Epigenomic tensor predicts disease subtypes and reveals constrained tumor evolution

Jacob R. Leistico, Priyanka Saini, Christopher R. Futtner, Miroslav Hejna, Yasuhiro Omura, Pritin N. Soni, Poorva Sandlesh, Magdy Milad, Jian Jun Wei, Serdar Bulun, J. Brandon Parker, Grant D. Barish, Jun S. Song, Debabrata Chakravarti

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number108927
JournalCell Reports
Issue number13
StatePublished - Mar 30 2021


  • HOXA13
  • cancer
  • epigenomics
  • leiomyoma
  • support tensor machine
  • tensor decomposition

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology


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