groups. However, in the intervention but not the control group, change in urinary PG as a marker of e-cig use and inhalation was significantly correlated with change in cell counts (cell concentrations, macrophages, and lymphocytes) and cytokines (IL8, IL13, and TNFα), although the absolute magnitude of changes was small. There were no significant changes in mRNA or miRNA gene expression. Although limited by study size and duration, this is the first experimental demonstration of an impact of e-cig use on inflammation in the human lung among never-smokers.

IntLIM: integration using linear models of metabolomics and gene expression data.

BACKGROUND: Integration of transcriptomic and metabolomic data improves functional interpretation of disease-related metabolomic phenotypes, and facilitates discovery of putative metabolite biomarkers and gene targets. For this reason, these data are increasingly collected in large (> 100 participants) cohorts, thereby driving a need for the development of user-friendly and open-source methods/tools for their integration. Of note, clinical/translational studies typically provide snapshot (e.g. one time point) gene and metabolite profiles and, oftentimes, most metabolites measured are not identified. Thus, in these types of studies, pathway/network approaches that take into account the complexity of transcript-metabolite relationships may neither be applicable nor readily uncover novel relationships. With this in mind, we propose a simple linear modeling approach to capture disease-(or other phenotype) specific gene-metabolite associations, with the assumption that co-regulation patterns reflect functionally related genes and metabolites. RESULTS: The proposed linear model, metabolite ~ gene + phenotype + gene:phenotype, specifically evaluates whether gene-metabolite relationships differ by phenotype, by testing whether the relationship in one phenotype is significantly different from the relationship in another phenotype (via a statistical interaction gene:phenotype p-value). Statistical interaction p-values for all possible gene-metabolite pairs are computed and significant pairs are then clustered by the directionality of associations (e.g. strong positive association in one phenotype, strong negative association in another phenotype). We implemented our approach as an R package, IntLIM, which includes a user-friendly R Shiny web interface, thereby making the integrative analyses accessible to non-computational experts. We applied IntLIM to two previously published datasets, collected in the NCI-60 cancer cell lines and in human breast tumor and non-tumor tissue, for which transcriptomic and metabolomic data are available. We demonstrate that IntLIM captures relevant tumor-specific gene-metabolite associations involved in known cancer-related pathways, including glutamine metabolism. Using IntLIM, we also uncover biologically relevant novel relationships that could be further tested experimentally. CONCLUSIONS: IntLIM provides a user-friendly, reproducible framework to integrate transcriptomic and metabolomic data and help interpret metabolomic data and uncover novel gene-metabolite relationships. The IntLIM R package is publicly available in GitHub ( https://github.com/mathelab/IntLIM ) and includes a user-friendly web application, vignettes, sample data and data/code to reproduce results.

RaMP: A Comprehensive Relational Database of Metabolomics Pathways for Pathway Enrichment Analysis of Genes and Metabolites.

The value of metabolomics in translational research is undeniable, and metabolomics data are increasingly generated in large cohorts. The functional interpretation of disease-associated metabolites though is difficult, and the biological mechanisms that underlie cell type or disease-specific metabolomics profiles are oftentimes unknown. To help fully exploit metabolomics data and to aid in its interpretation, analysis of metabolomics data with other complementary omics data, including transcriptomics, is helpful. To facilitate such analyses at a pathway level, we have developed RaMP (Relational database of Metabolomics Pathways), which combines biological pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, WikiPathways, and the Human Metabolome DataBase (HMDB). To the best of our knowledge, an off-the-shelf, public database that maps genes and metabolites to biochemical/disease pathways and can readily be integrated into other existing software is currently lacking. For consistent and comprehensive analysis, RaMP enables batch and complex queries (e.g., list all metabolites involved in glycolysis and lung cancer), can readily be integrated into pathway analysis tools, and supports pathway overrepresentation analysis given a list of genes and/or metabolites of interest. For usability, we have developed a RaMP R package (https://github.com/Mathelab/RaMP-DB), including a user-friendly RShiny web application, that supports basic simple and batch queries, pathway overrepresentation analysis given a list of genes or metabolites of interest, and network visualization of gene-metabolite relationships. The package also includes the raw database file (mysql dump), thereby providing a stand-alone downloadable framework for public use and integration with other tools. In addition, the Python code needed to recreate the database on another system is also publicly available (https://github.com/Mathelab/RaMP-BackEnd). Updates for databases in RaMP will be checked multiple times a year and RaMP will be updated accordingly.

BRD4 Profiling Identifies Critical Chronic Lymphocytic Leukemia Oncogenic Circuits and Reveals Sensitivity to PLX51107, a Novel Structurally Distinct BET Inhibitor.

