Institute for BioEngineering, University of Edinburgh, UK.
Faculty of Life Sciences, Rhine Waal University of Applied Sciences, Kleve, Germany.
Analyst. 2021 Jun 7;146(11):3709-3716. doi: 10.1039/d1an00387a. Epub 2021 May 10.
Radioresistance-a living cell's response to, and development of resistance to ionising radiation-can lead to radiotherapy failure and/or tumour recurrence. We used Raman spectroscopy and machine learning to characterise biochemical changes that occur in acquired radioresistance for breast cancer cells. We were able to distinguish between wild-type and acquired radioresistant cells by changes in chemical composition using Raman spectroscopy and machine learning with 100% accuracy. In studying both hormone receptor positive and negative cells, we found similar changes in chemical composition that occur with the development of acquired radioresistance; these radioresistant cells contained less lipids and proteins compared to their parental counterparts. As well as characterising acquired radioresistance in vitro, this approach has the potential to be translated into a clinical setting, to look for Raman signals of radioresistance in tumours or biopsies; that would lead to tailored clinical treatments.
放射抗性——活细胞对电离辐射的反应和发展产生的抗性——可导致放射疗法失败和/或肿瘤复发。我们使用拉曼光谱和机器学习来描述乳腺癌细胞获得性放射抗性中发生的生化变化。我们能够通过拉曼光谱和机器学习以 100%的准确率区分野生型和获得性放射抗性细胞，因为它们的化学成分发生了变化。在研究激素受体阳性和阴性细胞时，我们发现与获得性放射抗性发展相关的化学成分发生了类似的变化；与亲本细胞相比，这些放射抗性细胞的脂质和蛋白质含量较少。除了在体外描述获得性放射抗性外，这种方法还有望转化为临床环境，以寻找肿瘤或活检中放射抗性的拉曼信号；这将导致量身定制的临床治疗。
