Somatic copy number alterations (CNAs) are a hallmark of cancer, but their role in tumorigenesis and clinical relevance remain largely unclear. Here we developed CNApp, a web-based tool that allows a comprehensive exploration of CNAs by using purity-corrected segmented data from multiple genomic platforms. CNApp generates genome-wide profiles, computes CNA scores for broad, focal and global CNA burdens, and uses machine learning-based predictions to classify samples. We applied CNApp to the TCGA pan-cancer dataset of 10,635 genomes showing that CNAs classify cancer types according to their tissue-of-origin, and that each cancer type shows specific ranges of broad and focal CNA scores. Moreover, CNApp reproduces recurrent CNAs in hepatocellular carcinoma, and predicts colon cancer molecular subtypes and microsatellite instability based on broad CNA scores and discrete genomic imbalances. In summary, CNApp facilitates CNA-driven research by providing a unique framework to identify relevant clinical implications. CNApp is hosted at https://tools.idibaps.org/CNApp/.

中文翻译：

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CNApp，用于量化拷贝数变化和综合分析的工具，揭示了临床意义

体细胞拷贝数改变（CNA）是癌症的标志，但是它们在肿瘤发生和临床相关性中的作用仍不清楚。在这里，我们开发了CNApp，这是一个基于Web的工具，通过使用来自多个基因组平台的纯度校正的分段数据，可以全面探索CNA。CNApp生成全基因组概况，计算广泛，重点和全球CNA负担的CNA分数，并使用基于机器学习的预测对样本进行分类。我们将CNApp应用于10,635个基因组的TCGA泛癌数据集，显示CNA根据其起源组织对癌症类型进行分类，并且每种癌症类型均显示广泛且集中的CNA得分的特定范围。而且，CNApp可在肝细胞癌中复制复发性CNA，并根据广泛的CNA得分和离散的基因组失衡预测结肠癌分子亚型和微卫星不稳定性。总而言之，CNApp通过提供识别相关临床意义的独特框架，促进了CNA驱动的研究。CNApp托管在https://tools.idibaps.org/CNApp/。