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Glycogen drives tumour initiation and progression in lung adenocarcinoma

Nature Metabolism volume 7, pages 952–965 (2025)Cite this article

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Abstract

Lung adenocarcinoma (LUAD) is an aggressive cancer defined by oncogenic drivers and metabolic reprogramming. Here we leverage next-generation spatial screens to identify glycogen as a critical and previously underexplored oncogenic metabolite. High-throughput spatial analysis of human LUAD samples revealed that glycogen accumulation correlates with increased tumour grade and poor survival. Furthermore, we assessed the effect of increasing glycogen levels on LUAD via dietary intervention or via a genetic model. Approaches that increased glycogen levels provided compelling evidence that elevated glycogen substantially accelerates tumour progression, driving the formation of higher-grade tumours, while the genetic ablation of glycogen synthase effectively suppressed tumour growth. To further establish the connection between glycogen and cellular metabolism, we developed a multiplexed spatial technique to simultaneously assess glycogen and cellular metabolites, uncovering a direct relationship between glycogen levels and elevated central carbon metabolites essential for tumour growth. Our findings support the conclusion that glycogen accumulation drives LUAD cancer progression and provide a framework for integrating spatial metabolomics with translational models to uncover metabolic drivers of cancer.

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Fig. 1: Intratumoural glycogen uniquely accumulates in LUAD.
Fig. 2: Higher glycogen drives accelerated tumourigenesis.
Fig. 3: Glycogen synthesis is required for tumourigenesis.
Fig. 4: High glycogen supports metabolite pools in KP tumours.
Fig. 5: Glycogen contribution to cancer metabolism traced with 13C-glucose.

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Data availability

All MALDI files in imzML format generated in this study are available at https://sunlabresources.rc.ufl.edu. Proteomics datasets are available on ProteomeXchange at https://doi.org/10.25345/C5N01058W and accession number PXD060090. Source data are provided with this paper.

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Acknowledgements

This study was supported by National Institute of Health (NIH) grants R01AG066653, R01CA266004, R01AG078702, R01CA288696, V-Scholar Grant, and RM1NS133593 to R.C.S., R35NS116824 to M.S.G., R35GM142701 to L.C., T32HL134621 to H.A.C., and R01CA237643 and P20GM121327-03 to C.F.B. This research was also supported by the Biospecimen Procurement and Translational Pathology Shared Resource Facility of the University of Kentucky Markey Cancer Center (P30CA177558) to D.B.A. and supported in part by an NIH award, S10 OD030293, for MRI/S instrumentation. Z.L. is supported by the MBI Gator NeuroScholar Program. Large language models, for example, ChatGPT, were used to make minor grammatical improvements in the text. We thank N. R. Gough (BioSerendipity, LLC) for critical discussions and editorial assistance.

Author information

Author notes

  1. These authors contributed equally: Harrison A. Clarke, Tara R. Hawkinson.

  2. These authors jointly supervised this work.

Authors and Affiliations

  1. Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, FL, USA

    Harrison A. Clarke, Tara R. Hawkinson, Cameron J. Shedlock, Terrymar Medina, Roberto A. Ribas, Lei Wu, Zizhen Liu, Xin Ma, Anna Rushin, Matthew E. Merritt, Annette Mestas, Pankaj K. Singh, Craig W. Vander Kooi, Matthew S. Gentry & Ramon C. Sun

  2. Center for Advanced Spatial Biomolecule Research, University of Florida, Gainesville, FL, USA

    Harrison A. Clarke, Tara R. Hawkinson, Cameron J. Shedlock, Terrymar Medina, Roberto A. Ribas, Lei Wu, Zizhen Liu, Pankaj K. Singh, Craig W. Vander Kooi, Matthew S. Gentry & Ramon C. Sun

  3. Evelyn F. and William L. McKnight Brain Institute, University of Florida, Gainesville, FL, USA

    Zizhen Liu & Ramon C. Sun

  4. Department of Biostatistics College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA

    Xin Ma, Yi Xia & Li Chen

  5. Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA

    Yu Huang, Xing He & Jiang Bian

  6. Regenstrief Institute, Indianapolis, IN, USA

    Yu Huang, Xing He & Jiang Bian

  7. Department of Biostatistics and Health Data Science, School of Medicine, Indianapolis, IN, USA

    Yu Huang, Xing He & Jiang Bian

  8. Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, USA

    Josephine E. Chang, Jelena A. Juras, Michael D. Buoncristiani, Alexis N. James & Jessica F. Lamb

  9. Markey Cancer Center, University of Kentucky, Lexington, KY, USA

    Lyndsay E. A. Young, B. Mark Evers, Christine F. Brainson & Derek B. Allison

  10. Department of Molecular and Cellular Biochemistry, College of Medicine, University of Kentucky, Lexington, KY, USA

    Lyndsay E. A. Young, Elena C. Manauis & Grant L. Austin

  11. Department of Toxicology and Cancer Biology, College of Medicine, University of Kentucky, Lexington, KY, USA

    Christine F. Brainson

  12. Department of Pathology and Laboratory Medicine, College of Medicine, University of Kentucky, Lexington, KY, USA

    Derek B. Allison

Authors
  1. Harrison A. Clarke
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  2. Tara R. Hawkinson
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  3. Cameron J. Shedlock
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  30. Matthew S. Gentry
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  31. Ramon C. Sun
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Contributions

Conceptualization, R.C.S.; methodology, R.C.S.; investigation, H.A.C., T.R.H., C.J.S., T.M., R.A.R., L.W., J.E.C., L.E.A.Y., J.A.J., M.D.B., A.N.J., A.M., J.F.L., E.C.M., X.M., L.C., Z.L., Y.X., Y.H., X.H., A.R., M.E.M., P.K.S., J.B., G.L.A., B.M.E., C.W.V.K., C.F.B., D.B.A., M.S.G. and R.C.S.; writing—original draft, R.C.S.; writing—review and editing, R.C.S., H.A.C., C.J.S., T.R.H., C.F.B., L.E.A.Y., C.W.V.K., M.S.G. and D.B.A.; funding acquisition, M.S.G. and R.C.S.; resources, M.S.G. and R.C.S; supervision, M.S.G. and R.C.S.

Corresponding authors

Correspondence to Matthew S. Gentry or Ramon C. Sun.

Ethics declarations

Competing interests

R.C.S. has received research support and consultancy fees from Maze Therapeutics. R.C.S. is a member of the Medical Advisory Board for Little Warrior Foundation. M.S.G. has research support and research compounds from Maze Therapeutics, Valerion Therapeutics and Ionis Pharmaceuticals. M.S.G. also received consultancy fee from Maze Therapeutics, PTC Therapeutics and the Glut1-Deficiency Syndrome Foundation. D.B.A. receives book royalty from Wolters Kluwer. The other authors declare no competing interests.

Peer review

Peer review information

Nature Metabolism thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Alfredo Giménez-Cassina, in collaboration with the Nature Metabolism team.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Validation of MALDI glycogen imaging with anti-glycogen antibody staining in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) tissues.

a, Representative images of MALDI glycogen imaging (left) and corresponding anti-glycogen antibody staining (right) in two LUAD and one LUSC patient tissue samples. b, Detailed view of various tumor regions stained with anti-glycogen antibody showing predominately tumors positive for glycogen similar to MALDI analysis. Labels indicate tumor (T), stroma (S), normal adjacent tissue (N), vessels (V), necrosis (Ne), smooth muscle (SM), cartilage (C), and chondrocytes (Cc). c, Tissue overlays combining anti-glycogen antibody staining and MALDI glycogen imaging performed in SCILs software. The upper panels show the antibody staining with outlined regions for zoomed-in views, and the lower panels display the corresponding MALDI imaging overlays. c and c represent three biological replicates (individual patients). Black boxes indicate areas magnified in the zoomed-in images to illustrate the concordance between glycogen imaging and antibody staining.

