Developing and validating the model of tumor-infiltrating immune cell to predict survival in patients receiving radiation therapy for head and neck squamous cell carcinoma

Abstract

Background: Radiotherapy (RT) is a mainstay of head and neck squamous cell carcinoma (HNSCC) treatment. Due to the influence of RT on tumor cells and immune/stromal cells in microenvironment, some studies suggest that immunologic landscape could shape treatment response. To better predict the survival based on genomic data, we developed a prognostic model using tumor-infiltrating immune cell (TIIC) signature to predict survival in patients undergoing RT for HNSCC.

Methods: Gene expression data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Data from HNSCC patients undergoing RT were extracted for analysis. TIICs prevalence in HNSCC patients was quantified by gene set variation analysis (GSVA) algorithm. TIICs and post-RT survival were analyzed using univariate Cox regression analysis and used to construct and validate a tumor-infiltrating cells score (TICS).

Results: Five of 26 immune cells were significantly associated with HNSCC prognosis in the training cohort (all P<0.05). Kaplan-Meier (KM) survival curves showed that patients in the high TICS group had better survival outcomes (log-rank test, P<0.05). Univariate analyses demonstrated that the TICS had independent prognostic predictive ability for RT outcomes (P<0.05). Patients with high TICS scores showed significantly higher expression of immune-related genes. Functional pathway analyses further showed that the TICS was significantly related to immune-related biological process. Stratified analyses supported integrating TICS and tumor mutation burden (TMB) into individualized treatment planning, as an adjunct to classification by clinical stage and human papillomavirus (HPV) infection.

Conclusions: The TICS model supports a personalized medicine approach to RT for HNSCC. Increased prevalence of TIIC within the tumor microenvironment (TME) confers a better prognosis for patients undergoing treatment for HNSCC.

Keywords: Head and neck squamous cell carcinoma (HNSCC); gene set variation analysis (GSVA); human papillomavirus (HPV); radiotherapy (RT); tumor-infiltrating immune cell (TIIC).

Figure 1

Development of TICS and analysis of predictive model in the training cohort. (A) Forest plot of mortality risk (prognosis) associated with 26 immune cell types in HNSCC patients undergoing RT; (B) the KM curve of the TICS subgroups in TCGA training cohort for PFS; (C) the KM curve of the TICS subgroups in TCGA training cohort for overall survival; (D) time-dependent ROC curve analysis of the five prognostic immune cells and TICS; (E) Comparison of cancer survival for high vs. low TICS cell type subgroups, stratified by the best cut-off value in the training cohort. MDSC, Myeloid-derived suppressor cells; CI, confidence interval; TICS, tumor-infiltrating cell score; AUC, area under the curve; NK, natural killer; TCGA, The Cancer Genome Atlas; HNSCC, head and neck squamous cell carcinoma; RT, radiation therapy; KM, Kaplan-Meier; PFS, progression-free survival; ROC, receiver operating characteristic.

Figure 2

Prognostic analysis of TICS model in the validation cohort. (A) The KM curve of the TICS subgroups in GEO training cohort for recurrence-free survival probability; (B) the differences of the five prognostic immune cells between the high and low TICS subgroups stratified by the best cut-off value in the validation cohorts; (C) the univariate and multivariate Cox regression analyses of TICS and multiple clinical features in the training cohort; (D) the univariate Cox regression analyses of TICS and multiple clinical features in the validation cohort. TICS, tumor-infiltrating cell score; HR, hazard rations; CI, confidence interval; TCGA, The Cancer Genome Atlas; HNSCC, head and neck squamous cell carcinoma; HPV, human papillomavirus; GSE, GEO series; NK, natural killer; KM, Kaplan-Meier; GEO, Gene Expression Omnibus.

Figure 3

Clinical value of risk score by independent prognostic analysis. (A) Prognosis analysis between the HPV-positive and HPV-negative in HNSCC patients and the prognosis of the combination of TICS and HPV status; (B) the nomogram model based on risk model and clinical features; (C-E) the calibration plots of the nomogram. The closer to 45 degrees (gray lines), the better the fitting effect. HPV, human papillomavirus; TICS, tumor-infiltrating cell score; PFS, progression-free survival; HNSCC, head and neck squamous cell carcinoma.

Figure 4

Function richness analysis of differential expression genes in HNSCC. (A) The biological process of GO of 1,475 up-regulated genes; (B) the biological process of GO of 211 down-regulated genes. (C) GSEA of GO analysis results showing the enriched pathways in the high-TICS group. GO, Gene Ontology; HNSCC, head and neck squamous cell carcinoma; GSEA, gene set enrichment analysis; TICS, tumor-infiltrating cell score.

Figure 5

Characteristics of clinical factors on TICS subgroups in the TCGA cohort. (A) Specific immune checkpoints, MHC, chemokines, and clinical characteristics of the TICS subgroups; (B) estimate score in different TICS groups; (C) immune score in different risk groups; (D) stromal score in different TICS groups; (E) correlations between the TICS risk score and Immune score. ****, P<0.0001; TICS, tumor-infiltrating cell score; MHC, major histocompatibility complex; HPV, human papillomavirus; TCGA, The Cancer Genome Atlas.

Figure 6

m6A analysis and HNSCC somatic genome characteristics of TICS subgroups in the TCGA cohort. (A-D) The expression of 4 m6A regulators between high- and low-risk groups. Data are shown as means ± SD. *, P<0.05; **, P<0.01; ***, P<0.001. (E) Prognosis analysis between the low and high TMB in HNSCC patients. (F) The prognosis of the combination of TICS and TMB. (G) The difference of most frequent mutations genes in high TICS and low TICS subgroups. TICS, tumor-infiltrating cell score; TMB, tumor mutation burden; HNSCC, head and neck squamous cell carcinoma; TCGA, The Cancer Genome Atlas; SD, standard deviation.

Figure 7

Sensitivity of chemotherapy drugs, difference in the estimated IC50 levels of (A) bortezomib, (B) crizotinib, (C) phenformin, (D) bleomycin, (E) cetuximab, (F) cisplatin, (G) cytarabine, (H) docetaxel, (I) doxorubicin, (J) epothilone B, (K) gemcitabine, (L) tipifarnib. TICS, tumor-infiltrating cell score; IC50, 50% maximal inhibitory concentration.