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，避开正常组织，防止因杀死健康细胞而产生的不必要的副作用。近年来，新的先进治疗技术和更好的成像技术的引入，提高了肿瘤靶向性，显著改善了放射治疗后的患者预后。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。该项目试图通过开发细胞对辐射的反应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。这项工作试图回答一些问题，包括：1.初始辐射如何与细胞相互作用导致DNA损伤。这将使用物理科学的数学建模技术来计算能量是如何储存在单个细胞中的，以及这是如何对单个DNA链造成损害的。细胞如何对这种最初的损伤做出反应。在这里，我们将模拟细胞如何对不同分布的DNA损伤做出反应，包括它们修复这种损伤的可能性，以及细胞在给定的辐射暴露后存活的可能性。患者的遗传学如何影响这些反应。虽然我们知道决定细胞对辐射的敏感性的过程(例如，它修复DNA损伤的能力)，但很难测量每个患者的这些过程。取而代之的是，我们将开发方法，基于个人疾病的遗传学来预测这些过程的有效性，这些遗传学可以在治疗前直接测量。4.如何优化临床治疗以纳入这一知识。在这些模型的基础上，我们将开发一种工具，在设计最佳的放射治疗时，同时考虑物理和生物的个性化，以最大限度地增加每个患者的疾病被成功治疗的机会，并将副作用降至最低。如果成功，这项研究计划将提供一种独特的工具，使放射治疗能够利用物理和生物因素进行靶向治疗，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 and avoid 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 in recent years. 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, and is associated with a large number of different types of 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 project 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. This work seeks to answer a number of questions, including: 1. How the initial radiation interacts with the cell to cause DNA damage. This will use mathematical modelling techniques from the physical sciences to calculate how energy is deposited in individual cells, and how this causes damage to individual DNA strands.2. How cells respond to this initial damage. Here, we will model how cells respond to different distributions of DNA damage, including how likely they are to repair this damage, and how likely the cell is to survive following a given radiation exposure.3. How patient genetics impacts on these responses. While we know the processes which determine how sensitive a cell is to radiation (for example, its ability to repair DNA damage), it is difficult to measure these for each patient. Instead, we will develop methods to predict how effective these processes are based on the genetics of the individual's disease, which can be directly measured before treatment. 4. How clinical treatments can be optimised to incorporate this knowledge. Based on these models, we will then develop a tool which will allow for the best radiotherapy treatment to be designed taking into account both physical and biological personalisation, to maximize the chance that each patient's disease will be successfully treated with minimal side-effects.If successful, this research programme will deliver a unique tool to enable the targeting of radiotherapy using both physical and biological factors, offering more personalised therapy and better treatment outcomes for patients suffering from cancer in the future.