Recurrent successions of genomic changes, both within and between patients, reflect repeated evolutionary processes that are valuable for the anticipation of cancer progression. Multi-region sequencing allows the temporal order of some genomic changes in a tumor to be inferred, but the robust identification of repeated evolution across patients remains a challenge. We developed a machine-learning method based on transfer learning that allowed us to overcome the stochastic effects of cancer evolution and noise in data and identified hidden evolutionary patterns in cancer cohorts. When applied to multi-region sequencing datasets from lung, breast, renal, and colorectal cancer (768 samples from 178 patients), our method detected repeated evolutionary trajectories in subgroups of patients, which were reproduced in single-sample cohorts (n = 2,935). Our method provides a means of classifying patients on the basis of how their tumor evolved, with implications for the anticipation of disease progression.

患者内部和患者之间反复出现的基因组变化反映了重复的进化过程，这对于预测癌症进展很有价值。多区域测序可以推断肿瘤中某些基因组变化的时间顺序，但对患者之间重复进化的可靠识别仍然是一个挑战。我们开发了一种基于迁移学习的机器学习方法，使我们能够克服癌症进化和数据噪声的随机影响，并识别癌症队列中隐藏的进化模式。当应用于肺癌、乳腺癌、肾癌和结直肠癌的多区域测序数据集（来自 178 名患者的 768 个样本）时，我们的方法检测到患者亚组中重复的进化轨迹，这些轨迹在单样本队列中重现（ n = 2,935） 。我们的方法提供了一种根据肿瘤演变方式对患者进行分类的方法，这对疾病进展的预测具有重要意义。