Somatic copy number alterations are a frequent sign of genome instability in cancer. A precise characterization of the genome architecture would reveal underlying instability mechanisms and provide an instrument for outcome prediction and treatment guidance. Here we show that the local spatial behavior of copy number profiles conveys important information about this architecture. Six filters were defined to characterize regional traits in copy number profiles, and the resulting Copy Aberration Regional Mapping Analysis (CARMA) algorithm was applied to tumors in four breast cancer cohorts (n = 2919). The derived motifs represent a layer of information that complements established molecular classifications of breast cancer. A score reflecting presence or absence of motifs provided a highly significant independent prognostic predictor. Results were consistent between cohorts. The nonsite-specific occurrence of the detected patterns suggests that CARMA captures underlying replication and repair defects and could have a future potential in treatment stratification.

体细胞拷贝数改变是癌症基因组不稳定的常见迹象。基因组结构的精确表征将揭示潜在的不稳定机制，并为结果预测和治疗指导提供工具。在这里，我们表明拷贝数配置文件的局部空间行为传达了有关此体系结构的重要信息。定义了六个过滤器来表征拷贝数配置文件中的区域特征，并将由此产生的拷贝畸变区域映射分析 (CARMA) 算法应用于四个乳腺癌队列中的肿瘤 ( n = 2919）。衍生的基序代表了一层信息，补充了已建立的乳腺癌分子分类。反映图案存在或不存在的分数提供了非常重要的独立预后预测因子。结果在队列之间是一致的。检测到的模式的非位点特异性出现表明 CARMA 捕获了潜在的复制和修复缺陷，并且可能在未来的治疗分层中具有潜力。