In silico learning of tumor evolution through mutational time series.
cancer progression
driver mutations
machine learning
neural networks
passenger mutations
Journal
Proceedings of the National Academy of Sciences of the United States of America
ISSN: 1091-6490
Titre abrégé: Proc Natl Acad Sci U S A
Pays: United States
ID NLM: 7505876
Informations de publication
Date de publication:
07 05 2019
07 05 2019
Historique:
pubmed:
25
4
2019
medline:
17
3
2020
entrez:
25
4
2019
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 31015295
pii: 1901695116
doi: 10.1073/pnas.1901695116
pmc: PMC6510994
doi:
Types de publication
Journal Article
Research Support, N.I.H., Intramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
9501-9510Informations de copyright
Copyright © 2019 the Author(s). Published by PNAS.
Déclaration de conflit d'intérêts
The authors declare no conflict of interest.
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