Giglia Giuseppe, Gambino Giuditta, Sardo Pierangelo
Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), Section of Human Physiology, University of Palermo, 90134 Palermo, Italy.
Euro Mediterranean Institute of Science and Technology, I.E.ME.S.T, 90139 Palermo, Italy.
Brain Sci. 2020 Aug 28;10(9):594. doi: 10.3390/brainsci10090594.
Neuroblastoma (NBM) is a deadly form of solid tumor mostly observed in the pediatric age. Although survival rates largely differ depending on host factors and tumor-related features, treatment for clinically aggressive forms of NBM remains challenging. Scientific advances are paving the way to improved and safer therapeutic protocols, and immunotherapy is quickly rising as a promising treatment that is potentially safer and complementary to traditionally adopted surgical procedures, chemotherapy and radiotherapy. Improving therapeutic outcomes requires new approaches to be explored and validated. In-silico predictive models based on analysis of a plethora of data have been proposed by Lombardo et al. as an innovative tool for more efficacious immunotherapy against NBM. In particular, knowledge gained on intracellular signaling pathways linked to the development of NBM was used to predict how the different phenotypes could be modulated to respond to anti-programmed cell death-ligand-1 (PD-L1)/programmed cell death-1 (PD-1) immunotherapy. Prediction or forecasting are important targets of artificial intelligence and machine learning. Hopefully, similar systems could provide a reliable opportunity for a more targeted approach in the near future.
神经母细胞瘤（NBM）是一种主要在儿童期出现的致命性实体瘤。尽管生存率因宿主因素和肿瘤相关特征而有很大差异，但对于临床侵袭性形式的NBM的治疗仍然具有挑战性。科学进步正在为改进和更安全的治疗方案铺平道路，免疫疗法正在迅速崛起，成为一种有前景的治疗方法，可能更安全且是传统采用的手术、化疗和放疗的补充。改善治疗结果需要探索和验证新的方法。Lombardo等人提出了基于大量数据分析的计算机预测模型，作为一种针对NBM进行更有效免疫治疗的创新工具。特别是，关于与NBM发展相关的细胞内信号通路的知识被用于预测如何调节不同表型以对抗程序性细胞死亡配体1（PD-L1）/程序性细胞死亡1（PD-1）免疫疗法作出反应。预测是人工智能和机器学习的重要目标。有望在不久的将来，类似的系统能为更有针对性的方法提供可靠机会。
