癌症进化为预测肿瘤学奠定了基础。测试进化指标需要在受控临床试验中进行定量测量。我们使用来自 114 名参与临床试验且中位随访时间为 12 年的个体的 642 个样本，绘制了局部晚期前列腺癌的基因组肿瘤内异质性。我们同时使用深度学习评估了 250 名个体的 1,923 个组织学切片的形态异质性。遗传和形态学（格里森）多样性是复发的独立预测因子（风险比（HR） = 3.12和95%置信区间（95% CI） = 1.34-7.3；HR = 2.24和95% CI = 1.28-3.92）。综合起来，他们确定了一组中位复发时间只有一半的组。克隆的空间分离也是复发的独立标志（HR = 2.3和95% CI = 1.11-4.8）。我们确定了与格里森分级相关的拷贝数变化，并发现 6p 染色体丢失与免疫浸润减少相关。诊断几十年后复发的匹配分析证实，基因组不稳定性是前列腺癌进展的驱动力。这项研究表明，将基因组学与人工智能辅助的组织病理学相结合，可以识别进化的临床生物标志物。© 2024。作者。

Cancer evolution lays the groundwork for predictive oncology. Testing evolutionary metrics requires quantitative measurements in controlled clinical trials. We mapped genomic intratumor heterogeneity in locally advanced prostate cancer using 642 samples from 114 individuals enrolled in clinical trials with a 12-year median follow-up. We concomitantly assessed morphological heterogeneity using deep learning in 1,923 histological sections from 250 individuals. Genetic and morphological (Gleason) diversity were independent predictors of recurrence (hazard ratio (HR) = 3.12 and 95% confidence interval (95% CI) = 1.34-7.3; HR = 2.24 and 95% CI = 1.28-3.92). Combined, they identified a group with half the median time to recurrence. Spatial segregation of clones was also an independent marker of recurrence (HR = 2.3 and 95% CI = 1.11-4.8). We identified copy number changes associated with Gleason grade and found that chromosome 6p loss correlated with reduced immune infiltration. Matched profiling of relapse, decades after diagnosis, confirmed that genomic instability is a driving force in prostate cancer progression. This study shows that combining genomics with artificial intelligence-aided histopathology leads to the identification of clinical biomarkers of evolution.© 2024. The Author(s).