Quantifying Uncertainty of Predictions from Cancer Progression Models

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ID: 322294
2026
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Abstract
BACKGROUND: Cancer progresses through the accumulation of genomic events. Cancer progression models such as Mutual Hazard Networks (MHNs) describe this dynamic, enabling prediction of temporal event positions and patient-specific risks of acquiring mutations. However, current MHN analyses rely on single most likely models and do not quantify the uncertainty inherent to parameter estimation. Assessing forecast stability is essential before using them to anticipate treatment-relevant mutations, adapt targeted therapies, or prioritize monitoring of patients at elevated progression risk. RESULTS: We address a key prerequisite for the responsible clinical use of cancer progression models by making MHN-derived predictions uncertainty-aware. We present a Bayesian framework for MHN that uses Markov Chain Monte Carlo to sample from the posterior distributions of model parameters and derived predictions. For practical use we implemented the Random-Walk Metropolis, Metropolis-Adjusted Langevin Algorithm (MALA) and simplified manifold MALA samplers as part of the existing mhn Python package. Only MALA and smMALA were successful in sampling from MHN posteriors, with MALA performing best. While most MHN parameters and predictions showed low posterior variance, a small subset displayed greater variability across the posterior distribution. This differentiation cannot be obtained from a single most likely model, emphasizing the need for uncertainty quantification, especially in clinical contexts. As an illustrative example, posterior sampling identified a subgroup of STK11$-$, KRAS$+$ lung adenocarcinoma patients with a high predicted short-term risk - with low variance across posterior samples - to develop an STK11 mutation. This subgroup exhibited poorer survival under immunotherapy, resembling patterns observed in STK11+ patients. AVAILABILITY: Our implementation is part of version 1.2.0 of the mhn package (https://github.com/spang-lab/LearnMHN). All analyses including the code to produce all figures in this article can be found under https://github.com/huy29433/MCMC-sampling-for-MHN (https://doi.org/10.5281/zenodo.21160219).
Reference Key
openalex_W7170911200 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Y. Linda Hu, Simon Pfahler, A Lösch, Stefan Vocht, Stefan Hansch, Kevin Rupp, Niko Beerenwinkel, Tilo Wettig, Rudolf Schill, Rainer Spang
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag526
URL
https://doi.org/10.1093/bioinformatics/btag526
Keywords Keywords not found

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