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Mesenchymal and adrenergic cell lineage states in neuroblastoma possess distinct immunogenic phenotypes

Nature Cancer volume 3, pages 1228–1246 (2022)Cite this article

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Abstract

Apart from the anti-GD2 antibody, immunotherapy for neuroblastoma has had limited success due to immune evasion mechanisms, coupled with an incomplete understanding of predictors of response. Here, from bulk and single-cell transcriptomic analyses, we identify a subset of neuroblastomas enriched for transcripts associated with immune activation and inhibition and show that these are predominantly characterized by gene expression signatures of the mesenchymal lineage state. By contrast, tumors expressing adrenergic lineage signatures are less immunogenic. The inherent presence or induction of the mesenchymal state through transcriptional reprogramming or therapy resistance is accompanied by innate and adaptive immune gene activation through epigenetic remodeling. Mesenchymal lineage cells promote T cell infiltration by secreting inflammatory cytokines, are efficiently targeted by cytotoxic T and natural killer cells and respond to immune checkpoint blockade. Together, we demonstrate that distinct immunogenic phenotypes define the divergent lineage states of neuroblastoma and highlight the immunogenic potential of the mesenchymal lineage.

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Fig. 1: A subset of neuroblastomas expresses signatures of an immune response.
Fig. 2: The mesenchymal cell state is associated with an immunogenic signature.
Fig. 3: Reprogramming adrenergic cells to the mesenchymal lineage induces immune gene expression.
Fig. 4: Activation of immune gene expression associated with cell state transition is epigenetically regulated.
Fig. 5: Mesenchymal NB cells induce NKG2D-dependent NK activation.
Fig. 6: Mesenchymal NB cells are killed by antigen-specific CTLs.
Fig. 7: Mesenchymal NB cells functionally engage with cytotoxic T cells and are responsive to ICB.

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Data availability

The RNA-seq and ChIP–seq datasets generated and analyzed during the current study are available in the Gene Expression Omnibus (GEO) repository under accession number GSE165750. Source data are provided with this paper. The authors declare that all other data supporting the findings of this study are available within the paper and its Supplementary Information files or are available from the corresponding author on reasonable request.

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Acknowledgements

We thank C. Cardoso and M. Harlow from the George laboratory and S. Sen Santara, Y. Zhang and Z. Zhang from the Lieberman laboratory for helpful discussions. We thank M. Zimmerman, A. T. Look and K. Stegmaier for sharing cell lines. We thank the following members of the former Haining laboratory at DFCI for sharing resources and experimental advice: U. Gerdemann, D. Comstock, K. Yates, A. Word and A. Long. We thank F. Westermann’s group for sharing the Seurat object containing the count matrix derived from the snRNA-seq dataset. We thank H. Tillman and the St. Jude Veterinary Pathology Core for their assistance with IHC staining. The results shown here are in part based on data curated by the R2: Genomics Analysis and Visualization Platform: http://r2.amc.nl/. This work was supported by a St. Baldrick’s Foundation Childhood Cancer Research Grant, grant no. DOD CA191000 (R.E.G. and R.J.); NIH grant no. R01-CA197336 (R.E.G); and the Cookies for Kids’ Cancer Foundation (R.E.G); the Ted and Eileen Pasquarello Research Fund (R.R.); and Villa Joep Foundation, grant no. IWOV-Actief.51391.180034 (S.N.). S. Spranger is a recipient of a Pew Stewart Scholarship. S. Sengupta and M.K. were supported by the Rally Foundation for Childhood Cancer Research and Infinite Love for Kids Fighting Cancer, M. Campisi by an AIRC Fellowship for Abroad, B.C.M. by the National Center for Advancing Translational Sciences/NIH Award no. KL2 TR002542 and D.N.D. by an Alex’s Lemonade Stand Foundation Young Investigator Fellowship.

