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Liver tumour immune microenvironment subtypes and neutrophil heterogeneity

Nature volume 612, pages 141–147 (2022)Cite this article

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Abstract

The heterogeneity of the tumour immune microenvironment (TIME), organized by various immune and stromal cells, is a major contributing factor of tumour metastasis, relapse and drug resistance1,2,3, but how different TIME subtypes are connected to the clinical relevance in liver cancer remains unclear. Here we performed single-cell RNA-sequencing (scRNA-seq) analysis of 189 samples collected from 124 patients and 8 mice with liver cancer. With more than 1 million cells analysed, we stratified patients into five TIME subtypes, including immune activation, immune suppression mediated by myeloid or stromal cells, immune exclusion and immune residence phenotypes. Different TIME subtypes were spatially organized and associated with chemokine networks and genomic features. Notably, tumour-associated neutrophil (TAN) populations enriched in the myeloid-cell-enriched subtype were associated with an unfavourable prognosis. Through in vitro induction of TANs and ex vivo analyses of patient TANs, we showed that CCL4+ TANs can recruit macrophages and that PD-L1+ TANs can suppress T cell cytotoxicity. Furthermore, scRNA-seq analysis of mouse neutrophil subsets revealed that they are largely conserved with those of humans. In vivo neutrophil depletion in mouse models attenuated tumour progression, confirming the pro-tumour phenotypes of TANs. With this detailed cellular heterogeneity landscape of liver cancer, our study illustrates diverse TIME subtypes, highlights immunosuppressive functions of TANs and sheds light on potential immunotherapies targeting TANs.

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Fig. 1: The single-cell landscape of 124 patients with liver cancer.
Fig. 2: Five TIME subtypes of PLC.
Fig. 3: Neutrophil heterogeneity and functional validation in humans.
Fig. 4: Neutrophil heterogeneity and depletion in mouse models.

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Data availability

Raw sequencing data reported in this paper have been deposited at the Genome Sequence Archive at the National Genomics Data Center (Beijing, China) under the BioProject ID PRJCA007744. The data deposited and made public are compliant with the regulations of the Ministry of Science and Technology of China. To facilitate the use of our data by the wider research community, we developed an interactive web-based tool (http://meta-cancer.cn:3838/scPLC) for analysing and visualizing our single-cell data. Other public data used in this study include reference genomes for human (https://asia.ensembl.org/, GRCh38.p13) and mouse (https://asia.ensembl.org/, GRCm39) and TCGA datasets (https://portal.gdc.cancer.gov/). Source data are provided with this paper.

Code availability

Codes used in this study are available at GitHub (https://github.com/meta-cancer/scPLC).

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Acknowledgements

We thank Y. Guo, C. Shan and J. Ren from National Center for Protein Sciences at Peking University for FACS and CODEX assistance. This work is jointly supported by National Natural Science Foundation of China (81988101, 82173035, 82030079, 81972656, 81802813, 81902401, 81972735 and 81872508), the National Science and Technology Major Project of China (2018ZX10723204), Beijing Natural Science Foundation (7212108), Changping Laboratory, the Michigan Medicine and PKU-HSC JI for Translational and Clinical Research (BMU2020JI005) and Sino-Russian Math Center in PKU.

Author information

Author notes

  1. These authors contributed equally: Ruidong Xue, Qiming Zhang, Qi Cao, Ruirui Kong, Xiao Xiang

  2. These authors jointly supervised this work: Jiye Zhu, Zemin Zhang, Ning Zhang

Authors and Affiliations

  1. Translational Cancer Research Center, Peking University First Hospital, Beijing, China

    Ruidong Xue, Qi Cao, Ruirui Kong, Hengkang Liu, Mei Feng, Fangyanni Wang, Jinghui Cheng & Ning Zhang

  2. BIOPIC, Beijing Advanced Innovation Center for Genomics, School of Life Sciences, Peking University, Beijing, China

    Qiming Zhang & Zemin Zhang

  3. Beijing Key Surgical Basic Research Laboratory of Liver Cirrhosis and Liver Cancer, Department of Hepatobiliary Surgery, Peking University People’s Hospital, Beijing, China

