An evaluation of the National Institutes of Health grants portfolio: identifying opportunities and challenges for multi-omics research that leverage metabolomics data.

BACKGROUND: Through the systematic large-scale profiling of metabolites, metabolomics provides a tool for biomarker discovery and improving disease monitoring, diagnosis, prognosis, and treatment response, as well as for delineating disease mechanisms and etiology. As a downstream product of the genome and epigenome, transcriptome, and proteome activity, the metabolome can be considered as being the most proximal correlate to the phenotype. Integration of metabolomics data with other -omics data in multi-omics analyses has the potential to advance understanding of human disease development and treatment. AIM OF REVIEW: To understand the current funding and potential research opportunities for when metabolomics is used in human multi-omics studies, we cross-sectionally evaluated National Institutes of Health (NIH)-funded grants to examine the use of metabolomics data when collected with at least one other -omics data type. First, we aimed to determine what types of multi-omics studies included metabolomics data collection. Then, we looked at those multi-omics studies to examine how often grants employed an integrative analysis approach using metabolomics data. KEY SCIENTIFIC CONCEPTS OF REVIEW: We observed that the majority of NIH-funded multi-omics studies that include metabolomics data performed integration, but to a limited extent, with integration primarily incorporating only one other -omics data type. Some opportunities to improve data integration may include increasing confidence in metabolite identification, as well as addressing variability between -omics approach requirements and -omics data incompatibility.

Urinary Metabolites Diagnostic and Prognostic of Intrahepatic Cholangiocarcinoma.

BACKGROUND: Liver cancer is the second leading cause of cancer-related deaths worldwide. With a predicted 2.4-fold rise in liver cancer incidence by 2020, there is an urgent need for early, inexpensive diagnostic biomarkers to deploy in the clinic. METHODS: We employed ultraperformance liquid chromatography tandem mass-spectrometry (UPLC/MS-MS) for the quantitation of four metabolites, creatine riboside (CR), N-acetylneuraminic acid (NANA), cortisol sulfate, and a lipid molecule designated as 561+, in urine samples from the NCI-MD cohort comprising 98 hepatocellular carcinoma (HCC) cases, 101 high-risk subjects, and 95 controls. Validation was carried out in the TIGER-LC cohort [n = 370 HCC and intrahepatic cholangiocarcinoma (ICC) cases, 471 high-risk subjects, 251 controls], where ICC, the second most common primary hepatic malignancy, is highly prevalent. Metabolite quantitation was also conducted in TIGER-LC tissue samples (n = 48 ICC; n = 51 HCC). RESULTS: All profiled metabolites were significantly increased in liver cancer when compared with high-risk subjects and controls in the NCI-MD study. In the TIGER-LC cohort, the four-metabolite profile was superior at classifying ICC than a clinically utilized marker, CA19-9, and their combination led to a significantly improved model (AUC = 0.88, P = 4E-8). Metabolites CR and NANA were significantly elevated in ICC when compared with HCC cases in both urine and tissue samples. High levels of CR were associated with poorer prognosis in ICC. CONCLUSIONS: Four metabolites are significantly increased in HCC and ICC and are robust at classifying ICC in combination with the clinically utilized marker CA19-9. IMPACT: Noninvasive urinary metabolite biomarkers hold promise for diagnostic and prognostic evaluation of ICC.

Inverse association of vitamin D3 levels with lung cancer mediated by genetic variation.

