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BackgroundInhibitory checkpoints are promising antitumor targets and predictive biomarkers in a variety of cancers. We aimed to identify the expression levels and prognostic value of multiple inhibitory checkpoints supported by preclinical and clinical evidence in head and neck lymphoepithelioma-like carcinoma (HNLELC).MethodsThe expression of seven inhibitory checkpoints were evaluated in the tumor nest (TN) and tumor stroma (TS) of 102 HNLELC specimens using immunohistochemistry and digital pathology, and an inhibitory checkpoint-based signature (ICS) was subsequently constructed using the LASSO Cox regression model.ResultsPD-L1, B7H3, and IDO-1 were mostly expressed in the TN, with median H-score of TN vs TS: 63.6 vs 14.6; 8.1 vs 1.0; 61.5 vs 34.7 (all P < 0.001), whereas PD-1, TIM-3, LAG-3, and VISTA were mainly observed in the TS, with median H-score of TN vs TS: 0.2 vs 12.4, 3.4 vs 7.1, 6.2 vs 11.9, 16.4 vs 47.2 (all P < 0.001), respectively. The most common simultaneously expressed combinations consisted of PD-L1 + B7H3 + IDO-1 + TIM-3 + LAG-3 + VISTA and B7H3 + IDO-1 + TIM-3 + LAG-3 in the TN (both occurring in 8.8% of patients) and PD-L1 + B7H3 + IDO-1 in the TS (4.9%). In addition, high-ICS patients had shorter 5-year disease-free (40.6% vs 81.7%; P < 0.001), regional recurrence-free (63.5% vs 88.2%; P = 0.003), and overall survival (73.5% vs 92.9%; P = 0.006) than low-ICS patients. Multivariate analysis revealed that ICS represented an independent predictor, which could significantly complement the predictive performance of TNM stage for 3-year (AUC 0.724 vs 0.619, P = 0.014), 5-year (AUC 0.727 vs 0.640, P = 0.056), and 10-year disease-free survival (AUC 0.815 vs 0.709, P = 0.023).ConclusionsThe expression of inhibitory checkpoints and ICS classifier may increase the prognostic value of the TNM staging system and guide the rational design of personalized inhibitory checkpoint blockade therapy in HNLELC.

Additional file 1: Methods. Figure S1. Study flow. HTA, Human Transcriptome Array; ICI, immune checkpoint inhibitor; NMF, non-negative matrix factorization; NPC, nasopharyngeal carcinoma; PSM, propensity score matching. Figure S2. Identification of an NMF immune factor. (A) We applied NMF (k = 5 factors or expression patterns) to analyze the gene expression profiles of nasopharyngeal carcinoma (NPC) samples in the training cohort (n = 113). One of the five factors (green bar) showed the highest ssGSEA scores in both immune enrichment score and 6-gene IFN-γ signature, as shown in the heatmap, indicating that it is an immune factor (or an immune expression pattern). High and low ssGSEA scores are represented in red and blue, respectively. (B) The top 100 exemplar immune factor genes characterized using DAVID confirmed immune-related functions. (C) NMF consensus-clustering of the training cohort using exemplar immune factor genes was refined by random forest classification. As shown in the heatmap, an immune-enriched subtype and a non-immune subtype. The ssGSEA scores of immune enrichment score and 6-gene IFN-γ signature are indicated; high and low scores are represented in red and blue, respectively. NMF, non-negative matrix factorization; NPC, nasopharyngeal carcinoma; ssGSEA, single-sample gene set enrichment analysis. Figure S3. Association of immune subtypes with tumoural genomic features and survival outcome. (A) Box plot showing similar number of non-synonymous mutations among the immune subtypes. (B) Box plot showing similar numbers of gene-level amplifications and deletions among the immune subtypes. (C) Box plot showing significantly higher cell cycling scores in non-IS. The box plot centre corresponds to the median, with the box and whiskers corresponding to the interquartile range and 1.5× interquartile range, respectively. P-values were based on the Kruskal–Wallis rank-sum test. (D) The proportion of CDKN2A deletions was significantly higher in non-IS. P-values were based on the Fisher’s exact test. (E) Kaplan–Meier curves for progression-free survival according to immune subtypes. A trend of better survival was observed for A-IS compared to E-IS and non-IS in 88 patients with available survival outcomes. P-values were calculated by log-rank test. A-IS, active immune subtype; CD4+ Tconv, conventional CD4+ T cells; CD8+ Tcyt, cytotoxic CD8+ T cells; CD8+ Tdys, dysfunctional CD8+ T cells; CD8+ Tnaï, naïve CD8+ T cells; DCs, dendritic cells; E-IS, evaded immune subtype; NK, natural killer; non-IS, non-immune subtype. Figure S4. Genetic similarity of the immune subtypes in different groups of patients from two melanoma ICI cohorts. (A) SubMap analysis of the immune subtypes in validation cohort 1 and four groups (anti-PD-1 responsive and non-responsive, and anti-CTLA-4 responsive and non-responsive) in melanoma ICI cohort 1. (B) SubMap analysis of the immune subtypes in validation cohort 1 and four groups (CR/PR/SD > 12 months, CR/PR/SD 6–12 months, CR/PR/SD 12 months for anti-PD-1 therapy (P = 0.036). A-IS, active immune subtype; CR, complete response; E-IS, evaded immune subtype; ICI, immune checkpoint inhibitor; non-IS, non-immune subtype; PD, progressive disease; PR, partial response; SD, stable disease. Table S1. Clinical cohorts used in this study. Table S2. Publicly available gene signatures used in this study. Table S7. Clinical characteristics of patients in the validation cohort 1 and validation cohort 2 (ICI).

Additional file 2: Table S1. Clinicopathological characteristics of the patients in the training and validation cohorts stratified according to the ICS. Table S2. Immune checkpoint co-expression by tumour cells (TCs) in the training cohort. Table S3. Five-year OS, DFS and DMFS estimates for different groups. Table S4. Number of events for different groups. Table S5. Univariate analysis of factors associated with overall survival in the training and validation cohorts. Table S6. Univariate analysis of factors associated with disease-free survival in the training and validation cohorts. Table S7. Univariate analysis of factors associated with distant metastasis-free survival in the training and validation cohorts. Table S8. Multivariable Cox regression analysis of factors associated with survival in the training and validation cohorts. Table S9. Summary of the multivariable analyses of prognostic factors for OS, DFS, DMFS and corresponding risk score in the training set of 208 nasopharyngeal carcinoma patients.

Table S3. Univariate Cox regression analyses of the significant of different prognostic variables in nasopharyngeal carcinoma. (DOCX 19Â kb)

Table S2. Correlations between TIPE3 methylation levels and clinical features in patients with nasopharyngeal carcinoma from the training and validation cohorts. (DOCX 24Â kb)