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Integrative single-cell analysis of human colorectal cancer reveals patient stratification with distinct immune evasion mechanisms

Nature Cancer volume 5, pages 1409–1426 (2024)Cite this article

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Abstract

The tumor microenvironment (TME) considerably influences colorectal cancer (CRC) progression, therapeutic response and clinical outcome, but studies of interindividual heterogeneities of the TME in CRC are lacking. Here, by integrating human colorectal single-cell transcriptomic data from approximately 200 donors, we comprehensively characterized transcriptional remodeling in the TME compared to noncancer tissues and identified a rare tumor-specific subset of endothelial cells with T cell recruitment potential. The large sample size enabled us to stratify patients based on their TME heterogeneity, revealing divergent TME subtypes in which cancer cells exploit different immune evasion mechanisms. Additionally, by associating single-cell transcriptional profiling with risk genes identified by genome-wide association studies, we determined that stromal cells are major effector cell types in CRC genetic susceptibility. In summary, our results provide valuable insights into CRC pathogenesis and might help with the development of personalized immune therapies.

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Fig. 1: Human colorectal atlas and tissue preference of major cell types.
Fig. 2: Characterization of immune and stromal cell subsets in CRC.
Fig. 3: Characterization of EC subsets and cell subset co-occurrence in CRC.
Fig. 4: Characterization of interindividual heterogeneity across the TMEs of patients with CRC.
Fig. 5: Characterization of potential immune evasion mechanisms and regulations.
Fig. 6: Characterization of potential immune evasion mechanisms.
Fig. 7: TLS characterization by spatial transcriptome and single-cell transcriptome analyses.
Fig. 8: Transcriptional alterations of CRC genetic risk genes.

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Data availability

Data included in this paper were acquired from the National Center for Biotechnology Information’s Gene Expression Omnibus (GSE146771, GSE188711, GSE132465, GSE144735, GSE132257, GSE125527, GSE150115, GSE201349 and GSE178341), Single Cell Portal (https://singlecell.broadinstitute.org/single_cell, SCP259), VIB-KU Leuven Center for Cancer Biology (https://lambrechtslab.sites.vib.be/en/pan-cancer-blueprint-tumour-microenvironment-0, Lambrechts Lab), Supplementary Table S3 in ref. 19, Gut Cell Survey (https://www.gutcellatlas.org, Space–Time Gut Cell Atlas) and upon request from authors. To facilitate the use of our CRC atlas for the broader research community, we have shared the data via figshare at https://doi.org/10.6084/m9.figshare.25323397 (ref. 71) and developed an online interactive portal (http://118.190.148.166:8918/). The human CRC gene expression and clinical information data were derived from the TCGA Research Network (http://cancergenome.nih.gov/). All other data supporting the findings of this study have been provided as supplementary tables and source data files. Source data are provided with this paper.

Code availability

No algorithm or software was generated for this study. The code for reproducing major figures is available on GitHub (https://github.com/Chuxj/CRC-atlas). Any additional information required to reanalyze the data reported in this article is available from the lead contact upon request.

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Acknowledgements

We are grateful to F. Tang for suggestions on cell subset annotation. This study was supported by Changping Laboratory. Part of the analysis was performed on the High-Performance Computing Platform of the Center for Life Sciences, Peking University.

Author information

Author notes

  1. These authors contributed equally: Xiaojing Chu, Xiangjie Li, Yu Zhang.

  2. These authors jointly supervised this work: Zemin Zhang, Sijin Cheng.

Authors and Affiliations

  1. Changping Laboratory, Beijing, China

    Xiaojing Chu, Xiangjie Li, Yu Zhang, Guohui Dang, Yuhui Miao, Wenbin Xu, Jinyu Wang & Sijin Cheng

  2. Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

    Guohui Dang

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

    Zemin Zhang

  4. Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

    Zemin Zhang

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Contributions

S.C., Z.Z. and X.C. designed the study. X.C., X.L. and Y.Z. collected data and performed bioinformatics analysis, supervised by S.C. and Z.Z. X.C. and S.C. interpreted the data with help from G.D., Y.M., W.X. and J.W. X.C. and S.C. wrote the manuscript, supervised by Z.Z. with input from all authors. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Zemin Zhang or Sijin Cheng.