Vitamin D is an essential micronutrient required for normal physiological function and recognized for its role regulating calcium metabolism. Recent work is beginning to emerge demonstrating a role for vitamin D in chronic illnesses, such as cancer. Circulating serum levels of 25(OH)D2/3 were quantitatively measured using sensitive ultraperformance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) in 406 lung cancer cases and 437 population controls, while 1,25(OH)2 D2/3 levels were measured in a subset of 90 cases and 104 controls using the same method, from the NCI-MD case-control cohort. 25(OH)D3 levels were inversely associated with lung cancer status across quartiles (Q2 vs. Q1: ORadjusted  = 0.5, 95% CI = 0.3-0.8; Q3 vs. Q1: ORadjusted  = 0.5, 95% CI = 0.3-0.8; Q4 vs. Q1: ORadjusted  = 0.5, 95% CI = 0.2-0.9; Ptrend  = 0.004). Levels of 1,25(OH)2 D3 were also inversely associated with lung cancer status (Q2 vs. Q1: ORadjusted  = 0.2, 95% CI = 0.03-0.7; Q3 vs. Q1: ORadjusted  = 0.1, 95% CI = 0.01-0.4; Q4 vs. Q1: ORadjusted  = 0.04, 95% CI = 0.01-0.3; Ptrend <0.0001). Although the observed trends were similar for the 25(OH)D2 (Ptrend  = 0.08), no significant associations were seen between vitamin D2 and lung cancer status. Additionally, genotyping of 296 SNPs in the same subjects resulted in findings that 27 SNPs, predominantly in CYP24A1 and VDR genes, were significantly associated with lung cancer status, affected mRNA expression, and modulated vitamin D levels. These findings suggest a protective role for vitamin D3 in lung cancer, with similar trends but insignificant findings for D2 . Vitamin D3 levels appeared to be modulated by genetic variation in CYP24A1 and VDR genes. Additional research to illuminate the mechanism(s) through which vitamin D exacerbates effects against lung carcinogenesis is warranted.

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.

Identification of a functional SNP in the 3'UTR of CXCR2 that is associated with reduced risk of lung cancer.

Global changes in gene expression accompany the development of cancer. Thus, inherited variants in miRNA-binding sites are likely candidates for conferring inherited susceptibility. Using an in silico approach, we compiled a comprehensive list of SNPs predicted to modulate miRNA binding in genes from several key lung cancer pathways. We then investigated whether these SNPs were associated with lung cancer risk in two independent populations. In general, SNPs in miRNA-binding sites are rare. However, some allelic variation was observed. We found that rs1126579 in CXCR2 was associated with a reduced risk of lung cancer in both European American [ORTT vs. CC 0.56 (0.37-0.88); P = 0.008] and Japanese [ORTT vs. CC 0.62 (0.38-1.00); P = 0.049] populations. Furthermore, we found that the SNP disrupted a novel binding site for miR-516a-3p, led to a moderate increase in CXCR2 mRNA and protein expression, and increased MAPK signaling. Moreover, analysis of rs1126579 with serum levels of IL8, its endogenous ligand, supported an interaction whereby rs1126579-T and high serum IL8 conferred synergistic protection from lung cancer. Our findings demonstrate a function for a 3'UTR SNP in modulating CXCR2 expression, signaling, and susceptibility to lung cancer.

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.

Biomarkers of coordinate metabolic reprogramming in colorectal tumors in mice and humans.

BACKGROUND & AIMS: There are no robust noninvasive methods for colorectal cancer screening and diagnosis. Metabolomic and gene expression analyses of urine and tissue samples from mice and humans were used to identify markers of colorectal carcinogenesis. METHODS: Mass spectrometry-based metabolomic analysis of urine and tissues from wild-type C57BL/6J and Apc(Min/+) mice, as well as from mice with azoxymethane-induced tumors, was employed in tandem with gene expression analysis. Metabolic profiling was also performed on colon tumor and adjacent nontumor tissues from 39 patients. The effects of ß-catenin activity on metabolic profiles were assessed in mice with colon-specific disruption of Apc. RESULTS: Thirteen markers were found in urine associated with development of colorectal tumors in Apc(Min/+) mice. Metabolites related to polyamine metabolism, nucleic acid metabolism, and methylation, identified tumor-bearing mice with 100% accuracy, and also accurately identified mice with polyps. Changes in gene expression in tumor samples from mice revealed that derangement of metabolites were a reflection of coordinate metabolic reprogramming in tumor tissue. Similar changes in urinary metabolites were observed in mice with azoxymethane-induced tumors and in mice with colon-specific activation of ß-catenin. The metabolic alterations indicated by markers in urine, therefore, appear to occur during early stages of tumorigenesis, when cancer cells are proliferating. In tissues from patients, tumors had stage-dependent increases in 17 metabolites associated with the same metabolic pathways identified in mice. Ten metabolites that were increased in tumor tissues, compared with nontumor tissues (proline, threonine, glutamic acid, arginine, N1-acetylspermidine, xanthine, uracil, betaine, symmetric dimethylarginine, and asymmetric-dimethylarginine), were also increased in urine from tumor-bearing mice. CONCLUSIONS: Gene expression and metabolomic profiles of urine and tissue samples from mice with colorectal tumors and of colorectal tumor samples from patients revealed pathways associated with derangement of specific metabolic pathways that are indicative of early-stage tumor development. These urine and tissue markers might be used in early detection of colorectal cancer.