Bromodomain and extra-terminal (BET) family proteins are key regulators of gene expression in cancer. Herein, we utilize BRD4 profiling to identify critical pathways involved in pathogenesis of chronic lymphocytic leukemia (CLL). BRD4 is overexpressed in CLL and is enriched proximal to genes upregulated or de novo expressed in CLL with known functions in disease pathogenesis and progression. These genes, including key members of the B-cell receptor (BCR) signaling pathway, provide a rationale for this therapeutic approach to identify new targets in alternative types of cancer. Additionally, we describe PLX51107, a structurally distinct BET inhibitor with novel in vitro and in vivo pharmacologic properties that emulates or exceeds the efficacy of BCR signaling agents in preclinical models of CLL. Herein, the discovery of the involvement of BRD4 in the core CLL transcriptional program provides a compelling rationale for clinical investigation of PLX51107 as epigenetic therapy in CLL and application of BRD4 profiling in other cancers.Significance: To date, functional studies of BRD4 in CLL are lacking. Through integrated genomic, functional, and pharmacologic analyses, we uncover the existence of BRD4-regulated core CLL transcriptional programs and present preclinical proof-of-concept studies validating BET inhibition as an epigenetic approach to target BCR signaling in CLL. Cancer Discov; 8(4); 458-77. ©2018 AACR.This article is highlighted in the In This Issue feature, p. 371.

Experimental and Study Design Considerations for Uncovering Oncometabolites.

Metabolomics as a field has gained attention due to its potential for biomarker discovery, namely because it directly reflects disease phenotype and is the downstream effect of posttranslational modifications. The field provides a "top-down," integrated view of biochemistry in complex organisms, as opposed to the traditional "bottom-up" approach that aims to analyze networks of interactions between genes, proteins and metabolites. It also allows for the detection of thousands of endogenous metabolites in various clinical biospecimens in a high-throughput manner, including tissue and biofluids such as blood and urine. Of note, because biological fluid samples can be collected relatively easily, the time-dependent fluctuations of metabolites can be readily studied in detail.In this chapter, we aim to provide an overview of (1) analytical methods that are currently employed in the field, and (2) study design concepts that should be considered prior to conducting high-throughput metabolomics studies. While widely applicable, the concepts presented here are namely applicable to high-throughput untargeted studies that aim to search for metabolite biomarkers that are associated with a particular human disease.

Urinary Metabolite Risk Biomarkers of Lung Cancer: A Prospective Cohort Study.

BACKGROUND: Lung cancer is a major health burden causing 160,000 and 1.6 million deaths annually in the United States and worldwide, respectively. METHODS: While seeking to identify stable and reproducible biomarkers in noninvasively collected biofluids, we assessed whether previously identified metabolite urinary lung cancer biomarkers, creatine riboside (CR), N-acetylneuraminic acid (NANA), cortisol sulfate, and indeterminate metabolite 561+, were elevated in the urines of subjects prior to lung cancer diagnosis in a well-characterized prospective Southern Community Cohort Study (SCCS). Urine was examined from 178 patients and 351 nondiseased controls, confirming that one of four metabolites was associated with lung cancer risk in the overall case-control set, whereas two metabolites were associated with lung cancer risk in European-Americans. RESULTS: OR of lung cancer associated with elevated CR levels, and adjusted for smoking and other potential confounders, was 2.0 [95% confidence interval (CI), 1.2-3.4; P= 0.01]. In European-Americans, both CR and NANA were significantly associated with lung cancer risk (OR = 5.3; 95% CI, 1.6-17.6; P= 0.006 and OR=3.5; 95% CI, 1.5-8.4; P= 0.004, respectively). However, race itself did not significantly modify the associations. ROC analysis showed that adding CR and NANA to a model containing previously established lung cancer risk factors led to a significantly improved classifier (P= 0.01). Increasing urinary levels of CR and NANA displayed a positive association with increasing tumor size, strengthening a previously established link to altered tumor metabolism. CONCLUSION AND IMPACT: These replicated results provide evidence that identified urinary metabolite biomarkers have a potential utility as noninvasive, clinical screening tools for early diagnosis of lung cancer. Cancer Epidemiol Biomarkers Prev; 25(6); 978-86. ©2016 AACR.

An Integrated Prognostic Classifier for Stage I Lung Adenocarcinoma Based on mRNA, microRNA, and DNA Methylation Biomarkers.

