Spatial N-Glycan Imaging and Machine Learning Classify Hepatocellular Carcinoma and Predict Glutamine Synthetase Status

Muhammed F. Bayram; Jade K. Macdonald; Andrew DelaCourt; Peggi M. Angel; Richard R. Drake; Aatur Singhi; David Geller; Satdarshan P. Monga; Amit Singal; Anand Mehta

doi:10.1172/jci.insight.209628

Spatial N-Glycan Imaging and Machine Learning Classify Hepatocellular Carcinoma and Predict Glutamine Synthetase Status

Muhammed F. Bayram,¹ Jade K. Macdonald,¹ Andrew DelaCourt,¹ Peggi M. Angel,¹ Richard R. Drake,¹ Aatur Singhi,² David Geller,² Satdarshan P. Monga,² Amit Singal,³ and Anand Mehta¹

Published September 29, 2026 - More info

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Abstract

BACKGROUND. Hepatocellular carcinoma (HCC) exhibits molecular heterogeneity that challenges histopathologic classification and biomarker discovery. We assessed whether spatially resolved N-glycan imaging with machine learning could classify tumor regions and infer glutamine synthetase (GS) status.

METHODS. In this retrospective study, MALDI mass spectrometry imaging of N-glycans was performed on formalin-fixed, paraffin-embedded sections from two independent cohorts (discovery, n = 88; validation, n = 60) with pathologist annotation. An XGBoost classifier was trained on 90 discriminative N-glycan features using patient-grouped cross-validation. Performance was assessed by AUC for pixel- and biopsy-level discrimination of tumor from adjacent non-tumor tissue, and for GS status classification.

RESULTS. Pixel-level AUCs were 0.95 (cross-validation) and 0.89 (external validation); biopsy-level AUCs were 1.0 and 0.97, correctly identifying 97% of tumor-containing biopsies. Probability maps recapitulated pathologist-defined boundaries; UMAP embeddings captured inter- and intratumoral heterogeneity. Discriminative species (m/z 2393.846, 1905.634, 1743.579, 1809.639) reflected complex, fucosylated, branched remodeling. N-glycans bearing six GlcNAc residues were enriched in GS+ (n = 45) versus GS− (n = 17) tumors (P = 0.001) and discriminated GS status (AUC = 0.75), consistent with GLUL and MGAT5 upregulation in TCGA-LIHC.

CONCLUSION. MALDI N-glycan imaging with machine learning enables spatially resolved, objective classification of HCC and links glycan phenotypes to tumor-associated metabolic programs.

TRIAL REGISTRATION. Not applicable; retrospective analysis of archival, de-identified tissue.

FUNDING. NIH/NCI R01CA285370, 1R01CA289381, R33CA267226, R01CA282022, R21CA263464, R21CA286287, R01CA253460, S10OD030212, R01CA251155, R01CA250227, U01CA271887, P50CA295495, P30CA138313, P20GM130457, P30DK123704, P30DK120531,R24DK139775; NIH/NIA R01AG078702; Smart State Endowment, State of South Carolina; LeDucq Foundation.

Spatial N-Glycan Imaging and Machine Learning Classify Hepatocellular Carcinoma and Predict Glutamine Synthetase Status

Muhammed F. Bayram,¹ Jade K. Macdonald,¹ Andrew DelaCourt,¹ Peggi M. Angel,¹ Richard R. Drake,¹ Aatur Singhi,² David Geller,² Satdarshan P. Monga,² Amit Singal,³ and Anand Mehta¹

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Spatial N-Glycan Imaging and Machine Learning Classify Hepatocellular Carcinoma and Predict Glutamine Synthetase Status

Muhammed F. Bayram,1 Jade K. Macdonald,1 Andrew DelaCourt,1 Peggi M. Angel,1 Richard R. Drake,1 Aatur Singhi,2 David Geller,2 Satdarshan P. Monga,2 Amit Singal,3 and Anand Mehta1

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Muhammed F. Bayram,¹ Jade K. Macdonald,¹ Andrew DelaCourt,¹ Peggi M. Angel,¹ Richard R. Drake,¹ Aatur Singhi,² David Geller,² Satdarshan P. Monga,² Amit Singal,³ and Anand Mehta¹