In-vitro 3D modelling for charged particle therapy – Uncertainties and opportunities

抗辐射性 放射治疗 肿瘤微环境 医学 粒子疗法 放射生物学 相对生物效应 质子疗法 癌症研究 免疫系统 免疫学 内科学 辐照 物理 核物理学
作者
Anuradha Thiagarajan
出处
期刊:Advanced Drug Delivery Reviews [Elsevier BV]
卷期号:179: 114018-114018 被引量:12
标识
DOI:10.1016/j.addr.2021.114018
摘要

Radiation therapy is a critical component of oncologic management, with more than half of all cancer patients requiring radiotherapy at some point during their disease course. Over the last decade, there has been increasing interest in charged particle therapy due to its advantageous physical and radiobiologic properties, with the therapeutic use of proton beam therapy (PBT) expanding worldwide. However, there remain large gaps in our knowledge of the radiobiologic mechanisms that underlie key aspects of PBT, such as variations in relative biologic effectiveness (RBE), radioresistance, DNA damage response and repair pathways, as well as immunologic effects. In addition, while the emerging technique of ultra-high dose rate or FLASH radiotherapy, with its potential to further reduce normal tissue toxicities, is an exciting development, in-depth study is needed into the postulated biochemical mechanisms that underpin the FLASH effect such as the oxygen depletion hypothesis as well as the relative contributions of immune responses and the tumor microenvironment. Further investigation is also required to ensure that the FLASH effect is not diminished or lost in PBT. Current methods to evaluate the biologic effects of charged particle therapy rely heavily on 2D cell culture systems and/or animal models. However, both of these methods have well-recognized limitations which limit translatability of findings from bench to bedside. The advent of novel three-dimensional in-vitro tumor models offers a more physiologically relevant and high throughput in-vitro system for the study of tumor development as well as novel therapeutic approaches such as PBT. Advances in 3D cell culture methods, together with knowledge of disease mechanism, biomarkers, and genomic data, can be used to design personalized 3D models that most closely recapitulate tumor microenvironmental factors promoting a particular disease phenotype, moving 3D models and PBT into the age of precision medicine.
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