Forecasting tumor evolution: can the past reveal the future?

总结 克隆进化是当前许多公共卫生问题（如癌症和传染病）背后的驱动力。 疾病然而，在治疗和预防这些疾病方面投入的努力有限， 进化的观点。重要的是，预测肿瘤演变的能力取决于 确定性和随机性的过程。尽管对人类肿瘤进化的直接观察 不切实际的是，肿瘤内细胞之间的体细胞改变模式忠实地报告了它们的过去， 增殖史出乎意料的是，我们最近发现，在转化后，一些肿瘤生长在 缺乏严格的选择，与有效的中性进化相容。这导致了我们对一种 肿瘤生长的新大爆炸模型，其中肿瘤生长为单个终端扩展， 大量的异质性和有效的同样适合的亚克隆。这种新模式与事实上的 序贯克隆扩增模型，并表明，肿瘤启动事件是必要的和充分的 以促进随后的生长。此外，这些发现提出了一种诱人的可能性， 肿瘤生长过程中的事件塑造了其随后的进化轨迹。在这里我们严格测试小说 假设早期肿瘤演变是决定性的，并试图确定其偶然性。因此，我们执行 野生型人类类器官的癌基因工程和细胞条形码以表征克隆动力学 以及体外肿瘤演变过程中适应性增加的功能决定因素。这种创新的血统 追踪策略使得能够直接测量人类细胞中的进化参数， 肿瘤进展过程中的综合基因型-表型图谱。与此同时，我们将推断 转移性播散，并评估是否转移表型是指定的早期通过 患者基因组数据的计算和数学建模。这种系统生物学方法将 评估肿瘤演变的可预测性，以开发预测疾病的模型 这有助于癌症的早期诊断，从而降低癌症相关的死亡率。 ! !

Summary Clonal evolution is the driving force behind many current public health issues such as cancer and infectious disease. However, limited efforts have been invested in treating and preventing these conditions from an evolutionary perspective. Critically, the ability to forecast tumor evolution depends on the relative contribution of deterministic and stochastic processes. Although direct observations of human tumor evolution are impractical, patterns of somatic alterations amongst cells within a tumor faithfully report on their past proliferative history. Unexpectedly, we recently found that after transformation, some tumors grow in the absence of stringent selection, compatible with effectively neutral evolution. This led to our description of a novel Big Bang model of tumor growth where the tumor grows as a single terminal expansion populated by numerous heterogeneous—and effectively equally fit subclones. This new model contrasts with the de facto sequential clonal expansion model, and suggests that tumor-initiating events are both necessary and sufficient to propagate subsequent growth. Moreover, these findings raise the tantalizing possibility that the earliest events during tumor growth shape its subsequent evolutionary trajectory. Here we rigorously test the novel hypothesis that early tumor evolution is deterministic and seek to define its contingencies. We thus perform oncogene-engineering and cellular barcoding of wild-type human organoids to characterize clonal dynamics and the functional determinants of increased fitness during in vitro tumor evolution. This innovative lineage tracing strategy enables the direct measurement of evolutionary parameters in human cells, while rendering a comprehensive genotype to phenotype map during tumor progression. In parallel, we will infer the timing of metastatic dissemination and evaluate whether the metastatic phenotype is specified early through computational and mathematical modeling of patient genomic data. This systems biology approach will evaluate the predictability of tumor evolution towards the development of models to forecast disease progression and guide earlier detection, thereby reducing cancer related mortality. ! !