Biomedical subjects

Niko Beerenwinkel

Publications and source records attributed to Niko Beerenwinkel.

4 recordsLinked to original sources

PubMed2026

Quantifying uncertainty of predictions from cancer progression models.

MOTIVATION: 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 AND IMPLEMENTATION: 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).

Humans↗

PubMed2026

Wastewater-based sequencing of respiratory syncytial virus to investigate lineage dynamics and antigenic site mutations: a retrospective genomic epidemiology study.

BACKGROUND: Respiratory syncytial virus (RSV) infections pose a substantial health burden, particularly for clinically vulnerable populations such as infants and older adults. Although novel immunoprophylactic interventions show promise in providing protection, many countries may not have robust surveillance systems to monitor circulating RSV lineages and detect mutations that might reduce the effectiveness of these new interventions. We aimed to assess the diversity and temporal dynamics of circulating RSV lineages in urban populations through amplicon-based sequencing and analysis of wastewater extracts. METHODS: In this prospective observational wastewater-based genomic surveillance study, 32 raw influent 24-h composite samples were collected during the 2022-23 and 2023-24 RSV seasons from both Zurich and Geneva, Switzerland. We applied an RSV subtype-specific amplicon-based sequencing approach to obtain RSV-A and RSV-B sequences from all 64 samples. Mutations relative to reference genomes were identified at positions with read depth above 30. Relative abundances of RSV lineages were estimated from frequencies of lineage-signature mutations, present in greater than 90% of publicly available sequences of that lineage. FINDINGS: Relative abundances of RSV-B (2022-23) and RSV-A (2023-24) lineages were estimated over the two RSV seasons. During the 2022-23 season, the RSV-B B.D.E.1 lineage prevailed in both cities. In the 2023-24 season, multiple RSV-A lineages cocirculated, including A.D.1, A.D.3, A.D.5, and their sub-lineages. Identification and frequency estimation of mutations showed low-frequency, non-synonymous mutations in antigenic sites on the fusion gene of both RSV-A and RSV-B, some of which have not been reported in clinical sequences. The primary outcome was identification and relative abundance of RSV lineages in wastewater samples. INTERPRETATION: These findings show the potential of wastewater-based genomic surveillance to identify and track circulating RSV lineages and clinically relevant mutations. As novel RSV immunoprophylaxis measures are introduced in upcoming RSV seasons, wastewater-derived genomic RSV data provide a valuable baseline for understanding RSV diversity and future viral evolution under increased immunological pressure. FUNDING: This study was funded by the Swiss National Science Foundation and in part by the National Institute Of Allergy And Infectious Diseases of the National Institutes of Health. Funding for sample collection and processing was provided by the Swiss Federal Office of Public Health.

Humans↗

PubMed2025

Bayesian inference of fitness landscapes via tree-structured branching processes.

MOTIVATION: The complex dynamics of cancer evolution, driven by mutation and selection, underlies the molecular heterogeneity observed in tumors. The evolutionary histories of tumors of different patients can be encoded as mutation trees and reconstructed in high resolution from single-cell sequencing data, offering crucial insights for studying fitness effects of and epistasis among mutations. Existing models, however, either fail to separate mutation and selection or neglect the evolutionary histories encoded by the tumor phylogenetic trees. RESULTS: We introduce FiTree, a tree-structured multi-type branching process model with epistatic fitness parameterization and a Bayesian inference scheme to learn fitness landscapes from single-cell tumor mutation trees. Through simulations, we demonstrate that FiTree outperforms state-of-the-art methods in inferring the fitness landscape underlying tumor evolution. Applying FiTree to a single-cell acute myeloid leukemia dataset, we identify epistatic fitness effects consistent with known biological findings and quantify uncertainty in predicting future mutational events. The new model unifies probabilistic graphical models of cancer progression with population genetics, offering a principled framework for understanding tumor evolution and informing therapeutic strategies. AVAILABILITY AND IMPLEMENTATION: The Python package FiTree and the analysis workflows are available at https://github.com/cbg-ethz/FiTree.

Bayes Theorem↗

PubMed2025

Single-cell copy number calling and event history reconstruction.

MOTIVATION: Copy number alterations are driving forces of tumour development and the emergence of intra-tumour heterogeneity. A comprehensive picture of these genomic aberrations is therefore essential for the development of personalised and precise cancer diagnostics and therapies. Single-cell sequencing offers the highest resolution for copy number profiling down to the level of individual cells. Recent high-throughput protocols allow for the processing of hundreds of cells through shallow whole-genome DNA sequencing. The resulting low read-depth data poses substantial statistical and computational challenges to the identification of copy number alterations. RESULTS: We developed SCICoNE, a statistical model and MCMC algorithm tailored to single-cell copy number profiling from shallow whole-genome DNA sequencing data. SCICoNE reconstructs the history of copy number events in the tumour and uses these evolutionary relationships to identify the copy number profiles of the individual cells. We show the accuracy of this approach in evaluations on simulated data and demonstrate its practicability in applications to two breast cancer samples from different sequencing protocols. AVAILABILITY AND IMPLEMENTATION: SCICoNE is available at https://github.com/cbg-ethz/SCICoNE.

Single-Cell Analysis↗