Comprehensive analyses of tumor immunity: implications for cancer immunotherapy

Abstract

Background: Understanding the interactions between tumor and the host immune system is critical to finding prognostic biomarkers, reducing drug resistance, and developing new therapies. Novel computational methods are needed to estimate tumor-infiltrating immune cells and understand tumor-immune interactions in cancers.

Results: We analyze tumor-infiltrating immune cells in over 10,000 RNA-seq samples across 23 cancer types from The Cancer Genome Atlas (TCGA). Our computationally inferred immune infiltrates associate much more strongly with patient clinical features, viral infection status, and cancer genetic alterations than other computational approaches. Analysis of cancer/testis antigen expression and CD8 T-cell abundance suggests that MAGEA3 is a potential immune target in melanoma, but not in non-small cell lung cancer, and implicates SPAG5 as an alternative cancer vaccine target in multiple cancers. We find that melanomas expressing high levels of CTLA4 separate into two distinct groups with respect to CD8 T-cell infiltration, which might influence clinical responses to anti-CTLA4 agents. We observe similar dichotomy of TIM3 expression with respect to CD8 T cells in kidney cancer and validate it experimentally. The abundance of immune infiltration, together with our downstream analyses and findings, are accessible through TIMER, a public resource at http://cistrome.org/TIMER .

Conclusions: We develop a computational approach to study tumor-infiltrating immune cells and their interactions with cancer cells. Our resource of immune-infiltrate levels, clinical associations, as well as predicted therapeutic markers may inform effective cancer vaccine and checkpoint blockade therapies.

Keywords: Cancer immunity; Cancer immunotherapies; Cancer vaccine; Checkpoint blockade; Tumor immune infiltration.

Fig. 1

Computational method for estimating the abundance of tumor-infiltrating immune cells. Tumor purity was estimated for each sample using DNA single-nucleotide polymorphism (SNP) array data (a). B allele frequency (BAF) is the frequency of a randomly selected parental allele. The logR ratio (LRR) is the log₂(Y/2), Y being the marker intensity in the SNP array. TCGA gene expression profiles were combined with reference immune cell expression data after batch effect removal (b). Informative genes with expression levels inversely correlated with tumor purity (Pearson’s r ≤ −0.2 and P value ≤ 0.05) are selected (c) and tested for immune signature enrichment (Fisher’s exact test) (d). In all 23 cancers informative genes are significantly enriched for immune signature. Diffuse large B-cell lymphoma (DLBC) has the lowest enrichment (odds ratio = 1.6, q = 0.0005, Fisher’s exact test). In this study, we estimate the abundance of six immune cell types (B cells, CD4 T cells, CD8 T cells, neutrophil, macrophage, and dendritic cells) using selected immune signature genes through constrained least squares fitting (e). Asterisks in d indicate event significance at a 1 % false discovery rate

Fig. 2

Distribution of infiltrating immune cells and selective enrichment of B cells in the tumor microenvironment. a The abundance of infiltrating CD8 T cells, macrophages, and B cells in 18 cancer types, with both primary tumor and adjacent (Adj)/normal (Norm) tissue available. Normal tissue was from healthy donors where adjacent tissues were unavailable. Statistical significance was evaluated by Wilcoxon rank sum test. Blue arrowheads point to three cancers with B cells significantly enriched in the primary tumor and associated with clinical outcomes. q values are colored red, blue, or black for significant (false discovery rate ≤ 0.15) enrichment in tumor, adjacent or normal tissue, or insignificance, respectively. b B-cell infiltration level significantly predicted patient survival in selected cancer types. Tumors in the top 20th percentile of B-cell infiltration were compared with those in the bottom 20th percentile. The median survival time for the top 20 % of patients with brain, lung, and bladder cancers was 460, 1778, and 2000 days, respectively, and for the bottom 20 % 345, 976, and 575 days, respectively. Statistical significance and hazard ratios (HR) with 95 % confidence intervals were calculated for all the samples, not just the top and bottom 20 %, using multivariate Cox regression including all six immune cell types, patient age, and clinical stage

