Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
School of Informatics, University of Edinburgh, Edinburgh, UK.
Nat Methods. 2018 Sep;15(9):707-714. doi: 10.1038/s41592-018-0108-x. Epub 2018 Aug 31.
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.
在患者内部和之间，基因组变化的反复出现反映了反复的进化过程，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，但在患者之间稳健地识别反复进化仍然是一个挑战。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的随机效应和数据中的噪声，并在癌症队列中识别隐藏的进化模式。当应用于来自肺癌、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，我们的方法在患者亚组中检测到了反复的进化轨迹，在单样本队列（n=2935）中重现了这些轨迹。我们的方法提供了一种根据肿瘤进化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。
