Bayesian Inference of Fitness Landscapes via Tree-Structured Branching Processes

Authors

Xiang Ge Luo
Jack Kuipers
Kevin Rupp
Koichi Takahashi
Niko Beerenwinkel

Publication Date

7-1-2025

Journal

Bioinformatics

DOI

10.1093/bioinformatics/btaf193

PMID

40662787

PMCID

PMC12261431

PubMedCentral® Posted Date

7-15-2025

PubMedCentral® Full Text Version

Post-print

Abstract

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.

Keywords

Bayes Theorem, Humans, Mutation, Models, Genetic, Epistasis, Genetic, Leukemia, Myeloid, Acute, Genetic Fitness, Single-Cell Analysis, Evolution, Molecular, Algorithms, Neoplasms

Comments

The Python package FiTree and the analysis workflows are available at https://github.com/cbg-ethz/FiTree.

Published Open-Access

yes

Recommended Citation

Luo, Xiang Ge; Kuipers, Jack; Rupp, Kevin; et al., "Bayesian Inference of Fitness Landscapes via Tree-Structured Branching Processes" (2025). Faculty, Staff and Student Publications. 5048.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/5048