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Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment

Nature Genetics volume 54, pages 1192–1201 (2022)Cite this article

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Abstract

Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including ‘stress’, ‘interferon response’, ‘epithelial-mesenchymal transition’, ‘metal response’, ‘basal’ and ‘ciliated’. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.

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Fig. 1: Catalog of recurrent cancer gene modules.
Fig. 2: Expression of gene modules underlies cancer cell states.
Fig. 3: Spatial organization of the tumor microenvironment.
Fig. 4: Mapping cancer cell states and their interactions with the TME.
Fig. 5: Cancer cell states in perturbed tumor microenvironments.

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Data availability

Data from this manuscript has been submitted to GEO with accession number GSE203612.

Code availability

Code from this manuscript is available at https://github.com/yanailab, and also through Zenodo (https://doi.org/10.5281/zenodo.6611786)99.

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Acknowledgements

We thank R. White, F. Kuperwaser, R. Satija and B. Neel for critical readings and helpful suggestions. We thank E. Hernando for helpful discussions and for providing the PDX samples. This work was supported by the following NIH grants: P50 CA225450 (to I.O. and I.Y.), R01 LM013522 (to I.Y.), R21 CA264361 (to I.Y.), U01CA260432 (to I.Y.), GM126573 and F30 CA257400 (to D.B.). This work was also supported by a DOD Team grant ME200052 (to A.W.L. and I.Y.) and The Leon Lowenstein Foundation (to I.Y.); The Mary Kay Foundation (to I.Y.).

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Authors and Affiliations

  1. Institute for Computational Medicine, NYU Langone Health, New York, NY, USA

    Dalia Barkley, Reuben Moncada, Maayan Pour, Deborah A. Liberman, Maayan Baron, Anjali Rao, Bo Xia, Gustavo S. França & Itai Yanai

  2. Department of Dermatology, NYU Langone Medical Center, New York, NY, USA

    Ian Dryg, Amanda W. Lund & Iman Osman

  3. Department of Surgery, NYU Langone Health, New York, NY, USA

    Gregor Werba, Wei Wang & Amanda W. Lund

  4. Department of Pathology, NYU Langone Medical Center, New York, NY, USA

    Gregor Werba, Wei Wang, Alejandro Weil, Deborah F. Delair, Cristina Hajdu & Iman Osman

  5. Perlmutter Cancer Center, NYU School of Medicine, New York, NY, USA

    Amanda W. Lund, Iman Osman & Itai Yanai

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  1. Dalia Barkley
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Contributions

D.B., R.M., G.W., W.W., M.B., A.R. and B.X. performed transcriptomic experiments. D.B. performed the transcriptomic data analysis with contributions from I.Y., A.R., G.S.F. A.W. and I.O. collected patient samples. D.F.D. and C.H. annotated patient samples. M.P., D.A.L., I.D. and A.W.L. performed CODEX experiments and analysis. D.B. and I.Y. designed the experiments and wrote the manuscript.

Corresponding author

Correspondence to Itai Yanai.

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The authors declare no competing interests.

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Nature Genetics thanks Alexander Swarbrick 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 Quality control and cell type annotation in the single-cell RNA-Seq data.

a. Violin plots of the number of UMIs per cell in each tumor sample. b. Violin plots of the number of genes per cell in each tumor sample. c. Heatmap of average scaled gene expression per cell type per sample. Top bar represents cell type (colored as indicated) and sample (colored as in Fig. 1a).

Extended Data Fig. 2 Control analysis for purity of single-cell RNA-Seq data.

a. UMAP embeddings of cells annotated as malignant per cancer type or organ system, colored by sample. b. Control analysis for annotations of cells as malignant, using the method described by Kim et al.1. Briefly, inferred CNV profiles (from the scRNA-Seq data) were scored as the sum of the squared values (shown as the x-axis). The cells with the top 10 scores are assumed to be malignant and each cell is then correlated with the average profile of the top 10 cells (y-axis). In tumors with CNVs these two measures are consistent. Color indicates the annotation as malignant or normal cells, per sample.

