Abstract
Deciphering the composition of the tumor microenvironment (TME) is critical for understanding tumorigenesis and to design immunotherapies. In the present study, we mapped genetic effects on cell-type proportions using single-cell and bulk RNA sequencing data, identifying 3,494 immunity quantitative trait loci (immunQTLs) across 23 cancer types from The Cancer Genome Atlas. Functional annotation revealed regulatory potential and we further assigned 1,668 genes that regulate TME composition. We constructed a combined immunQTL map by integrating data from European and Chinese colorectal cancer (CRC) samples. A polygenic risk score that incorporates these immunQTLs and hits on a genome-wide association study outperformed in CRC risk stratification within 447,495 multiethnic individuals. Using large-scale population cohorts, we identified that the immunQTL rs1360948 is associated with CRC risk and prognosis. Mechanistically, the rs1360948-G-allele increases CCL2 expression, recruiting regulatory T cells that can exert immunosuppressive effects on CRC progression. Blocking the CCL2–CCR2 axis enhanced anti-programmed cell death protein 1 ligand therapy. Finally, we have established a database (CancerlmmunityQTL2) to serve the research community and advance our understanding of immunogenomic interactions in cancer pathogenesis.
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Data availability
All accession codes or web links for publicly available scRNA-seq datasets are described in Supplementary Table 1. RNA-seq profiles (level 3) and genotype data (level 2) of 7,707 samples were obtained from TCGA data portal (https://portal.gdc.cancer.gov). RNA-seq data conducted using tumor tissues and adjacent normal tissues from our 154 CRC samples have been deposited in the GSA under accession no. HRA007986. Genotype data for our 154 CRC samples have been deposited in GSA under accession no. GVM000801. GWAS summary statistics for 124 traits in UK Biobank were obtained from a pancancer GWAS (https://github.com/Wittelab/pancancer_pleiotropy) and gwasATLAS (https://atlas.ctglab.nl). Pancancer GWAS summary statistics for Asians was available through the BBJ (http://jenger.riken.jp/en/result). ChIP–seq peaks, TF-binding sites and eCLIP–seq data among human cancer cell lines were downloaded from the ENCODE portal (https://www.encodeproject.org/data/annotations). Expression profile data and drug sensitivity data of cancer cell lines were obtained from the Genomics of Drug Sensitivity in Cancer (GDSC) project (https://www.cancerrxgene.org). Genotype data of GECCO were downloaded from dbGaP under accession nos. phs001078.v1.p1, phs001315.v1.p1 and phs001415.v1.p1. Genotype data of PLCO were downloaded from dbGaP under accession nos. phs000346.v2.p2, phs001554.v1.p1, phs001286.v2.p2 and phs001524.v1.p1. Genotype data from UK Biobank (http://www.ukbiobank.ac.uk) were obtained under application no. 94939. Source data are provided with this paper.
Code availability
Data-processing scripts used to perform the analyses described herein are available at https://github.com/MiaoLab2024/ImmunQTL_Analysis.
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Acknowledgements
We thank the study participants, research staff and students who participated in this work, especially the sample donors. We also thank W. Nie from Wuhan Matrix Union Information Technology Co., Ltd for helping design the interface and construct the framework of our database. This work was supported by: National Science Fund for Distinguished Young Scholars of China (grant no. NSFC-81925032 to X.M.); National Science Fund for Excellent Young Scholars (grant no. NSFC-82322058 to J.T.); Key Program of National Natural Science Foundation of China (grant no. NSFC-82130098), the Leading Talent Program of the Health Commission of Hubei Province, Knowledge Innovation Program of Wuhan (grant no. 2023020201010060) and Fundamental Research Funds for the Central Universities (grant nos. 2042022rc0026 and 2042023kf1005) to X.M.; Program of National Natural Science Foundation of China (grant nos. NSFC-82103929 and NSFC-82273713), Young Elite Scientists Sponsorship Program by CAST (grant no. 2022QNRC001), National Science Fund for Distinguished Young Scholars of Hubei Province of China (grant no. 2023AFA046), Fundamental Research Funds for the Central Universities (2042024kf1012) and Knowledge Innovation Program of Wuhan (grant nos. whkxjsj011 and 2023020201010073) to J.T.; Program of National Natural Science Foundation of China (grant nos. NSFC-82003547 and NSFC-82373663), Program of Health Commission of Hubei Province (grant no. WJ2023M045) and Knowledge Innovation Program of Wuhan (grant no. 2023020201020244) to Y.Z.
