Intratumor heterogeneity arises from ongoing somatic evolution complicating cancer diagnosis, prognosis, and treatment. Here we present TEATIME (es t imating e volution a ry events t hrough s i ngle-ti m epoint s e quencing), a novel computational framework that models tumors as mixtures of two competing cell populations: an ancestral clone with baseline fitness and a derived subclone with elevated fitness. Using cross-sectional bulk sequencing data, TEATIME estimates mutation rates, timing of subclone emergence, relative fitness, and number of generations of growth. To quantify intratumor fitness asymmetries, we introduce a novel metric-fitness diversity-which captures the imbalance between competing cell populations and serves as a measure of functional intratumor heterogeneity. Applying TEATIME to 33 tumor types from The Cancer Genome Atlas, we revealed divergent as well as convergent evolutionary patterns. Notably, we found that immune-hot microenvironments constraint subclonal expansion and limit fitness diversity. Moreover, we detected temporal dependencies in mutation acquisition, where early driver mutations in ancestral clones epistatically shape the fitness landscape, predisposing specific subclones to selective advantages. These findings underscore the importance of intratumor competition and tumor-microenvironment interactions in shaping evolutionary trajectories, driving intratumor heterogeneity. Lastly, we demonstrate that TEATIME-derived evolutionary parameters and fitness diversity offer novel prognostic insights across multiple cancer types.

肿瘤内的异质性源于持续的体细胞进化，这使得癌症的诊断、预后和治疗变得复杂。在这里，我们提出了一个新颖的计算框架TEATIME（通过单时间点测序估计进化事件），该框架将肿瘤建模为两种竞争的细胞群体的混合物：一种是具有基线适应度的祖系克隆和另一种是具有较高适应度的派生亚克隆。利用横断面批量测序数据，TEATIME 可以估算突变率、亚克隆出现的时间、相对适应度以及增长代数的数量。为了量化肿瘤内的适应性不对称，我们引入了一个新的指标——适应度多样性，它捕捉了竞争细胞群体之间的不平衡，并作为功能性的肿瘤内异质性的衡量标准。将TEATIME 应用于来自癌症基因组图谱的33种不同类型的肿瘤后，我们揭示了不同的进化模式以及趋同进化的存在。值得注意的是，我们发现免疫活跃的小环境限制了亚克隆扩张并减少了适应度多样性。此外，我们还检测到突变获得的时间依赖性，在早期祖先克隆中的驱动突变表观遗传地塑造了适应度景观，并使特定的亚克隆具有选择优势。这些发现强调了肿瘤内竞争和肿瘤-微环境相互作用在塑造进化轨迹、驱动肿瘤内异质性方面的重要性。最后，我们展示了TEATIME 导出的进化参数和适应度多样性为多种癌症类型提供了新的预后见解。