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Competing interests

Z.Z. is a founder of Analytical Bioscience and also serves on the advisory board of Cell. All financial interests are unrelated to this study. The other authors declare no competing interests.

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Nature Cancer thanks Julio Garcia-Aguilar, Liza Konnikova and Iain Tan for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Immune and stromal cell subset annotation.

UMAP plots showing different expression patterns of selective well-known marker genes of major cell types. n = 671192 cells.

Extended Data Fig. 2 Immune and stromal cell subset annotation.

Dotplots showing expression level of top ten marker genes of B cells (a), T, NK cells and ILCs (b) myeloid cells (c), fibroblasts, pericytes and smooth muscle cells (d) and endothelial cells (e). Dot size indicates fraction of expressing cells. Dot color indicates normalized expression levels.

Extended Data Fig. 3 Data robusticity and batch effect evaluation and tissue preference of major cell types.

(a) UMAP showing cell clustering colored by datasets. n = 671192 cells. (b) PCA plots showing sample clustering based on cell subset abundance, colored by sampling sites (left) and datasets (right), n = 245 samples. (c) Boxplots (top and bottom quartiles with horizontal lines at the median) showing immune and stromal cell proportion in relative to all non-epithelial/malignant cells in different tissues. Each dot indicates a sample, n = 28 (healthy), 10 (uninflamed), 15 (inflamed), 83 (paracancerous), 16 (polyp) and 180 (tumor). Student’s t-test (two-sided). P values were shown in Supplementary Table 3. (d) Boxplots (top and bottom quartiles with horizontal lines at the median) showing immune cell proportion in relative to all CD45+ cells. Each dot indicates a sample, n = 28 (healthy), 10 (uninflamed), 15 (inflamed), 83 (paracancerous), 16 (polyp), 180 (tumor). Student’s t-test (two-sided). P values were shown in Supplementary Table 4.

Source data

Extended Data Fig. 4 Tissue preference of major cell types.

(a) Hematoxylin-eosin staining and IgG, IgA genes expression in spatial transcriptomic spots of tumors and adjacent normal regions. The results were replicated in two patients. (b) Box plots (top and bottom quartiles with horizontal lines at the median) showing the relative proportion of certain cell types in the validation cohort. Each dot indicates a sample, n = 17 (paracancerous) and 98 (tumor). Student’s t-test (two-sided). P values = 0.0019, 1e-12, 0.056, 0.063, 9.4e-5, 4.9e-13 from left to right.

Source data

Extended Data Fig. 5 Data robusticity evaluation, tissue similarity and characterization of fibroblast subsets in CRC.

(a) Box plots (top and bottom quartiles with horizontal lines at the median) showing cell proportion in relative to all non-epithelial/malignant cells in different tissues after randomly excluding (Excl.) datasets. Each dot indicates a sample. From left to right, n = 28, 24, 24, 28, 28, 28, 28, 28, 28 (healthy), 10, 6, 6, 10, 10, 10, 10, 10, 10 (uninflamed), 15, 11, 11, 15, 15, 13, 15, 15, 15 (inflamed), 83, 83, 78, 68, 77, 83, 76, 71, 42 (paracancerous), 16, 16, 16, 16, 16, 16, 16, 16, 16 (polyp), 180, 180, 175, 154, 164, 180, 162, 163, 63 (tumor). (b) Box and violin plots (top and bottom quartiles with horizontal lines at the median) showing Bhattacharyya distance difference between tumors and polyps in different cell types, n = 100 for each cell type. (c) Lollipop plot showing upregulated pathways in monocyte/macrophages in tumors compared to polyp tissues. Numbers in the dots indicate gene counts matched to corresponding biological pathways. Over-representation analysis (BH adjustment). (d) UMAP plots showing fibroblast subsets composition (top) and distribution in tissues (bottom). n = 45581 cells. (e) UMAP plots showing expression patterns of POSTN (top) and CXCL12 (bottom). n = 45581 cells. (f) Lollipop plots showing upregulated pathways in CAF subsets. Numbers in the dots indicate gene counts matched to corresponding biological pathways. Over-representation analysis (BH adjustment).

