Novel Prognostic Model Based on Immune Signature for Head and Neck Squamous Cell Carcinoma

Population-Based Prognostic Models for Head and Neck Cancers Using National Cancer Registry Data from Taiwan

PURPOSE

This study aims to raise awareness of the disparities in survival predictions among races in head and neck cancer (HNC) patients by developing and validating population-based prognostic models specifically tailored for Taiwanese and Asian populations.

METHODS

A total of 49,137 patients diagnosed with HNCs were included from the Taiwan Cancer Registry (TCR). Six prognostic models, divided into three categories based on surgical status, were developed to predict both overall survival (OS) and cancer-specific survival using the registered demographic and clinicopathological characteristics in the Cox proportional hazards model. The prognostic models underwent internal evaluation through a tenfold cross-validation among the TCR Taiwanese datasets and external validation across three primary racial populations using the Surveillance, Epidemiology, and End Results database. Predictive performance was assessed using discrimination analysis employing Harrell's c-index and calibration analysis with proportion tests.

RESULTS

The TCR training and testing datasets demonstrated stable and favorable predictive performance, with all Harrell's c-index values ≥ 0.7 and almost all differences in proportion between the predicted and observed mortality being < 5%. In external validation, Asians exhibited the best performance compared with white and black populations, particularly in predicting OS, with all Harrell's c-index values > 0.7.

CONCLUSIONS

Survival predictive disparities exist among different racial groups in HNCs. We have developed population-based prognostic models for Asians that can enhance clinical practice and treatment plans.

Development and Validation of an ADME-Related Gene Signature for Survival, Treatment Outcome and Immune Cell Infiltration in Head and Neck Squamous Cell Carcinoma

ADME genes are a set of genes which are involved in drug absorption, distribution, metabolism, and excretion (ADME). However, prognostic value and function of ADME genes in head and neck squamous cell carcinoma (HNSCC) remain largely unclear. In this study, we established an ADME-related prognostic model through the least absolute shrinkage and selection operator (LASSO) analysis in the Cancer Genome Atla (TCGA) training cohort and its robustness was validated by TCGA internal validation cohort and a Gene Expression Omnibus (GEO) external cohort. The 14-gene signature stratified patients into high- or low-risk groups. Patients with high-risk scores exhibited significantly poorer overall survival (OS) and disease-free survival (DFS) than those with low-risk scores. Receiver operating characteristic (ROC) curve analysis was used to confirm the signature’s predictive efficacy for OS and DFS. Furthermore, gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analyses showed that immune-related functions and pathways were enriched, such as lymphocyte activation, leukocyte cell-cell adhesion and T-helper cell differentiation. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) and other analyses revealed that immune cell (especially B cell and T cell) infiltration levels were significantly higher in the low-risk group. Moreover, patients with low-risk scores were significantly associated with immunotherapy and chemotherapy treatment benefit. In conclusion, we constructed a novel ADME-related prognostic and therapeutic biomarker associated with immune cell infiltration of HNSCC patients.

CD3D: a prognostic biomarker associated with immune infiltration and immunotherapeutic response in head and neck squamous cell carcinoma

Recent studies have demonstrated that CD3D activates T-cell-related signal transduction and is associated with the antitumor immune response in several cancers. This study explored the role of CD3D in head and neck squamous cell carcinoma (HNSCC). A total of 499 HNSCC tissues and 44 normal controls were acquired from The Cancer Genome Atlas as the training cohort. GSE65858 included 270 HNSCC patients and was obtained from the Gene Expression Omnibus database as the test cohort. Overall, 172 HNSCC patients were collected as the validation cohort. CD3D expression in the validation cohort was measured by quantitative real-time polymerase chain reaction. The Kaplan-Meier plot revealed that high CD3D expression was associated with longer overall survival in HNSCC patients. Univariate and multivariate analyses showed that CD3D expression was an independent prognostic factor for HNSCC patients, which was confirmed in the test cohort and validation cohort. Furthermore, GO, KEGG, and GSEA analyses revealed the association of CD3D with immune-related pathways. Subsequently, ESTIMATE analysis showed the association between CD3D and the tumor microenvironment, while ssGSEA showed a remarkable positive link between CD3D and immune-related functions. Multiple algorithms demonstrated that high CD3D expression was associated with more immune effector cell infiltration. Finally, the tumor immune dysfunction and exclusion (TIDE) score and immunophenoscore (IPS) showed that patients with high CD3D could benefit from immunotherapy. In summary, CD3D was an independent favorable prognostic biomarker and correlated with immune cell infiltration and immune-related function, as well as an efficient indicator of immunotherapy response for HNSCC patients.

Feature screening for survival trait with application to TCGA high-dimensional genomic data

Background

In high-dimensional survival genomic data, identifying cancer-related genes is a challenging and important subject in the field of bioinformatics. In recent years, many feature screening approaches for survival outcomes with high-dimensional survival genomic data have been developed; however, few studies have systematically compared these methods. The primary purpose of this article is to conduct a series of simulation studies for systematic comparison; the second purpose of this article is to use these feature screening methods to further establish a more accurate prediction model for patient survival based on the survival genomic datasets of The Cancer Genome Atlas (TCGA).