    Xiao Xiang, Zhao Li & Jiye Zhu

  4. International Cancer Institute, Peking University Health Science Center, Beijing, China

    Qimin Zhan, Mi Deng & Ning Zhang

  5. Changping Laboratory, Beijing, China

    Zemin Zhang

  6. Yunnan Baiyao Group, Kunming, China

    Ning Zhang

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Contributions

R.X., Z.Z. and N.Z. conceived and designed the project. R.X., X.X., Z.L. and J.Z. collected the human samples and clinical information. X.X., Z.L. and J.Z. performed pathological examination. R.X. and X.X. performed the scRNA-seq experiments. Q.C., Q. Zhang and R.X. performed bioinformatic analyses. Q. Zhang, R.X. and Q.C. performed IHC, mIHC and CODEX experiments. R.K., R.X., M.F. and F.W. performed functional experiments of neutrophils. R.K. and R.X. constructed the mouse models and analysed the in vivo data. R.X., Q. Zhang, Q.C., R.K., X.X., H.L., Q. Zhan, M.D., J.Z., Z.Z. and N.Z. discussed and interpreted the data. Q.C., R.X. and J.C. built the online website. R.X., Q. Zhang, Q.C. and R.K. wrote the manuscript with help from Z.Z. and N.Z.; Z.Z., J.Z. and N.Z. supervised the project.

Corresponding authors

Correspondence to Jiye Zhu, Zemin Zhang or Ning Zhang.

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Competing interests

Z.Z. is a founder of Analytical BioSciences and is a consultant for InnoCare Pharma and ArsenalBio. N.Z. is the CSO of Yunnan Baiyao Group. The other authors declare no competing interests.

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Nature thanks Andres Hidalgo, Alexander Swarbrick and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Patient cohort and cluster information.

a, Pie charts showing the composition of cancer types in our cohort. HCC, hepatocellular carcinoma; ICC, intrahepatic cholangiocarcinoma; CHC, combined hepatocellular and cholangiocarcinoma; HH, hepatic hemangioma; ASC, adenosquamous carcinoma; SAR, sarcomatoid carcinoma; SLC, secondary liver cancer. CRC_M, liver metastasis from colorectal cancer, PAN_M, liver metastasis from pancreatic cancer, LYM_M, liver metastasis from lymphoma, GAS_M, liver metastasis from gastric cancer, BRC_M, liver metastasis from breast cancer. b, UMAP plots showing the distribution of patients, cancer types, viruses and liver cirrhosis states. Dots represent individual cells. PB, peripheral blood; AL, adjacent liver; HBV, hepatitis B virus, HCV, hepatitis C virus, NBNC, double negative of HBV and HCV. c, UMAP plots showing expression of canonical marker genes of major cell populations including T cells (CD3D, CD8A, FOXP3), NK cells (NKG7), B cells (CD79A), macrophages (CD68), neutrophils (CSF3R), dendritic cells (CLEC10A), mast cells (TPSAB1), fibroblasts (COL1A1), endothelial cells (VWF), and epithelial cells (EPCAM). d, Stacked barplot showing the distribution of major cell types in each sample. e, UMAP plots showing the distribution of cell identities for tumour cells and TIME cells. Tumour cells were further coloured by patient, cancer type, virus, and cirrhosis. f, CNV profiles inferred from scRNA-seq data for each cell and from matched bulk exome data in the sample A014_HCC. g, Boxplots showing hepatic scores and biliary epithelial scores in tumour (n = 193,877 cells) and TIME cells (n = 898,295 cells). Cells are from 124 patients. h, Boxplots showing hepatic scores and biliary epithelial scores in tumour cells of different PLC subtypes (HCC, n = 96,211 cells from 79 cases, ICC, n = 52,345 cells from 25 cases, CHC, n = 15,493 cells from 7 cases). Cells are from 111 patients. i, Pie charts showing the patient number (top) and cell number (bottom) of our study and published single cell studies for PLC. Colours represent different studies. j, Stacked barplot showing proportions of major cell populations among different studies. Colours represent major cell populations. In g-h, n denotes individual cells. Two-sided Wilcoxon rank-sum test is used. For boxplots, centre line shows median, box limits indicate upper and lower quartiles, and whiskers extend 1.5 times the interquartile range, while data beyond the end of the whiskers are outlying points that are plotted individually. ***, P < 0.001.