Navigating the road ahead: addressing challenges for use of metabolomics in epidemiology studies.

Metabolomics platforms allow for the measurement of hundreds to thousands of unique small chemical entities, as well as offer extensive coverage of metabolic markers related to obesity, diet, smoking, and other exposures of high interest to health scientists. Nevertheless, its potential use as a tool in population-based study design has not been fully explored. As the field of metabolomics continues to mature, and in part, accelerate through the National Institutes of Health (NIH) investment of ≤65 million in the Common Fund's Metabolomics Program (https://common fund.nih.gov/metabolomics/index), it is time to consider those challenges most pertinent to epidemiologic studies.

High-order SNP combinations associated with complex diseases: efficient discovery, statistical power and functional interactions.

There has been increased interest in discovering combinations of single-nucleotide polymorphisms (SNPs) that are strongly associated with a phenotype even if each SNP has little individual effect. Efficient approaches have been proposed for searching two-locus combinations from genome-wide datasets. However, for high-order combinations, existing methods either adopt a brute-force search which only handles a small number of SNPs (up to few hundreds), or use heuristic search that may miss informative combinations. In addition, existing approaches lack statistical power because of the use of statistics with high degrees-of-freedom and the huge number of hypotheses tested during combinatorial search. Due to these challenges, functional interactions in high-order combinations have not been systematically explored. We leverage discriminative-pattern-mining algorithms from the data-mining community to search for high-order combinations in case-control datasets. The substantially improved efficiency and scalability demonstrated on synthetic and real datasets with several thousands of SNPs allows the study of several important mathematical and statistical properties of SNP combinations with order as high as eleven. We further explore functional interactions in high-order combinations and reveal a general connection between the increase in discriminative power of a combination over its subsets and the functional coherence among the genes comprising the combination, supported by multiple datasets. Finally, we study several significant high-order combinations discovered from a lung-cancer dataset and a kidney-transplant-rejection dataset in detail to provide novel insights on the complex diseases. Interestingly, many of these associations involve combinations of common variations that occur in small fractions of population. Thus, our approach is an alternative methodology for exploring the genetics of rare diseases for which the current focus is on individually rare variations.

3'-UTR and functional secretor haplotypes in mannose-binding lectin 2 are associated with increased colon cancer risk in African Americans.

Because chronic intestinal inflammation is a risk factor for colorectal cancer, we hypothesized that genetic variants of inflammatory mediators, such as mannose-binding lectin 2 (MBL2), are associated with colon cancer susceptibility. Here, we report the association of 24 MBL2 single-nucleotide polymorphisms (SNP) and corresponding haplotypes with colon cancer risk in a case-control study. Four SNPs in the 3'-untranslated region (UTR) of the gene (rs10082466, rs2120132, rs2099902, and rs10450310) were associated with an increased risk of colon cancer in African Americans. ORs for homozygous variants versus wild-type ranged from 3.17 [95% confidence interval (CI), 1.57-6.40] to 4.51 (95% CI, 1.94-10.50), whereas the 3'-UTR region haplotype consisting of these four variants had an OR of 2.10 (95% CI, 1.42-3.12). The C allele of rs10082466 exhibited a binding affinity of miR-27a and this allele was associated with both lower MBL plasma levels and activity. We found that 5' secretor haplotypes known to correlate with moderate and low MBL serum levels exhibited associations with increased risk of colon cancer in African Americans, specifically as driven by two haplotypes, LYPA and LYQC, relative to the referent HYPA haplotype (LYPA: OR, 2.60; 95% CI, 1.33-5.08 and LYQC: OR, 2.28; 95% CI, 1.20-4.30). Similar associations were not observed in Caucasians. Together, our results support the hypothesis that genetic variations in MBL2 increase colon cancer susceptibility in African Americans.

