Abstract
Response to radiotherapy (RT) in cancers varies widely among patients. Therefore, it is very important to predict who will benefit from RT before clinical treatment. Consideration of the immune tumor microenvironment (TME) could provide novel insight into tumor treatment options. In this study, we investigated the link between immune infiltration status and clinical RT outcome in order to identify certain leukocyte subsets that could potentially influence the clinical RT benefit across cancers. By integrally analyzing the TCGA data across seven cancers, we identified complex associations between immune infiltration and patients RT outcomes. Besides, immune cells showed large differences in their populations in various cancers, and the most abundant cells were resting memory CD4 T cells. Additionally, the proportion of activated CD4 memory T cells and activated mast cells, albeit at low number, were closely related to RT overall survival in multiple cancers. Furthermore, a prognostic model for RT outcomes was established with good performance based on the immune infiltration status. Summarized, immune infiltration was found to be of significant clinical relevance to RT outcomes. These findings may help to shed light on the impact of tumor-associated immune cell infiltration on cancer RT outcomes, and identify biomarkers and therapeutic targets.
Highlights
Radiotherapy (RT) is the primary method for cancer treatment given to approximately 60%of all newly diagnosed patients [1]
Based on the seven cancer types from the The Cancer Genome Atlas (TCGA) dataset, we found that immune treatment of patients with solid tumors, RT has a wide range of applications [29]
Our study firstly demonstrated the relationship between clinical RT outcomes to relieve symptoms and improve the quality of life of patients with CESC or UCEC
Summary
Radiotherapy (RT) is the primary method for cancer treatment given to approximately 60%of all newly diagnosed patients [1]. Radiotherapy (RT) is the primary method for cancer treatment given to approximately 60%. Significant physical advances in RT have been achieved by developing methods of treatment planning and delivery [2]. Due to differences in tumor radiosensitivity, not all patients derive survival benefit from RT, while suffering serious adverse consequences [3,4]. Radiosensitivity prediction has always been a topic of primary importance in the field of biologically guided personalized treatment strategies in radiation oncology [5,6]. In the current era of precision medicine, high-throughput technologies have provided an opportunity to approach the development of radiosensitivity biomarkers from a different perspective. Based on a 10-gene signature, Eschrich et al developed the radiosensitivity index (RSI), which is directly proportional to tumor radioresistance [7].
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