Department: Department of New Biology
Daegu Gyeongbuk Institute of Science and Technology (DGIST),
Republic of Korea
During Ph.D. study at MIT, he conducted several systems biology projects and developed bioinformatic tools for integrative analyses of gene expression and metabolomic data. In 2003, he started his postdoctoral career at ISB and developed a data integration tool, Pointillist; proteomic data analysis tools, MS-BID and Prequips; a systems approach to prion disease; and a prion disease database. In 2006, he joined POSTECH in Korea and has developed systems approaches for understanding complex human diseases. In 2010, he became the director of System Biodynamics-National Core Research Center. In 2013, he moved to DGIST in Korea and joined the Center for Plant Aging Research, Institute for Basic Science, as a group leader. He has developed tools for understanding spatiotemporal operations of biological networks through integrative analysis of multi-dimensional global datasets.
Proteogenomic analysis of diffuse gastric cancers
Daehee Hwang1, Sang-Won Lee2, Eunok Paek3, Hark Kyun Kim4, Sanghyuk Lee5, Henry Rodriguez6, Myeong-Hee Yu7, and Eun Gyeong Yang7
1Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, DGIST, Daegu 711-873, Republic of Korea; 2Department of Chemistry, Research Institute for Natural Sciences, Korea University, Seoul 136-701, Republic of Korea; 3Department of Computer Science and Engineering, Hanyang University, Seoul 133-791, Republic of Korea; 4National Cancer Center, Goyang 410-769, Republic of Korea; 5Department of Life Science and Ewha Research Center for Systems Biology, Ewha Womans University, Seoul 120-750, Republic of Korea; 6Office of Cancer Clinical Proteomics Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892; and 7Biomedical Research Institute, Korea Institute of Science and Technology, Seoul 136-791, Republic of Korea.
We report an integrated proteogenomic analysis of diffuse gastric cancers. Genomic alterations supported by proteomic data enabled prioritization of cancer genes associated with patient survival. Correlations between mRNA and protein abundance changes also enabled selection of oncogenes and tumor suppressors affecting patient survival. Integrated analysis of mRNA, protein, phosphorylation, and glycosylation data identified four subtypes of gastric cancers with subtypes 1-4 associated with cell proliferation, immune response, metabolism, and invasion, respectively; subtypes 1/3 and subtypes 2/4 distinguishable only by proteomic data; and association of subtypes 2 and 4 with immune- and invasion-related pathways identified uniquely by N-glycoproteome and/or phosphoproteome data. Finally, correlation between mutation and phosphorylation enabled effective identification of mutation-signaling interplays associated with subtype-dependent patient survival. Therefore, integrated proteogenomic analysis affords more enhanced understanding of cancer biology and patient stratification than genomic analysis alone.