Integrating clinical and multiple omics data for prognostic assessment across human cancers Journal Article


Authors: Zhu, B.; Song, N.; Shen, R.; Arora, A.; Machiela, M. J.; Song, L.; Landi, M. T.; Ghosh, D.; Chatterjee, N.; Baladandayuthapani, V.; Zhao, H.
Article Title: Integrating clinical and multiple omics data for prognostic assessment across human cancers
Abstract: Multiple omic profiles have been generated for many cancer types; however, comprehensive assessment of their prognostic values across cancers is limited. We conducted a pan-cancer prognostic assessment and presented a multi-omic kernel machine learning method to systematically quantify the prognostic values of high-throughput genomic, epigenomic, and transcriptomic profiles individually, integratively, and in combination with clinical factors for 3,382 samples across 14 cancer types. We found that the prognostic performance varied substantially across cancer types. mRNA and miRNA expression profile frequently performed the best, followed by DNA methylation profile. Germline susceptibility variants displayed low prognostic performance consistently across cancer types. The integration of omic profiles with clinical variables can lead to substantially improved prognostic performance over the use of clinical variables alone in half of cancer types examined. Moreover, we showed that the kernel machine learning method consistently outperformed existing prognostic signatures, suggesting that including a large number of omic biomarkers may provide substantial improvement in prognostic assessment. Our study provides a comprehensive portrait of omic architecture for tumor prognosis across cancers, and highlights the prognostic value of genome-wide omic biomarker aggregation, which may facilitate refined prognostic assessment in the era of precision oncology. © 2017 The Author(s).
Journal Title: Scientific Reports
Volume: 7
ISSN: 2045-2322
Publisher: Nature Publishing Group  
Date Published: 2017-12-05
Start Page: 16954
Language: English
DOI: 10.1038/s41598-017-17031-8
PROVIDER: scopus
PMCID: PMC5717223
PUBMED: 29209073
DOI/URL:
Notes: Article -- Export Date: 2 January 2018 -- Source: Scopus
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MSK Authors
  1. Ronglai Shen
    192 Shen
  2. Arshi Arora
    35 Arora