Comprehensive characterization of the tumor microenvironment for assessing immunotherapy outcome in patients with head and neck squamous cell carcinoma

Results

Overview of TME characteristics in HNSCC

To investigate TME characteristics in HNSCC, based on the RNA-seq data of 502 patients with HNSCC from TCGA database, we systematically investigated 22 subpopulations of infiltrating immune cells using CIBERSORT. The distribution ratio of infiltrating immune cells among the samples showed a significant difference (Figure 1A), and the TME cell network depicted a comprehensive landscape of tumor–immune cell interactions, cell lineages and their effects on the overall survival (OS) of patients with HNSCC (Figure 1B; Supplementary Table 1).

Figure 1. Overview of TME characteristics. (A) Relative percentage of each immune cell type in 502 patients with HNSCC from TCGA database. (B) Tumor–immune cell interactions. The size of each cell represents the impact of each TME cell type on survival and was calculated using log10 (log-rank test P value). Risk factors for overall survival are indicated in pink, and favourable factors are in green. The lines connecting TME cell types represent cellular interactions. The thickness of the lines represents the strength of correlation, which was estimated using Spearman correlation analysis. Negative correlation is indicated in grey and positive correlation in black. (C) The elbow criterion determines the optimal number of TME clusters (K = 3). (D) Consensus clustering analysis identification of the three TME clusters (samples, n = 500). The white (consensus value = 0, samples never clustered together) and blue (consensus value = 1, samples always clustered together) heatmap display sample consensus. (E) Kaplan–Meier curves for survival probability of the three clusters. Log-rank test was used for data analysis.

Further, we performed unsupervised consensus clustering of all tumor samples for the molecular classification of HNSCC. The optimal number of clusters was determined by the K value. After assessing relative changes in the area under the cumulative distribution function curve and consensus matrix heatmap, we selected a three-cluster solution (K = 3), which showed no appreciable increase in the area under the cumulative distribution function curve (Figure 1C). To gain further insights into the molecular heterogeneity of HNSCC, unsupervised consensus clustering was performed to explore discernible patterns of the TME clusters. Based on the consensus matrix heatmap, three distinct TME-based molecular clusters were identified (Figure 1D). To further clarify the clinical implications of these clusters, we performed Kaplan–Meier curve analyses to elucidate the association between them and clinical prognosis. Notably, survival analysis based on the TME phenotypes indicated that TME cluster 3 (n = 124) was significantly associated with better prognosis and that TME cluster 2 (n = 150) was associated with poorer prognosis (log-rank test, P = 0.0018). Of the 500 patients with HNSCC, 226 belonged to TME cluster 1, which was associated with intermediate prognosis (log-rank test, P > 0.05) (Figure 1E).

Signature and functional annotation of the TME clusters

To determine the characteristics of immune cells in the three TME clusters, infiltrating immune cell subpopulations were analysed using CIBERSORT. Tumor ploidy and malignant cell purity showed no differences among the three clusters (Supplementary Figure 1A and 1B), whereas the proportion of infiltrating immune cells among the clusters showed significant differences, particularly that of resting memory CD4+ T cells, M0 macrophages, naïve B cells and plasma cells (Figure 2A). Further, the clusters were subjected to unsupervised hierarchical clustering, and the obtained results revealed that the 22 subpopulations of infiltrating immune cells showed differential patterns among the clusters (Figure 2B). To identify and elucidate differences in infiltrating immune cells, the relative fraction of 22 leukocyte subpopulations in each sample was estimated based on the differential expression of 547 genes [20]. Comparing the TME clusters revealed several differences, including increased levels of naïve B cells, regulatory T cells and eosinophils; in contrast, the levels of resting memory CD4+ T cells, resting NK cells and M2 macrophages were markedly reduced (Figure 2C).

