GNPS IBD Stool Samples Metabolomics
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Q Exactive
ORGANISM(S): Homo Sapiens (ncbitaxon:9606)
SUBMITTER: 
Brigid Boland
PROVIDER: MSV000087942 | GNPS | Wed Aug 04 08:31:00 BST 2021
REPOSITORIES: GNPS
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Project description:Fecal samples (n=52) of a cohort of IBD patients were collected before and after 14 weeks of treatment with three different biologics. Clinical disease activity scores were used to determine the clinical response and remission. Fecal metaproteomes of remitting patients (n=12) and of non-remitting patients (n=12) were compared before treatment and changes within both groups were assessed over sampling time to identify functional changes and potential human and microbial biomarkers. (2 patients were excluded from finala analyses as the started the therapy in a remitting disease state.)
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Project description:This is a prospective, multi-centered study to assess whether urine metabolomics can play a role in the screening of colorectal cancer (CRC). Urine samples will be collected from 1000 patients going through an established CRC screening program, and from a further 500 patients who already have a diagnosis of CRC. Using nuclear magnetic resonance (NMR) spectroscopy, the 1H NMR spectrum of urine samples will be analyzed for specific metabolites, and establish the metabolomic signature of colorectal cancer. The results from metabolomic urinalysis of this screening cohort will be compared with results from colonoscopy, histological descriptions, fecal occult blood testing (FOBT), and fecal immune testing (FIT) to assess the accuracy of urine metabolomics in identifying patients with polyps and malignancies. The urine metabolomic results from the colorectal cancer group will be correlated with operative, histological and clinical staging to define the role of urine metabolomics in assessing colorectal cancer type, location and stage. Additionally approximately 300 urine samples from breast cancer patients and 300 from prostate cancer patients will be collected to validate that the colorectal cancer signature is unique.
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Project description:Introduction Monitoring disease activity in inflammatory bowel disease (IBD) is essential for guiding therapy and preventing irreversible tissue damage. Colonoscopy, although the gold standard, is invasive and unsuitable for frequent monitoring, while fecal calprotectin lacks accuracy within its diagnostic gray zone (fecal calprotectin 100–250 µg/g). Stool proteomics offers a non-invasive alternative by directly capturing molecular signatures of intestinal inflammation. We conducted a proof-of-concept study to determine whether stool-derived peptides can accurately classify IBD activity (Active vs Remission) using a fully unbiased and reproducible nested cross-validation machine-learning framework. Methods A total of 174 stool samples from IBD patients were collected and profiled using SWATH-DIA mass spectrometry. Feature selection was performed within the training loops only (Boruta, LASSO, RFE) across repeated subsampling, retaining peptides consistently identified in ≥70 % of runs. Stable features were used to train four classifiers (GLMNet, SVM-Radial, SVM-Linear, Naïve Bayes) under inner 5-fold tuning. Outer test folds provided fully unseen evaluation, and model performance was additionally assessed exclusively on gray zone samples extracted from the outer test splits to quantify diagnostic resolution in this clinically challenging subgroup. Results Nested cross-validation identified a consensus panel of nine stool-derived peptides from five proteins. Across candidate classifiers, performance was broadly similar, with GLMNet consistently achieving the best trade-off between metrics. For GLMNet, outer-fold mean AUC was 0.93 and balanced accuracy 0.88, with specificity 0.94, sensitivity 0.82, and F1-score 0.85; close agreement between inner- and outer-fold metrics indicated minimal overfitting. Approximately half of the misclassified test patients were shared with the other models, suggesting intrinsically ambiguous cases rather than GLMNet-specific errors. Within the calprotectin gray zone subgroup (n = 34), GLMNet maintained good performance (accuracy 0.76, balanced accuracy 0.78, F1 0.79, AUC 0.80), confirming that the peptide signature remains informative in this diagnostically challenging range. SHAP and correlation analyses confirmed directionally consistent, largely non-redundant peptide contributions, and network analysis showed that the underlying proteins participate in related but distinct inflammatory pathways. Conclusions A stool-based multi-peptide signature, evaluated with a rigorously nested, leakage-free machine-learning framework, can reliably classify IBD activity and retain discriminative power within the gray zone. This biologically interpretable five-protein panel provides a strong basis for targeted mass-spectrometry assay development and prospective validation as a non-invasive tool for personalized IBD monitoring.
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