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Multi-omic analysis of gallbladder cancer identifies distinct tumor microenvironments associated with disease progression

Nature Genetics volume 57, pages 1935–1949 (2025)Cite this article

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Abstract

Gallbladder carcinoma (GBC) is the most aggressive biliary tract cancer and is associated with a high mortality rate. Treatment of GBC faces therapeutic challenges owing to the elusive nature of in situ drivers within the local tumor microenvironment that drive its progression. Here, we created a single-cell atlas of 1,117,245 cells and a mutational landscape from 102 patients, which unveiled spatial–temporal characterizations of cellular constitutions, spatial interplays and molecular functions, and generalized five local ecosystems stratifying clinical outcomes. An integrated epithelial program, AI-EPI, combined with spatial transcriptome analysis, revealed the concurrent localization of a highly malignant tumor subtype (GM16) and AREG+ T cell, B cell, dendritic cell and macrophage subtypes within the pro-metastatic niche of primary adenocarcinomas. In vitro and in vivo experiments suggest that in addition to promoting metastasis, AREG facilitates CXCL5 expression in tumor cells through EGFR–pERK–EGR1 signaling, leading to increased neutrophil infiltration and impeding the effectiveness of immunotherapy. Our study provides a spatial–temporal landscape of the GBC microenvironment and sheds light on potential strategies for preventing immunotherapy resistance.

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Fig. 1: A single-cell atlas of GBC.
Fig. 2: Macrophage-mediated TAN-rich P_Mets.
Fig. 3: DC dynamics during tumorigenesis.
Fig. 4: Heterogeneity and temporal property of lymphocyte.
Fig. 5: Stratification of gallbladder adenocarcinomas.
Fig. 6: Spatial proximity of tumor cells and TIME subtypes.
Fig. 7: AREG–CXCL5 recruits neutrophils and resists therapy.

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Data availability

The raw sequencing data have been deposited in two publicly accessible repositories: the CNGB Sequence Archive (CNSA)69 of China National GeneBank DataBase (CNGBdb) under accession number CNP0004660, accessible at https://db.cngb.org/cnsa, and the Genome Sequence Archive (GSA)70 in National Genomics Data Center71, China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences under accession number HRA009841, accessible at https://ngdc.cncb.ac.cn/gsa-human. All processed data can be accessed on Zenodo (https://zenodo.org/records/15400138)72. The bulk RNA-seq raw sequencing data have been deposited in GSA under accession number HRA011357. The reference genome used in this study is GRCh38 (https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000001405.26). The published datasets from nine external cohorts are available as follows: Cohort 1 data are in the original paper’s Supplementary Table 7 (ref. 24); Cohort 2 data are available in the database of Genotypes and Phenotypes (dbGaP) under accession number phs001404.v1.p1 (ref. 27); Cohort 3 data are on cBioPortal under accession number gbc_mskcc_2022 (ref. 28). Cohorts 4–8 data are deposited in the European Genome–Phenome Archive (EGA) under accession numbers EGAD00001004853, EGAD00001004854 and EGAD00001004855 (ref. 20); Gene Expression Omnibus (GEO) under accession numbers GSE201425 (ref. 64) and GSE126030 (ref. 65); GSA under BioProject ID PRJCA007744 (ref. 16); and an online data browser (http://cancer-pku.cn:3838/PanC_T)42. Cohort 9 data are available from the original study’s corresponding author upon request66. Source data are provided with this paper.

Code availability

The software and analysis code for reproducing the results are available on GitHub (https://github.com/JulieBaker1/AIEPI and https://github.com/JulieBaker1/GBC_analysis)73,74.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (no. 82588201 to H.W., no. 82425038 to L.C., no. U21A20376 to L.C., no. 82273277 to X.Q., no. 82421005 to L.C., no. 82372872 to S.Y. and no. 62133006 to J.G.), National Key R&D Program of China (no. 2022YFC3400903 to L.C. and no. 2023YFC2507500 to L.C.), the National Science Foundation of Shanghai (no. 22140901000 to L.C.) and Shanghai Municipal Science and Technology Major Project.

Author information

Author notes

  1. These authors contributed equally: Tao Zhou, Yanhong Wu, Shuai Li, Xinyao Qiu, Erdong Liu, Zhihua Xie.

  2. These authors jointly supervised this work: Xiaoqing Jiang, Jing Fu, Hongyang Wang, Jin Gu, Lei Chen.

Authors and Affiliations

  1. The International Cooperation Laboratory on Signal Transduction, National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China

    Tao Zhou, Xinyao Qiu, Zhihua Xie, Xuebing Shi, Guosheng Ma, Ji Hu, Siyun Shen, Jing Fu, Hongyang Wang & Lei Chen

  2. MOE Key Laboratory of Bioinformatics, BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, Beijing, China

    Yanhong Wu, Wenbo Guo & Jin Gu

  3. Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, China

    Shuai Li, Erdong Liu, Yani Zhang, Kaiting Wang & Hongyang Wang

  4. Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China

    Xinyao Qiu, Shuai Yang, Hongyang Wang & Lei Chen

  5. Department I of Biliary Tract, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China

    Zhihua Xie, Xuebing Shi & Xiaoqing Jiang

  6. Department II of Biliary Tract, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China

    Xiang Wang

  7. Department of Hepatobiliary Medicine, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China

    Xiaomeng Yao

  8. Key Laboratory of Signaling Regulation and Targeting Therapy of Liver Cancer, Ministry of Education, Shanghai, China

    Jing Fu

  9. Shanghai Key Laboratory of Hepatobiliary Tumor Biology (EHBH), Shanghai, China

    Jing Fu & Hongyang Wang

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  1. Tao Zhou
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Contributions

T.Z., Y.W., S.L., G.M. and W.G. analyzed sequencing data. T.Z., S.L., X.Q., E.L., X.Y., Y.Z., J.H., S.S. and S.Y. conducted experiments. Z.X., X.S. and X.W. collected clinical samples for sequencing and organized clinical information. K.W. collected clinical samples for experiments. T.Z., Y.W. and S.L. organized the figures. T.Z. wrote the manuscript. L.C., J.G., H.W., J.F. and X.J. designed the research, supervised the study, guided the discussion and revised the manuscript.

Corresponding authors

Correspondence to Xiaoqing Jiang, Jing Fu, Hongyang Wang, Jin Gu or Lei Chen.

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Extended data

Extended Data Fig. 1 Cell type definition and patient cohort information, related to Fig. 1.

a, UMAP plots showing the expression of classic marker genes of cell types, including B cells (CD79A+), dendritic cells (CLEC10A+), endothelial cells (VWF+), epithelial cells (EPCAM+), mesenchymal cells (PDGFRB+), mast cells (CPA3+), monocytes & macrophages (CD14+, FCGR3A+, and CD68+), neutrophils (FCGR3B+ and CSF3R+), NK & T cells (CD3D+ and NKG7+), plasma cells (MZB1+), and neuroendocrine tumor cells (NET cells) (INSM1+). b, Stacked barplots showing the distribution of non-tumor cell types in each sample, with detailed clinical attributes. c, Pie charts depicting the composition of different gallbladder diseases in our cohort. d, Pie charts depicting the composition of different sites for Adeno, Adeno Squa, and Neuro respectively. e-g, Pie charts depicting the composition of risk factors for GBC, including age (e), polyp & gallstone (f), and sex (g). F, female; M, male. XGC, xanthogranulomatous cholecystitis; CC, chronic cholecystitis; LG, low-grade neoplasia; HG, high-grade neoplasia; Adeno, adenocarcinomas; Adeno Squa, adenosquamous carcinomas; Neuro, neuroendocrine neoplasms; Undiff, undifferentiated carcinomas; Squa, squamous carcinoma; GB, gallbladder; Pri, primary tumors; PO, polyps; LI, liver invasive lesions; LN, lymph node metastases; LM, liver metastases; OM, omentum metastases.

Extended Data Fig. 2 Analysis of cell types and definition of subtypes, related to Fig. 1.