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Y.C. performed data analyses. Y.C. and Z.L. conducted the experiments. C.C. and Y.Z. wrote the paper. Z.C., Z.W., J.P., X.Z. and Z.L. assisted in data analyses. B.L., M.Z. and J.H. were responsible for dataset curation. Y.L., Y.L., Q.M., C.H. and S.C. implemented the web design. W.T., L.F., C.N., H.G., B.X., H.L., X.Z., J.F., X.W., S.Z., M.J., C.H. and X.Y. were responsible for patient recruitment and sample preparation. J.T. conceived the study and revised the paper. X.M. supervised the whole project and revised the paper.
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Extended data
Extended Data Fig. 1 Deconvolution results derived from CIBERSORTx, MuSic and Bisque.
The stacked bar chart displays each cell type’s proportions predicted by CIBERSORTx (a), MuSic (b) and Bisque (c) in TCGA 23 cancer types.
Extended Data Fig. 2 Workflow to test deconvolution performance of CIBERSORTx.
Public single-cell RNA sequencing (scRNA-seq) data from 23 tumor and adjacent normal tissues of patients with colorectal cancer (CRC) were applied to build a signature matrix, which could distinguish the abundance of 25 cell subtypes. Additionally, we collected 20 samples from 18 CRC patients to conduct both bulk RNA-seq and scRNA-seq. Using the signature matrix generated above as the reference, we tested the concordance between the deconvolution result estimated by CIBERSORTx for our bulk RNA-seq and ground truth cell proportions measured by scRNA-seq.
Extended Data Fig. 3 Assessment of the deconvolution performance of MuSic and Bisque.
a,b. Cellular proportions predicted by MuSic (a) and Bisque (b) for 20 samples from Chinese CRC patients, with both scRNA-seq dataset and bulk transcriptome simultaneously profiled. c,d. Concordance between the ground truth cell proportions measured by scRNA-seq and deconvolution results derived from MuSic (c) and Bisque (d) at both cell-level and sample-level. Correlation coefficients were calculated by a two-sided Pearson’s correlation analysis.
Extended Data Fig. 4 Manhattan plots for the immunQTLs identified in 23 cancer type.
The x-axis reflects the chromosomal position, and y-axis shows −log10(P-value), which were calculated by linear regression models with the adjustment of top 5 principal components, top 3 PEER factors, patients’ age, sex and tumor stage. The red line indicates the significance threshold at P-value < 1 × 10−6.
Extended Data Fig. 5 GWAS loci of complex traits that could be interpreted by immunQTLs.
a. Distribution of the number of trait-associated immunQTLs across 98 common traits and complex diseases based on colocalization analysis. b,c. Manhattan plot showing the genomic position (y axis) and –log10P-value (x axis) of immunQTLs (points) among traits (colors) that show colocalization of immunQTLs with GWAS variants in BRCA (b) and CRC (c). The black, red, and yellow color represent common traits, traits related with cancer risk and cancers, respectively. BRCA, breast invasive carcinoma; CRC, colorectal cancer.
Extended Data Fig. 6 iGenes are critical in cancer progression and clinical utility.
a. Gene Ontology (GO) term enrichment analyses for immunQTL-related genes (iGenes), only top 20 significant pathways among biological process, cell component, and molecular function are visualized. The circle color represents –log10(P-value), and circle size represents gene count. b. Enrichment analyses of iGenes within the immune-related gene sets in 23 cancer types. The circle size represents the significance of enrichment, and the rectangle color denotes normalized enrichment score (NES) within each pathway. c. Bubble plot displays the cell types associated with patients’ overall survival in each cancer type. The associations were evaluated by multivariate Cox regression with age, sex and tumor stage adjusted.
