Radiation-sensitive genetic prognostic model to identify individuals at risk for radiation resistance in head and neck squamous cell carcinoma.

person Peimeng You Department of Radiation Oncology, Cancer Hospital of Nanchang University, Jiangxi Key Laboratory of Translational Cancer Research (Jiangxi Cancer Hospital of Nanchang University), Nanchang, China info_outline Peimeng You, Qiaxuan Li, HaiYu Zhou

Authors person Peimeng You Department of Radiation Oncology, Cancer Hospital of Nanchang University, Jiangxi Key Laboratory of Translational Cancer Research (Jiangxi Cancer Hospital of Nanchang University), Nanchang, China info_outline Peimeng You, Qiaxuan Li, HaiYu Zhou Organizations Department of Radiation Oncology, Cancer Hospital of Nanchang University, Jiangxi Key Laboratory of Translational Cancer Research (Jiangxi Cancer Hospital of Nanchang University), Nanchang, China, Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China Abstract Disclosures Research Funding No funding received None. Background: The advantages of radiotherapy for head and neck squamous cell carcinoma (HNSCC) depend on the radiation sensitivity of the patient. Here, we established and verified radiological factor-related gene signature and built a prognostic risk model to predict whether radiotherapy would be beneficial. Methods: Data from The Cancer Genome Atlas, Gene Expression Omnibus, and RadAtlas databases were subjected to LASSO regression, univariate COX regression, and multivariate COX regression analyses to integrate genomic and clinical information from patients with HNSCC. HNSCC radiation-related prognostic genes were identified, and patients classified into high- and low-risk groups, based on risk scores. Variations in radiation sensitivity according to immunological microenvironment, functional pathways, and immunotherapy response were investigated. Results: We built a clinical risk prediction model comprising a 15-genes signature and used it to divide patients into two groups based on their susceptibility to radiation: radiation-sensitive and radiation-resistant. Overall survival was significantly greater in the radiation-sensitive than the radiation-resistant group. Further, our model was an independent predictor of radiotherapy response, outperforming other clinical parameters, and could be combined with tumor mutational burden, to identify the target population with good predictive value for prognosis at 1, 2, and 3 years. Additionally, the radiation-resistant group was more vulnerable to low levels of immune infiltration, which are significantly associated with DNA damage repair, hypoxia, and cell cycle regulation. Tumor Immune Dysfunction and Exclusion scores also suggested that the resistant group would respond less favorably to immunotherapy. Conclusions: Our prognostic model based on a radiation-related gene signature has potential for application as a tool for risk stratification of radiation therapy for patients with HNSCC, helping to identify candidates for radiation therapy and overcome radiation resistance.