Fig. 3

Immune cell infiltration predicts clinical outcome. a Association of tumor infiltrating immune cells with patient survival. For each cancer type, multivariate Cox regression was performed, with covariates including the abundance of six immune cell types, patient age at diagnosis, clinical stage, and viral infection status when available. Each entry on the first six rows of the heatmap represents the hazard ratio (HR) of a corresponding immune cell type, with larger size indicating statistical significance at a false discovery rate (FDR) of 0.15 and color indicating the value of the HR. The last row of the heatmap records the Cox model HRs and statistical significance using cytolytic activity (CYT) scores adjusted for the same covariates. Multiple test correction was performed using q value across cancer types and six immune components. b Kaplan–Meier curves of melanoma (SKCM) and head and neck cancer (HNSC) stratified by infiltration CD8 T-cell abundance. Median survival time for the top 20 % of patients in melanoma and head and neck cancers is 4507 and 1838 days, respectively, and for bottom 20 % 2005 and 862 days, respectively. Statistical significance, hazard ratios, and 95 % confidence intervals were calculated using multivariate Cox regression and all the samples as described above. c CD8 T-cell infiltration in primary tumors (metastatic samples for SKCM) significantly (FDR ≤ 0.15) predicts tumor relapse in selected cancers. Statistical significance was evaluated using logistic regression correcting for patient age and clinical stage

Fig. 4

Potential causes of inter-tumor immune infiltration heterogeneity. a In selected cancer types, counts of total somatic coding mutations positively associated with the level of infiltrating immune cells. The y-axis is the residual of corresponding immune cell abundance after linear regression against tumor purity. Statistical significance was evaluated using partial Spearman’s correlation adjusted for tumor purity. The asterisk indicates only HPV-negative tumors were selected for head and neck cancer. Multiple test correction was performed across cancer types and six immune components. Gray hues indicate previously known results (HNSC, LGG, and LUSC), while other findings are novel in this study. b CD8 T-cell infiltration is associated with microsatellite instability (MSI) status in cancers commonly with MSI. MSI-H high level of microsatellite instability, MSI-L low level of microsatellite instability; MSS microsatellite stable. Statistical significance was evaluated using a Wilcoxon rank sum test. c, d Chemokine/receptor networks for immune infiltration in diverse cancer types. Vertexes are ligands (green) and receptors (purple) and edges indicate known molecular interactions. For each cancer, the partial correlations (corrected for purity) between the chemokine gene expression and infiltration of CD8 T cells (c) or macrophages (d) were calculated. For a pair of interacting chemokine and receptor genes, if both are significantly correlated with immune cell infiltration in one cancer, a colored dot represents the cancer type is placed on the edge connecting the chemokine and receptor. Statistical significance was evaluated using partial Spearman’s correlation at a false discovery rate threshold of 0.01. Heatmap visualizations of the same results are shown in Additional file 1: Figure S6c, d

Fig. 5

Association of immune cell infiltration and cancer/testis (CT) antigen expression in non-small cell lung carcinomas (a-b) and melanoma (c). Only genes with expression levels positively correlated with tumor purity (Pearson’s r > 0.1, q < 0.1) were selected to ensure cancer cell-specific expression. The heatmap presents correlations of gene expression and tumor infiltrating immune cells, which were calculated using partial Spearman’s correlation correcting for tumor purity. Asterisks indicate events significant at a 15 % FDR. Red arrowheads point to MAGEA3

Fig. 6

Varied levels of CD8 T-cell infiltration in tumors highly expressing inhibitory receptors. a, b High CTLA4/TIM3-expressing tumors in melanoma/KIRC show different CD8 T-cell infiltration levels. Dashed lines in both panels are the hypothetical high CTLA4 or TIM3 cutoff. Tumor purity is indicated by color. Arrows in b point to selected TCGA samples for immunohistochemistry (IHC) analysis. c Sample with low TIM3 expression and CD8 T-cell infiltration used as a negative control. TIM3- or CD8-expressing cells are brown in color. Selected samples with (1) high TIM3 expression and (2) low (d) or high (e) CD8 T-cell infiltration showed the existence of two KIRC sample groups. TIM3 expression in d is twice as high as in e according to RNA-seq data. d Image represents about 15 % TCGA KIRC samples while e represents 5 %. The upper and lower panels were synchronized. TIM3 was expressed in cancer cells (d, e) as well as in lymphocytes (e). High magnification insets are presented in d and e to illustrate TIM3 expression in different cell types. Yellow boxes indicate lymphocytes; red boxes indicate tumor cells