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Author notes

  1. These authors contributed equally: Satyaki Sengupta, Sanjukta Das.

Authors and Affiliations

  1. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Satyaki Sengupta, Sanjukta Das, Bandana Sharma, Jared H. Rowe, Hao Huang, David N. Debruyne, Esther D. Cerda, Malgorzata Krajewska, Ruben Dries, Luigi Soriano & Rani E. George

  2. Department of Pediatrics, Harvard Medical School, Boston, MA, USA

    Satyaki Sengupta, Sanjukta Das, Angela C. Crespo, Bandana Sharma, Hao Huang, David N. Debruyne, Malgorzata Krajewska, Ruben Dries, Judy Lieberman & Rani E. George

  3. Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA, USA

    Angela C. Crespo & Judy Lieberman

  4. Center for Translational Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands

    Annelisa M. Cornel & Stefan Nierkens

  5. Princess Máxima Center for Pediatric Oncology, Utrecht University, Utrecht, The Netherlands

    Annelisa M. Cornel & Stefan Nierkens

  6. Department of Developmental Neurobiology, St Jude Children’s Research Hospital, Memphis, TN, USA

    Anand G. Patel & Michael A. Dyer

  7. Department of Oncology, St Jude Children’s Research Hospital, Memphis, TN, USA

    Anand G. Patel

  8. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Navin R. Mahadevan, Marco Campisi, Alaa K. Ali, Minyue Chen, Rizwan Romee, Brian C. Miller & David A. Barbie

  9. Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA

    Navin R. Mahadevan

  10. Cellular Therapy and Stem Cell Transplant Program, Dana-Farber Cancer Institute, Boston, MA, USA

    Alaa K. Ali & Rizwan Romee

  11. Whitehead Institute for Biomedical Research, Cambridge, MA, USA

    Shupei Zhang, Malkiel A. Cohen & Rudolf Jaenisch

  12. Department of Oncogenomics, University Medical Center Amsterdam, University of Amsterdam, Amsterdam, The Netherlands

    Rogier Versteeg

  13. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Rudolf Jaenisch & Stefani Spranger

  14. Koch Institute for Integrative Cancer Research, Cambridge, MA, USA

    Stefani Spranger

  15. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Brian C. Miller

  16. Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA

    Brian C. Miller

  17. Evergrande Center for Immunological Diseases, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA, USA

    Brian C. Miller

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  1. Satyaki Sengupta
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Contributions

S. Sengupta and R.E.G. conceived the study. S. Sengupta, A.C.C., A.M.C., A.K.A., B.C.M., J.L. and R.E.G. designed the experiments. S. Sengupta performed the molecular, cellular and genomic studies. S.D. conceived and performed the genomic and computational analysis with inputs from R.D., S. Sengupta and R.E.G. S. Sengupta, A.C.C., A.M.C., A.K.A. and B.C.M. performed the T and NK cell studies. A.G.P. contributed to scRNA-seq and IHC analysis of primary tumors. N.R.M. and M. Campisi performed the ELISA and luminex assays with samples prepared by S. Sengupta. M. Chen helped with the analysis of luminex data. N.R.M. and M. Campisi designed and performed the 3D-migration assays. B.S. generated the PRRX1 constructs. B.S., E.D.C. and S. Sengupta performed the animal experiments with advice from B.C.M. and J.H.R. B.C.M. and J.H.R. performed FACS analysis of mouse tumors. S.Z. performed quantitative analysis of IHC images. H.H. performed the cell migration assays. M.K., D.N.D. and L.S. contributed to FACS analysis, generation of LDK378-resistant SH-SY5Y cells and compound testing, respectively. M.A.C., R.V., R.J., S. Spranger, R.R., D.A.B., S.N. and M.A.D. contributed ideas toward regulation of immune function and cell lineage state. S. Sengupta, S.D. and R.E.G. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Rani E. George.

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The authors declare no competing interests.

