Format

Send to

Choose Destination
Sci Rep. 2017 Dec 5;7(1):16954. doi: 10.1038/s41598-017-17031-8.

Integrating Clinical and Multiple Omics Data for Prognostic Assessment across Human Cancers.

Author information

1
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA. bin.zhu@nih.gov.
2
NSABP Foundation, Pittsburgh, PA, 15212, USA.
3
Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10021, USA.
4
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA.
5
Department of Biostatistics and Informatics, University of Colorado Denver, Aurora, CO, 80045, USA.
6
Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA.
7
Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA.
8
Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, TX, 77230, USA.
9
Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06520, USA.

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.

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center