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Presence of onco-fetal neighborhoods in hepatocellular carcinoma is associated with relapse and response to immunotherapy

Nature Cancer volume 5, pages 167–186 (2024)Cite this article

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Abstract

Onco-fetal reprogramming of the tumor ecosystem induces fetal developmental signatures in the tumor microenvironment, leading to immunosuppressive features. Here, we employed single-cell RNA sequencing, spatial transcriptomics and bulk RNA sequencing to delineate specific cell subsets involved in hepatocellular carcinoma (HCC) relapse and response to immunotherapy. We identified POSTN+ extracellular matrix cancer-associated fibroblasts (EM CAFs) as a prominent onco-fetal interacting hub, promoting tumor progression. Cell–cell communication and spatial transcriptomics analysis revealed crosstalk and co-localization of onco-fetal cells, including POSTN+ CAFs, FOLR2+ macrophages and PLVAP+ endothelial cells. Further analyses suggest an association between onco-fetal reprogramming and epithelial–mesenchymal transition (EMT), tumor cell proliferation and recruitment of Treg cells, ultimately influencing early relapse and response to immunotherapy. In summary, our study identifies POSTN+ CAFs as part of the HCC onco-fetal niche and highlights its potential influence in EMT, relapse and immunotherapy response, paving the way for the use of onco-fetal signatures for therapeutic stratification.

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Fig. 1: Construct a single-cell atlas of HCC.
Fig. 2: Characterization of onco-fetal CAFs in HCC and fetal liver.
Fig. 3: Predicted cell–cell interactions of onco-fetal reprogramming in HCC.
Fig. 4: Spatial mapping of onco-fetal cell co-localization.
Fig. 5: Association between onco-fetal cells and relapse in HCC patients.
Fig. 6: Single-cell and spatial validation of onco-fetal cells enriched in relapsed HCC patients.
Fig. 7: Connections between onco-fetal ecosystem and combination immunotherapy.

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Data availability

The HCC01 and HCC03 Stereo-seq data have been deposited in the China National GeneBank Sequence Archive (CNSA) under accession code CNP0004497. The M311 and M316 Stereo-seq data have been deposited in the Sequence Read Archive (SRA) under accession code PRJNA1005053. The original data used to construct the HCC atlas are available with accession codes GSE156337, GSE149614, GSE151530, GSE212046 (GEO), HRA000069 (Genome Sequence Archive) and CNP0000650 (CNSA). The original data of the PLANET cohort and GO30140 and IMbrave150 trials are available with accession codes EGAS00001003813 and EGAS00001005503 (European Genome-phenome Archive). The TCGA LIHC datasets were downloaded from UCSC Xena (http://xena.ucsc.edu). All processed expression data have been uploaded to Figshare, which can be visited through the following links: HCC Atlas, https://doi.org/10.6084/m9.figshare.22332568; onco-fetal CAF identification, https://doi.org/10.6084/m9.figshare.22332655; HCC01 and HCC03 Stereo-seq data, https://doi.org/10.6084/m9.figshare.22332223; NanoString CosMx data, https://doi.org/10.6084/m9.figshare.23972991; PLANET cohort data, https://doi.org/10.6084/m9.figshare.23732370; HCC and fetal liver Nanostring DSP data, https://doi.org/10.6084/m9.figshare.22332397; HCC immunotherapy Stereo-seq data, https://doi.org/10.6084/m9.figshare.22332352. Source data for Figs. 1–7 and Extended Data Figs. 1–10 have been provided as Source Data files. All other data supporting the findings of this study are available from the corresponding author upon reasonable request. Source data are provided with this paper.

Code availability

All codes generated for analysis are available at https://github.com/liziyie/Onco-fetal-CAF-in-HCC.

