Dataset Information

Modeling metastatic progression from cross-sectional cancer genomics data.

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.

SUBMITTER: Rupp K

PROVIDER: S-EPMC11245855 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature

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Publications

Modeling metastatic progression from cross-sectional cancer genomics data.

Rupp Kevin K Lösch Andreas A Hu Yanren Linda YL Nie Chenxi C Schill Rudolf R Klever Maren M Pfahler Simon S Grasedyck Lars L Wettig Tilo T Beerenwinkel Niko N Spang Rainer R

Bioinformatics (Oxford, England) 20240601 Suppl 1

<h4>Motivation</h4>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.<h4>Results</h4>We present metMHN, a cancer progression model designed to deduce the joint progression of primary tumors and metastases using cross-sectional cancer gen ...[more]

PMID: 38940126

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Dataset Information

Modeling metastatic progression from cross-sectional cancer genomics data.

Motivation

Results

Availability and implementation

Publications

Modeling metastatic progression from cross-sectional cancer genomics data.

Similar Datasets

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