Extended Data Fig. 2 MALDI glycogen imaging workflow and profiling of human tissue microarray (TMA) and mouse GEMM tumors.

a, Schematic of the MALDI glycogen imaging workflow. Tissue sections are subjected to antigen retrieval, followed by treatment with isoamylase and application of the CHCA matrix. The isoamylase enzyme digests glycogen into glucose polymers, which are then detected and visualized using MALDI mass spectrometry. Created with BioRender.com. b, Representative H&E staining (top row) and MALDI glycogen imaging (bottom row) of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) tissue cores from a TMA, image represents single run from three repeats, Scale bar: 2 mm. c, Representative H&E staining (top row) and MALDI glycogen imaging (bottom row) of normal lung tissue and various genetically engineered mouse model (GEMM) tumors. The GEMM tumors include K (Kras), KP (Kras; Pten), KL (Kras; Lkb1), and E (Egfr). Scale bar represents 3 mm. d, Quantification of total glycogen levels in normal lung tissue and GEMM tumors, as measured by MALDI glycogen imaging. Bar graph shows the relative abundance of glycogen in normal, K, KP, KL, and E tissues (n = 6/group; mean +/−s.e.m. p-values indicated; one-way ANOVA and Tukey’s multiple comparison test).

Source data

Extended Data Fig. 3 Determination of absolute glycogen concentration using spotted standards by MALDI imaging.

a, Scanned image showing the location of spotted glycogen standards (0.06, 0.18, 0.55, 1.6, and 5 ng) next to a tissue section on a microscope slide. b, Mass spectra of different glycogen concentrations spotted on the slide, indicating the glucose polymer 7 m/z peaks corresponding to varying amounts of glycogen. c, XY plots showing the relationship between glycogen concentration (ng) and relative intensity per pixel for each spotted standard. d, Log-transformed plots of relative intensity versus glycogen concentration for the standards for different glucose chain length indicated above. The linear regression lines indicate the strong correlation used for glycogen quantification. R² values for each plot are shown, line equations are displayed on top. e, Absolute quantification of glycogen in four human lung adenocarcinoma (LUAD) and one lung squamous cell carcinoma (LUSC) tissue sections (n = 3 ROIs of 500 pixels/tissue; mean +/− s.e.m. p-values indicated; one-way ANOVA adjusted for Tukey’s multiple comparisons).

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Extended Data Fig. 4 Glycogen accumulation correlates with tumor grade and predicts survival outcomes.

a, Representative immunohistochemical staining of glycogen in well differentiated (top) and poorly differentiated (bottom) tumour regions. T, tumour tissue; S, stromal tissue. b, Quantification of normalized glycogen intensity (adjusted for stromal regions) across tumour samples stratified by differentiation status: well, moderate, and poor in 247 LUAD patients (mean +/- s.e.m. p-values indicated). c, Kaplan-Meier survival curves illustrating the relationship between glycogen levels and patient survival. Top: Patients divided by the median glycogen expression level (low vs. high). Bottom: Survival analysis stratified into glycogen low (quartile), mid (50th percentile), and high (quartile) groups. Log-rank test p-value indicates a significant association between high glycogen content and decreased survival (p < 0.0001). d, Predictive performance of a random forest model for glycogen intensity in relation to survival outcomes, shown through the area under the receiver operating characteristic (AUROC = 0.846) and the area under the precision-recall curve (AUPRC = 0.888).

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Extended Data Fig. 5 Impact of different diets on glycogen and glucose metabolism in C57BL/6 J mice.

