Modeling and predicting cancer clonal evolution with reinforcement learning

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...

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Bibliographic Details
Published in:Genome research 2023-07, Vol.33 (7), p.1078-1088
Main Authors: Ivanovic, Stefan, El-Kebir, Mohammed
Format: Article
Language:English
Subjects:
Cancer
Clonal Evolution - genetics
Evolution
Humans
Leukemia
Methods
Mutation
Neoplasms - genetics
Neural networks
Neural Networks, Computer
Predictions
Probability learning
Reinforcement
Reproductive fitness
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Summary: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 sets with a large number of mutations. Using simulations, we show that CloMu either matches or outperforms current methods on a wide variety of prediction tasks. In particular, for simulated data with interchangeable mutations, current methods are unable to uncover causal relationships as effectively as CloMu. On breast cancer and leukemia cohorts, we show that CloMu determines similarities and causal relationships between mutations as well as the fitness of mutations. We validate CloMu's inferred mutation fitness values for the leukemia cohort by comparing them to clonal proportion data not used during training, showing high concordance. In summary, CloMu's low-parameter model facilitates a wide range of prediction tasks regarding cancer evolution on increasingly available cohort-level data sets.
ISSN:1088-9051
1549-5469
DOI:10.1101/gr.277672.123