Evolution forecasting for real-time blood-cancer risk prediction

癌症是一种进化的疾病，在我们的身体里发生了几十年。当我们的细胞分裂时，DNA复制中偶尔发生的错误（“突变”）会破坏细胞的正常功能。如果这些“突变克隆”存活的时间足够长，它们就会获得进一步的致癌突变，最终导致受控细胞增殖的完全崩溃。这一进化过程的第一步在癌症发展前几年就开始了。这就提出了一种可能性，即这些早期事件可以作为预测谁有患癌症风险的风向标。然而，由于很难在如此长的时间尺度上测量我们体内发生的进化，我们对导致早期癌症的进化动力学的理解仍然是粗略的。我们认识上的一个关键差距是，我们无法确定哪些突变克隆会发展成致命的癌症，哪些会保持良性。每年从成千上万的健康人身上收集的连续血液样本给了我们一个超能力，可以填补这些理解上的空白。我们可以“放大”癌症患者，然后通过分析癌症确诊前几年采集的血液样本来“倒带”时间。这提供了一个详细的疾病“化石记录”，使人们能够确定癌症首次出现的时间，并观察肿瘤的整个进化生活史，一次一个DNA错误。这一雄心勃勃的愿景的一个有吸引力的初始目标是侵袭性血癌急性髓性白血病（AML），因为它的特点是相对较少的致癌突变，这些突变很容易在血液中检测到。这项UKRI FLF应用程序为血癌预测和早期检测制定了一项创新的长期研究计划。为了实现这一目标，首先，我们将把群体遗传学理论与来自50万个人血液的大量测序数据结合起来，以描述定义“正常”的进化动态。其次，通过利用数百例AML病例和无癌对照的一组非凡的系列血液样本，我们将生成一个独特的动态数据集，描绘出AML如何从健康组织进化的高度定量的遗传肖像。第三，通过挖掘这一丰富的数据资源，我们将训练一组数学和统计模型，这些模型使用进化动力学理论和模拟（我们之前率先使用）来从血液样本中对白血病风险进行概率“预测”。通过结合独特的纵向样本、新颖的测序技术和新兴的统计方法来预测血癌风险，该FLF应用程序将建立血癌作为早期癌症检测的“模型系统”。

Cancer is a disease of evolution that plays out in our bodies over decades. As our cells divide, occasional errors ("mutations") in DNA replication occur which can disrupt the normal function of the cell. If these "mutant clones" survive long enough they can acquire further cancer-causing mutations that eventually drive the total break-down of controlled cell proliferation. The first steps of this evolutionary process begin years before the development of cancer. This raises the possibility that these early events could be used as a bellwether for predicting who is at risk of cancer. However, because it is difficult to measure the evolution taking place inside our bodies over such long timescales, our understanding of the evolutionary dynamics driving early cancer remains cursory. A key gap in our understanding is our inability to identify which mutant clones will progress to lethal cancers and which will remain benign. Serial blood samples collected annually from hundreds of thousands of healthy people give us a superpower that can fill in these gaps in understanding. We can "zoom in" on the people who develop cancer, then "rewind" time by analysing blood samples collected years before the cancer was diagnosed. This provides a detailed "fossil record" of the disease, enabling one to determine when the cancer first arose and to watch the entire evolutionary life-history of the tumour unfold, one DNA error at a time. An attractive initial target for this ambitious vision is the aggressive blood cancer Acute Myeloid Leukemia (AML) because it is characterised by a relatively small number of cancer-causing mutations, which are readily detectable in the blood. This UKRI FLF application sets out an innovative long-term research programme for blood cancer prediction and early detection. To achieve this, first we will combine population genetic theory with the vast amounts of sequencing data from the blood of >50,000 individuals to characterise the evolutionary dynamics that defines "normal". Second, by exploiting an extraordinary set of serial blood samples in hundreds of AML cases and cancer-free controls we will generate a unique dynamic dataset that paints a highly quantitative genetic portrait of how AML evolves from healthy tissue. Third, by mining this rich data resource, we will train a set of mathematical and statistical models that use evolutionary dynamics theory and simulation (which we have previously pioneered) to make probabilistic "forecasts" of leukemia risk from a blood sample. This FLF application will thus establish blood-cancers as a "model system" for early cancer detection by combining unique longitudinal samples, novel sequencing technologies and emerging statistical methods to predict blood cancer risk.