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

Bioinformatics Advances, vol. 6, pp. vbaf283

Abstract

Abstract Summary Mutual Hazard Networks (MHNs) are statistical models for analyzing (genetic) cancer progression. Many cancers develop silently and are only noticeable when they have significantly progressed, creating an observational gap until diagnosis. MHNs bridge this gap by reconstructing the underlying dynamics of disease progression. We present mhn, a Python package for dynamic cancer progression analysis using MHNs. It trains an MHN model from tumor genotypes. mhn overcomes challenges of numerical efficiency in model training by making use of state space restriction, allowing training MHNs with >100 mutational events, 5 times more than was possible before. The package offers (i) reconstruction of the most likely evolutionary history of tumors, (ii) sampling of artificial tumor histories, and (iii) visualization of genomic interactions and likely progression trajectories. These features substantially extend earlier implementations, providing a fast and user-friendly framework for researchers and clinicians to study cancer dynamics. Availability and implementation mhn can be installed from PyPI using pip and is available under the MIT License on GitHub (https://github.com/spang-lab/LearnMHN). Installation instructions and package functionalities are detailed on GitHub and PyPI, with a comprehensive guide on Read the Docs (https://learnmhn.readthedocs.io/en/latest/index.html) and a Jupyter notebook on GitHub to help users explore the package.

Authors 8

1. Stefan Vocht

   University of Regensburg

   Affiliation as printed

   Department of Statistical Bioinformatics, University of Regensburg , 93053 Regensburg,

   Department of Statistical Bioinformatics, University of Regensburg, Regensburg, Germany

2. Y. Linda Hu

   University of Regensburg

   Affiliation as printed

   Department of Statistical Bioinformatics, University of Regensburg , 93053 Regensburg,

   Department of Statistical Bioinformatics, University of Regensburg, Regensburg, Germany

3. Kevin Rupp

   ETH Zurich

   Affiliation as printed

   Department of Biosystems Science and Engineering, ETH Zürich , 4056 Basel,

   ETH Zürich Department of Biosystems Science and Engineering, , Basel,

4. Tilo Wettig

   University of Regensburg

   Affiliation as printed

   Faculty of Physics, University of Regensburg , 93040 Regensburg,

   Faculty of Physics, University of Regensburg, Regensburg, Germany

5. Lars Grasedyck Aachen Institute for Geometry and Applied Mathematics

   RWTH Aachen University

   Affiliation as printed

   Institute for Geometry and Applied Mathematics, RWTH Aachen , 52056 Aachen,

   Institute for Geometry and Applied Mathematics, RWTH Aachen, Aachen, Germany

6. Niko Beerenwinkel

   ETH Zurich

   Affiliation as printed

   Department of Biosystems Science and Engineering, ETH Zürich , 4056 Basel,

   ETH Zürich Department of Biosystems Science and Engineering, , Basel,

7. Rainer Spang

   University of Regensburg

   Affiliation as printed

   Department of Statistical Bioinformatics, University of Regensburg , 93053 Regensburg,

   Department of Statistical Bioinformatics, University of Regensburg, Regensburg, Germany

8. Rudolf Schill corresponding

   ETH Zurich

   Affiliation as printed

   Department of Biosystems Science and Engineering, ETH Zürich , 4056 Basel,

   ETH Zürich Department of Biosystems Science and Engineering, , Basel,