| Literature DB >> 31499184 |
Olga Lyudovyk1, Yufeng Shen1, Nicholas P Tatonetti1, Susan J Hsiao2, Mahesh M Mansukhani2, Chunhua Weng3.
Abstract
Genomic test results collected during the provision of medical care and stored in Electronic Health Record (EHR) systems represent an opportunity for clinical research into disease heterogeneity and clinical outcomes. In this paper, we evaluate the use of genomic test reports ordered for cancer patients in order to derive cancer subtypes and to identify biological pathways predictive of poor survival outcomes. A novel method is proposed to calculate patient similarity based on affected biological pathways rather than gene mutations. We demonstrate that this approach identifies subtypes of prognostic value and biological pathways linked to survival, with implications for precision treatment selection and a better understanding of the underlying disease. We also share lessons learned regarding the opportunities and challenges of secondary use of observational genomic data to conduct such research.Entities:
Keywords: Computational cancer subtyping; Deep phenotyping; Pathway analysis; Secondary use of genomic data; Survival analysis
Mesh:
Year: 2019 PMID: 31499184 PMCID: PMC7136846 DOI: 10.1016/j.jbi.2019.103286
Source DB: PubMed Journal: J Biomed Inform ISSN: 1532-0464 Impact factor: 6.317