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Modeling and predicting cancer clonal evolution with reinforcement learning.
Ivanovic, Stefan; El-Kebir, Mohammed.
Affiliation
  • Ivanovic S; Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.
  • El-Kebir M; Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA; melkebir@illinois.edu.
Genome Res ; 33(7): 1078-1088, 2023 07.
Article in En | MEDLINE | ID: mdl-37344104
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.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Leukemia / Neoplasms Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Genome Res Journal subject: BIOLOGIA MOLECULAR / GENETICA Year: 2023 Type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Leukemia / Neoplasms Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Genome Res Journal subject: BIOLOGIA MOLECULAR / GENETICA Year: 2023 Type: Article Affiliation country: United States