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A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis

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Modeling metastatic progression from cross-sectional cancer genomics data.

Kevin Rupp^1,2,3, Andreas Lösch¹, Yanren Linda Hu¹

¹Faculty of Informatics and Data Science-Statistical Bioinformatics Group, University of Regensburg, Regensburg 93053, Germany.

Bioinformatics (Oxford, England)

|June 28, 2024

Summary

This study introduces metMHN, a novel cancer progression model. It reconstructs tumor evolution and metastasis timing using genomic data, identifying key genes like TP53 and EGFR in lung adenocarcinoma metastasis.

Keywords:

Markov chains Mutual Hazard Networks cancer genomics cancer progression models lung cancer metastasis

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Area of Science:

Cancer genomics
Computational biology
Evolutionary medicine

Background:

Metastasis formation is a critical factor in cancer mortality.
Early stages of cancer dissemination and spread are difficult to observe.
Genomic data from primary tumors and metastases can reveal insights into metastasis dynamics.

Purpose of the Study:

To develop a computational model (metMHN) for analyzing joint progression of primary tumors and metastases.
To elucidate the relationships between genomic events, metastasis formation, and clinical emergence.
To enable chronological reconstruction of mutational sequences and estimate metastatic seeding times.

Main Methods:

Developed metMHN, a cancer progression model utilizing cross-sectional cancer genomics data.
Applied the model to a dataset of nearly 5000 lung adenocarcinomas.
Analyzed statistical dependencies among genomic events and metastasis formation.

Main Results:

metMHN successfully deduced joint progression of primary tumors and metastases.
Identified TP53 and EGFR as key mediators in lung adenocarcinoma metastasis.
Revealed that copy number alterations predominantly influence post-seeding adaptation.

Conclusions:

metMHN provides a powerful tool for understanding cancer metastasis using genomic data.
The findings highlight specific genes and genomic alterations involved in metastasis.
This approach aids in reconstructing tumor evolution and estimating metastasis timing.