a, Kaplan-Meier plots illustrating the overall survival (OS) of patients grouped by cell percentages across cell types. The log-rank test was used. b, Ro/e indicating tissue site preference of cell types in gallbladder adenocarcinoma patients. c, Boxplots showing tissue site preference of B, dendritic, mast, and mesenchymal cells. n for Pri, PO, LN, LI, LM, and OM groups are 75, 1, 11, 10, 5, and 2. The Benjamini-Hochberg-adjusted two-sided Wilcoxon test was used. d, Boxplots showing sex preference of plasma and dendritic cells. n for male and female groups are 24 and 51. The two-sided Wilcoxon test was used. e-i, UMAP plots showing the expression of canonical marker genes of cell subtypes, including CD8+ T cell subtypes (e), CD4+ T cell subtypes (f), DC subtypes (g), endothelial cell subtypes (h), mesenchymal cell subtypes (i). Different histological classifications (XGC, CC, LG, etc.) combined with different tissue sites (Pri, PO, LI, etc.) form more refined groups. For example, Adeno_Pri represents primary tumors of adenocarcinoma patients. For boxplots in c and d, the center line represents the median, box edges show quartiles, whiskers extend 1.5 times the interquartile range, and points beyond are individual outliers. n represents biologically independent samples.

Extended Data Fig. 3 Epithelial classification and genomic landscape of GBC, related to Fig. 1.

a, The gene expression UMAP plots showing group identifications in epithelial cells, colored by histological types, sites, as well as gallstone and polyp information. b, The copy number variation (cnv) UMAP plots showing epithelial cnv_leiden, cnv_score, and cnv_status. The cnv_status was also labeled in the gene expression UMAP plot. c, Significantly mutated genes in GBC. Detailed clinical features were annotated. The top barplot shows the somatic mutation number. The right barplot shows the mutation frequency. The left bar plot shows the significance: -Log10 (P) values are shown in light gray, and -Log (q) values are shown in dark gray. The red vertical line represents a P value of 0.05. The permutation test was used. d-e, Boxplots showing the enrichment of TMB across TNM stages (d) and tissue sites (e). According to the TNM stages, n are sequentially 3, 8, 6, 16, 18, and 35. n for Pri, PO, LN, LI, LM, and OM groups are 86, 2, 12, 11, 6, and 2. The one-way ANOVA test was used. TMB, tumor mutational burden. f, Comparison of mutation frequencies among 22 genes that occur in more than 5% of samples in Cohort 3. For boxplots in d and e, the center line represents the median, box edges show quartiles, whiskers extend 1.5 times the interquartile range, and points beyond are individual outliers. n represents biologically independent samples.

Extended Data Fig. 4 Paired comparisons between primary tumors and metastases, related to Fig. 1.

a, CNV landscape of paired samples in GBC patients. b, Boxplot of the Jaccard similarity coefficient of paired samples (n = 20). The center line represents the median, box edges show quartiles, whiskers extend 1.5 times the interquartile range, and points beyond are individual outliers. c, Venn diagram of the shared mutations among multi-lesions of a patient. d, Clone-level and copy-number-level phylogenies inferred for the 4 patients. n represents biologically independent samples.

Extended Data Fig. 5 Macrophage changes during disease progression, related to Fig. 2.

a, The expression of differentially expressed genes (DEGs) across macrophage subtypes. b, The scores of functional signatures of tumor-associated macrophage (TAM) subsets across macrophage subtypes. c, Heatmap showing distinct disease prevalence estimated by Ro/e. The top barplot shows cell composition, and the right barplot shows sample composition across distinct diseases. d-f, Boxplots showing the proportions of macrophage subtypes between Non-tumor (containing CC_GB, XGC_GB, LG_GB, and HG_GB, n = 16) and Adeno_Pri (n = 75) (d), paired NAT (normal tissues adjacent to the tumors, n = 21) and Adeno_Pri (n = 21) (e), and P (n = 20) and P_Mets (n = 55) (f). g, Differentially enriched pathways of M_C1_S100A8 between Non-tumor and Adeno_Pri. The Benjamini-Hochberg-adjusted one-sided hypergeometric test was used. h, Heatmap showing distinct tissue site prevalence estimated by Ro/e across macrophage subtypes. i, Pearson correlation between the percentages of M_C1_S100A8 in Adeno_Pri and Adeno_LN, with 95% confidence interval shown. The two-sided t-test was used. j, Volcano plots showing DEGs in P_Mets versus P across macrophage subtypes. Up-regulated chemokines were displayed. The Benjamini-Hochberg-adjusted two-sided Wilcoxon test was used. For d, e, and f, the two-sided Wilcoxon test was used. For boxplots in d, e, and f, the center line represents the median, box edges show quartiles, whiskers extend 1.5 times the interquartile range, and points beyond are individual outliers. n represents biologically independent samples.