Source data

Extended Data Fig. 6 Characterization of endothelial subsets, data robusticity and dataset effect evaluation referring to patient stratification.

(a) UMAP plots showing expression patterns of ACKR1 (top) and SELE (bottom). n = 11233 cells. (b) Scatter plot showing a positive correlation between HEV-CXCL10 and T cell signature. HEV-CXCL10 signature was indicated by the expression score of the top ten HEV-CXCL10 subset markers (CXCL10, CXCL11, GBP1, CXCL9, ISG15, GBP4, WARS, IL32, CCL2, CTSS and IGFBP5) and two endothelial cell markers (PECAM1 and VWF), and T cell signature was indicated by expression score of the top ten T cell markers (CCL5, CD3D, GZMA, TRBC2, CD2, TRAC, CD7, KLRB1, GNLY and CD3E), n = 647. Pearson’s correlation. (c–e) PCA plots showing patient clustering based on cell subset abundance, colored by group (c), MSI status (d) and dataset (e). (f, g) ANOVA tests estimating datasets, groups and MSI status contributions to PC1 (f) and PC2 (g). *Df (Dataset) is different from dataset number -1, because missing values were excluded in the analysis, n = 116. (h) Heatmap showing cell subset abundance of different groups in patients from the Pelka dataset.

Source data

Extended Data Fig. 7 Characterization of six CRC groups and validation of classification and cell-cell interaction.

(a) Density plot showing the distribution of group 6 signature score in TCGA patients, n = 87 (MSI) and 193 (MSS) samples. (b) Boxplots (top and bottom quartiles with horizontal lines at the median) showing the proportion of EMP1+ (left) and LGR5+ (right) malignant cells in patients of different groups, n = 7 (G1), 23 (G2), 10 (G3), 15 (G4), 25 (G5) and 30 (G6). Student’s t-test, (one-sided). (c) Violin plots showing iCMS marker genes score in different groups. n = 4, 23, 10, 14, 22, 21 and 53 patients. (d) Heatmap showing the relative abundance of cell subsets in the validation cohort. (e) Hematoxylin-eosin staining, ADGRE5 and CD55 expression in transcriptomic spots. This pattern was replicated in two patients. (f) Scatter plot showing correlation between expression density of ADGRE5 and CD55 in transcriptomic data. P value is less than the smallest non-zero normalized floating-point number of R, n = 4007. Pearson’s correlation.

Source data

Extended Data Fig. 8 Characterization of potential immune evasion mechanisms and regulations.

(a) Heatmaps showing scaled mean expression of co-inhibitory molecules in CD8+ T cells and PDL1/2 in malignant cells in the validation cohort. (b) Box and violin plots (top and bottom quartiles with horizontal lines at the median) showing Co-inhibitory scores of patients in different groups, n = 42 (G1), 998 (G2), 1386 (G3), 1785 (G4), 7698 (G5) and 6840 (G6). Student’s t-test (two sided). (c) Heatmap showing regulatory potentials of top 15 prioritized ligands in regulating target genes in CD4-CXCL13 subset. (d) Dotplots showing the expression level of the top ten prioritized ligands identified for CD4-CXCL13 in each cell subset. Dot size indicates the fraction of expressing cells. Dot color indicates normalized expression levels. (e) Scatter plot showing the correlation between the normalized expression level of LAMP3 and IL15 in the tumor samples of CRC patients of TCGA datasets. Shading indicates a confidence interval of 0.95, n = 647. Pearson’s correlation. (f) Expression of SIRPA and CD47 in the spatial transcriptomic spots. This pattern was replicated in two patients. (g) Scatter plot showing the correlation between the expression density of SIRPA and CD47. P value is less than the smallest non-zero normalized floating-point number of R, n = 4007. Pearson’s correlation. (h) Violin plot showing the expression level of CD24 in malignant cells of patients in groups. n = 6195, 32816, 15402, 25928, 23391 and 20431 cells from left to right. (i) Violin plot showing expression level of FCGR3A in NK cell subsets. n = 3046 (NK-GZMH) and 2316 (NK-XCL1) cells. (j) Violin plot showing expression level of FCGR3A in NK cell subsets in the validation cohort. n = 1496 (NK-GZMH) and 1553 (NK-XCL1) cells. (k) Scatter plot showing the correlation between plasma signature score and FcR genes score regressing out macrophage and NK cell variance. Shading indicates a confidence interval of 0.95, n = 647. Pearson’s correlation.

