Personalising the treatment of brain tumours through advanced graphical modelling

脑癌是英国第九种最常见的癌症，是导致死亡和残疾的主要原因，但仍然顽固地抵制治疗。尽管进行了密集的研究，但在过去的30年里，存活率基本上没有变化。胶质母细胞瘤是一种常见的快速生长的脑瘤，每20名患者中只有一人能在确诊后五年内存活，而且通常生活质量严重受损。近几十年来，其他类型的癌症在治疗结果方面取得了显着的改善：为什么大脑会不同？一个可能的答案是脑瘤异常复杂。导致不受控制的、无序的细胞分裂--所有癌症的标志--的疾病机制似乎既多又多样，而且每个患者之间的差异很大。这里的治疗必须非常个人化，才能有效，然而，多样性使得确定哪种治疗有效非常困难。我们陷入了两难境地：为了确定对每个患者最好的方案，我们需要通过对许多患者的研究来指导，但个人结果是如此不同，以至于一个大群体的平均水平几乎不能告诉我们每个人的情况。我们怎么才能摆脱这一困境？与所有人类多样性一样，要了解这种生物多样性，我们必须首先对其进行足够详细的描述，使每个个体都与众不同和可辨认。这种丰富的描述或表型可以被认为是每个人独一无二的、但能够与其他人进行比较的特有的“指纹”模式。然后，能够理解复杂模式的机器--比如现在正在给世界带来革命性变化的人工智能系统--可以被部署到为合适的患者识别合适的治疗方法的任务中，帮助提供既个性化又有确凿证据的护理。至关重要的是，在处理脑癌的所有NHS医院的常规护理过程中，已经收集了丰富的详细信息-脑扫描、组织检查、基因分析-可以从中提取这种模式，使我们能够在不干扰既定护理途径的情况下采用这种方法。但目前还不存在在NHS范围内提供它的框架：我们的任务是创建它的基础，确定它的可行性，并对它的应用进行试验。我们的任务分为两部分。首先，我们必须创建复杂的计算机算法，理解从脑部扫描、组织样本和遗传学中提取的特征肿瘤模式，将它们放在彼此之间有意义的关系中，以便建立它们与患者的病程和治疗的联系。其次，我们必须使用来自过去和未来的许多不同患者的大量数据来评估算法的准确性，以验证发现并确保在不同患者群体和医院地点的普适性。我们提议的方法的核心是将生物模式理解为网络的想法。以此类推，细胞从正常到癌变的路径可以被视为一段穿越伦敦地铁的旅程，在那里，恶性肿瘤的最终目的地是通过一组定义了一条特征路径的基因停顿来到达的。捕捉所有可能的路径--一张“地下肿瘤”的地图--然后给我们一种方法来了解发生了什么，以及疾病过程如何在不同的患者之间发生变化。成功将提供一个框架，使个人能够更好地预测患者的结果，更准确、个性化地开出治疗处方，并提供一种强大的手段，阐明未来治疗创新所依赖的疾病机制。

Cancer of the brain - the 9th commonest form in the UK - is a major cause of death and disability that remains stubbornly resistant to treatment. Despite intense research, survival has remained essentially unchanged over the past thirty years. Only one in twenty patients with glioblastoma - a common, fast-growing brain tumour - can expect to be alive five years after diagnosis, and often with severely impaired quality of life. Other kinds of cancer have seen striking improvements in treatment outcomes over recent decades: why is the brain different? One possible answer is the unusual complexity of brain tumours. The disease mechanisms that cause the uncontrolled, disorganised cell division - the hallmark of all cancer - appear to be both many and diverse, and vary greatly from one patient to another. Treatment here must be closely personalised if it is to be effective, yet the diversity makes determining what treatment works very difficult. We are caught in a Catch 22 situation: to determine what is best for each patient we need to be guided by the study of many patients together, yet individual outcomes are so diverse that the average of a large group tells us little about each individual. How do we break out of this? As with all human diversity, to understand this biological diversity we must first describe it in sufficient detail to render each individual distinct and recognizable. Such a rich description or phenotype can be thought of as a characteristic "fingerprint" pattern unique to each individual yet enabling comparison with others. Machines able to make sense of complex patterns - such as the artificial intelligence systems now revolutionising the world - can then be deployed to the task of identifying the right treatment for the right patient, helping deliver care that is both personalised and founded on robust evidence. Crucially, there is already a wealth of detailed information collected during routine care across all NHS hospitals dealing with brain cancer - brain scans, tissue examinations, genetic analyses - from which such patterns may be extracted, allowing us to adopt this approach without disturbing established pathways of care. But no framework to deliver it across the NHS currently exists: our task is to create its foundations, establish its feasibility, and pilot its application. Our task has two parts. First, we must create complex computer algorithms that make sense of characteristic tumour patterns extracted from brain scans, tissue samples, and genetics, placing them into meaningful relation to each other so that their connection with the patient's disease course and treatment can be established. Second, we must assess the accuracy of the algorithms with copious data from many, diverse patients, from both the past and the future, to validate findings and ensure generalisability across both different patient groups and hospital sites. At the heart of our proposed approach lies the idea of making sense of biological patterns as networks. By way of analogy, the path of a cell from normal to cancerous may be seen as a journey through the London Underground, where the final destination of malignancy is reached via a set of genetic stops defining a characteristic path. Capturing the set of all possible paths - a map of the "tumour underground" - then gives us a means of understanding what is going on, and how the disease process varies from one patient to another. Success will provide a framework that will enable better individual prediction of patient outcomes, more accurate, personalised prescription of treatments, and a powerful means of illuminating disease mechanisms on which future treatment innovation depends.