Extended Data Fig. 3 Additional analyses for the derivation of the cancer gene modules catalog.

a-e. Heatmap of the significance of overlap (hypergeometric test) of the consensus modules across (a) the indicated Gene Ontology terms, (b) cell type markers, (c) signatures derived by Neftel et al.2, (d) signatures derived from Puram et al.3, and (e) signatures derived from Ji et al.4. f. Network of genes belonging to the consensus modules, colored as in Fig. 1f. Lines connect genes that are found together in at least 2 individual tumor modules (see Methods). g. Heatmap of the significance of the overlap between consensus modules and individual tumor modules (hypergeometric test). The bottom bar indicates the significance of the overlap with consensus modules (hypergeometric test). The top bar indicates the identity of the tumor samples, colored as in Fig. 1a. h. Heatmap of the Jaccard similarity (intersect/union) between consensus modules and SCENIC regulons obtained for individual tumors. The bar indicates the identity of the tumor samples, colored as in Fig. 1a. To test whether the catalog of 16 modules can also be detected using an independent approach, we used SCENIC5, a method that identifies genes that are both correlated in their expression and regulated by the same transcription factor. We found that each module of our catalog had significant overlap with several SCENIC regulons (Supplementary Table 4, see Methods). For instance, the interferon response module overlapped with several SCENIC regulons annotated with the transcription factors STAT1 and IRF1.

Extended Data Fig. 4 Pattern of presence and absence of the catalog gene modules across malignant and normal epithelial cells.

a-c. Heatmap of the significance of the presence of each module (see Methods) in the malignant cells of each tumor sample (a), in epithelial cells of normal samples6,7,8 (b), and for malignant and epithelial cells from paired normal and tumor samples (c). Gray indicates a complete lack of gene expression of the module. FTE: Fallopian tube epithelium. BRE: Breast epithelium. LE: Liver epithelium. d. Volcano plots of differential gene expression between malignant and normal epithelial cells from paired LUAD samples (Kim et al.1 T20 vs. N20). Each panel highlights genes from the indicated module.

Extended Data Fig. 5 Cell states across tumors.

a-c. Module score TSNE embedding of the cancer cells of all 19 tumors, colored by the most highly expressed module (a), by module score as in Fig. 1f(b) and by entropy of tumor of origin (c). d. Gene expression UMAP embedding of the cancer cells of OVCA NYU3 and BRCA NYU1, colored by module score for the ciliated and cycle module respectively. Unlike other modules, the cycle and cilium module were expressed by cells forming discrete clusters. These clusters are also identified when examining tumors individually in gene expression-based dimensionality reductions, and are therefore not artifacts of the module score dimensionality reduction (Extended Data Fig. 6d). e. Heatmap of the frequency of expression of each module in the malignant cells of each sample. f. Module expression frequencies in normal vs malignant epithelia. Points represent individual samples, bars indicate mean + /- standard error calculated across individual tumors of the same cancer type.

Extended Data Fig. 6 Spatial organization of the tumor microenvironment.

a. OVCA NYU1 H&E image with spots colored by annotation (scale bar represents 1 mm). b-c. Joint dimensionality reduction after mutual nearest neighbor integration (MNN) of single-cell and ST spots for the OVCA NYU1 sample, with (b) single-cell transcriptomes in gray and ST spots colored according to their annotation and (c) spots in gray and single-cell transcriptomes colored by their annotated cell type. The single cells form clusters at the periphery, indicating distinct cell types. The ST spots are either mixed with individual single-cell clusters, indicating a pure population, or bridge multiple clusters, indicating a combination of cell types. Specifically, ‘Malignant’ spots are mixed with the malignant cell cluster, ‘Normal’ spots are in the region of nonmalignant cell types, and ‘Both’ spots span both malignant and nonmalignant single-cell clusters. d. LIHC NYU1 H&E image with spots colored by annotation as in a. (scale bar represents 1 mm). e-g. Joint dimensionality reduction of single-cell and ST spots for the LIHC NYU1 sample, with (e) single-cell transcriptomes in gray and ST spots colored according to their annotation, (f) spots in gray and single-cell transcriptomes colored by their annotated cell type and (g) spots colored according to their coordinate along the x-axis. This sample has two spatially distinct tumor nodules, with the left having substantial mixing between malignant and nonmalignant cells and the right consisting almost exclusively of malignant cells. The joint dimensionality reduction analysis reflects the two corresponding malignant clusters, which were not distinct when considering the single-cell dimensionality reduction alone.