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Acknowledgements

We thank all patients and their families for participating in this study. We also thank J. Li, C. Bleriot and B. Su for discussions; J. Li from Renji Hospital for clinical sample preparation; Y. Ye for providing the computing platform; and the BGI STOmics team for Stereo-seq studies. Z.Y.L. was supported by the National Natural Science Foundation of China (NSFC) (32100722). P.K.-H.C. is supported by the National Medical Research Council (NMRC) OFLCG21jun-0001 and TCR/015-NCC/2016. A. Sharmaʼs laboratory is supported by funding from the National Health and Medical Research Council (NHMRC) Ideas Grant [2021/GNT2010795], Medical Research Future Fund (MRFF) Early to Mid-Career Researchers (EMCR) grant [2022/MRF2016215], Perkins-Curtin start-up fellowship and Cancer Research Trust (CRT) Programme Grant to the Liver Cancer Collaborative. F.G. is supported by the Shanghai Institute of Immunology funding, the Biomedical Research Council (BMRC) use-inspired basic research (UIBR) award, and the Singapore National Research Foundation Senior Investigatorship (NRFI) NRF2016NRF-NRFI001-02.

Author information

Author notes

  1. These authors contributed equally: Rhea Pai, Saurabh Gupta, Jennifer Currenti, Wei Guo.

Authors and Affiliations

  1. Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Ziyi Li, Wei Guo & Florent Ginhoux

  2. Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, Perth, Western Australia, Australia

    Rhea Pai, Saurabh Gupta, Jennifer Currenti, Abhishek Singh & Ankur Sharma

  3. Curtin Medical School, Curtin University, Perth, Western Australia, Australia

    Rhea Pai, Saurabh Gupta, Jennifer Currenti & Ankur Sharma

  4. Storr Liver Centre, The Westmead Institute for Medical Research and Westmead Hospital, University of Sydney, Sydney, New South Wales, Australia

    Anna Di Bartolomeo, Liang Qiao & Jacob George

  5. Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Hao Feng, Zijie Zhang & Qiang Xia

  6. Shanghai Institute of Transplantation, Shanghai, China

    Hao Feng & Qiang Xia

  7. Department of Biliary Tract Surgery I, Eastern Hepatobiliary Surgery Hospital, Shanghai, China

    Zhizhen Li

  8. BGI-Shenzhen, Beishan Industrial Zone, Shenzhen, P. R. China

    Longqi Liu & Yinqi Bai

  9. BGI-Australia, Herston, Queensland, Australia

    Bicheng Yang

  10. Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore

    Archita Mishra, Katharine Yang & Florent Ginhoux

  11. Telethon Kids Institute, University of Western Australia, Perth Children’s Hospital, Nedlands, Western Australia, Australia

    Archita Mishra

  12. Department of Hepatology and Western Australian Liver Transplant Service, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia

    Michael Wallace

  13. Medical School, University of Western Australia, Nedlands, Western Australia, Australia

    Michael Wallace

  14. Department of Obstetrics and Gynecology, Xinhua Hospital Affiliated to Shanghai Jiaotong University Medicine School, Shanghai, China

    Yujia Yin

  15. Department of Reproductive Medicine, KK Women’s and Children’s Hospital, Singapore, Singapore

    Jerry Kok Yen Chan

  16. Academic Clinical Program in Obstetrics and Gynaecology, Duke-NUS Medical School, Singapore, Singapore

    Jerry Kok Yen Chan

  17. Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Jerry Kok Yen Chan

  18. Department of Hepatopancreatobiliary and Transplant Surgery, Singapore General Hospital and National Cancer Centre Singapore, Singapore, Singapore

    Pierce Kah-Hoe Chow

  19. Surgery Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore

    Pierce Kah-Hoe Chow

  20. Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore

    Florent Ginhoux

  21. Gustave Roussy Cancer Campus, Villejuif, France

    Florent Ginhoux

  22. Institute of Molecular and Cell Biology, A∗STAR, Singapore, Singapore

    Ankur Sharma

  23. KK Research Centre, KK Women’s and Children’s Hospital, Singapore, Singapore

    Ankur Sharma

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  1. Ziyi Li
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  2. Rhea Pai
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Contributions

A. Sharma, F.G. and P.K.-H.C. conceptualized the study and supervised the project; Z.Y.L., R.P., J.C., W.G., L.Q.L., Y.Q.B., B.C.Y., A.M. and K.Y. conducted the experiments; Z.Y.L., R.P., S.G., J.C., W.G., A. Sharma and F.G. analyzed the data; A.D.B, H.F., Z.J.Z., Z.Z.L., A. Singh, M.W., Q.X., J.C., J.G. and P.K.-H.C. collected clinical samples; Z.Y.L., R.P., S.G., J.C., W.G. and Y.J.Y. generated figures; Z.Y.L., A. Sharma and F.G. wrote the manuscript, with all the authors contributing to writing and providing feedback.