Interaction of CYP1B1, cigarette-smoke carcinogen metabolism, and lung cancer risk.

A previously published case-control study nested in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial found a significant relationship of serum levels of total NNAL (4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol and its glucuronides) to prospective lung cancer risk. The present paper examines this relationship in the context of single-nucleotide polymorphisms (SNPs) in genes important in the metabolism of tobacco smoke carcinogens. DNA was extracted from the subjects' lymphocytes and analyzed for SNPs in 11 locations on four genes related to tobacco carcinogen metabolism. Logistic regressions on case-control status were used to estimate main effects of SNPs and biomarkers and their interactions adjusting for potential confounders. Of the 11 SNPs, only one, in CYP1B1, significantly interacted with total NNAL affecting risk for lung cancer. At low NNAL levels, the variant appeared protective. However, for those with the minor variant, the risk for lung cancer increased with increasing NNAL five times as rapidly compared to those without it, so that at high NNAL levels, this SNP's protection disappears. Analyzing only adenocarcinomas, the effect of the variant was even stronger, with the risk of cancer increasing six times as fast. A common polymorphism of CYP1B1 may play a role in the risk of NNK, a powerful lung carcinogen, in the development of lung cancer in smokers.

Genomic variation in myeloma: design, content, and initial application of the Bank On A Cure SNP Panel to detect associations with progression-free survival.

BACKGROUND: We have engaged in an international program designated the Bank On A Cure, which has established DNA banks from multiple cooperative and institutional clinical trials, and a platform for examining the association of genetic variations with disease risk and outcomes in multiple myeloma. We describe the development and content of a novel custom SNP panel that contains 3404 SNPs in 983 genes, representing cellular functions and pathways that may influence disease severity at diagnosis, toxicity, progression or other treatment outcomes. A systematic search of national databases was used to identify non-synonymous coding SNPs and SNPs within transcriptional regulatory regions. To explore SNP associations with PFS we compared SNP profiles of short term (less than 1 year, n = 70) versus long term progression-free survivors (greater than 3 years, n = 73) in two phase III clinical trials. RESULTS: Quality controls were established, demonstrating an accurate and robust screening panel for genetic variations, and some initial racial comparisons of allelic variation were done. A variety of analytical approaches, including machine learning tools for data mining and recursive partitioning analyses, demonstrated predictive value of the SNP panel in survival. While the entire SNP panel showed genotype predictive association with PFS, some SNP subsets were identified within drug response, cellular signaling and cell cycle genes. CONCLUSION: A targeted gene approach was undertaken to develop an SNP panel that can test for associations with clinical outcomes in myeloma. The initial analysis provided some predictive power, demonstrating that genetic variations in the myeloma patient population may influence PFS.

Saturation with oxygen for ductal dependent congenital heart diseases before and after the prostaglandin therapy.

Ductal dependent congenital heart diseases represent 14-20% of all congenital heart diseases. A primary goal of the treatment of these diseases is to retain ductus open until the final cardiosurgical treatment. Prostaglandins are presently the only medicaments, which have a capability to keep ductus open. By means of a retrospective study in a period from January, 2000 until December, 2002 at the Paediatric clinic of the Clinical centre of the University in Sarajevo, 14 patients (treated with prostaglandins) diagnosed with ductal dependent congenital heart diseases were analyzed. In our sample, there are 9/14 male patients (64.3%), 11/14 (78.6%) were full-term newborns, while 10/14 (71.4%) were eutrophic at birth. An average saturation increase, after the prostaglandin therapy, measured in blood from the capillaries is 29, and measured transcutanlly is 32 units. Duration of prostaglandin therapy in our study was on average 17.2 days. The most common cause of death was insufficientia cardiorespiratoria (4 out of 11), but sepsis/infection (3 out of 11) and insufficientia renalis were also common. 78.6% (11 out of 14) patients died partly because of the complexity of these diseases, but also because a cardiosurgical treatment is delayed. A goal of this study is evaluation of saturation with oxygen before and after the prostaglandin therapy.