Extended Data Fig. 2 Gene expression and tissue preference of 89 TIME cell clusters.

a, UMAP plots showing the expression of canonical marker genes for clusters in each major cell population. Normalized expression level was abbreviated as Exp. b, Heatmap showing tissue preferences of clusters in each major cell population revealed by Ro/e. c, Boxplots showing proportions of several tumour-enriched TIME clusters divided by PLC subtypes. *, P < 0.05; **, P < 0.01; ***, P < 0.001. (HCC, n = 79 cases, ICC, n = 25 cases, CHC, n = 7 cases). d, Boxplots showing proportions of several cell clusters associated with virus or cirrhosis. (HBV, n = 57 cases, HCV, n = 6 cases, NBNC, n = 50 cases; cirrhosis, n = 46 cases, non-cirrhosis, n = 67 cases). In c-d, n denotes biologically independent samples. Two-sided Wilcoxon rank-sum test is used. For boxplots, centre line shows median, box limits indicate upper and lower quartiles, and whiskers extend 1.5 times the interquartile range, while data beyond the end of the whiskers are outlying points that are plotted individually.

Extended Data Fig. 3 Clusters, signatures, and prognosis of five TIMELASER subtypes.

a, Heatmap showing frequencies of TIME cell clusters in 5 CMs. b, Forest plot showing the clinical relevance of clusters in each CM revealed by log10(hazard ratio) based on PFS. Cox regression. Log-rank test. c, Dot heatmap showing enriched pathways across TIMELASER subtypes. Benjamini–Hochberg-adjusted hypergeometric test. d, Boxplots showing the expression of given signatures in different TIMELASER subtypes. Signature scores of TIMELASER subtypes with overhead asterisk are significantly higher than that of subtypes with corresponding asterisk colour. Wilcoxon rank-sum test, two-sided. (TIME-IA, n = 18 cases, TIME-ISM, n = 8 cases, TIME-ISS, n = 12 cases, TIME-IE, n = 42 cases, TIME-IR, n = 31 cases, n denotes biologically independent patients). For boxplots, centre line shows median, box limits indicate upper and lower quartiles, and whiskers extend 1.5 times the interquartile range, while data beyond the end of the whiskers are outlying points that are plotted individually. e, Overall survival (OS) with each patient assigned to a single CM. Log-rank test. f, OS of cases stratified by each TIMELASER module. Log-rank test. In b and d, *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Extended Data Fig. 4 Validation of five TIMELASER subtypes.

a, Boxplots showing the percentage of TIMELASER modules across 3 PLC subtypes. (HCC, n = 79 cases, ICC, n = 25 cases, CHC, n = 7 cases). b, Heatmap showing the percentage of CM1–5 across tumours in our cohort and three published scRNA-seq cohorts. c, Expression of signature genes for the five TIMELASER subtypes in 453 published liver cancer bulk RNA-seq data. d, Boxplot showing z-scores of signature genes for five TIMELASER subtypes in different cancer types. Colours represents HCC (orange, n = 369 cases), ICC (green, n = 33 cases) and CHC (purple, n = 51 cases). e, Pie charts showing the proportion of TIMELASER subtypes in c. f, Representative CODEX results showing four different TIMELASER subtypes. For each sample, only six representative antibodies staining are displayed in the figure along with DAPI. Scale bar, 500 μm. g, Validation of TIMELASER by a published spatial transcriptomic study of liver cancer. H&E staining and the corresponding spatial feature plots of different marker genes of cell types are shown in different samples. In a and d, n denotes biologically independent samples. Two-sided Wilcoxon rank-sum test is used. For boxplots, centre line shows median, box limits indicate upper and lower quartiles, and whiskers extend 1.5 times the interquartile range, while data beyond the end of the whiskers are outlying points that are plotted individually. **, P < 0.01; ***, P < 0.001.