INTRODUCTION: Up to 30% stage I lung cancer patients suffer recurrence within 5 years of curative surgery. We sought to improve existing protein-coding gene and microRNA expression prognostic classifiers by incorporating epigenetic biomarkers. METHODS: Genome-wide screening of DNA methylation and pyrosequencing analysis of HOXA9 promoter methylation were performed in two independently collected cohorts of stage I lung adenocarcinoma. The prognostic value of HOXA9 promoter methylation alone and in combination with mRNA and miRNA biomarkers was assessed by Cox regression and Kaplan-Meier survival analysis in both cohorts. RESULTS: Promoters of genes marked by polycomb in embryonic stem cells were methylated de novo in tumors and identified patients with poor prognosis. The HOXA9 locus was methylated de novo in stage I tumors (p < 0.0005). High HOXA9 promoter methylation was associated with worse cancer-specific survival (hazard ratio [HR], 2.6; p = 0.02) and recurrence-free survival (HR, 3.0; p = 0.01), and identified high-risk patients in stratified analysis of stages IA and IB. Four protein-coding gene (XPO1, BRCA1, HIF1α, and DLC1), miR-21 expression, and HOXA9 promoter methylation were each independently associated with outcome (HR, 2.8; p = 0.002; HR, 2.3; p = 0.01; and HR, 2.4; p = 0.005, respectively), and when combined, identified high-risk, therapy naive, stage I patients (HR, 10.2; p = 3 × 10). All associations were confirmed in two independently collected cohorts. CONCLUSION: A prognostic classifier comprising three types of genomic and epigenomic data may help guide the postoperative management of stage I lung cancer patients at high risk of recurrence.

Noninvasive urinary metabolomic profiling identifies diagnostic and prognostic markers in lung cancer.

Lung cancer remains the most common cause of cancer deaths worldwide, yet there is currently a lack of diagnostic noninvasive biomarkers that could guide treatment decisions. Small molecules (<1,500 Da) were measured in urine collected from 469 patients with lung cancer and 536 population controls using unbiased liquid chromatography/mass spectrometry. Clinical putative diagnostic and prognostic biomarkers were validated by quantitation and normalized to creatinine levels at two different time points and further confirmed in an independent sample set, which comprises 80 cases and 78 population controls, with similar demographic and clinical characteristics when compared with the training set. Creatine riboside (IUPAC name: 2-{2-[(2R,3R,4S,5R)-3,4-dihydroxy-5-(hydroxymethyl)-oxolan-2-yl]-1-methylcarbamimidamido}acetic acid), a novel molecule identified in this study, and N-acetylneuraminic acid (NANA) were each significantly (P < 0.00001) elevated in non-small cell lung cancer and associated with worse prognosis [HR = 1.81 (P = 0.0002), and 1.54 (P = 0.025), respectively]. Creatine riboside was the strongest classifier of lung cancer status in all and stage I-II cases, important for early detection, and also associated with worse prognosis in stage I-II lung cancer (HR = 1.71, P = 0.048). All measurements were highly reproducible with intraclass correlation coefficients ranging from 0.82 to 0.99. Both metabolites were significantly (P < 0.03) enriched in tumor tissue compared with adjacent nontumor tissue (N = 48), thus revealing their direct association with tumor metabolism. Creatine riboside and NANA may be robust urinary clinical metabolomic markers that are elevated in tumor tissue and associated with early lung cancer diagnosis and worse prognosis.

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.

microRNA and inflammatory gene expression as prognostic marker for overall survival in esophageal squamous cell carcinoma.

MicroRNAs (miRNAs) and inflammatory genes have a role in the initiation and development of esophageal squamous cell carcinoma (ESCC). In our study, we examined the potential of using miRNA and inflammatory gene expression patterns as prognostic classifiers for ESCC. Five miRNAs and 25 inflammatory-related genes were measured by quantitative reverse transcriptase PCR in tumor tissues and adjacent noncancerous tissues from 178 Chinese patients with ESCC. The expression levels of miR-21 (p = 0.027), miR-181b (p = 0.002) and miR-146b (p = 0.021) in tumor tissue and miR-21 (p = 0.003) in noncancerous tissue were associated with overall survival of patients. These data were combined to generate a miRNA risk score that was significantly associated with worse prognosis (p = 0.0001), suggesting that these miRNAs may be useful prognostic classifiers for ESCC. To construct an inflammatory gene prognostic classifier, we divided the population into training (n = 124) and test cohorts (n = 54). The expression levels of CRY61, CTGF and IL-18 in tumor tissue and VEGF in adjacent noncancerous tissue were modestly associated with prognosis in the training cohort |Z-score| > 1.5 and were subsequently used to construct a Cox regression-based inflammatory risk score (IRS). IRS was significantly associated with survival in both the training cohort (p = 0.002) and the test cohort (p = 0.005). Furthermore, Cox regression models combining both miRNA risk score and IRS performed significantly better than models with either alone (p < 0.001 likelihood ratio test). Therefore, miRNA and inflammatory gene expression patterns, alone or in combination, have potential as prognostic classifiers for ESCC and may help to guide therapeutic decisions.