a, Top: Schematic of the experimental design where C57BL/6 J mice were fed with vehicle, corn oil, high-fructose corn syrup (HFCS), or a combination (Combo) diet for 2 weeks. Created with BioRender.com. Bottom: MALDI glycogen imaging of lung tissues from mice subjected to different dietary treatments. Scale bar: 2 mm. b, Glycogen structure analysis in the lung tissues of WT mice treated with different diets. The graph shows the relative abundance of glycogen chain lengths in mice fed with corn oil, HFCS, or the Combo diet (n = 3 mice; mean +/− s.e.m. p-values indicated; two-way ANOVA adjusted for Tukey’s multiple comparisons). c, Glucose tolerance test results for mice on Day 1 (left) and Day 14 (right) post-treatment. The graphs show changes in blood glucose levels (mg/dL) over time following glucose gavage. No significant differences (ns) were observed among the different diet groups (n = 3 mice; mean +/− s.e.m. p-values indicated; two-way ANOVA adjusted for Tukey’s multiple comparisons). d, Fasting blood glucose levels (mg/dL) in the same cohort of mice. No significant differences (ns) were observed among the different diet groups (n = 3 mice; mean +/− s.e.m. p-values indicated; two-way ANOVA adjusted for Tukey’s multiple comparisons). e, Body weight (g) of mice over the 14-day treatment period, showing no significant differences among the diet groups (n = 3 mice; mean +/− s.e.m. p-values indicated; two-way ANOVA adjusted for Tukey’s multiple comparisons). f, Changes in blood glucose levels (mg/dL) over the 14-day treatment period, showing no differences among the diet groups (n = 3 mice; mean +/− s.e.m. p-values indicated; two-way ANOVA adjusted for Tukey’s multiple comparisons). g, Liver glycogen imaging using CL6 staining in mice fed with vehicle, corn oil, HFCS, or the Combo diet. Scale bar: 2 mm. h, Quantification of total glycogen in liver tissues from mice subjected to different dietary treatments (n = 3 mice; mean +/− s.e.m. p-values indicated; one-way ANOVA adjusted for Tukey’s multiple comparison). i, Glycogen chain length analysis in normal adjacent tissues from KP and KP Combo-treated mice related to Fig. 3. The graph shows the relative intensity of glycogen chain lengths, (n = 6 mice; mean +/− s.e.m. two-way ANOVA for Tukey’s multiple comparison).

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Extended Data Fig. 6 Laforin expression, phosphorylated glycogen analysis, and glycogen dynamics in LKO animals.

a, Representative IHC images of tissue cores and zoomed-in views stained with antibodies against Laforin in LUSC (leftmost), Laforin in LUAD (second from left), GP (glycogen phosphorylase), and GYS (glycogen synthase,), image represents single replicates from three repeats. b, Phosphorylated glycogen chain length analysis in LUAD and LUSC tissue cores from TMA (n = 67 patients for LUAD, 52 for LUSC, mean +/− s.e.m. p-values indicated; two-way ANOVA adjusted for Tukey’s multiple comparisons). c, Glycogen chain length analysis in the lungs of WT and LKO (Laforin knockout) mice over 1-, 2, and 4- months (n = 4 animals/group mean +/−s.e.m. p-values indicated; two-way ANOVA adjusted for Tukey’s multiple comparisons).

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Extended Data Fig. 7 Lung stem cell growth in vitro, H&E and MALDI glycogen imaging of different mouse models.

a, Schematic of isolating bronchoalveolar stem cells and differentiation to bronchiolar and alveolar organoids in matrigel. b, Representative bright field images of bronchiolar and alveolar organoids derived from WT and laforin−/− (LKO) animals. c, Number of colonies and colony size from bronchiolar and alveolar organoids derived from WT and laforin−/− (LKO) animals. Values are presented as mean +/− s.e.m. p-values were calculated using two-tailed t-test. d, Additional representative H&E images and MALDI imaging of an adjacent tissue section showing tumor formation and glycogen levels LSL-KrasG12D/p53fl/fl (KP) and LSL-KrasG12D/p53fl/fl /LKO(KPL) (n = 3 each). e, Additional representative H&E images and MALDI imaging of an adjacent tissue section showing tumor formation and glycogen levels LSL-KrasG12D/p53fl/fl (KP); LSL-KrasG12D/p53fl/fl/Gysfl/fl (KPG); LSL-KrasG12D/p53fl/fl/Gysfl/fl:Vehicle (KPG:V); LSL-KrasG12D/p53fl/fl/Gysfl/fl:Combination diet (KPG/C)animals (n = 2 each). f, Distribution of tumor grades across KP and KPL cohorts of mice (n = 3 mice; mean +/− s.e.m. p-values indicated; one-way ANOVA adjusted for Tukey’s multiple comparisons). g. Kaplan Meier survival analysis for mice in different groups. Log-rank test stated.