Extended Data Fig. 5 L-R networks and feature summary of five TIMELASER subtypes.

a, Heatmap showing Ro/e enrichment values of TIMELASER-specific L-R pairs. b, Chord diagrams showing the interactions within each TIMELASER subtype mediated by specific L-R pairs. Line width is proportional to interaction intensity and coloured by TIMELASER subtypes. c, Barplots showing the number of ligand-receptor pairs significantly enriched in TIME-IA and TIME-ISM modules. d, Heatmap showing the expression of chemokines and the corresponding receptors in TIME-IA and TIME-ISM patients. Exp, normalized mean expression. e, Summary of key features across TIMELASER subtypes. f, Schematic for five TIMELASER subtypes. Selected cell populations are shown for each TIMELASER subtype with tumour cells as background. Tex, exhausted T cell; NK, nature killer cell; TAN, tumour-associated neutrophil; TAM, tumour-associated macrophage; DC, dendritic cell.

Extended Data Fig. 6 Mutational landscape and GMs of malignant cells.

a, Heatmap showing frequencies of five TIMELASER subtypes across 111 PLC patient samples. Detailed clinical and molecular attributes of individual tumour samples are annotated. P values to the right indicate significant non-random distributions for each attribute. Chi-square test is used for categorical variables. Two-way ANOVA test is used for continuous variables. b, Stacked barplots showing the distribution of cancer types, virus and cirrhosis state across TIMELASER subtypes. Chi-square test. c, Boxplots showing the distribution of tumour purity, CNA and TMB inferred by WES data across TIMELASER subtypes. Two-way ANOVA test is used for comparison of multiple groups. Two-sided Wilcoxon rank-sum test is used for comparison between any two groups. d, Heatmap showing the mutational rate of somatic mutations enriched in different TIMELASER subtypes. e, Barplots showing mutational frequencies of TP53, KRAS, IDH1, and CTNNB1 in different TIMELASER subtypes. Colours represent different TIMELASER subtypes. One-sided Fisher’s exact test. Tests are performed between the denoted TIME subtype (P value colour coded) and a combination of all others. f, Heatmaps showing the eight common gene modules (GMs) extracted from tumour cells. g, Boxplots showing the distributions of signature scores of GMs across tumours stratified into five TIMELASER subtypes. Overhead asterisk is significantly higher than that of subtypes with corresponding asterisk colour. Wilcoxon rank-sum test, two-sided. In c and e, (TIME-IA, n = 13 cases, TIME-ISM, n = 7 cases, TIME-ISS, n = 7 cases, TIME-IE, n = 32 cases, TIME-IR, n = 20 cases). In g, (TIME-IA, n = 18 cases, TIME-ISM, n = 8 cases, TIME-ISS, n = 12 cases, TIME-IE, n = 42 cases, TIME-IR, n = 31 cases). In c, e, and g, n denotes biologically independent patients. For boxplots, centre line shows median, box limits indicate upper and lower quartiles, and whiskers extend 1.5 times the interquartile range, while data beyond the end of the whiskers are outlying points that are plotted individually. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Extended Data Fig. 7 Neutrophil heterogeneity in human PLC.

a, H&E and IHC plots showing the neutrophil frequencies in HCC and ICC patients. Scale bar, 20 μm. Boxplot to the right is the quantitative result. Student’s t-test, two sided. (HCC, n = 5 cases, ICC, n = 8 cases, n denotes biologically independent samples.) In the boxplot, centre line shows median, box limits indicate upper and lower quartiles, and whiskers extend 1.5 times the interquartile range, while data beyond the end of the whiskers are outlying points that are plotted individually. b, Dot heatmap showing the row-scaled expression of marker genes for neutrophil clusters. c, UMAP plots showing the expression of typical marker genes for neutrophil subsets. Exp, normalized expression. d, Distribution of neutrophil clusters by patient. e, Monocle trajectories of neutrophils coloured by tissues (left), cluster identities (middle) and CytoTRACE scores (right). Each dot represents a single cell. Cell orders are inferred based on the expression of the most variable genes across neutrophil clusters. f, Heatmap showing similarity scores of peripheral blood neutrophil clusters from Xie et al. and lung cancer neutrophil clusters from Zillionis et al. compared with liver cancer neutrophil clusters inferred by singleR. g, OS and PFS of patients stratified by the proportion of all neutrophils and neutrophil clusters (Neu_09/10/11) in TIME-ISM. Log-rank test. h, Average expression of classic neutrophil scores and TAN-specific gene scores in neutrophil clusters. i, Gene ontology analysis showing the enrichment of specific pathways in neutrophil clusters. Benjamini–Hochberg-adjusted hypergeometric test.