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Nature Cancer thanks William A. Weiss 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 Tumors within the immunogenic cluster express both IA and IE markers.

a, Distribution of the 498 primary NB tumors in the data set (SEQC-498; GSE49711) within the indicated prognostic categories. Risk stratification was based on the Children’s Oncology Group risk classification. INSS, International Neuroblastoma Staging System32. b, Scatter plot of the standardized variance in expression of all protein coding genes within the 498 tumors. Red dots indicate the top 5000 variably expressed genes. c, Elbow plot representing the percentage variance for the top 20 principal components, PCs (n = 20). d, Violin plots showing the expression of representative marker genes across the four clusters in 498 tumors. e, Stacked bar plots showing the distribution of tumors within the defined prognostic features within each cluster. Amp, amplified; Nonamp, nonamplified. f, Two-dimensional UMAP representations of the gene expression profiles in 394 NB tumors (GSE120572). Each dot represents a tumor. The top 3000 highly variable genes were selected based on the variance-stabilizing method33 and 20 significant principal components (PCs) selected and processed in UMAP to generate three clusters representing three NB subtypes. The DEGs were identified for each cluster using the receiver operating characteristics (ROC) curve to compare one cluster with others (log2 FC > 0.25). g, Heat map of expression values of 10 representative DEGs within each cluster in (f). Rows are z-score scaled average expression levels for each gene in all three clusters. h, i, Box plots comparing IA (h) and IE (i) scores within the four clusters (n = 103 tumors in Hi-MYCN, 241 in neuronal, 140 in Immunogenic and 14 in metabolic) from the SEQC-498 tumor data set. All box plots are defined by center lines (medians), box limits (25th and 75th percentiles) and whiskers (minima and maxima; the smallest and largest data range). Significance was determined by the two-tailed Wilcoxon rank-sum test. j, UMAP visualization of the distribution of IA and IE scores among the three tumor clusters derived from the 394 NBs in the GSE120572 dataset. Color bar represents normalized z-scores. Values <2.5 and >2.5 were set to -2.5 and +2.5, respectively, to reduce the effects of extreme outliers.

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Extended Data Fig. 2 Cell lineage is significantly associated with immune gene signatures in NB.

a, Bar plots representing the numbers of co-expressed genes within each module. b, GO analysis of co-expressed genes associated with indicated modules (KEGG database). The vertical dashed lines indicate the adjusted P-value of 0.05, two-tailed hypergeometric test. c, Gene network representing all possible interactions in module M1. The topmost connected genes (hubs) are indicated. Hubs derived from module M1 are colored blue (co-expression) and those from the STRING database are indicated in red (interaction). The size of each node corresponds to the degree of interaction. d, Summary of the overlap between the DEGs associated with the four tumor clusters and the adrenergic or mesenchymal signature genes as per Groningen et al., 201738. Significance determined by two-tailed Fisher’s exact test. e, Box plots comparing the expression of adrenergic and mesenchymal scores within the four clusters (n = 498 tumors [Hi-MYCN (n = 103), neuronal (241), immunogenic (140), metabolic (14)] derived from the SEQC-498 tumor data set. All box plots are defined by center lines (medians), box limits (25th and 75th percentiles) and whiskers (minima and maxima; the smallest and largest data range). The immunogenic cluster was compared with the other three clusters and significance determined by the two-tailed Wilcoxon rank-sum test. f, Scatter plot of the 498 primary NB tumors ranked based on increasing M-A scores. g, Pearson correlation matrix showing pairwise correlation values among the indicated parameters. The colors and sizes of the circles indicate the correlation coefficient values, with the least (smaller blue circles) to the most (larger brown circles) degree of association between the parameters shown.

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Extended Data Fig. 3 The relative mesenchymal score (M-A score) is positively correlated with an immunogenic signature.