Figure 2. Signature and functional annotation of the TME clusters. (A) Relative percentage of each immune cell type in the three TME clusters. (B) Unsupervised hierarchical clustering of the clusters. (C) Relative populations of TME cells present in the three clusters. Within each group, the scattered dots represent expression values of TME cells. We also plotted the Immunoscore for the three clusters. The thick line represents the median value. The lower and upper ends of the boxes are the 25^th and 75^th percentiles. The whiskers encompass 1.5 times the interquartile range. Statistical differences in the three clusters were compared using the Kruskal–Wallis test. The range of P values are labelled above each boxplot with asterisks (* P < 0.05, ** P < 0.01, *** P < 0.001, ****P < 0.0001, ns = not significant). (D–F) Gene ontology enrichment analysis of TME signature genes in the cellular component (D), biological process (E) and molecular function (F) categories. The x-axis indicates the number of genes within each gene ontology term.

To identify biological characteristics underlying each TME phenotype, differentially expressed genes (DEGs) among the three clusters were analyzed using the limma package. In total, 306, 998 and 676 DEGs were obtained upon three comparisons (TME cluster 1 vs. TME cluster 2, TME cluster 2 vs. TME cluster 3 and TME cluster 1 vs. TME cluster 3). Overall, 622 common DEGs were further screened in the comparisons. Next, we used random forest algorithms for dimension reduction to extract phenotype signatures. Unsupervised hierarchical clustering was based on the expression of the 312 most representative D EGs (Supplementary Table 2). Gene ontology enrichment analysis of TME signature genes was conducted using the R package. Significantly enriched biological processes are summarized in Supplementary Table 3. Analyses of TME signature mRNAs indicated that within the cellular component category, immunoglobulin complex, external side of plasma membrane, T cell receptor complex and receptor complex were significantly annotated (Figure 2D). Further, within the biological process category, antigen receptor-mediated signalling pathway, regulation of lymphocyte activation, positive regulation of cell activation and positive regulation of leukocyte activation were significantly annotated (Figure 2E), and within the molecular function category, pathways involved in antigen binding, immunoglobulin receptor binding and cytokine receptor activity were significantly annotated (Figure 2F). These findings indicated that the immune landscape of the three TME clusters plays a key role in immune regulation.

Clinical characteristics of the TME phenotypes

Next, we systematically classified the TME clusters according to the gene coefficient value and calculated TME score. The maxstat R package was used to identify TME score breakpoint. The distribution of risk scores and overall survival status of patients are shown in Figure 3A and 3B, respectively. TME score was calculated for all patients, and the TME score breakpoint was used to classify them into the high (n = 220) and low (n = 280) TME score groups. High TME score and was associated with favourable outcomes, and low TME score and was associated with poor outcomes (Figure 3C). Kaplan–Meier curve and Cox regression analyses further suggested that patients in the high TME score group had significantly better OS probability than those in the low TME score group [HR 1.401 (1.074–1.827), log-rank test, P = 0.013] (Figure 3D). To understand the pathway two TME score groups involved in, gene set enrichment analysis was performed, which indicated that allograft rejection (E score = −0.414; P = 0.0091), inflammatory response (E score = −0.388; P = 0.032), interferon-α response (E score = −0.495; P = 0.002), interferon-γ response (E score = −0.450; P = 0.001), reactive oxygen species (E score = −0.468; P = 0.048) and xenobiotic metabolism (E score = −0.390; P = 0.0343) pathways were significantly downregulated in the high TME score group (Figure 3E).

Figure 3. Clinical characteristics of the TME phenotypes. (A) TME score distribution in patients with HNSCC. (B) Overall survival status of patients with HNSCC. (C) Alluvial diagram of TME gene clusters in groups with different TME clusters, DEG clusters, TME scores and survival outcomes. (D) Gene set enrichment analysis of hallmark gene sets between the high and low TME score groups. (E) Kaplan–Meier curves for the high and low TME score groups. As evident, the high TME score group was associated with better outcomes than the low TME score group (log-rank test, P < 0.001).