Source data

Extended Data Fig. 8 Transcription factors, mIHC and in vitro validation of neutrophil clusters.

a, UMAP plots showing regulon activities of five representative transcription factors for specific neutrophil clusters. Binding motifs of these transcription factors are shown on the top. b, Normalized ATAC-seq sequencing tracks of selected transcription factor loci in matched PBN, ALN, and TAN isolated from the same patient. ATAC peaks detected by MACS3 are denoted with the grey box above the gene body and highlighted with light red shading. c, Workflow of co-culture experiments of PBNs with or without cell line (liver cancer cell line HepG2, HCCLM3, and MHCC97H, control cell line HEK293T). d, Survival curve of PBN in culture condition (n = 3, n denotes biologically independent samples). Data are presented as mean values ± SEM. e, Expression of TAN-related signatures in PBNs co-cultured with or without different cell lines for 0 h, 18 h, 24 h, and 30 h. f, Expression of gene signatures of different neutrophil subsets in PBNs co-cultured with or without different cell lines. g, White arrows mark CCL4+CD66b+ neutrophils, with one cell highlighted by the four enlarged panels on the right. Middle panels show another representative CCL4+ CD66b+ neutrophil while right panels show a representative CCL4− CD66b+ neutrophil. Scale bars are 20 μm and 2 μm. h, Expression of selected genes in PBNs co-cultured with or without different cell lines for 0 h, 18 h, 24 h, and 30 h. i, Chord diagrams showing interactions between neutrophils and other cell types mediated by CCL3-CCR1 and CCL4-CCR5. Line width is proportional to interaction intensity, coloured by cell types with receptors. j, Crystal violet staining of migrated monocytes co-cultured with matched TAN or non-TAN. Scale bar, 100 μm. k, FACS analysis showing the PD-L1 expression of PBNs co-cultured with or without different cell lines for 24 h. Two-way ANOVA test is used for e and h.

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Extended Data Fig. 9 Co-culture experiment of cell line-PBN-CD8+ T cells and analyses of two IFIT+ neutrophil subsets.

a, Experimental workflow. b, Gating strategy separating neutrophils from CD8+ T cells in the bottom chamber of co-culture system in a. c, FACS analysis showing the expression of CD25 (n = 3), CD69 (n = 3), and IFNγ (n = 4) in PBNs co-cultured with different cell lines. Student’s t-test, one-sided. Data are presented as mean values ± SEM. d, FACS analysis showing the expression of IFNγ in CD8+ T cells when anti-PD-L1 or the IgG control is added to the co-culture system. e, FACS analysis showing the expression of CD25, IFNγ, GZMB, and PRF1 in CD8+ T cells co-cultured with matched TAN or non-TAN isolated from patients with liver cancer. f, Volcano plot showing differentially expressed genes between Neu_03_ISG15 and Neu_09_IFIT1. Benjamini-Hochberg adjusted Wilcoxon rank-sum test, two-sided. g, Heatmap showing the predicted ligand activity by NicheNet on genes highly expressed in Neu_09_IFIT1. Pearson correlation indicates the ability of each ligand to predict the target genes, and better predictive ligands are thus ranked higher. h, Dot heatmap showing the selected ligand-receptor pairs between different cell populations and Neu_09_IFIT1. Benjamini-Hochberg adjusted permutation test. i, Boxplots showing the proportion of two IFNG+ populations between patients with or without Neu_09_IFIT1. Wilcoxon rank-sum test, two-sided. (Yes, n = 33 cases, No, n = 78 cases). For boxplots, centre line shows median, box limits indicate upper and lower quartiles, and whiskers extend 1.5 times the interquartile range, while data beyond the end of the whiskers are outlying points that are plotted individually. j, Pearson correlation between the expression of CD274 and IFNG in TIME cells in this study (left) or in the collected bulk RNA-seq datasets (right). In c and i, n denotes biologically independent samples.

