Modeling and predicting cancer clonal evolution with reinforcement learning.

ABSTRACT: Cancer results from an evolutionary process that typically yields multiple clones with varying sets of mutations within the same tumor. Accurately modeling this process is key to understanding and predicting cancer evolution. Here, we introduce clone to mutation (CloMu), a flexible and low-parameter tree generative model of cancer evolution. CloMu uses a two-layer neural network trained via reinforcement learning to determine the probability of new mutations based on the existing mutations on a clone. CloMu supports several prediction tasks, including the determination of evolutionary trajectories, tree selection, causality and interchangeability between mutations, and mutation fitness. Importantly, previous methods support only some of these tasks, and many suffer from overfitting on data

SUBMITTER: Ivanovic S 

PROVIDER: S-EPMC10538496 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature