Radiotherapy (RT) is currently considered as an essential treatment for non-small cell lung cancer (NSCLC); it can induce cell death directly and indirectly via promoting systemic immune responses. However, there still exist obstacles that affect the efficacy of RT such as tumor hypoxia and immunosuppressive tumor microenvironment (TME). Herein, we report that the biomineralized manganese oxide nanoparticles (Bio-MnO2 NPs) prepared by mild enzymatic reaction could be a promising candidate to synergistically enhance RT and RT-induced immune responses by relieving tumor hypoxia and activating cGAS-STING pathway. Bio-MnO2 NPs could convert endogenic H2O2 to O2 and catalyze the generation of reactive oxygen species so as to sensitize the radiosensitivity of NSCLC cells. Meanwhile, the release of Mn2+ into the TME significantly enhanced the cGAS-STING activity to activate radio-immune responses, boosting immunogenic cell death and increasing cytotoxic T cell infiltration. Collectively, this work presents the great promise of TME reversal with Bio-MnO2 NPs to collaborate RT-induced antitumor immune responses in NSCLC.
Background Radiotherapy has been widely used to treat various cancers, but its efficacy depends on the individual involved. Traditional gene-based machine-learning models have been widely used to predict radiosensitivity. However, there is still a lack of emerging powerful models, artificial neural networks (ANN), in the practice of gene-based radiosensitivity prediction. In addition, ANN may overfit and learn biologically irrelevant features. Methods We developed a novel ANN with Selective Connection based on Gene Patterns (namely ANN-SCGP) to predict radiosensitivity and radiocurability. We creatively used gene patterns (gene similarity or gene interaction information) to control the "on–off" of the first layer of weights, enabling the low-dimensional features to learn the gene pattern information. ANN-SCGP was trained and tested in 82 cell lines and 1,101 patients from the 11 pan-cancer cohorts. Results For survival fraction at 2 Gy, the root mean squared errors (RMSE) of prediction in ANN-SCGP was the smallest among all algorithms (mean RMSE: 0.1587–0.1654). For radiocurability, ANN-SCGP achieved the first and second largest C-index in the 12/20 and 4/20 tests, respectively. The low dimensional output of ANN-SCGP reproduced the patterns of gene similarity. Moreover, the pan-cancer analysis indicated that immune signals and DNA damage responses were associated with radiocurability. Conclusions As a model including gene pattern information, ANN-SCGP had superior prediction abilities than traditional models. Our work provided novel insights into radiosensitivity and radiocurability.
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