Corresponding authors

Correspondence to Pierce Kah-Hoe Chow, Florent Ginhoux or Ankur Sharma.

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The authors declare no competing interests.

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Nature Cancer thanks Florian Greten, Mara Sherman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Characteristics of CAF subsets from HCC atlas.

a, Stacked bar plots showing proportions of cells from each study among different mesenchymal subsets. b, Bubble heatmap showing expression of marker genes across 9 mesenchymal subsets in HCC atlas. Dot size indicates the fraction of expressing cells, colored according to z-score normalized expression levels. c, Violin plots showing the specific high expression levels of PI16, CD34 and DPT in cluster F6. d, Volcano plot showing differentially expressed genes between VSMC-like cells and EM CAFs. P-value < 0.05, Two-sided unpaired limma-moderated t test; log2(FC) > 0.5. e, Violin plots showing the expression of myHSC and cyHSC signature score in each cluster. For a-e, n = 6,163 mesenchymal cells. f, Spatial visualization of selected marker genes of CAF subpopulations in VISIUM_1 (left) and VISIUM_2 (right) slides. In VISIUM 1, Normal, n = 2,956 spots; Leading-edge, n = 2,791 spots; Tumor, n = 3,184 spots. In VISIUM 2, Normal, n = 4,628 spots; Leading-edge, n = 4,672 spots; Tumor, n = 4,733 spots.

Source data

Extended Data Fig. 2 Characteristics of fibroblast subsets from HCC.

a, UMAP projection of fibroblast subsets identified in HCC tumors (left) and adjacent normal tissues (right). b, Bubble heatmap showing expression of marker genes across 9 fibroblast clusters. Dot size indicates the fraction of expressing cells, colored according to z-score normalized expression levels. c, Projection of 9 fibroblast clusters generated from Sharma data back onto the mesenchymal cells from HCC atlas. d, UMAP plot showing the developmental trajectories (black skeleton lines) of fibroblasts inferred by Monocle3. Points are colored by clusters. e, UMAP plot showing the developmental trajectories of fibroblasts inferred by RNA velocity. Points are colored by Monocle3 pseudotime. f, Boxplot showing the Monocle3 pseudotime distribution of fibroblasts in the two major paths to POSTN+ CAF or MYH11+ CAF. Center line indicates the median value, lower and upper hinges represent the 25th and 75th percentiles, respectively, and whiskers denote 1.5× interquartile range. g, Scatter plot showing the specificity scores of regulons of POSTN+ CAF and MYH11+ CAF. h, Volcano plot showing differentially expressed genes between POSTN+ CAF and MYH11+ CAF. P-value < 0.05, Two-sided unpaired limma-moderated t test; log2(FC) > 0.5. i, FACs analyses showing a much higher expression of CD105 (ENG) in POSTN+ CAF compared to MYH11+ CAF. For a-f, ABCA8+ Fib, n = 25 cells; Fib, n = 341 cells; MT1M+ Fib, n = 83 cells; CAF, n = 384 cells; HSP+ CAF, n = 190 cells; MYH11+ CAF, n = 213 cells; SDC2+ CAF, n = 283 cells; POSTN+ CAF, n = 97 cells; APOA2+ CAF, n = 96 cells; In total, n = 1,712 cells.

