Patient-specific multi-omics models and the application in personalized combination therapy

August John, Bo Qin, Krishna R. Kalari, Liewei M Wang, Jia Yu

Research output: Contribution to journalReview article

1 Scopus citations

Abstract

The rapid advancement of high-throughput technologies and sharp decrease in cost have opened up the possibility to generate large amount of multi-omics data on an individual basis. The development of high-throughput -omics, including genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiomics, enables the application of multi-omics technologies in the clinical settings. Combination therapy, defined as disease treatment with two or more drugs to achieve efficacy with lower doses or lower drug toxicity, is the basis for the care of diseases like cancer. Patient-specific multi-omics data integration can help the identification and development of combination therapies. In this review, we provide an overview of different -omics platforms, and discuss the methods for multi-omics, high-throughput, data integration, personalized combination therapy.

Original languageEnglish (US)
Pages (from-to)1737-1750
Number of pages14
JournalFuture Oncology
Volume16
Issue number23
DOIs
StatePublished - Aug 2020

Keywords

  • Data integration
  • High-throughput
  • Multi-omics
  • Personalized combination therapy

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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