Related Experiment Video
Updated: Aug 6, 2026
05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Quantifying uncertainty of predictions from cancer progression models
Yanren Linda Hu1, Simon Pfahler2, Andreas Lösch1
1Department for Statistical Bioinformatics, University of Regensburg, Regensburg 93053, Germany.
Bioinformatics (Oxford, England)
|July 24, 2026
Summary
We developed a Bayesian framework for Mutual Hazard Networks (MHNs) to quantify uncertainty in cancer progression models. This approach improves prediction accuracy for treatment-relevant mutations and patient risk stratification.
Area of Science:
- Computational biology
- Cancer genomics
- Statistical modeling
Background:
- Cancer progression involves accumulating genomic events.
- Mutual Hazard Networks (MHNs) model cancer dynamics for predicting mutations and patient risks.
- Current MHN analyses lack uncertainty quantification, limiting clinical application.
Purpose of the Study:
- To develop an uncertainty-aware Bayesian framework for MHNs.
- To enable reliable clinical use of cancer progression models.
- To improve prediction of treatment-relevant mutations and patient monitoring.
Main Methods:
- Implemented a Bayesian framework for MHNs using Markov Chain Monte Carlo (MCMC).
- Utilized Metropolis-Adjusted Langevin Algorithm (MALA) and simplified manifold MALA (smMALA) samplers.
- Integrated MALA and smMALA into the existing mhn Python package for posterior sampling.
Main Results:
- MALA and smMALA successfully sampled MHN posteriors, with MALA performing optimally.
- Most MHN parameters and predictions showed low posterior variance, but a subset exhibited higher variability.
- Identified a lung adenocarcinoma subgroup (STK11-, KRAS+) with high, low-variance predicted risk for STK11 mutation, correlating with poorer immunotherapy survival.
Conclusions:
- The Bayesian MHN framework provides essential uncertainty quantification for clinical decision-making.
- Uncertainty assessment reveals critical parameter and prediction variability not evident in single-model analyses.
- This method enhances risk stratification for targeted therapies and patient monitoring in cancer genomics.
Related Concept Videos
01:21Cancer Survival Analysis
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
00:59Propagation of Uncertainty from Random Error
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
02:07Tumor Progression
Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
00:59Uncertainty: Overview
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.