a, Dot plots showing the distribution of MYCN-nonamplified tumors (n = 400) within each of the clusters based on ranked M-A scores. Tumors from the upper (high M-A) and lower (low M-A) M-A score quartiles are shown (n = 200 tumors; P = 0.0011 for C3). Statistical significance assessed by the two-tailed Fisher’s exact test. b, UMAP visualization of the distribution of adrenergic, mesenchymal, and M-A scores among the three tumor clusters derived from 394 NBs in the GSE120572 dataset. Color bar represents normalized z-scores. Values <2.5 and >2.5 were set to -2.5 and +2.5, respectively, to reduce the effects of extreme outliers. c, Left, Heat map of the indicated immune cell signatures in MYCN-nonamplified tumors (n = 401), ranked by increasing M-A scores. Log2 gene expression values were z-score transformed for heat map visualization. Right, violin plots comparing the quantitative scores of the indicated immune cell signatures in 100 tumors each from the upper (mesenchymal) and lower (adrenergic) quartiles of the tumor M-A scores using the two-sided K-S test. Box plots within the violin plots are defined by center lines (medians), box limits (25th and 75th percentiles) and whiskers (minima and maxima; 1.5X the interquartile range). d, e, Heat maps depicting IA (d) and IE (e) signatures in MYCN-nonamplified tumors, ranked by increasing M-A score. Log2 gene expression values were z-score transformed for visualization. Violin plots comparing the distribution of IA (d) and IE (e) signatures in 100 tumors each from upper (mesenchymal) and lower (adrenergic) quartiles of the tumor M-A scores are shown next to the heat maps. Significance determined by the two-sided K-S test. Box plots within the violin plots are defined as in (c). f, Bar diagram comparing the CIBERSORT-estimated fractional content of the indicated tumor-infiltrating leukocytes between MYCN-nonamplified adrenergic and mesenchymal tumors. Adrenergic and mesenchymal tumors were assigned as in (c). Data represent the mean, n = 100 tumors, P determined by the two-tailed Welch’s t-test.

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Extended Data Fig. 4 Cellular heterogeneity predicts immune gene expression in NB tumors.

a, UMAP embedding of 691 (top left) and 6498 (bottom left) cell signatures derived from two representative MYCN-amplified and nonamplified tumors (NB11 and NB07), respectively. Clusters of transcriptionally similar cells are colored by cell type. UMAP representation of 573 (top right) and 6299 (bottom right) malignant cell signatures from the same samples as in left panel. b, UMAP representation of the distribution of malignant cells in 12 NB tumors. Cells are colored based on the score calculated from the average expression of genes per cell38. c, Ridge plot representing the distributions of the ADR and MES scores of the malignant cells within the indicated clusters. d, Box plot representing ADR and MES scores of 56,597 malignant cells from 14 primary NB tumor samples based on transcriptional diversity (n = 4543 cells in NB01, 66 in NB02, 4429 in NB03, 3914 in NB04, 3769 in NB05, 7782 in NB06, 6047 in NB07, 10675 in NB08, 2353 in NB09, 587 in NB10, 568 in NB11, 3596 in NB12, 1024 in NB13 and 7244 in NB14). Box plots are defined by center lines (medians), box limits (25th and 75th percentiles) and whiskers (minima and maxima; 1.5X the interquartile range).

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Extended Data Fig. 5 NB tumors are highly heterogeneous at the inter- and intra-tumoral level.

a, b, c, UMAP visualization of 63,291 cells from 14 NB tumors42, colored by samples (a), cells (b) and cell type (c). d, Scatter plot of M-A scores derived from bulk RNA-seq data from 16 NB tumors (St Jude Cloud43). Samples are arranged based on increasing M-A scores. The samples analyzed by IHC are colored in red.

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Extended Data Fig. 6 Tumor cell-intrinsic immune marker genes are upregulated in mesenchymal NBs.

