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Therapy Testing in a Spheroid-based 3D Cell Culture Model for Head and Neck Squamous Cell Carcinoma
Published on: April 20, 2018
Systematic Profiling of Immune Risk Model to Predict Survival and Immunotherapy Response in Head and Neck Squamous
Xingyu Liu1, Jiarui Chen1, Wei Lu2
1Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Background And Purpose:
Head and neck squamous carcinoma (HNSCC), characterized by immunosuppression, is a group of highly heterogeneous cancers. Although immunotherapy exerts a promising influence on HNSCC, the response rate remains low and varies in assorted primary sites. Immunological mechanisms underlying HNSCC pathogenesis and treatment response are not fully understood. This study aimed to develop a differentially expressed genes (DEGs)-based risk model to predict immunotherapy efficacy and stratify prognosis of HNSCC patients.
Materials And Methods:
The expression profiles of HNSCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The tumor microenvironment and immune response were estimated by cell type identification via estimating relative subset of known RNA transcripts (CIBERSORT) and immunophenoscore (IPS). The differential expression pattern based on human papillomavirus status was identified. A DEGs-based prognostic risk model was developed and validated. All statistical analyses were performed with R software (version 3.6.3).
Results:
By using the TCGA database, we identified DKK1, HBEGF, RNASE7, TNFRSF12A, INHBA, and IPIK3R3 as DEGs that were associated with patients' overall survival (OS). Patients were stratified into the high- and low-risk subgroups according to a DEGs-based prognostic risk model. Significant difference in OS was found between the high- and low-risk patients (1.64 vs. 2.18 years, P = 0.0017). In multivariate Cox analysis, the risk model was an independent prognostic factor for OS (hazard radio = 1.06, 95% confidence interval [1.02-1.10], P = 0.004). More CD8+ T cells and regulatory T cells were observed in the low-risk group and associated with a favorable prognosis. The IPS analysis suggested that the low-risk patients possessed a higher IPS score and a higher immunoreactivity phenotype, which were correlated with better immunotherapy response.
Conclusion:
Collectively, we established a reliable DEGs-based risk model with potential prognostic value and capacity to predict the immunophenotype of HNSCC patients.
Insights
This study developed a risk model using differentially expressed genes (DEGs) to predict immunotherapy response and prognosis in head and neck squamous cell carcinoma (HNSCC). The model effectively stratified patients, identifying a low-risk group with better survival and higher immune cell infiltration.
Area of Science:
- Oncology
- Immunology
- Genomics
Background:
- Head and neck squamous cell carcinoma (HNSCC) is highly heterogeneous and characterized by immunosuppression.
- Despite immunotherapy's promise, response rates in HNSCC are low and vary by primary site.
- The immunological mechanisms driving HNSCC and treatment response require further elucidation.
Purpose of the Study:
- To develop a risk model based on differentially expressed genes (DEGs) for predicting immunotherapy efficacy in HNSCC.
- To stratify HNSCC patients based on their risk profile for improved prognosis prediction.
Main Methods:
- Utilized The Cancer Genome Atlas (TCGA) database for HNSCC expression profiles.
- Employed CIBERSORT and immunophenoscore (IPS) to assess tumor microenvironment and immune response.
- Developed and validated a DEGs-based prognostic risk model using R software.
Main Results:
- Identified six DEGs (DKK1, HBEGF, RNASE7, TNFRSF12A, INHBA, IPIK3R3) associated with overall survival (OS).
- The DEGs-based risk model significantly stratified patients into high- and low-risk groups with distinct OS (1.64 vs. 2.18 years, P = 0.0017).
- Low-risk patients exhibited higher CD8+ T cell and regulatory T cell infiltration, a higher IPS score, and better predicted immunotherapy response.
Conclusions:
- Established a reliable DEGs-based risk model for HNSCC prognosis.
- The model demonstrates potential in predicting patient immunophenotype and immunotherapy response.
- This tool can aid in stratifying HNSCC patients for tailored treatment strategies.
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