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

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 simulat

SUBMITTER: Luo XG 

PROVIDER: S-EPMC12261431 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature