Univariate and multivariate regression analysis for factors associated with survival in nonsmokers from both cohorts.

K-M plots of overall survival of lung cancer cases stratified by the median cutoff value of CR and NANA for smokers in (A) Exploratory cohort and (B) Validation cohort.

Box plots showing the distribution of CR and NANA urinary metabolite levels in the (A) exploratory cohort and (B) validation cohort were quantitatively measured by UPLC–MS/MS in the study participants. Kruskal–Wallis ANOVA; posthoc multiple comparisons, ****P < 0.0001; **P < 0.01. LCC, lung cancer cases; x͂ = median.

Distribution of CR and NANA metabolite levels in late-stage (III & IV) cases. **** p<0.0001, *** p<0.001; LS, late-stage (III & IV); x͂ = median

Abstract Purpose: Nonsmokers account for 10% to 13% of all lung cancer cases in the United States. Etiology is attributed to multiple risk factors including exposure to secondhand smoking, asbestos, environmental pollution, and radon, but these exposures are not within the current eligibility criteria for early lung cancer screening by low-dose CT (LDCT). Experimental Design: Urine samples were collected from two independent cohorts comprising 846 participants (exploratory cohort) and 505 participants (validation cohort). The cancer urinary biomarkers, creatine riboside (CR) and N-acetylneuraminic acid (NANA), were analyzed and quantified using liquid chromatography–mass spectrometry to determine if nonsmoker cases can be distinguished from sex and age-matched controls in comparison with tobacco smoker cases and controls, potentially leading to more precise eligibility criteria for LDCT screening. Results: Urinary levels of CR and NANA were significantly higher and comparable in nonsmokers and tobacco smoker cases than population controls in both cohorts. Receiver operating characteristic analysis for combined CR and NANA levels in nonsmokers of the exploratory cohort resulted in better predictive performance with the AUC of 0.94, whereas the validation cohort nonsmokers had an AUC of 0.80. Kaplan–Meier survival curves showed that high levels of CR and NANA were associated with increased cancer-specific death in nonsmokers as well as tobacco smoker cases in both cohorts. Conclusions: Measuring CR and NANA in urine liquid biopsies could identify nonsmokers at high risk for lung cancer as candidates for LDCT screening and warrant prospective studies of these biomarkers.

ROC curves represent the accuracy of metabolite CR, NANA, and the combination with AUC for smoking status in both cohorts: (A) nonsmokers and (B) smokers. PC, population controls.

K–M plots of the overall survival of lung cancer cases stratified by the median cutoff value of CR and NANA for nonsmokers in the (A) exploratory cohort and (B) validation cohort.

Distribution of CR and NANA metabolite levels in early-stage (I & II) cases. ****, p < 0.0001, ***, p < 0.001; ES, early-stage (I & II); x͂ = median

Glyphosate [N-(phosphonomethyl) glycine], a systemic herbicide, is used globally (825 million kg/year) in 750+ formulations. The International Agency for Research on Cancer classified glyphosate is a probable human carcinogen (Group 2A), but epidemiological studies have been lacking for its association with liver cancer and chronic liver disease. We analyzed urine specimens from 591 patients with newly diagnosed liver cancer, chronic liver disease (CLD), and healthy individuals from five different medical centers between 2011 to 2016 in Thailand. Gas chromatography electrospray ionization mass spectrometry (GC-ESI/MS) was used to quantify glyphosate and its metabolites, aminomethylphosphonic acid (AMPA) and phosphoric acid (PPA) to study their levels in urine of hepatocellular carcinoma (HCC) and CLD patients in comparison to matched healthy individuals. Significantly higher levels of glyphosate were found in CLD patients compared to HCC cases and hospital controls, while significantly elevated levels of both AMPA and PPA were observed in HCC and CLD patients compared to hospital controls. Glyphosate and its metabolites were also detected at low to moderately high levels in convenience samples of food products and drinking water. These results raise concerns about the potential role of glyphosate in chronic liver disease and liver cancer risk.

The diagnostic efficiency of models in non-smoking exploratory and validation cohorts. LCC, Lung cancer cases; PC, Population controls; AUC, area under the curve; CI, confidence interval; SN, Sensitivity; SP, Specificity; NPV, negative predictive values; PPV, positive predictive values; CR, Creatine riboside; NANA- N-acetyl neuraminic acid.

Distribution of CR and NANA metabolite levels in AA and EA participants. **** p<0.0001* p<0.05; AA, African American; EA, European American; LCC, lung cancer cases; x͂ = median

Survival analysis for Non-smokers and Smokers by stage and histology. K-M plot for (A) Early-stage (I &II) Lung cancer cases; Late-stage (III & IV) Lung cancer cases and (B) LUAD cases; LUSC cases. NS, Non-smokers; SK, Smokers; LUAD, Lung adenocarcinoma; LUSC, Lung squamous cell carcinoma

Distribution of CR and NANA metabolite levels in non-smoker cases without and with (A) Childhood parental smoking exposure and (B) Secondhand smoking exposure. **** p<0.0001; NS, non-smokers; CPS, Childhood parental smoking exposure; SHS, Secondhand smoking exposure, LCC, lung cancer cases; x͂ = median

Cox proportional analysis for factors associated with survival

Clinical characteristics of the study participants by smoking status.

Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS).

List of SNPs successfully genotypes in the NCI/MD Case Control study.

PDF file - 1526KB, Supplementary Table 1 shows random forest analysis results for predictions of lung cancer status in the training set. Supplementary Table 2 shows associations with survival in the training set when the top four predictive metabolites are combined in all cases. Supplementary Table 3 shows associations with survival in the training set, stratified by self-reported race. Supplementary Table 4 shows intraclass correlation coefficients in the quantitated subset. Supplementary Figure 1 depicts workflow of the classification analysis. Supplementary Figure 2 depicts quality control assessment in the training set. Supplementary Figure 3 shows predictions of smoking status in the training set determined by random forest analysis and abundances of tobacco-related metabolites. Supplementary Figure 4 shows overlap of metabolites predictive of lung cancer status in the training set based on random forest analysis, stratified by gender, race and smoking status. Supplementary Figure 5 shows fragmentation patterns of top four predictive metabolites determined by tandem mass spectrometry. Supplementary Figure 6 depicts identification of creatine riboside by NMR. Supplementary Figure 7 shows diurnal effects on top four predictive metabolites. Supplementary Figure 8 shows top four predictive metabolite abundances stratified by smoking status. Supplementary Figure 9 shows Kaplan-Meier survival estimates in the training set depicted for the top four predictive metabolites in stages I-II and their combination. Supplementary Figure 10 shows metabolite abundances stratified by chemotherapy/radiation status and surgery status.

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.