Extended Data Fig. 7 Validation of spot annotation and module presence using PDX samples.

a. UMAP of single-cell RNA-Seq data for sample 1, colored by the number of UMIs corresponding to human (left) or mouse (right) genes. b. Heatmap of module overdispersion in malignant cells of sample 1 (see Extended Data Fig. 4a-c and Methods). Since human malignant cells can unambiguously be distinguished from mouse TME cells in this system, we used the single-cell data to confirm that the modules are differentially expressed by malignant cells themselves and rule out the possibility of an artifact stemming from TME contamination. For example, the pEMT module includes genes normally expressed by fibroblasts, but we detected its overdispersion in malignant cells. c-d. Spatial transcriptomic spots colored by the number of UMIs corresponding to human (left) or mouse (right) genes for sample 1 (c) and sample 2 (d). Scale bar represents 1 mm. e-f. Spatial transcriptomic spots colored by annotation as ‘Malignant’, ‘Both’ or ‘Normal’ using the NNLS method on the full transcriptome (left) or on human orthologs (right) in sample 1 (e) and sample 2 (f). To test the accuracy of the NNLS method to annotate spots, we performed paired scRNA-Seq and ST on two patient-derived melanoma xenografts (PDX). In this setting, only malignant cells are of human origin and therefore express human genes, enabling us to reliably identify malignant cells or spots. Using the NNLS method on the full mouse and human transcriptomes, we first established a ‘ground truth’ for spot identities. We then simulated the patient samples by converting mouse genes to their human orthologs, thereby removing the species information. This resulted in 99% (sample 1) and 89% (sample 2) specificity ‘Malignant’ spots. g-h. ‘Malignant’ spatial transcriptomic spots colored by expression score for the cycle, stress, hypoxia and pEMT modules for sample 1 (g) and sample 2 (h).

Extended Data Fig. 8 Relationship between pEMT and cancer spot depth.

a. Sample UCEC NYU3, ‘Malignant’ only spots colored by their depth: the distance to the nearest spot containing nonmalignant cells. b. Sample UCEC NYU3, ‘Malignant’ only spots colored by pEMT module score. c. Boxplots of correlation scores (±log10(p-value)) between module scores and depth of malignant spots across 10 samples, colored as in Fig. 1a. For each boxplot, the line indicates the median, the box indicates the 1st and 3rd quartile, the whiskers indicate the minimum and maximum values. Positive scores correspond to positive correlations. Dashed lines indicate p-value=0.05. Plots of the relationship between the pEMT module score and depth in the 10 ST samples, colored as in Fig. 1a. Lines are drawn for correlations with p-value<0.05.

Extended Data Fig. 9 CODEX analysis of samples from four cancer types supporting a proximity of interferon response-expressing malignant cells to macrophages and T cells.

a. Cell populations and marker expression in a region of OVCA NYU1. Top row displays an entire tile, bottom row displays an enlargement. Top and bottom left: Colored by populations as defined in Extended Data Fig. 15. Top right and bottom center: Colored by expression of markers used to define cell types, as indicated. Bottom right: Colored by expression of PanCK and of HLA-DRA, used to define interferon response positive and negative malignant cells. Scale bar represents 50 µm. b. For the tile shown in a., histogram showing the distance between malignant cells and the nearest macrophage, for interferon response positive (light green) and negative (dark green) malignant cells. Lines indicate the mean distance for each population, used to calculate the log2(proximity ratio). c-d. Boxplots of the distribution of log2(proximity ratio) (c) and log2(neighborhood ratio) (d) of macrophages, T cells and malignant cells across tiles of each sample (*, p-value<0.05; ***, p-value<0.001; two-sided t-test). For each boxplot, the line indicates the median, the box indicates the 1st and 3rd quartile, the whiskers indicate the minimum and maximum values.

Extended Data Fig. 10 Additional experiments relating to the orthotopic and heterotopic mouse experiments.

a. UMAP embedding of cells from 16 orthotopic pancreatic tumors across the 3 experiments, colored by annotation as malignant or nonmalignant cells. b. Same as a, colored by sample. c. Violin plots of module expression scores in individual tumors across the 3 experiments. d. Barplots of the average expression of the interferon response module genes in cancer cells in the WT and Rag1-/- tumors according to their interferon response expression.

Supplementary information

Supplementary Information

Supplementary Figs. 1–4, captions for Data 1–3 and captions for Tables 1–7.

