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Abstract To improve radiotherapy effect by inducing more toxicity for tumors and less for normal tissue and switching immunosuppressive microenvironment caused by expression of PD-L1 and tumor-associated macrophages (TAMs) to immunoreactive microenvironment, we designed a PD-L1-targeted nanoplatform consisting of gold nanoparticles and superparamagnetic iron oxide nanoparticles (antiPD-L1-SPIOs@PLGA@Au). In vivo T2-weighted images, the best contrast effect of tumor was achieved two hours after intravenous injection of antiPD-L1-SPIOs@PLGA@Au. The tumor control caused by irradiation combined with antiPD-L1-SPIOs@PLGA@Au was better than that by radiotherapy alone in clone formation assay and B16F10 subcutaneous tumor model. Radiosensitivity enhancement induced by the addition of antiPD-L1-SPIOs@PLGA@Au was achieved by increasing ROS production and attenuating DNA damage repair. AntiPD-L1-SPIOs@PLGA@Au could promote the polarization of tumor-associated macrophages (TAMs) to M1 and reverse the immunosuppression caused by TAMs. By increasing the expression of CRT in tumor and blocking the PD-L1/PD pathway, antiPD-L1-SPIOs@PLGA@Au with radiation activated the anti-tumor immune response. In conclusion, antiPD-L1-SPIOs@PLGA@Au could be used as a radiosensitizer and a MRI contrast targeting PD-L1, with the functions of blocking the PD-L1/PD-1 immune checkpoint pathway and reversing the immunosuppression caused by TAMs.

Abstract This study aimed to develop prognosis signatures through a radiomics analysis for patients with nasopharyngeal carcinoma (NPC) by their pretreatment diagnosis magnetic resonance imaging (MRI). A total of 208 radiomics features were extracted for each patient from a database of 303 patients. The patients were split into the training and validation cohorts according to their pretreatment diagnosis date. The radiomics feature analysis consisted of cluster analysis and prognosis model analysis for disease free-survival (DFS), overall survival (OS), distant metastasis-free survival (DMFS) and locoregional recurrence-free survival (LRFS). Additionally, two prognosis models using clinical features only and combined radiomics and clinical features were generated to estimate the incremental prognostic value of radiomics features. Patients were clustered by non-negative matrix factorization (NMF) into two groups. It showed high correspondence with patients’ T stage (p