Given the implications of tumor dynamics for precision medicine, there is a need to systematically characterize the mode of evolution across diverse solid tumor types. In particular, methods to infer the role of natural selection within established human tumors are lacking. By simulating spatial tumor growth under different evolutionary modes and examining patterns of between-region subclonal genetic divergence from multiregion sequencing (MRS) data, we demonstrate that it is feasible to distinguish tumors driven by strong positive subclonal selection from those evolving neutrally or under weak selection, as the latter fail to dramatically alter subclonal composition. We developed a classifier based on measures of between-region subclonal genetic divergence and projected patient data into model space, finding different modes of evolution both within and between solid tumor types. Our findings have broad implications for how human tumors progress, how they accumulate intratumoral heterogeneity, and ultimately how they may be more effectively treated.

中文翻译：

---

区域间的遗传差异反映了肿瘤进化的方式和速度。

考虑到肿瘤动力学对精密医学的影响，有必要系统地表征多种实体瘤类型之间的进化模式。特别地，缺乏推断自然选择在已建立的人类肿瘤中的作用的方法。通过模拟不同进化模式下的空间肿瘤生长并从多区域测序（MRS）数据检查区域间亚克隆遗传差异的模式，我们证明了将强阳性亚克隆选择驱动的肿瘤与中性进化或弱选择的肿瘤区分开来是可行的，因为后者无法显着改变亚克隆的组成。我们基于区域间亚克隆遗传差异的度量方法开发了分类器，并将患者数据投射到模型空间中，在实体瘤类型之内和之间找到不同的进化模式。我们的发现对人类肿瘤如何进展，如何累积肿瘤内异质性以及最终如何更有效地治疗具有广泛的意义。