Somatic mutations of the TME phenotypes

To detect driver mutations in the TME, we analysed HNSCC-related SNP data and consequently detected 21 putative driver genes, including TP53, TTN, CSMD3, CDKN2A and NOTCH1 (Figure 4A and 4B), which were associated with the TME, using random forest algorithm with 1000 iterations. The TP53 gene showed the highest mutation rate (74% vs. 70% in the high and low TME score groups, respectively), which was consistent with the findings of other studies [21–25]. The somatic mutations of TP53, TTN, CSMD3, CDKN2A, FRG1B, FAT1 and NOTCH1 were all >20% in the high and low TME score groups; in particular, FAT1 was significantly different between high TME score and low TME score groups (P < 0.05) (Figure 4C). We also found that several genes involved in the cell cycle pathway (e.g., TP53, CDKN2A, NOTCH1 and PIK3CA) were frequently altered. Clinical reports have described associations between individual altered genes and response or resistance to ICIs [26, 27]. These mutations may be associated with a change in the TME. Consequently, we further detected and classified candidate somatic mutations, which led to the identification of nine classifications; nonstop, in-frame (insert and delete), frame-shift (insert and delete), translation start, splice, nonsense and mutations were the common somatic mutations (Figure 4D). Mutation type and spectrum analyses showed that single nucleotide mutations, particularly C > T transitions at TpCpW trinucleotide sites, were the predominant type of mutations (Figure 4E, 4F).

Figure 4. Somatic mutations in HNSCC. (A, B) Distribution of highly variant mutated genes correlated with TME score groups. The upper bar plot indicates overall survival (OS), TMB and TME score for each patient, whereas the left bar plot shows the mutation frequency of each gene in separate TME score groups [high (A) and low (B) TME score groups]. TME score, grade, overall survival status, gender, age, smoking, alcohol frequency, daily alcohol, HPV P16 status and HPV ISH status are shown as patient annotations. (C) Mutation percentage of common mutated genes in the TME score groups. (D) Genome variant classification. (E) Genome variant type. (F) Single nucleotide variant class.

Mutational signatures

Mutational signatures in the cancer genome might reflect and help trace DNA damage caused by DNA-damaging agents to which cells have been exposed. Thus, we counted the number of SNVs in the matrix of 96 possible mutations occurring in a trinucleotide context in each HNSCC sample and found that the predominant mutations were C > T and/or C > G transitions at TpCpW trinucleotide sites in both the high and low TME score groups (Figure 5A, 5B).

Figure 5. Mutational signature of the TME score groups. (A, B) Distribution of mutation type frequency in the high (A) and low (B) TME score groups. (C–H) Mutational signatures identified in the high (C–F) and low (G–H) TME score groups, respectively. The y-axis indicates exposure of 96 trinucleotide motifs to overall signature. The plot title indicates best match against validated COSMIC signatures and cosine similarity value along with the proposed aetiology.

Using 1,000 iterations of non-negative matrix factorization [28], we then performed cosine similarity analyses to compare mutational signatures in HNSCC with current COSMIC; consequently, six independent and stable mutational signatures were identified. De novo signatures identified in the high TME score group were enriched in APOBEC cytidine deaminase signature (COSMIC Signature 13; cosine similarity: 0.842), UV exposure (COSMIC Signature 7; cosine similarity: 0.93), tobacco mutagens (COSMIC Signature 4; cosine similarity: 0.917) and spontaneous deamination of 5-methylcytosine (COSMIC Signature 1; cosine similarity: 0.942) (Figure 5C–5F). In contrast, de novo signatures identified in the low TME score group were enriched in APOBEC cytidine deaminase signature (COSMIC Signature 13; cosine similarity: 0.809) and defective DNA mismatch repair (COSMIC Signature 6; cosine similarity: 0.849) (Figure 5G, 5H). C > T and C > G mutations at TpCpN trinucleotides were attributed to the overactivity of the AID/APOBEC family of cytidine deaminases. These data indicate that the overactivity of APOBEC family genes may be involved in HNSCC tumorigenesis and immune outcome.

