Individualising Radiotherapy Through Mechanistic Models

放射治疗通过精确地将高剂量的辐射传递到肿瘤来治疗癌症，通过破坏癌细胞的DNA来杀死癌细胞。放射治疗非常有效，因为辐射可以准确地瞄准肿瘤，同时避开正常组织，防止杀死健康细胞所产生的不必要的副作用。新的先进治疗技术和更好的成像技术的引入改善了肿瘤靶向性，大大改善了放射治疗后的患者结果。然而，虽然放射治疗受益于高度的物理个性化，但还可以做更多的工作来改善治疗结果。癌症是一种高度复杂的疾病，与大量不同类型的基因突变有关。这些突变会显著影响特定患者癌症的辐射敏感性。尽管如此，所有患有特定器官癌症的患者通常都接受相同的剂量和治疗计划。虽然这些剂量是针对人群水平的癌症量身定做的，但几乎可以肯定的是，这对一些患者治疗不足和过度治疗。如果在治疗前能够准确地确定个体的放射敏感性，根据患者的特殊遗传学，在改善肿瘤控制或减少副作用方面，结果可能会有显著的改善。该奖学金旨在通过开发细胞如何对辐射做出反应的模型来应对这一挑战，该模型可以根据特定癌症中存在的突变准确地预测个人疾病的敏感性。到目前为止，我们已经开发了与DNA修复相关的模型和表征的反应，并表明我们可以有效地预测和量化DNA修复失败对辐射敏感性的影响。然而，这项工作也表明，尽管DNA修复的丢失很重要，但它只解释了不同癌症和不同患者反应之间的一小部分差异。因此，这种方法需要扩展，以更好地了解临床上看到的反应范围。在这个更新阶段，我们将努力更好地表征这些差异，测量其他生物过程--如与细胞生长和细胞死亡相关的过程--的变化对辐射敏感性的影响，并将其整合到一个组合模型中。我们将使用本地产生的新数据来演示这个模型的有效性，然后开发一种方法，通过它可以将这些预测应用于临床治疗计划，使其预测能够在真实患者数据中得到验证。如果成功，这项研究计划将提供一个独特的工具，使放射治疗能够使用物理和生物因素，为未来的癌症患者提供更个性化的治疗和更好的治疗结果。

Radiotherapy treats cancer through the precise delivery of high doses of radiation to tumours, killing cancerous cells by damaging their DNA. Radiotherapy is highly effective because radiation can be accurately targeted to tumours while avoiding normal tissue, preventing the unwanted side effects which would result from killing healthy cells. The introduction of new advanced treatment techniques and better imaging to improve tumour targeting has significantly improved patient outcomes following radiotherapy. However, while radiotherapy benefits from a high degree of physical personalisation, more can be done to improve treatment outcomes. Cancer is a highly complex disease, associated with a large number of different types of genetic mutation. These mutations can significantly affect the radiation sensitivity of a given patient's cancer. Despite this, all patients with cancer in a particular organ are typically treated with the same dose and treatment schedule. While these doses have been tailored to cancer at a population level, this almost certainly under- and over-treats some patients. If individual radiosensitivity can be precisely defined before treatment, significant improvements in outcome could be achieved, in terms of improved tumour control or reduced side effects, depending on the patient's particular genetics. This fellowship seeks to address this challenge by developing models of how cells respond to radiation, which can accurately predict the sensitivity of an individual's disease based on the mutations present in their particular cancer. In work to date, we have developed models and characterised responses related to DNA repair, and shown that we can effectively predict and quantify quantify how DNA repair failure impacts on radiosensitivity. However, this work has also shown that although loss of DNA repair is important, it only explains a small fraction of the variability between the responses of different cancers and different patients. As a result, this approach needs to be expanded to better understand the range of responses seen in the clinic.In this renewal phase, we will work to better characterise these differences, measuring the impact that changes in other biological processes - such as those related to cell growth and cell death - have on radiation sensitivity, and integrate this into a combined model. We will demonstrate the efficacy of this model using new data generated locally, and then develop a method by which these predictions can be applied in clinical treatment plans, to enable its predictions to be tested in real patient data.If successful, this research programme will deliver a unique tool to enable the tailoring of radiotherapy using both physical and biological factors, offering more personalised therapy and better treatment outcomes for patients suffering from cancer in the future.