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Serum Metabolite Profiles in Participants of Lung Cancer Screening Study; Comparison of Two Independent Cohorts.

ABSTRACT: Serum metabolome is a promising source of molecular biomarkers that could support early detection of lung cancer in screening programs based on low-dose computed tomography. Several panels of metabolites that differentiate lung cancer patients and healthy individuals were reported, yet none of them were validated in the population at high-risk of developing cancer. Here we analyzed serum metabolome profiles in participants of two lung cancer screening studies: MOLTEST-BIS (Poland, n = 369) and SMAC-1 (Italy, n = 93). Three groups of screening participants were included: lung cancer patients, individuals with benign pulmonary nodules, and those without any lung alterations. Concentrations of about 400 metabolites (lipids, amino acids, and biogenic amines) were measured by a mass spectrometry-based approach. We observed a reduced level of lipids, in particular cholesteryl esters, in sera of cancer patients from both studies. Despite several specific compounds showing significant differences between cancer patients and healthy controls within each study, only a few cancer-related features were common when both cohorts were compared, which included a reduced concentration of lysophosphatidylcholine LPC (18:0). Moreover, serum metabolome profiles in both noncancer groups were similar, and differences between cancer patients and both groups of healthy participants were comparable. Large heterogeneity in levels of specific metabolites was observed, both within and between cohorts, which markedly impaired the accuracy of classification models: The overall AUC values of three-state classifiers were 0.60 and 0.51 for the test (MOLTEST) and validation (SMAC) cohorts, respectively. Therefore, a hypothetical metabolite-based biomarker for early detection of lung cancer would require adjustment to lifestyle-related confounding factors that putatively affect the composition of serum metabolome.

SUBMITTER: Widlak P

PROVIDER: S-EPMC8198431 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Prediabetes is a metabolic condition associated with gut microbiome composition, although mechanisms remain elusive. We searched for fecal metabolites, a readout of gut microbiome function, associated with impaired fasting glucose (IFG) in 142 individuals with IFG and 1,105 healthy individuals from the UK Adult Twin Registry (TwinsUK). We used the Cooperative Health Research in the Region of Augsburg (KORA) cohort (318 IFG individuals, 689 healthy individuals) to replicate our findings. We linearly combined eight IFG-positively associated metabolites (1-methylxantine, nicotinate, glucuronate, uridine, cholesterol, serine, caffeine, and protoporphyrin IX) into an IFG-metabolite score, which was significantly associated with higher odds ratios (ORs) for IFG (TwinsUK: OR 3.9 [95% CI 3.02-5.02], P < 0.0001, KORA: OR 1.3 [95% CI 1.16-1.52], P < 0.0001) and incident type 2 diabetes (T2D; TwinsUK: hazard ratio 4 [95% CI 1.97-8], P = 0.0002). Although these are host-produced metabolites, we found that the gut microbiome is strongly associated with their fecal levels (area under the curve >70%). Abundances of Faecalibacillus intestinalis, Dorea formicigenerans, Ruminococcus torques, and Dorea sp. AF24-7LB were positively associated with IFG, and such associations were partially mediated by 1-methylxanthine and nicotinate (variance accounted for mean 14.4% [SD 5.1], P < 0.05). Our results suggest that the gut microbiome is linked to prediabetes not only via the production of microbial metabolites but also by affecting intestinal absorption/excretion of host-produced metabolites and xenobiotics, which are correlated with the risk of IFG. Fecal metabolites enable modeling of another mechanism of gut microbiome effect on prediabetes and T2D onset.Article highlightsPrediabetes is a metabolic condition associated with gut microbiome composition, although mechanisms remain elusive. We investigated whether there is a fecal metabolite signature of impaired fasting glucose (IFG) and the possible underlying mechanisms of action. We identified a fecal metabolite signature of IFG associated with prevalent IFG in two independent cohorts and incident type 2 diabetes in a subanalysis. Although the signature consists of metabolites of nonmicrobial origin, it is strongly correlated with gut microbiome composition. Fecal metabolites enable modeling of another mechanism of gut microbiome effect on prediabetes by affecting intestinal absorption or excretion of host compounds and xenobiotics.