a, Heat maps of lineage marker (blue, adrenergic; red, mesenchymal) and MHC and APM gene (black) expression in the indicated adrenergic and mesenchymal cells (n = 2 independent experiments). Rows are z-scores calculated for each transcript in each cell type. b, FACS analysis of cell surface HLA expression in the cells depicted in a. Numbers on bottom right indicate isotype-normalized HLA gMFI values expressed as mean ± SD, n = 3 independent experiments. c, Heat map of lineage marker (blue, adrenergic; red, mesenchymal), MHC and APM gene (black) expression in SH-SY5Y and SH-EP adrenergic and mesenchymal cells, respectively (n = 2 independent experiments). Rows are z-scores calculated for each transcript in each cell type. d, GO analysis of differentially upregulated genes in mesenchymal SH-EP and adrenergic SH-SY5Y cells. Significance determined by the Fisher’s exact test. e, Volcano plot showing the gene expression changes between mesenchymal SH-EP and adrenergic SH-SY5Y cells. The top ten lineage marker genes are highlighted. The fold changes are represented in log2 scale (X-axis) and the -log10 of the P-values depicted on Y-axis (FDR < 0.1 and log2FC > 1.5). Significance was determined by two-tailed Wald test. f, RT-qPCR analysis of PRRX1 expression in SH-SY5Y cells expressing dox-inducible PRRX1 ± dox for 10 days. Data represent mean ± SD, n = 4 independent experiments. g, Dose-response curves of ceritinib-sensitive (5Y-par.) and -resistant (5Y-res.) cells treated with increasing concentrations of LDK378 for 72 h. Data show mean ± SD, n = 3 technical replicates and is representative of n = 3 independent experiments. h, Venn diagrams depicting the overlap between the DEGs in 5Y-res. cells (compared to 5Y-par. cells) and mesenchymal or adrenergic signatures. P-values were determined by two-tailed Fisher’s exact test. i, Waterfall plot of the fold-change in RNA expression levels of up- and down-regulated genes in 5Y-res. cells compared to 5Y-par. cells; selected lineage and immune genes are highlighted. j, GO analysis of differentially upregulated genes in 5Y-res. compared to 5Y-par. cells. Significance determined by the two-tailed Fisher’s exact test.

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Extended Data Fig. 7 NBs that acquire mesenchymal phenotypes at relapse show increased immune gene expression.

a, PCA plot showing the segregation of the depicted tumors using the 1st and 2nd PCs in 7 matched pairs and 4 single primary tumors. b, c, UMAP plot showing the segregation of primary and relapsed tumors (b) overlaid with the sample numbers (c). d, e, Line plots depicting changes in ADR, MES and M-A (MES-ADR) scores between primary and relapsed tumor pairs in clusters 2 (d) and 3 (e). f, Heat map of z-score-transformed log2 normalized expression values of tumor cell-intrinsic IA and IE genes in the 7 matched tumor pairs (n = 14 tumor samples). g, h, Line plots depicting the changes in the indicated immune cell scores in primary and relapsed tumor pairs in clusters 2 (g) and 3 (h).

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Extended Data Fig. 8 Enrichment of immune cells is seen at relapse in a matched diagnosis-relapsed NB tumor pair.

a, UMAP embedding of single cell profiles (dots) from diagnosis (left; n = 4349 cells) and relapsed (right; n = 771 cells) samples, colored by cell subset signatures. b, UMAP visualization of the distribution of ADR and MES scores of malignant cells at diagnosis (top; n = 4266 cells) and relapse (bottom; n = 113 cells). c, Box plot depicting the ADR (top) and MES (bottom) scores of the malignant cells in each sample [diagnosis (n = 4266), relapse (n = 113)]. Box plots are defined by center lines (medians), box limits (25th and 75th percentiles) and whiskers (minima and maxima; the smallest and largest data range). d, Bar plot representing the proportion of neuroendocrine cells (tumor), fibroblasts, endothelial cells and immune cells in each sample.

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Extended Data Fig. 9 Immune gene activation associated with the mesenchymal state is epigenetically regulated.