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Extended Data Fig. 3 Characteristics of fetal liver fibroblast subsets and identification of onco-fetal fibroblasts.

a, Heatmap showing the expression patterns of selected genes across fetal liver fibroblast clusters. b, UMAP plots displaying the developmental trajectories (black skeleton lines) of fetal liver fibroblasts inferred by Monocle3. Points are colored by clusters (left) and pseudotime (right). c, Boxplot showing the pseudotime distribution of fetal liver fibroblasts in the two major paths to COL1A1+ Fib or ITM2C+ Fib. Center line indicates the median value, lower and upper hinges represent the 25th and 75th percentiles, respectively, and whiskers denote 1.5× interquartile range. d, Fetal age preference of each cluster estimated by Ro/e score. +++, Ro/e > 1; ++, 0.8 < Ro/e ≤ 1; +, 0.2 ≤ Ro/e ≤ 0.8; +/-, 0 < Ro/e < 0.2. e, UMAP plot of integrated analyses showing 12 clusters of fibroblasts from healthy livers, fetal livers, HCC tumors and HCC adjacent normal tissues. f, UMAP plot showing the developmental trajectories (black skeleton lines) of joint clusters by Monocle 3. g, Bar plot showing tissue distribution of each joint cluster. h, Similarity analysis of fibroblast clusters from human fetal liver and HCC. i, Heatmap depicting expression levels of selected genes across fibroblast clusters identified from human fetal liver and HCC. j,k, Bar plot showing enriched GO terms of genes upregulated in both ITM2C+ Fib and POSTN+ CAF (j) and of POSTN+ CAF-specific upregulated genes (k). Hypergeometric test. For a-d, IGFBP3+ Fib, n = 810 cells; BGN+ Fib, n = 250 cells; PLEK+ Fib, n = 66 cells; ACTA2+ Fib, n = 333 cells; COL1A1+ Fib, n = 310 cells; APOC3+ Fib, n = 150 cells; ITM2C+ Fib, n = 210 cells; In total, n = 2,130 cells. For e-i, healthy liver, n = 1,564 cells; fetal liver, n = 2,130 cells; HCC tumor, n = 1,440 cells; adjacent normal liver, n = 272 cells; In total, n = 5,406 cells.

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Extended Data Fig. 4 Onco-fetal interactions in HCC.

a, Heatmaps depicting the expression of potential ligands regulating the onco-fetal cell types. b, Lollipop plot showing number of significant interaction events between onco-fetal cell types with other clusters. c, Heatmap depicting the expression of shared potential ligands driving onco-fetal reprogramming. d, Bubble heatmap showing the cell-cell communications from malignant hepatocytes to onco-fetal cell types. Dot size indicates P-value, colored by communication probability. e, Bubble heatmap showing expression of selected POSTN+ CAF functional genes across fibroblasts in fetal liver. Dot size indicates the fraction of expressing cells, colored according to z-score normalized expression levels. For a-e, n = 72,330 cells were used for cell-cell communication analyses.

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Extended Data Fig. 5 Spatial transcriptomic profiles of two HCC samples by Stereo-seq.

a, The number of genes captured by Stereo-seq at 10–200 bin resolutions. b, The number of transcripts captured by Stereo-seq at 10–200 bin resolutions. c, The DAPI fluorescence images (left), H&E staining images (middle) and number of genes detected at 100 bin resolution (right) of HCC01 (upper) and HCC03 (bottom). No replicates for these micrographs. d, Spatial visualization of onco-fetal markers FOLR2, PLVAP and POSTN in HCC01 analyzed by Stereo-seq at bin 100 resolution. e, Spatial visualization of onco-fetal cells in HCC01. The bins enriched with onco-fetal cells are highlighted with red. f, Spatial visualization of expression of indicated genes in HCC01 (upper) and HCC03 (bottom) at bin 100 resolution. g, Boxplot showing the expression of POSTN+ CAF signature in bins with different enrichment patterns of PLVAP+ EC or FOLR2+ TAM1 in HCC01. PLVAP+ EC low, n = 29,964 bins; PLVAP+ EC high, n = 6,812 bins; FOLR2+ TAM1 low, n = 29,649 bins; FOLR2+ TAM1 high, n = 7,127 bins. h, Bubble heatmap showing expression of POSTN+, POSTN- and panCAF signature genes in different CAF subsets. Dot size indicates the fraction of expressing cells, colored according to z-score normalized expression levels. i, Spatial visualization of selected cluster signatures in HCC03 analyzed by Stereo-seq at bin 100 resolution. j, Boxplot showing the expression of POSTN- and panCAF signature in bins with different enrichment patterns of PLVAP+ EC or FOLR2+ TAM1 in HCC03. PLVAP+ EC low, n = 19,905 bins; PLVAP+ EC high, n = 4,036 bins; FOLR2+ TAM1 low, n = 20,013 bins; FOLR2+ TAM1 high, n = 3,928 bins. For c-f and i, HCC01, n = 36,776 bins; HCC03, n = 23,941 bins. For g and j, Center line indicates the median value, lower and upper hinges represent the 25th and 75th percentiles, respectively, and whiskers denote 1.5× interquartile range. *P < 0.05, **P < 0.01, ***P < 0.001. Two-sided Wilcoxon test. Spatial cross-correlation test.