Project description:BackgroundHuman plasma and serum are widely used matrices in clinical and biological studies. However, different collecting procedures and the coagulation cascade influence concentrations of both proteins and metabolites in these matrices. The effects on metabolite concentration profiles have not been fully characterized.Methodology/principal findingsWe analyzed the concentrations of 163 metabolites in plasma and serum samples collected simultaneously from 377 fasting individuals. To ensure data quality, 41 metabolites with low measurement stability were excluded from further analysis. In addition, plasma and corresponding serum samples from 83 individuals were re-measured in the same plates and mean correlation coefficients (r) of all metabolites between the duplicates were 0.83 and 0.80 in plasma and serum, respectively, indicating significantly better stability of plasma compared to serum (p?=?0.01). Metabolite profiles from plasma and serum were clearly distinct with 104 metabolites showing significantly higher concentrations in serum. In particular, 9 metabolites showed relative concentration differences larger than 20%. Despite differences in absolute concentration between the two matrices, for most metabolites the overall correlation was high (mean r?=?0.81±0.10), which reflects a proportional change in concentration. Furthermore, when two groups of individuals with different phenotypes were compared with each other using both matrices, more metabolites with significantly different concentrations could be identified in serum than in plasma. For example, when 51 type 2 diabetes (T2D) patients were compared with 326 non-T2D individuals, 15 more significantly different metabolites were found in serum, in addition to the 25 common to both matrices.Conclusions/significanceOur study shows that reproducibility was good in both plasma and serum, and better in plasma. Furthermore, as long as the same blood preparation procedure is used, either matrix should generate similar results in clinical and biological studies. The higher metabolite concentrations in serum, however, make it possible to provide more sensitive results in biomarker detection.

Project description:Introduction: Effective predictive biomarkers for selection of patients benefiting from adjuvant platinum-based chemotherapy in non-small cell lung cancer (NSCLC) are needed. Based on a previously validated methodology, molecular profiles of predicted sensitivity in two patient cohorts are presented. Methods: The profiles are correlations between in vitro sensitivity to cisplatin and vinorelbine and baseline mRNA expression of the 60 cell lines in the National Cancer Institute panel. An applied clinical samples filter focused the profiles to clinically relevant genes. The profiles were tested on 1) snap-frozen tumors from 133 patients with completely resected stage 1B-2 NSCLC randomized to adjuvant cisplatin and vinorelbine (ACV, n=71) or no adjuvant treatment (OBS, n=62) [GSE14814] and 2) formalin-fixed paraffin-embedded (FFPE) pre-treatment tumors from 95 patients with completely resected stage 1A-3B NSCLC receiving adjuvant cisplatin and vinorelbine. Results: The combined cisplatin and vinorelbine profiles showed: 1) univariate Hazard Ratio (HR) for sensitive versus resistant of 0.265 (95% CI:0.079-0.889, p=0.032) in the ACV cohort and a HR of 0.28 in a multivariate model (95% CI:0.08-1.04, p=0.0573); 2) significant prediction at 3 year survival from surgery in univariate (HR=0.138 (95% CI:0.035-0.537), p=0.004) and multivariate analysis (HR=0.14 (95% CI:0.030-0.6), p=0.0081). No discrimination was found in the OBS cohort (HR=1.328, p=0.60). The cisplatin predictor alone had similar figures with 1) univariate HR of 0.37 (95% CI:0.12-1.15, p=0.09) in the ACV cohort and 2) univariate HR of 0.14 (95% CI:0.03-0.59, p=0.0076) to three years. Functional analysis on the cisplatin profile revealed a group of upregulated genes related to RNA splicing as a part of DNA damage repair and apoptosis. Conclusions: Profiles derived from snap-frozen and FFPE NSCLC tissue were prognostic and predictive in the patients that received cisplatin and vinorelbine but not in the cohort that did not receive adjuvant treatment.

