NMR metabolomics and glycomics for cancer detection in patients with non-specific symptoms: a prospective observational cohort study
Year: 2026
Type: article
Study design: Observational Study
Abstract: BACKGROUND: H NMR-based metabolomics can identify cancer with high accuracy. SCAN2 evaluated whether integrating metabolomics with glycomics provides complementary molecular information and improves discrimination in a clinically complex, real-world population. METHODS: Serum from 369 SCAN patients (59 cancers) was analysed using AXINON® System-derived NMR metabolomics and HPLC-MS glycomics. Machine-learning models were trained to predict cancer status, with performance assessed by receiver operating characteristic (ROC) analysis of pooled cross-validated predictions. To place cancer risk in a broader clinical context, a second classifier modelling alternative non-cancer diagnosis was incorporated, and mean predicted probabilities from both models were jointly projected into a two-dimensional space, maintaining strict separation of training and test data. FINDINGS: In the full cohort, integration of glycomics with metabolomics achieved an AUC of 0.814 (95% CI 0.808-0.820). In a refined sub-cohort excluding major comorbidities and selected cancer types (32 cancers, 277 non-cancers), performance improved to an AUC of 0.884 (95% CI 0.879-0.890). Discriminatory features included cancer-associated biantennary fucosylated glycans alongside amino acid metabolites (glutamate, histidine) and lipoprotein-related measures. A classifier distinguishing metastatic from non-metastatic disease (n = 29 vs. 30) achieved an AUC of 0.80. Joint probability analysis in the full cohort preserved cancer-associated signatures across comorbidity burden, with projection-based classification achieving an accuracy of 89.2% (95% CI 85.7-92.6). INTERPRETATION: These findings validate the SCAN1 metabolomic signature in a more clinically complex cohort and indicate that integrating glycomics with metabolomics provides complementary biological information for cancer discrimination. Joint probability analysis provides an interpretable framework for cancer risk stratification within multimorbid diagnostic pathways, supporting the clinical potential of scalable multi-omics blood testing. FUNDING: EPSRC, EU Horizon 2020, Wellcome/MLSTF, Novo Nordisk Foundation.
Source: EBioMedicine
Authors: Tereza Kacerova, Abi G. Yates, James R. Larkin, Boris Shulgin, Jack J.J.J. Miller, Philippa L. Harris Gleave, Sebastian de Jel, Jack Cheeseman, Georgia Elgood-Hunt, Hasan Al-Sattar, Niamh Appleby, Guanke Bao, Gagandeep Batth, Rachel Benamore, Andrew Blake, Georgeta Ciuban, Kiana K. Collins, Isabella de Vere Hunt, Helene Dreau, Monica Evans, Nick Gibb, Fergus Gleeson, Shelley Hayles, Laura Heath, Ashley Jackson, Catherine Johnson, Zoe Kaveney, David Lewis, Rosie Lomas, Daniel Mitchener, Alexandra Montagu, Julia-Ann Moreland, Niall Moore, Brian Nicholson, Sophie Roberts, Neelaksh Sadhoo, Shyamal Saujani, Anna Schuh, Andrew Slater, Sarah Smith, Nia Taylor, Sharon Tonner, Suzanne Waterfield, Louise Wing, Eric Schiffer, Daniel I.R. Spencer, Suzie Anthony, Daniel C. Anthony
Institutions: University of Oxford, Aarhus University, Culham Science Centre
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,780
OpenAlex ID: W7203440458