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Extended Data Fig. 6 Characterization of Lymphoid subtypes in Adeno_Pri, related to Fig. 4.

a-d, Bubble plots showing the expression of DEGs across subtypes for each cell type, including CD8+ T cells (a), CD4+ T cells (b), NK cells (c), and B cells (d). Heatmaps show the prevalence of distinct diseases estimated by Ro/e. The right barplots show sample composition. The bottom barplots show cellular composition. e, Boxplots compare the proportions of lymphoid subtypes between Non-tumor (n = 16) and Adeno_Pri (n = 75). The center line represents the median, box edges show quartiles, whiskers extend 1.5 times the interquartile range, and points beyond are individual outliers. f, The scores of functional signatures and the expression of immune checkpoint genes across lymphoid subtypes. g, Kaplan-Meier plots illustrating OS of patients grouped by the percentages of lymphoid subtypes. The log-rank test was used. h, The comparison of the score of the immune checkpoint gene set in CD4T_C1_FOXP3_TREG between Non-tumor and Adeno_Pri. The inner boxplot displays the median (center dot), the interquartile range (box limits), and whiskers that extend to 1.5 times the interquartile range from the quartiles. i, DEGs of NK_C3_CAPG versus other NK cells. For e, h, and i, the two-sided Wilcoxon test was used. n represents biologically independent samples.

Extended Data Fig. 7 Characterization of Lymphoid subtypes in P_Mets, related to Fig. 4.

a, The proportions of lymphoid subtypes between P (n = 20) and P_Mets (n = 55). The center line represents the median, box edges show quartiles, whiskers extend 1.5 times the interquartile range, and points beyond are individual outliers. b, Scores of functional gene sets in CD4T_C2_CCR7_TN between P (n = 20) and P_Mets (n = 55). c, DEGs of CD4T_C2_CCR7_TN between P and P_Mets. d, AREG expression in CD4T_C2_CCR7_TN in Adeno_Pri. e, The dynamic change of AREG expression in CD4T_C2_CCR7_TN across TNM stages. f, Scatter plots illustrating the proportions of subtypes per patient. Lines represent the results of linear regression fitting (two-sided t-test). g, Pearson correlation across the percentages of CD4+ T cell subtypes. h, The scores of different functional gene sets in CD4T_C2_CCR7_TN across TNM stages. The right line plots show the dynamic changes in functions of CD4T_C2_CCR7_TN along with TNM stages. i, DEGs of CD4T_C2_CCR7_TN between Adeno_Pri and Adeno_LN. j, DEGs and enriched pathways of naïve CD4+ T cells between Normal_LN and Adeno_LN. k, AREG expression of naïve CD4+ T cells in another GBC cohort. l, The UMAP of CD4+ T cell subclusters in an external HCC cohort. Sample information of the TN cluster is denoted in the lower right box. m, AREG expression of CD4+ T cell subtypes in a pan-cancer dataset. The bottom panel shows the expression of AREG and classical naïve T cell markers. n, Differentially enriched pathways of CD4T_C2_CCR7_TN among LG, HG, XGC, and Adeno_Pri. For a and b, the two-sided Wilcoxon test was used. For c and i, the Benjamini-Hochberg-adjusted two-sided Wilcoxon test was used. For j and n, the Benjamini-Hochberg-adjusted one-sided hypergeometric test was used. For violin plots in b and m, the inner boxplot displays the median (center line or dot), the interquartile range (box limits), and whiskers that extend to 1.5 times the interquartile range from the quartiles. n represents biologically independent samples.

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Extended Data Fig. 8 The pro-angiogenic role of stromal cell subtypes, related to Fig. 5.

