JCI insight · 2026
Spatial N-Glycan Imaging and Machine Learning Classify Hepatocellular Carcinoma and Predict Glutamine Synthetase Status.
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Bayram Muhammed F, Macdonald Jade K, DelaCourt Andrew, Angel Peggi M, Drake Richard R, Singhi Aatur, Geller David, Monga Satdarshan P, Singal Amit, Mehta Anand
Аннотация
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.
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.