1. Modeling metastatic progression from cross-sectional cancer genomics data.
- Author
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Rupp K, Lösch A, Hu YL, Nie C, Schill R, Klever M, Pfahler S, Grasedyck L, Wettig T, Beerenwinkel N, and Spang R
- Subjects
- Humans, Lung Neoplasms genetics, Lung Neoplasms pathology, Disease Progression, Neoplasms genetics, Neoplasms pathology, Adenocarcinoma of Lung genetics, Adenocarcinoma of Lung pathology, Mutation, Tumor Suppressor Protein p53 genetics, Tumor Suppressor Protein p53 metabolism, Cross-Sectional Studies, ErbB Receptors genetics, Genomics methods, Neoplasm Metastasis genetics
- Abstract
Motivation: 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., Results: 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., Availability and Implementation: All datasets and code are available on GitHub at https://github.com/cbg-ethz/metMHN., (© The Author(s) 2024. Published by Oxford University Press.)
- Published
- 2024
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