Reporting Summary

Supplementary Data 1

Differential gene expression in scRNA-seq data. For each of the tumors collected for scRNA-Seq, the heatmap shows the scaled gene expression across cell types. Top bar represents cell type annotation. For each cell type the top ten differentially expressed genes are selected (Methods).

Supplementary Data 2

Differential gene expression in ST data. For each of the ten tumors studied by spatial transcriptomics, the heatmap shows the scaled gene expression across spots (columns) annotated as ‘Malignant’, ‘Both’ and ‘Normal’ for genes (rows) identified as differentially expressed across cell types in the paired single-cell RNA-seq data.

Supplementary Data 3

Key markers from CODEX used for proximity analysis of interferon response-expressing malignant cells to macrophages and T cells. a, OVCA NYU1. b, UCEC NYU3. c, LIHC NYU1. d, GIST NYU1. White dotted lines signify areas excluded from analysis due to obvious folding or bubbles. 1. Tiles; scale bar, 500 µm. 2. Single stain, DAPI (white); scale bar, 500 µm. 3. Single stain, DAPI (white), inset from b; scale bar, 50 µm. 4. Single stain, DAPI (white) with segmentation, inset from c; scale bar, 25 µm. Single stain, CD3 (magenta); scale bar, 500 µm. 5. Single stain, DAPI (white) with segmentation, inset from c; scale bar, 25 µm. Single stain, CD3 (magenta); scale bar, 500 µm. 6. Single stain, CD68 (red), scale bar, 500 µm. 7. Single stain, Pan-Cytokeratin (green) in a–c or podoplanin (blue) in d; scale bar, 500 µm. 8. Single stain, HLA-DR (white); scale bar, 500 µm. 9. Single stain, CD3 (magenta), inset from e; scale bar, 50 µm. 10. Single stain, CD68 (red), inset from f; scale bar, 50 µm. 11. Single stain, pan-cytokeratin (green) in a–c or podoplanin (blue) in d; inset from g; scale bar, 50 µm. 12. Single stain, HLA-DR (white), inset from h; scale bar, 50 µm. 13. Composite image of CD3 (magenta), CD68 (red), Pan-Cytokeratin (green) in a–c or podoplanin (blue) in d and HLA-DR (white); scale bar, 500 µm. 14. Composite image of CD3 (magenta), CD68 (red), Pan-Cytokeratin (green) in a–c or podoplanin (blue) in d and HLA-DR (white), inset from m; scale bar, 50 µm. 15. Composite image of CD3 (magenta), CD68 (red), Pan-Cytokeratin (green) in a–c or podoplanin (blue) in d and HLA-DR (white) with populations as colored in Extended Data Fig. 15a: vascular (yellow), macrophages (red), T cells (magenta), B cells (orange), DC (purple), epithelial malignant cells that are interferon response positive (light green), epithelial malignant cells that are interferon response negative (dark green), stroma in epithelial cancers (blue), stromal malignant cells that are interferon response positive (cyan), stromal malignant cells that are interferon response negative (dark blue) and smooth muscle (light gray); scale bar, 500 µm. 16. Composite image of CD3 (magenta), CD68 (red), Pan-Cytokeratin (green) in a–c or podoplanin (blue) in d, and HLA-DR (white) with populations as colored in Extended Data Figure 15a: vascular (yellow), macrophages (red), T cells (magenta), B cells (orange), DC (purple), epithelial malignant cells that are interferon response positive (light green), epithelial malignant cells that are interferon response negative (dark green), stroma in epithelial cancers (blue), stromal malignant cells that are interferon response positive (cyan), stromal malignant cells that are interferon response negative (dark blue) and smooth muscle (light gray), inset from o; scale bar, 50 µm.

Supplementary Tables

Table 1. Pathology annotation of patient samples. Table 2. Annotation of cells from 19 patient samples. Table 3. Composition of recurrent gene modules. Table 4. Transcription factors identified using SCENIC. Table 5. Composition of mouse pancreatic cancer gener modules. Table 6. Differentially expressed genes between M1 and M2 macrophages. Table 7. Antibodies used for CODEX workflow.

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Barkley, D., Moncada, R., Pour, M. et al. Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment. Nat Genet 54, 1192–1201 (2022). https://doi.org/10.1038/s41588-022-01141-9

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