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Extended Data Fig. 8 Comparative proteomics, phosphoproteomic, and spatial metabolomics analysis in KP and KPL tumors.

a, left: Principal component analysis (PCA) of 5993 proteins from proteomics data, showing no major changes between KP and KPL tumors. right: Volcano plot of proteomics data, with log2 fold change (log2FC) on the x-axis and -log10 p-value on the y-axis, indicating no significant differential expression between KP and KPL tumors. b, Left: Principal component analysis (PCA) of 5323 proteins from phosphoproteomic data, showing no major changes between KP and KPL tumors. Right: Volcano plot of phosphoproteomic data, with log2 fold change (log2FC) on the x-axis and -log10 p-value on the y-axis, indicating no significant differential phosphorylation between KP and KPL tumors. c, H&E-stained images of adjacent tissue slices used on MALDI analysis from KP (left) and KPL (right) tumors, image represents single analysis from three repeats. d, MALDI imaging of various metabolites in the lungs of KP and KPL GEMM tumors. Metabolites analyzed include glutamate, glycerol-3-phosphate (G3P), aspartate, glutathione (GSH), aconitate, ascorbic acid, uridine monophosphate (UMP), cyclic adenosine monophosphate (cAMP), glutamine, citric acid, adenosine monophosphate (AMP), ADP, malate, succinate, adenosine triphosphate (ATP), pyruvate, glucose/inositol, inosine, lactate, and arachidonic acid.

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Extended Data Fig. 9 Multiplexed spatial metabolomics and glycogen analysis with 13C-glucose tracer enrichment.

a, Schematic of the multiplexed spatial metabolomics and glycogen workflow. Cells grown in chamber are grown in 12C- or 13C-glucose. After enrichment period, cells in chamber wells are imaged for metabolomics with NEDC, followed by antigen retrieval, isoamylase treatment, and CHCA matrix application for glycogen imaging. Created with BioRender.com. b, Layout of tracer enrichment in a chamber well format. Cells are exposed to 12C-glucose or 13C-glucose for 24, 48, and 72 hours in different wells. c, Metabolic pathway of 13C-glucose showing its incorporation into glycolysis and the TCA cycle, and 13C-glycogen formation. Key metabolic intermediates such as pyruvate (P), phosphoenolpyruvate (PEP), citrate (Cit), and oxaloacetate (OAA) are highlighted, along with enzymes pyruvate carboxylase (PC) and pyruvate dehydrogenase (PDH). d, Enrichment of various isotopologues in metabolites of glycolysis, TCA cycle, nucleotides, and glycogen. Fraction labeled graphs for m42 (glycogen), m6 (glucose and G6P), m5 (AMP), m3 (pyruvate, PEP, 3PG, lactate), and m2 (citric acid, GSH, glutamate, glutamine, malate) are shown at 0, 24, 48, and 72 hours. (n = 6697 individual pixels measured by MALDI imaging. e, Representative examples of 13C isotopologues detected through MALDI imaging, with 12C-glucose wells as controls. Metabolites visualized include citric acid, AMP, pyruvate, and glutamate.

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Extended Data Fig. 10 Ion mobility and MALDI imaging of 12C and 13C-labeled glycogen species.

a, Mass spectra showing both 12C and 13C-labeled glycogen-released glucose chain lengths. b, Schematic of ion mobility drift separation for 13C-labeled species. The diagram illustrates how 12C and 13C glycogen species are separated based on their drift times in the ion mobility spectrometer. Created with BioRender.com. c, 2D plot of ion mobility drift time versus m/z (mass-to-charge ratio), with a zoomed-in view highlighting the separation between 12C-GP7 and 13C-GP7 species. The plot shows similar drift times for both 12C and 13C glycogen species, indicating effective alignment. d, Overlay of mass spectra from unlabeled (12C) and 13C-glucose labeled wells (24 hours) for glycogen. The overlay highlights the differences in peak intensities, indicating the incorporation of 13C into the glycogen molecules. e, Detailed overlay of 12C and 13C-labeled glycogen species, showing the enrichment of fully labeled 13C-GP7 at m/z 42. Representative MALDI images are included to show the spatial distribution of glycogen species at 0 (12 C), 24, 48, and 72 hours of 13C labeling.

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Clarke, H.A., Hawkinson, T.R., Shedlock, C.J. et al. Glycogen drives tumour initiation and progression in lung adenocarcinoma. Nat Metab 7, 952–965 (2025). https://doi.org/10.1038/s42255-025-01243-8

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