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Extended Data Fig. 10 scRNA-seq and functional analyses of mouse models.

a, Schematic of liver cancer mouse models. Intrahepatic delivery of the transposable vectors pTMC (encoding Myc and ∆90Ctnnb1) or pTMK (encoding Myc and KrasG12D) via HDTV in Alb-Cre × Trp53fl/fl mice. b, Representative photos, H&E, and IHC staining of HCC and ICC mouse models. Rulers in the photo show a minimum unit of mm. Scale bar on the staining slides is 20 μm. c, Survival curve of liver cancer mouse model. Log-rank test. d, UMAP plot showing major cell types of mice with liver cancer. Dots represent individual cells, and colours represent the major cell populations. mILC: innate lymphoid cells, mNeu: neutrophils, mMph: macrophages, mMono: monocytes, mEC: endothelial cells; mFb: fibroblasts, mEpithelial: hepatocytes, biliary cells and progenitors; the first letter m indicates mouse clusters. The two small UMAP plots show the distribution of mouse models (left) and tissue types (right). e, UMAP plot showing myeloid clusters including 5 DC, 2 monocyte and 7 macrophage clusters for liver cancer mouse models. f, Dot heatmap showing the row-scaled expression of typical marker genes for neutrophil clusters in mice. g, Stacked barplot showing the fraction of 12 mouse neutrophil subsets across PB, AL, and tumour. h, The trajectory path of mouse neutrophil clusters inferred by Monocle2. Each dot represents a single cell. Cell orders are inferred from the expression of the most variable genes. The trajectory direction is determined by biological prior. i, Heatmap showing Pearson’s correlations across neutrophil clusters in human and mouse. j, UMAP plots showing the integration of mouse and human neutrophil clusters. k, Sankey plot showing the similarities of the joint clusters, mouse tissue isolated neutrophil clusters, and human sample isolated neutrophil clusters. l, FACS analysis on neutrophil, macrophage, and CD8+ T cell populations in isotype and anti-Ly6G groups. The right barplot shows the decreased neutrophil number in anti-Ly6G group (n = 10). m, FACS analyses and coloured histogram showing reduced PD-L1 expression in TANs and reduced PD-1 and TIM3 expression in tumour-infiltrated CD8+ T cells of the anti-Ly6G group compared with isotype control. The left barplot shows the decreased PD-L1 expression of neutrophils in anti-Ly6G group (n = 8). n, IHC of CD68 in tumour regions of mice treated with isotype control or anti-Ly6G antibody (n = 6). o, FACS analysis showing the expression of surface and intracellular Ly6G in the isotype control and anti-Ly6G treatment groups. p, Bar plot showing the statistical analysis of FACS results (n = 3). In l-p, n denotes biologically independent samples, data are presented as mean values ± SEM, and two-sided Student’s t-test is used.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–5, Supplementary Notes. 1–9 and Supplementary Discussion.

Reporting Summary

Supplementary Table 1

Clinical information of the patients and library information of the samples.

Supplementary Table 2

Mutations and CNVs detected from WES.

Supplementary Table 3

Statistics of 89 cell clusters and 5 TIMELASER subtypes.

Supplementary Table 4

Statistics of cell clusters in mouse liver tumours.

Supplementary Table 5

Summary of antibodies and primers.

Supplementary Data

Source Data for Supplementary Fig. 3

Supplementary Data

Source Data for Supplementary Fig. 4

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Xue, R., Zhang, Q., Cao, Q. et al. Liver tumour immune microenvironment subtypes and neutrophil heterogeneity. Nature 612, 141–147 (2022). https://doi.org/10.1038/s41586-022-05400-x

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