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Extended Data Fig. 6 Colocalization of onco-fetal cells in Stereo-seq data.

a, Spatial visualization of onco-fetal niche (upper) and core-ligands score (bottom) in HCC03 analyzed by Stereo-seq at bin 100 resolution. b, Boxplot showing the expression of 10 core ligands driving onco-fetal reprogramming in bins with different enrichment patterns of onco-fetal niche. c, Spatial visualization of selected core-ligands expression in HCC03 analyzed by Stereo-seq at bin 100 resolution. d-g, Spatial visualization of CXCL12-CXCR4 and DLL4-NOTCH3 ligand-receptor pairs expression. h, Boxplot showing the distance quantification between onco-fetal cells and either onco-fetal cells or other non-onco-fetal (Non-OF) cells. A distance of 1000 units is ∼0.16454 mm. POSTN+ CAF, n = 103 cells; PLVAP+ EC, n = 90 cells; FOLR2+ TAM, n = 28 cells; Non-OF Fibroblast, n = 472 cells; Non-OF EC, n = 103 cells; Non-OF Myeloid, n = 198 cells. For a-g, HCC01, n = 36,776 bins; HCC03, n = 23,941 bins. For b and h, Center line indicates the median value, lower and upper hinges represent the 25th and 75th percentiles, respectively, and whiskers denote 1.5× interquartile range. *P < 0.05, **P < 0.01, ***P < 0.001. Two-sided Wilcoxon test. For c-g, *P < 0.05, **P < 0.01, ***P < 0.001. Spatial cross-correlation test.

Extended Data Fig. 7 Bulk RNA-seq profiles of 55 HCC patients.

