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mhn: a Python package for analyzing cancer progression with Mutual Hazard Networks.

Stefan Vocht1, Yanren Linda Hu1, Andreas Lösch1

  • 1Department of Statistical Bioinformatics, University of Regensburg, 93053 Regensburg, Germany.

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Mutual Hazard Networks (MHNs) model cancer progression by reconstructing tumor evolutionary history. The new mhn Python package enables efficient analysis of over 100 mutational events, advancing cancer dynamics research.

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Area of Science:

  • Computational biology
  • Genomics
  • Cancer research

Background:

  • Cancers often progress undetected due to an observational gap until diagnosis.
  • Mutual Hazard Networks (MHNs) are statistical models designed to reconstruct the dynamics of cancer progression.
  • Existing MHN models face limitations in numerical efficiency, restricting analysis to fewer mutational events.

Purpose of the Study:

  • To introduce mhn, a novel Python package for dynamic cancer progression analysis using MHNs.
  • To overcome numerical efficiency challenges in training MHNs, enabling analysis of a larger number of mutational events.
  • To provide researchers and clinicians with a fast, user-friendly framework for studying cancer dynamics.

Main Methods:

  • The mhn package trains MHN models using tumor genotypes.
  • It employs a state space restriction technique to enhance numerical efficiency during model training.
  • The package supports the analysis of cancer progression involving more than 100 mutational events.

Main Results:

  • The mhn package facilitates the reconstruction of the most likely evolutionary history of tumors.
  • It enables the sampling of artificial tumor histories for further analysis.
  • mhn provides visualization tools for genomic interactions and likely cancer progression trajectories.

Conclusions:

  • The mhn Python package significantly extends previous MHN implementations by improving numerical efficiency.
  • It allows for the analysis of complex cancer progression models with over 100 mutational events.
  • mhn offers a powerful and accessible tool for advancing the study of cancer dynamics.