Results

Simulation studies prove that network-adjusted feature screening measurement performs well and outperforms existing popular univariate independent feature screening methods. In the application of real data, we show that the proposed network-adjusted feature screening approach leads to more accurate survival prediction than alternative methods that do not account for gene-gene dependency information. We also use TCGA clinical survival genetic data to identify biomarkers associated with clinical survival outcomes in patients with various cancers including esophageal, pancreatic, head and neck squamous cell, lung, and breast invasive carcinomas.

Conclusions

These applications reveal advantages of the new proposed network-adjusted feature selection method over alternative methods that do not consider gene-gene dependency information. We also identify cancer-related genes that are almost detected in the literature. As a result, the network-based screening method is reliable and credible.

The Cardiac Glycoside Deslanoside Exerts Anticancer Activity in Prostate Cancer Cells by Modulating Multiple Signaling Pathways

Simple Summary

Prostate cancer is a leading cause of cancer-related deaths among men, and novel therapies for advanced PCa are urgently needed. Cardiac glycosides are a group of attractive candidates for anticancer repurposing, but deslanoside has not been tested for a potential anticancer effect so far. This study aims to test the anticancer effect of deslanoside in PCa and investigate the underlying mechanisms. Deslanoside effectively inhibited colony formation and tumor growth in multiple prostate cancer cell lines. Such an inhibitory effect involved both the cell cycle arrest at G2/M and the induction of apoptosis. Deslanoside altered the expression of many genes, which belonged to various cancer-associated cellular processes and signaling pathways. Altered expression levels for 15 deslanoside-modulated genes correlate with recurrence-free survival or overall survival in PCa patients, some of which have not been implicated in cancer before. Therefore, deslanoside exerts anticancer activity in PCa cells by modulating gene expression.

Abstract

Prostate cancer (PCa) is a leading cause of cancer-related deaths among men worldwide, and novel therapies for advanced PCa are urgently needed. Cardiac glycosides represent an attractive group of candidates for anticancer repurposing, but the cardiac glycoside deslanoside has not been tested for potential anticancer activity so far. We found that deslanoside effectively inhibited colony formation in vitro and tumor growth in nude mice of PCa cell lines 22Rv1, PC-3, and DU 145. Such an anticancer activity was mediated by both the cell cycle arrest at G2/M and the induction of apoptosis, as demonstrated by different functional assays and the expression status of regulatory proteins of cell cycle and apoptosis in cultured cells. Moreover, deslanoside suppressed the invasion and migration of PCa cell lines. Genome-wide expression profiling and bioinformatic analyses revealed that 130 genes were either upregulated or downregulated by deslanoside in both 22Rv1 and PC-3 cell lines. These genes enriched multiple cellular processes, such as response to steroid hormones, regulation of lipid metabolism, epithelial cell proliferation and its regulation, and negative regulation of cell migration. They also enriched multiple signaling pathways, such as necroptosis, MAPK, NOD-like receptor, and focal adhesion. Survival analyses of the 130 genes in the TCGA PCa database revealed that 10 of the deslanoside-downregulated genes (ITG2B, CNIH2, FBF1, PABPC1L, MMP11, DUSP9, TMEM121, SOX18, CMPK2, and MAMDC4) inversely correlated, while one deslanoside-upregulated gene (RASD1) positively correlated, with disease-free survival in PCa patients. In addition, one deslanoside-downregulated gene (ENG) inversely correlated, while three upregulated genes (JUN, MXD1, and AQP3) positively correlated with overall survival in PCa patients. Some of the 15 genes have not been implicated in cancer before. These findings provide another candidate for repurposing cardiac glycosides for anticancer drugs. They also suggest that a diverse range of molecular events underlie deslanoside’s anticancer activity in PCa cells.

Immunotherapy Advances in Locally Advanced and Recurrent/Metastatic Head and Neck Squamous Cell Carcinoma and Its Relationship With Human Papillomavirus

Head and neck cancer (HNC) is the sixth most common malignancy worldwide; head and neck squamous cell carcinoma (HNSCC) account for the most cases of HNC. Past smoking and alcohol consumption are common risk factors of HNSCC; however, an increasing number of cases associated with human papillomavirus (HPV) infection have been reported in recent years. The treatment of HNSCC is integrated and multimodal including traditional surgery, radiotherapy, chemotherapy, and targeted therapy. Since pembrolizumab was approved in 2016, an increasing number of studies have focused on immunotherapy. However, not all of HNSCC patients have a better outcome on immunotherapy. Immunotherapy has been reported to be more effective in HPV-positive patients, but its molecular mechanism is still unclear. Some researchers have proposed that the high proportion of infiltrating immune cells in HPV-positive tumors and the difference in immune checkpoint expression level may be the reasons for their better response. As a result, a series of individualized immunotherapy trials have also been conducted in HPV-positive patients. This paper summarizes the current status of HNSCC immunotherapy, individualized immunotherapy in HPV-positive patients, and immune differences in HPV-positive tumors to provide new insights into HNSCC immunotherapy and try to identify patients who may benefit from immunotherapy.