Extended Data Fig. 7 Intercellular communication between sender cells and Treg cells in UVM.
a. CellChat predicted significant interactions (ligand-receptor pairs) from sender cell groups to regulatory T cells (Tregs), dot color reflects communication probability where dark and red colors correspond to the smallest and largest values, dot size represents computed P-value (left). Circle plot displays the signaling sent from one cell group to others in IL-1 signaling pathway, circle sizes are proportional to the number of cells in each cell group, and thicker edge line indicates a stronger signal. b. Intercellular communication assessed by NicheNet, showing the ranked ligand activity prediction, expression level of ligands in sender cells, interaction potential of ligands to the receptors in Treg cells and regulatory potential of ligands to the target genes in Treg cells.
Extended Data Fig. 8 Study design to estimate CRC genetic risk in multi-ethnic populations.
Three polygenic risk score (PRS) approaches for CRC risk stratification were constructed in East Asian (EAS) and European (EUR) populations. Next, the evaluation of three PRS models for assessing genetic susceptibility to CRC were performed in the case-control set from Chinese and European populations. Lastly, the predictive performance on CRC of these three PRS models were further assessed in the UK Biobank cohort.
Extended Data Fig. 9 Work flow to perform association analysis of rs1360948 and CRC risk.
Our association analyses were conducted in case-control studies from Chinese and European populations, respectively.
Extended Data Fig. 10 An illustration of modules and outputs in CancerImmunityQTL2.
a. The body map demonstrates the cancer types analyzed in our study (created using biorender.com). b. CancerImmunityQTL2 provides box plot to visualize the association between germline variant and estimated cell fraction in the ‘ImmunQTL’ module, which was calculated by linear regression model. The center line of the box presentation as the median, box limits indicated upper and lower quartiles, and whiskers indicated the maximum and minimum. c. Kaplan-Meier plot (Log-rank test) from the ‘Survival-immunQTL’ module shows the difference of overall survival among patients stratified by the genotype. d. In the ‘iGene identification’ module, scatter plot is equipped to display the correlation of iGene expression with cellular infiltration. e. In the ‘iGene-Cell-Drug Association’ module, scatter plot is equipped to display the correlation between cellular infiltration and drug response. f. CancerImmunityQTL2 generates both bubble plot and circle plot for the visualization of cell-cell communication. The bubble plot shows all significant interactions (ligand-receptor pairs) from a given cell group to other groups, dot color reflects communication probability where dark and yellow colors correspond to the smallest and largest values, dot size represents computed P-value. Circle plot displays the signaling sent from one cell group to others, above and below diagrams represent the number of interactions and the total interaction strength, respectively; circle sizes are proportional to the number of cells in each cell group, and thicker edge line indicates a stronger signal. g. The interface showing required files and selection of parameters in the ‘Cell Fraction Estimation’ module. h. The interface displaying required files and selection of parameters in the ‘ImmunQTL Calculation’ module.
Supplementary information
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Supplementary Methods, Supplementary References and Supplementary Figs. 1–13.
Supplementary Tables
Supplementary Tables 1 to 14.
Supplementary Data
Source data for Supplementary Figs.
Source data
Source Data
Statistical source data for Figs 1–7.
Source Data Extended Data Figs
Statistical source data for Extended Data Figs. 1, 3, 5, 6, 7 and 10.
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Cai, Y., Lu, Z., Chen, C. et al. An atlas of genetic effects on cellular composition of the tumor microenvironment. Nat Immunol 25, 1959–1975 (2024). https://doi.org/10.1038/s41590-024-01945-3
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DOI: https://doi.org/10.1038/s41590-024-01945-3