a, Schema for analysis of onco-fetal ecosystem in n = 250 bulk HCC specimens. b, Framework for evaluating CIBERSORTx performance. c, Boxplots showing Pearson correlation coefficients between cell fractions estimated by CIBERSORTx and ground truth simulated proportions. n = 5 replicates. d, Boxplot showing the cosine similarity of cell fractions estimated by CIBERSORTx and MuSiC in n = 198 specimens. e, Boxplots showing the inferred proportions of FOLR2+ TAM1 and POSTN+ CAF in specimens with high (n = 99) versus low (n = 99) proportions of PLVAP+ EC. f, Histogram showing the number of patients with Simpsons’ index of diversity measuring the diversity of onco-fetal status of specimens from each patient. n = 54 patients. g, Contingency tables showing relapse (left) and HBV viral status (right) with patients with high onco-fetal score versus low onco-fetal score in TCGA LIHC dataset (left, n = 291 patients) and PLANET cohort (right, n = 54 patients), respectively. Fisher’s exact test. h, Onco-fetal score showing the largest odds ratio (5.99) associated with relapse. Among individual cell types, POSTN+ CAF showed the highest odds ratio (5.28). i, TNM stage preference of n = 124 relapsed specimens with high or low onco-fetal score estimated by Ro/e. j, Boxplots showing the inferred proportions of onco-fetal cell types in different TNM tumor stages. Stage I, n = 87 specimens; Stage II, n = 63 specimens; Stage III, n = 48 specimens. k, The Kaplan-Meier curves of time-to-recurrence stratified by onco-fetal score in n = 29 relapsed patients. l, Volcano plot showing differentially expressed genes between relapsed specimens with low or high onco-fetal score. P-value < 0.05, Two-sided unpaired limma-moderated t test; log2(FC) > 1. m, Heatmap depicting the expression of differentially expressed genes between relapsed HCC samples with high versus low onco-fetal score across tumor cells from scRNA-seq. n, The Kaplan-Meier curves of overall survival (OS) stratified by EMT-like malignant cell signatures in n = 364 patients. o, Boxplot showing the expression of cell cycle, cEMT and epiHR signatures in relapsed specimens with low or high onco-fetal score. Non-relapsed, n = 71 specimens; OFlow relapsed, n = 93 specimens; OFhi relapsed, n = 31 specimens. p, Spatial visualization of non-EMT and EMT-like malignant cell signatures in VISIUM_1 (upper) and VISIUM_2 (bottom) tumor and adjacent normal liver slides. In VISIUM 1, Normal, n = 2,956 spots; Tumor, n = 3,184 spots. In VISIUM 2, Normal, n = 4,628 spots; Tumor, n = 4,733 spots. q, Ligand activities (left) and expression levels across three onco-fetal cell types (right). Prioritized ligands are identified by NicheNet analysis based on upregulated genes in relapsed specimens with high onco-fetal score. Dot size indicates the fraction of expressing cells, colored according to expression levels. r, Charactering Stereo-seq bins by neighborhood index. In the upper-left schematic, grey bins indicate the presence of interested cells, red bins indicate the targets, and dashed lines frame their surrounding neighborhood bins. Other panels showing the neighborhood index of three onco-fetal cell types at bin 100 resolution in HCC03. s, Boxplot depicting the changes in proportions of onco-fetal high bins with the increase of distance to EMT-like tumor cells. Grey boxes indicate the randomly sampled bins (n = 10 replicates) as control, and red dots indicate the bins with EMT-like tumor cells, which exhibited a significant enrichment of onco-fetal high bins surrounding the EMT-like tumor cells. For c-e, j, o and s, center line indicates the median value, lower and upper hinges represent the 25th and 75th percentiles, respectively, and whiskers denote 1.5× interquartile range. For e, j and o, *P < 0.05, **P < 0.01, ***P < 0.001. Two-sided Wilcoxon test. For k and n, HR, hazard ratio. P-value was determined by Kaplan–Meier survival curves and log-rank test. *P < 0.05, **P < 0.01, ***P < 0.001.

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Extended Data Fig. 8 Intercellular communication analysis in relapsed versus non-relapsed patients.

a, Expression of onco-fetal ligands in onco-fetal ligand producing cells from HCC scRNA-seq data. b, Bar plots showing the total number of interactions and interaction strength of the inferred cell-cell communication networks from non-relapsed and relapsed patients by CellChat analysis. c, Scatter plots showing the outgoing and incoming interaction strength in non-relapsed and relapsed patients, respectively. The onco-fetal cell types are highlighted with magenta, while blue points denote other clusters. d, Heatmap depicting the differential expression strength of the inferred cell-cell communication networks. Red (or blue) color indicates the increased (or decreased) signaling in relapse. The top and right colored bar plots represent the sum of column (incoming signaling) and row (outgoing signaling), respectively. e, Bar plot showing the overall information flow of indicated signaling pathways in relapsed and non-relapsed patients. f, Bubble heatmap showing the significantly different cell-cell communication in no-relapsed and relapsed patients. Dot size indicates P-value, colored by communication probability. g, Bubble heatmap showing the expression of prioritized ligands regulating EMT-like tumor cells across onco-fetal cells. Dot size indicates the fraction of expressing cells, colored according to expression levels. For a-g, n = 21,997 cells from relapsed patients and n = 34,232 cells from non-relapsed patients in tumor tissues were used for cell-cell communication analyses.

