Using knowledge-driven genomic interactions for multi-omics data analysis

Metadimensional models for predicting clinical outcomes in ovarian carcinoma

Dokyoon Kim, Ruowang Li, Anastasia Lucas, Shefali S. Verma, Scott M. Dudek, Marylyn Deriggi Ritchie

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

It is common that cancer patients have different molecular signatures even though they have similar clinical features, such as histology, due to the heterogeneity of tumors. To overcome this variability, we previously developed a new approach incorporating prior biological knowledge that identifies knowledge-driven genomic interactions associated with outcomes of interest. However, no systematic approach has been proposed to identify interactionmodels between pathways based on multi-omics data. Here we have proposed such a novel methodological framework, called metadimensional knowledge-driven genomic interactions (MKGIs). To test the utility of the proposed framework, we applied it to an ovarian cancer dataset including multi-omics profiles from The Cancer Genome Atlas to predict grade, stage, and survival outcome. We found that each knowledge-driven genomic interaction model, based on different genomic datasets, contains different sets of pathway features, which suggests that each genomic data type may contribute to outcomes in ovarian cancer via a different pathway. In addition, MKGI models significantly outperformed the single knowledge-driven genomic interaction model. From the MKGI models, many interactions between pathways associated with outcomes were found, including the mitogen-activated protein kinase (MAPK) signaling pathway and the gonadotropin-releasing hormone (GnRH) signaling pathway, which are known to play important roles in cancer pathogenesis. The beauty of incorporating biological knowledge into the model based on multi-omics data is the ability to improve diagnosis and prognosis and provide better interpretability. Thus, determining variability in molecular signatures based on these interactions between pathways may lead to better diagnostic/treatment strategies for better precisionmedicine.

Original languageEnglish (US)
Pages (from-to)577-587
Number of pages11
JournalJournal of the American Medical Informatics Association
Volume24
Issue number3
DOIs
StatePublished - Jan 1 2017

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Carcinoma
Ovarian Neoplasms
Neoplasms
Beauty
Aptitude
Atlases
Mitogen-Activated Protein Kinases
Gonadotropin-Releasing Hormone
Histology
Genome
Survival
Datasets
Therapeutics

All Science Journal Classification (ASJC) codes

  • Health Informatics

Cite this

Kim, Dokyoon ; Li, Ruowang ; Lucas, Anastasia ; Verma, Shefali S. ; Dudek, Scott M. ; Ritchie, Marylyn Deriggi. / Using knowledge-driven genomic interactions for multi-omics data analysis : Metadimensional models for predicting clinical outcomes in ovarian carcinoma. In: Journal of the American Medical Informatics Association. 2017 ; Vol. 24, No. 3. pp. 577-587.
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Using knowledge-driven genomic interactions for multi-omics data analysis : Metadimensional models for predicting clinical outcomes in ovarian carcinoma. / Kim, Dokyoon; Li, Ruowang; Lucas, Anastasia; Verma, Shefali S.; Dudek, Scott M.; Ritchie, Marylyn Deriggi.

In: Journal of the American Medical Informatics Association, Vol. 24, No. 3, 01.01.2017, p. 577-587.

Research output: Contribution to journalArticle

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