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Extended Data Fig. 9 TLS characterization by spatial transcriptome.

(a) Hematoxylin-eosin staining of tumor tissue sections (top) and spatial transcriptomic profiles of TLS 12 classical markers score (middle) and G6 signature genes score (bottom). This pattern was replicated in six patients, and one of them is shown in Fig. 7a. (b) LTB expression in spatial transcriptomic spots. This pattern was replicated in six patients, and one of them is shown in Fig. 7d.

Extended Data Fig. 10 Transcriptional alteration in genetic risk genes.

(a) Violin plots showing LTB expression in group enriching cell subsets in the validation cohort. n = 47, 3384, 5529, 2131, 94, 847, 382, 1615, 1015, 214, 574, 2245, 5501, 325, 1989, 3423, 3995, 13606, 3022, 5471, 12352, 4065, 382, 346, 2382, 589, 30185, 10975, 1473, 2766, 5573, 6809, 10709, 1454, 1057, 1535, 1104, 5355, 12749, 1501, 6124, 6621, 4144, 7359, 824 and 448 cells from left to right. (b) Bar plot showing lambda statistics of each cell type in each patient group. From left to right, n = 256, 229, 191, 112, 113, 142, 167, 94, 125, 97, 91 and 119 genes (G1); 270, 267, 199, 106, 109, 225, 183, 109, 115, 113, 93 and 88 genes (G2); 253, 223, 156, 109, 117, 188, 155, 85, 129, 97, 88, 94 (G3); 276, 255, 215, 122, 210, 269, 181, 129, 144, 119, 106 and 133 (G4); 284, 276, 179, 102, 118, 218, 183, 108, 120, 123, 107 and 93 (G5); 311, 287, 215, 142, 133, 215, 218, 129, 159, 134, 111 and 117 genes (G6). (c) Heatmap showing log2 fold change of each risk gene in each cell type in each patient group. (d) Violin plot showing expression of SP110 in CD8 T cells from tumors and paracancerous tissues in each patient group. n = 391, 147, 2115, 2865, 676, 1180, 946, 2539, 1090, 9099, 5731 and 10459 cells from left to right.

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Supplementary Tables

Supplementary Table 1: Basic characteristics of involved donors. Supplementary Table 2: Clinical and classification information of patients with CRC. Supplementary Table 3: P values in the comparison of cell-type proportions in all nonepithelial/malignant cells among healthy, inflamed, uninflamed, paracancerous, polyp and tumor samples. Supplementary Table 4: P values in the comparison of cell-type proportions in all immune cells among healthy, inflamed, uninflamed, paracancerous, polyp and tumor samples. Supplementary Table 5: Summary statistics in differential expression analysis between endothelial cells in tumors and inflamed tissues. Supplementary Table 6: Summary statistics in differential expression analysis between fibroblasts in tumors and inflamed tissues. Supplementary Table 7: List of group 1-specific interactions inferred by CellChat. Supplementary Table 8: Summary statistics in the differential expression analysis of CD8-CXCL13 and CD4-CXCL13. Supplementary Table 9: Group-specific transcriptional alterations of genetic risk genes. Supplementary Table 10: Classification of samples and patients from the CRC-SG1 dataset.

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Chu, X., Li, X., Zhang, Y. et al. Integrative single-cell analysis of human colorectal cancer reveals patient stratification with distinct immune evasion mechanisms. Nat Cancer 5, 1409–1426 (2024). https://doi.org/10.1038/s43018-024-00807-z

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