ArticleProceedings of the National Academy of Sciences of the United States of America2019
In silico learning of tumor evolution through mutational time series.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Review
- Identification of cancer mini-drivers by deciphering selective landscape in the cancer genome.Briefings in bioinformatics · 2026Article
- Medical digital twins: enabling precision medicine and medical artificial intelligence.The Lancet. Digital health · 2025Review
- Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools.Journal of molecular evolution · 2023Review
- Single-Cell Analysis in theGenes · 2023Review
- Autoformer-Based Model for Predicting and Assessing Wheat Quality Changes of Pesticide Residues during Storage.Foods (Basel, Switzerland) · 2023Article
- Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications.Cancers · 2023Review
- Inferring parameters of cancer evolution in chronic lymphocytic leukemia.PLoS computational biology · 2022Article
- Ectopic expression of meiotic cohesin generates chromosome instability in cancer cell line.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- DEGBOE: Discrete time Evolution modeling of Gene mutation through Bayesian inference using qualitative Observation of mutation Events.Journal of biomedical informatics · 2022Article
- Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey.International journal of molecular sciences · 2021Review
- Incorporating Machine Learning into Established Bioinformatics Frameworks.International journal of molecular sciences · 2021Review
- The Burden of Post-Translational Modification (PTM)-Disrupting Mutations in the Tumor Matrisome.Cancers · 2021Article
- Deep phylogeny of cancer drivers and compensatory mutations.Communications biology · 2020Article
- Executable cancer models: successes and challenges.Nature reviews. Cancer · 2020Review
- Over a century of cancer research: Inconvenient truths and promising leads.PLoS biology · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Cancer arises through the accumulation of somatic mutations over time. Understanding the sequence of mutation occurrence during cancer progression can assist early and accurate diagnosis and improve clinical decision-making. Here we employ long short-term memory (LSTM) networks, a class of recurrent neural network, to learn the evolution of a tumor through an ordered sequence of mutations. We demonstrate the capacity of LSTMs to learn complex dynamics of the mutational time series governing tumor progression, allowing accurate prediction of the mutational burden and the occurrence of mutations in the sequence. Using the probabilities learned by the LSTM, we simulate mutational data and show that the simulation results are statistically indistinguishable from the empirical data. We identify passenger mutations that are significantly associated with established cancer drivers in the sequence and demonstrate that the genes carrying these mutations are substantially enriched in interactions with the corresponding driver genes. Breaking the network into modules consisting of driver genes and their interactors, we show that these interactions are associated with poor patient prognosis, thus likely conferring growth advantage for tumor progression. Thus, application of LSTM provides for prediction of numerous additional conditional drivers and reveals hitherto unknown aspects of cancer evolution.
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