Project description:Effective predictive biomarkers for selection of patients benefiting from adjuvant platinum-based chemotherapy in non-small cell lung cancer (NSCLC) are needed. Based on a previously validated methodology, molecular profiles of predicted sensitivity in two patient cohorts are presented.The profiles are correlations between in vitro sensitivity to cisplatin and vinorelbine and baseline mRNA expression of the 60 cell lines in the National Cancer Institute panel. An applied clinical samples filter focused the profiles to clinically relevant genes. The profiles were tested on 1) snap-frozen tumors from 133 patients with completely resected stage 1B-2 NSCLC randomized to adjuvant cisplatin and vinorelbine (ACV, n = 71) or no adjuvant treatment (OBS, n = 62) and 2) formalin-fixed paraffin-embedded (FFPE) tumors from 95 patients with completely resected stage 1A-3B NSCLC receiving adjuvant cisplatin and vinorelbine.The combined cisplatin and vinorelbine profiles showed: 1) univariate Hazard Ratio (HR) for sensitive versus resistant of 0.265 (95% CI:0.079-0.889, p = 0.032) in the ACV cohort and a HR of 0.28 in a multivariate model (95% CI:0.08-1.04, p = 0.0573); 2) significant prediction at 3 year survival from surgery in univariate (HR = 0.138 (95% CI:0.035-0.537), p = 0.004) and multivariate analysis (HR = 0.14 (95% CI:0.030-0.6), p = 0.0081). No discrimination was found in the OBS cohort (HR = 1.328, p = 0.60). The cisplatin predictor alone had similar figures with 1) univariate HR of 0.37 (95% CI:0.12-1.15, p = 0.09) in the ACV cohort and 2) univariate HR of 0.14 (95% CI:0.03-0.59, p = 0.0076) to three years. Functional analysis on the cisplatin profile revealed a group of upregulated genes related to RNA splicing as a part of DNA damage repair and apoptosis.Profiles derived from snap-frozen and FFPE NSCLC tissue were prognostic and predictive in the patients that received cisplatin and vinorelbine but not in the cohort that did not receive adjuvant treatment.

Project description:ObjectivePredictors of treatment resistance and relapse have been identified in patients with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis in the Glomerular Disease Collaborative Network (GDCN) in the southeastern US. This study was undertaken to evaluate the applicability of those predictors in an independent cohort followed up by the French Vasculitis Study Group.MethodsPredictors of treatment resistance were evaluated using logistic regression models and reported as odds ratios (ORs) with 95% confidence intervals (95% CIs). Predictors of relapse were evaluated using Cox proportional hazards models and reported as hazard ratios (HRs) with 95% CIs. Models were controlled for age, sex, race, baseline serum creatinine level, and cyclophosphamide therapy.ResultsThe French cohort (n = 434) and the GDCN cohort (n = 350) had similar median followup periods (44 months versus 45 months) and initial percentages of patients taking cyclophosphamide (82% versus 78%). The French cohort included more patients with proteinase 3 (PR3) ANCA (58% versus 40%), lung involvement (58% versus 49%), and upper respiratory tract involvement (62% versus 31%). Of the predictors of treatment resistance in the GDCN cohort (female sex, African American race, presence of myeloperoxidase ANCA, elevated creatinine level, and age), only age predicted treatment resistance in the French cohort (OR 1.32 per 10 years [95% CI 1.05-1.66]). Predictors of relapse in the GDCN cohort were PR3 ANCA (HR 1.77 [95% CI 1.11-2.82]), lung involvement (HR 1.68 [95% CI 1.10-2.57), and upper respiratory tract involvement (HR 1.58 [95% CI 1.00-2.48]), while predictors in the French cohort were PR3 ANCA (HR 1.66 [95% CI 1.15-2.39]) and lung involvement (HR 1.56 [95% CI 1.11-2.20]), but not upper respiratory tract involvement (HR 0.96 [95% CI 0.67-1.38]).ConclusionOur findings indicate that older age is a predictor of treatment resistance, and that PR3 ANCA and lung involvement are predictors of relapse in both cohorts. Discrepancies in predictors of treatment tract resistance may reflect differences in access to care, and differences in predictors of relapse may reflect variations in disease expression.

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Serum Metabolite Profiles in Participants of Lung Cancer Screening Study; Comparison of Two Independent Cohorts.

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