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Extended Data Fig. 9 NanoString WTA of HCC and fetal liver specimens.

a, Boxplots showing the inferred proportions of POSTN+ CAF in FOLR2+ TAM1 and PLVAP+ EC high (n = 11) or low (n = 11) ROIs. b, PCA analysis of n = 48 ROIs from adjacent normal and tumor tissues. Orange (or skyblue) points indicate the ROIs from tumor (or adjacent normal) tissues. Circle (or triangle) points indicate the ROIs from non-relapsed B015 (or relapsed B017). c, Boxplots showing the expression of onco-fetal cell signatures in B015 and B017. B015 normal, n = 14 ROIs; B017 normal, n = 12 ROIs; B015 tumor, n = 10 ROIs; B017 tumor, n = 12 ROIs. d, Volcano plot showing differentially expressed genes between ROIs in cluster C1 and C2. P-value < 0.05, Two-sided unpaired limma-moderated t test; log2(FC) > 0.5. e, UMAP plots displaying the cluster (left) and AUC score of C1/C2 signature (right) in n = 72,330 cells from scRNA-seq dataset. f, Heatmap showing expression levels of selected genes across n = 72 ROIs. g, PCA analysis of n = 72 ROIs from adjacent normal, tumor and fetal liver tissues, colored by tissue source (upper) and cluster (bottom). h, Stacked bar plot depicting the proportions of tissue source in each cluster. i, Boxplots showing the expression levels of onco-fetal high relapse-related tumor cell signatures between ROIs in cluster C1 (n = 12 ROIs) and C2 (n = 8 ROIs). For a, c and i, center line indicates the median value, lower and upper hinges represent the 25th and 75th percentiles, respectively, and whiskers denote 1.5× interquartile range. *P < 0.05, **P < 0.01, ***P < 0.001. Two-sided Wilcoxon test.

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Extended Data Fig. 10 Associations among onco-fetal ecosystem, ABRS genes and Tregs.

a, Boxplots showing the expression of ABRS genes in scRNA-seq data (left), whose expression patterns are also shown in heatmap (right). b, Boxplots showing the expression of the Treg signature in scRNA-seq data (left), whose expression patterns are also shown in heatmap (right). c, The correlation between ABRS score and Treg signature in GO30140 and IMbrave150 cohorts. Linear regression and Pearson’s correlation. d, The Kaplan–Meier curves of PFS in GO30140 stratified by median expression score of the Treg signature. Multivariate Cox regression. HR, hazard ratio. P-value was determined by Kaplan–Meier survival curves and log-rank test. *P < 0.05, **P < 0.01, ***P < 0.001. e, Boxplots showing the onco-fetal score in patients with high or low levels of ABRS. f, The correlation between ABRS score and onco-fetal cell signatures in GO30140 and IMbrave150 cohorts. Linear regression and Pearson’s correlation. g, Boxplots showing the expression levels of onco-fetal cell signatures in patients with high or low levels of the Treg signature. h, Boxplots showing the onco-fetal score in patients with high or low levels of Treg signature. i, Boxplots showing the expression levels of EMT-like and non-EMT signatures in patients with high or low levels of ABRS score and the Treg signature. j, Schematic of the complete treatment process and gadolinium enhanced MRI images of M311 responder in M0, M11 and M16. M refers to month. k, Spatial visualization of Treg, EMT-like and non-EMT signatures using neighborhood index (NI) in M311 (responder) and M316 (non-responder). For a,b, n = 72,330 cells. For c-i, In GO30140, ABRS low, n = 43 patients; ABRS high, n = 43 patients; Treg low, n = 43 patients; Treg high, n = 43 patients. In IMbave150, ABRS low, n = 60 patients; ABRS high, n = 59 patients; Treg low, n = 60 patients; Treg high, n = 59 patients. For e and g-i, center line indicates the median value, lower and upper hinges represent the 25th and 75th percentiles, respectively, and whiskers denote 1.5× interquartile range. *P < 0.05, **P < 0.01, ***P < 0.001. Two-sided Wilcoxon test.

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Li, Z., Pai, R., Gupta, S. et al. Presence of onco-fetal neighborhoods in hepatocellular carcinoma is associated with relapse and response to immunotherapy. Nat Cancer 5, 167–186 (2024). https://doi.org/10.1038/s43018-023-00672-2

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