Deep learning with evolutionary and genomic profiles for identifying cancer subtypes

Chun Yu Lin, Peiying Ruan, Ruiming Li, Jinn Moon Yang, Simon See, Jiangning Song, Tatsuya Akutsu

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)

Abstract

Cancer subtype identification is an unmet need in precision diagnosis. Recently, evolutionary conservation has been indicated to contain informative signatures for functional significance in cancers. However, the importance of evolutionary conservation in distinguishing cancer subtypes remains largely unclear. Here, we identified the evolutionarily conserved genes (i.e. core genes) and observed that they are primarily involved in cellular pathways relevant to cell growth and metabolisms. By using these core genes, we developed two novel strategies, namely a feature-based strategy (FES) and an image-based strategy (IMS) by integrating their evolutionary and genomic profiles with the deep learning algorithm. In comparison with the FES using the random set and the strategy using the PAM50 classifier, the core gene set-based FES achieved a higher accuracy for identifying breast cancer subtypes. The IMS and FES using the core gene set yielded better performances than the other strategies, in terms of classifying both breast cancer subtypes and multiple cancer types. Moreover, the IMS is reproducible even using different gene expression data (i.e. RNA-seq and microarray). Comprehensive analysis of eight cancer types demonstrates that our evolutionary conservation-based models represent a valid and helpful approach for identifying cancer subtypes and the core gene set offers distinguishable clues of cancer subtypes.

Original languageEnglish
Article number1940005
Number of pages15
JournalJournal of Bioinformatics and Computational Biology
Volume17
Issue number3
DOIs
Publication statusPublished - 27 Jun 2019

Keywords

  • cancer genomics
  • Cancer subtype
  • convolutional neural network
  • copy number alteration
  • deep learning
  • evolutionary conservation
  • gene expression

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