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Modeling metastatic progression from cross-sectional cancer genomics data.
- Source :
-
Bioinformatics (Oxford, England) [Bioinformatics] 2024 Jun 28; Vol. 40 (Supplement_1), pp. i140-i150. - Publication Year :
- 2024
-
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.<br />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.<br />Availability and Implementation: All datasets and code are available on GitHub at https://github.com/cbg-ethz/metMHN.<br /> (© The Author(s) 2024. Published by Oxford University Press.)
- 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
Subjects
Details
- Language :
- English
- ISSN :
- 1367-4811
- Volume :
- 40
- Issue :
- Supplement_1
- Database :
- MEDLINE
- Journal :
- Bioinformatics (Oxford, England)
- Publication Type :
- Academic Journal
- Accession number :
- 38940126
- Full Text :
- https://doi.org/10.1093/bioinformatics/btae250