a, DEG expression across mesenchymal subtypes. b, Heatmap showing distinct disease prevalence estimated by Ro/e. The top barplot shows cell composition and the right barplot shows sample composition across distinct diseases. c-e, Boxplots compare the proportions of mesenchymal subtypes between Non-tumor (n = 16) and Adeno_Pri (n = 75) (c), paired NAT (n = 21) and Adeno_Pri (n = 21) (d), and P (n = 20) and P_Mets (n = 55) (e). f, Boxplots showing tissue site preference of F_C7_COL4A2. g-h, The DEGs (g) and enriched pathways (h) of F_C1_CFD_iCAF between P and P_Mets. i, VEGFB expression across mesenchymal subtypes. j, Kaplan-Meier plots illustrating OS of patients grouped by the percentage of F_C1_CFD_iCAF. k-l, The DEGs (k) and enriched pathways (l) of endothelial cell subtypes. m, Heatmap showing distinct disease prevalence estimated by Ro/e. The top barplot shows cell composition and the right barplot shows sample composition across distinct diseases. n-p, Boxplots compare the proportions of endothelial cell subtypes between Non-tumor (n = 16) and Adeno_Pri (n = 75) (n), paired NAT (n = 21) and Adeno_Pri (n = 21) (o), and P (n = 20) and P_Mets (n = 55) (p). q, Pearson correlation between the percentages of EC_C2_CXCR4_Tip and EC_C0_ACKR1_Vein (left panel) or EC_C3_GJA5_Artery (right panel), with 95% confidence interval shown. The two-sided t-test was used. r, FLT1 expression across classic endothelial cell subtypes. s, Kaplan-Meier plots illustrating OS of patients grouped by the percentage of EC_C3_GJA5_Artery. For c, d, e, n, o, and p, the two-sided Wilcoxon test was used. For f and g, the Benjamini-Hochberg-adjusted two-sided Wilcoxon test was used. For h and l, the Benjamini-Hochberg-adjusted one-sided hypergeometric test was used. For j and s, the log-rank test was used. For boxplots in c, d, e, n, o, and p, the center line represents the median, box edges show quartiles, whiskers extend 1.5 times the interquartile range, and points beyond are individual outliers. n represents biologically independent samples.

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Extended Data Fig. 9 Stratification of adenocarcinoma microenvironments, related to Fig. 5.

a, The dotplot showing the significance of enrichment of cell subtypes in each somatic mutation group (The number of samples in each group needs to be greater than 3). The size of the dots represents the P value, with gray dots indicating non-significance, red dots indicating a significant increase in cell proportion, and green dots indicating a significant decrease. The two-sided Wilcoxon test was used. b, The dotplot showing the significance of enrichment of cell subtypes in each MI group. The size of the dots represents the P value, with gray dots indicating non-significance, red dots indicating a significant increase in cell proportion, and green dots indicating a significant decrease. Colors indicate different types of MI. The two-sided Wilcoxon test was used. c, PCA visualization of samples. Colors represent different MI types. d, The heatmap showing the accuracy between actual and predicted results. e, Somatic mutations across 5 MIs. The right values show the mutation frequency.

Extended Data Fig. 10 AREG-CXCL5 recruits neutrophils and resists therapy, related to Fig. 7.

a, Relative proliferative rates of GBC-SD cells under the treatment with AREG or not as detected by CCK8 assay. b, Representative images of migration and invasion assays under different concentrations of AREG treatment using GBC-SD cells. Scale bar: 100 μm. c, mRNA and protein expression of CXCL5 as detected by qPCR and ELISA respectively under different conditions as denoted. Both assays were conducted on SGC996 cells. For qPCR, values were normalized against actin expression. d, mRNA and protein expression of CXCL5 as detected by qPCR and ELISA respectively under different conditions as denoted. Both assays were conducted on organoids. For qPCR, values were normalized against actin expression. The two-sided paired t-test was used. e, Protein expression of EGR1, pERK1/2, and total ERK1/2 as detected by western blotting under the conditions as denoted using SGC996 cells. Internal control was β-Tubulin. f, Protein expression of EGR1, pERK1/2, and total ERK1/2 as detected by western blotting under the conditions as denoted using organoids. Internal control was β-Tubulin. g, The ratio of liver weight to body weight for each mouse across the four groups. h, Neutrophil infiltration as detected by IHC with CD66b staining in tumor sections across groups. CI, control SGC996 cells and IgG; CP, control SGC996 cells and anti-PD-L1; AI, SGC996 cells treated by AREG and IgG; AP, SGC996 cells treated by AREG and anti-PD-L1. Scale bar: 50 μm. For a, b, c, and g, the two-sided unpaired t-test was used. Data are presented as mean values ± SD. n represents biologically independent samples and all experiments were performed at least three times, unless specified otherwise.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–5, Supplementary Notes 1–11, Supplementary Discussion and Supplementary References.

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Supplementary Tables 1–28.

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Zhou, T., Wu, Y., Li, S. et al. Multi-omic analysis of gallbladder cancer identifies distinct tumor microenvironments associated with disease progression. Nat Genet 57, 1935–1949 (2025). https://doi.org/10.1038/s41588-025-02236-9

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