Copy number alterations of the TME phenotypes

We observed that 98% HNSCC samples had CNVs at the chromosome arm level, including loss at 3p, 4p, 5q, 8p, 9p, 11q, 13q, 18q and 21q and gain at 1q, 3q, 5p, 8q, 14q, 20p and 20q. Analyses using GISTIC indicated that the chromosome arm level had a significant gain at 8q and 20q in the high TME score group (Figure 6A) and at 8q, 3q and 3p in the low TME score group (Figure 6B); moreover, the chromosome arm level had a significant loss at 8p, 3p and 11q in the high TME score group and at 9p, 5q and 4p in the low TME score group (Figure 6C, 6D). Similar SNV and CNV profiles along with similar RNA expression patterns supported that HNSCC belongs to the “squamous” molecular subtype, as identified by Hoadley et al. [29]. We also identified 13 focal regions with recurrent gain of copy number and 20 regions with recurrent loss of copy number (both q < 1e−4), in which many genes have previously been identified to be tumor-associated genes (Supplementary Table 4). Among them, three recurrent focal amplifications (11q13.3, 7p11.2 and 3q26.33) (Figure 6E, 6F) and three recurrent focal deletions (9p21.3, 8p23.2 and 4q35.2) (Figure 6G, 6H) were identified for the first time in the TME score groups.

Figure 6. CNV analysis in HNSCC. (A–D) CNV at arm level. The bar graphs show the frequency of arm-level CNV amplification (A, B) and deletion (C, D), the vertical axis denotes chromosome arms. (E–H) CNV at focal regions detected by GISTIC v2·0. Regions of recurrent focal amplifications (E, F) and focal deletions (G, H) in the high and low TME score groups are plotted by false discovery rate (x-axis) for each chromosome (y-axis). Dashed lines represent the centromere of each chromosome.

Integrated genomic landscape of the TME score phenotype

To elucidate the molecular characteristics of HNSCC, according to information pertaining to the TME clusters and TME score groups, as well as tumor purity, malignant cell ploidy, tumor mutation burden (TMB) and clinical information (age, grade and OS status), a comprehensive genomic landscape of HNSCC samples was integrated and has been depicted in Supplementary Table 5. As shown in Figure 7A and Supplementary Figure 1C and 1D, no difference was present between the high and low TME score groups with regard to tumor ploidy and malignant cell purity. However, consistent with TMB, TME score was able to significantly differentiate between high- and low-risk HNSCC. TMB, in concert with PD-L1 expression, is reportedly a useful biomarker for immune checkpoint blockade selection in diverse cancers [30]. Herein ROC analysis showed that in comparison with TMB, TME score is a similar efficacious biomarker to determine the effectiveness of immunotherapy in patients with HNSCC (area under ROC: 0.549 vs. 0.572, P = 0.64) (Figure 7B).

Figure 7. Clinical and integrated genomic landscape of HNSCC with the TME score phenotype. (A) Comprehensive genomic landscape of HNSCC. (B) Prediction immunotherapy effect of TME score by ROC analysis.

Abbreviations

HNSCC: head and neck squamous cell carcinoma; SNVs: single nucleotide variants; CNVs: copy number variations; TMB: tumor mutational burden; TME: tumor microenvironment; TCGA: The Cancer Genome Atlas database; RNA-seq: RNA sequencing; DEGs: Differentially expressed genes; COSMIC: Catalogue of Somatic Mutations in Cancer; APOBEC: apolipoprotein B-editing catalytic polypeptide-like subunit; GISTIC: Genomic Identification of Significant Targets in Cancer; OS: overall survival.

Author Contributions

JZ, XZ, HLiJ and HLoJ performed all experiments, prepared figures and drafted the manuscript. JZ, TX, YHT, RL and BYW participated in data analysis and interpretation of results. JZ, JXZ and YWY designed the study and participated in data analysis. All authors have read and approved the manuscript.

Acknowledgments

We thank Dr. Robert L. Ferris (Cancer Immunology Program, UPMC Hillman Cancer Center, Pittsburgh, PA, USA) for his helpful comments on this study. We thank the staff members of The Cancer Genome Atlas for their involvement in the cBioPortal for Cancer Genomics Program.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Funding

This study was supported by grants from the Science and Technology Program of Guangzhou (201803010024), Social Science and Technology Development Key Project of Dongguan (201750715046462), Guangzhou Key Medical Discipline Construction Project Fund (B195002004042) and Open Funds of State Key Laboratory of Oncology in South China (KY013711).

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