Faculty of Informatics and Data Science-Statistical Bioinformatics Group, University of Regensburg, Regensburg 93053, Germany.
Department of Biosystems Science and Engineering, ETH Zurich, Basel 4056, Switzerland.
Bioinformatics. 2024 Jun 28;40(Suppl 1):i140-i150. doi: 10.1093/bioinformatics/btae250.
Metastasis formation is a hallmark of cancer lethality. Yet, metastases are generally unobservable during their early stages of dissemination and spread to distant organs. Genomic datasets of matched primary tumors and metastases may offer insights into the underpinnings and the dynamics of metastasis formation.
We present metMHN, a cancer progression model designed to deduce the joint progression of primary tumors and metastases using cross-sectional cancer genomics data. The model elucidates the statistical dependencies among genomic events, the formation of metastasis, and the clinical emergence of both primary tumors and their metastatic counterparts. metMHN enables the chronological reconstruction of mutational sequences and facilitates estimation of the timing of metastatic seeding. In a study of nearly 5000 lung adenocarcinomas, metMHN pinpointed TP53 and EGFR as mediators of metastasis formation. Furthermore, the study revealed that post-seeding adaptation is predominantly influenced by frequent copy number alterations.
All datasets and code are available on GitHub at https://github.com/cbg-ethz/metMHN.
转移形成是癌症致命性的一个标志。然而，转移通常在其早期传播和扩散到远处器官时无法观察到。配对的原发肿瘤和转移瘤的基因组数据集可能提供对转移形成基础和动态的深入了解。
我们提出了 metMHN，这是一种癌症进展模型，旨在使用横截面癌症基因组学数据推断原发肿瘤和转移瘤的联合进展。该模型阐明了基因组事件之间的统计相关性、转移的形成以及原发肿瘤及其转移性对应物的临床出现。metMHN 能够对突变序列进行时间顺序重建，并有助于估计转移播种的时间。在对近 5000 例肺腺癌的研究中，metMHN 指出 TP53 和 EGFR 是转移形成的中介。此外，该研究表明，播种后的适应主要受频繁的拷贝数改变影响。
所有数据集和代码都可在 GitHub 上获得，网址为 https://github.com/cbg-ethz/metMHN。
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