a, ChIP-qPCR analysis of the indicated histone marks at the indicated immune gene promoters in SH-SY5Y cells expressing dox-inducible PRRX1 ± dox. Data points represent mean of 2 technical replicates of 2 independent experiments. Enrichments at TAP1 and PSMB9 loci represent the data corresponding to amplicons 4 and 6, respectively, in Fig. 4a. b, Enhancer regions in 5Y-par., 5Y-res. and SH-EP cells. H3K27ac bound regions identified as significant peaks were stitched together if they were within 12.5 kb of each other (typical enhancers; grey). SEs (red) were defined as stitched enhancers surpassing the threshold signal based on the inclination point in all cell types. In 5Y-par., 5Y-res. and SH-EP cells, 2.94% (915/31116), 6.56% (1880/28635) and 4.18% (1215/29057) of the enhancers were classified as SEs, respectively. The top five SE-associated lineage-specific genes are highlighted. c, ChIP-qPCR analysis of EZH2 and SUZ12 enrichment at the indicated immune genes in 5Y-par. cells. Negative control loci, PHOX2B and LIN28B. Data represent mean of n = 2 independent experiments. d, e, Scatter plots representing the differential binding of the indicated histone marks at the promoter regions of all protein coding genes between 5Y-par and 5Y-res. (d), and 5Y-par. and SH-EP cells (e). rpm/bp, reads per million per base pair. A ≥ 0.75 log2FC threshold was used to identify unique peaks for each histone mark. f, Bar plots representing the numbers of immune genes with increased promoter deposition of H3K4me3 and H3K27ac (log2 FC ≥ 0.75) and loss of H3K27me3 (log2 FC ≥ 0.75) marks, together with increased RNA expression (log2 FC ≥ 1) in 5Y-res. (left) or SH-EP (right) compared to 5Y-par. cells. g, Violin plots of the ratios of active to repressive histone marks surrounding immune gene promoters in the indicated cells. Significance determined by the two-tailed Wilcoxon rank-sum test. Box plots within the violin plots defined by center lines (medians), box limits (25th and 75th percentiles) and whiskers (minima and maxima; 1.5X the interquartile range).

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Extended Data Fig. 10 Tumors derived from NB-9464 H-2Kbhi cells show mesenchymal properties.

a, FACS plot showing gating conditions for sorting NB-9464 cells into H-2Kbhi and H-2Kblo populations. X-axis, side scatter; Y axis, H-2Kb fluorescence intensity. A logscale expression value of 103 was used to gate H-2Kbhi (≥ 103) and H-2Kblo (<103) populations. b, FACS analyses of H-2Kb expression (dark colored histograms) in H-2Kblo and H-2Kbhi cells compared to isotype controls (light colored histograms). Plots representative of 2 independent experiments. c, Left, Bright field images of crystal violet-stained H-2Kblo and H-2Kbhi cells in transwell migration assays. Scale bar, 100 µm. Right, Quantification of migrating cells per high-power field (HPF). Data represent mean ± SD of cells from three HPFs per cell line, significance derived using unpaired two-tailed Student’s t-test. Data representative of n = 3 independent experiments. d, Quantification of the relative invasiveness of H-2Kblo and H-2Kbhi cells. Data represent mean ± SD from three technical replicates, significance derived using unpaired two-tailed Student’s t-test. Data representative of 2 independent experiments. e, RT-qPCR analysis of H-2Kb expression in H-2Kblo and H-2Kbhi tumors and the cell lines from which they were derived. Data were normalized to H-2Kb expression in H-2Kblo cells and represent mean ± SD, n = 3 tumors per group, P determined by the unpaired two-tailed Student’s t-test. f, Tumor volumes in C57BL/6 mice injected subcutaneously with 1 ×106 H-2Kblo or H-2Kbhi cells and treated with anti-PD1 + anti-CTLA4 antibodies or isotype control (black arrowheads) on days 7, 10, and 13 after tumor inoculation. Data represent growth of individual mouse tumors, n = 8, H-2Kblo isotype; n = 7, H-2Kblo anti-PD1 + anti-CTLA4; n = 8, H-2Kbhi isotype; n = 8, H-2Kbhi anti-PD1 + anti-CTLA4. g, FACS analysis of the ratios between tumor infiltrating CD8 + T or conventional T (Tcon) and Treg cells (CD8:Treg and Tcon:Treg, respectively) in H-2Kblo and H-2Kbhi tumors treated with isotype control or anti-PD1 + anti-CTLA4 antibodies. Data represent medians with interquartile ranges, n = 5 tumors per group.

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Sengupta, S., Das, S., Crespo, A.C. et al. Mesenchymal and adrenergic cell lineage states in neuroblastoma possess distinct immunogenic phenotypes. Nat Cancer 3, 1228–1246 (2022). https://doi.org/10.1038/s43018-022-00427-5

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