Epigenomic tensor predicts disease subtypes and reveals constrained tumorevolution.

Leistico, Jacob R et al.

Understanding the epigenomic evolution and specificity of disease subtypes from complex patient data remains a major biomedical problem. We here present DeCET (decomposition and classification of epigenomic tensors), an integrative computational approach for simultaneously analyzing hierarchical heterogeneous data, to identify robust epigenomic differences among tissue types, differentiation states, and disease subtypes. Applying DeCET to our own data from 21 uterine benign tumor (leiomyoma) patients identifies distinct epigenomic features discriminating normal myometrium and leiomyoma subtypes. Leiomyomas possess preponderant alterations in distal enhancers and long-range histone modifications confined to chromatin contact domains that constrain the evolution of pathological epigenomes. Moreover, we demonstrate the power and advantage of DeCET on multiple publicly available epigenomic datasets representing different cancers and cellular states. Epigenomic features extracted by DeCET can thus help improve our understanding of disease states, cellular development, and differentiation, thereby facilitating future therapeutic, diagnostic, and prognostic strategies.


Share this article

March, 2021


Products used in this publication

  • cut and tag antibody icon
    H3K4me3 Antibody
  • cut and tag antibody icon
    H3K4me1 Antibody


  • London Calling 2024
    London, UK
    May 21-May 24, 2024
  • Symposium of the Young Scientist Association
    Vienna, Austria
    May 28-May 29, 2024
  • ESHG 2024
    Berlin, Germany
    Jun 1-Jun 4, 2024
  • CLEPIC 2024
    Warsaw, Poland
    Jun 5-Jun 7, 2024
  • EACR 2024
    Rotterdam, Netherlands
    Jun 10-Jun 13, 2024
  • Chromatin meets South 2024
    Marseille, France
    Jun 13-Jun 14, 2024
 See all events


       Site map   |   Contact us   |   Conditions of sales   |   Conditions of purchase   |   Privacy policy   |   Diagenode Diagnostics