title: A systems approach to model metastatic progression.
abstract: Proteomic profiling of human disease has seen much early activity with the accessibility of the newest generation of high-throughput platforms and technologies. Nevertheless, the nature of the dynamic physiologic milieu and high dimensionality of the data has complicated major diagnostic and prognostic breakthroughs. Our recent article in Cancer Cell delineates an integrative model for culling a molecular signature of metastatic progression in prostate cancer from proteomic and transcriptomic analyses and shows its facility as a predictor of prognosis. The study leveraged direct proteomic analysis of tumor tissue extracts, differential feature selection characterizing the proteomic alterations of prostate cancer subclasses, and integration with public and study-derived genomic data to construct a multiplex gene signature representing progression of indolent cancer to aggressive disease. This further predicted clinical outcome in a variety of solid tumors. This review describes the context of the work, the framework for the analysis itself, and a look forward to the promise of this systems approach to human disease.
authors.affiliation: Department of Pathology, University of Michigan Medical School, Ann Arbor, Michigan, USA.
authors.forename: Barry S
authors.initials: BS
authors.lastname: Taylor
authors.forename: Sooryanarayana
authors.initials: S
authors.lastname: Varambally
authors.forename: Arul M
authors.initials: AM
authors.lastname: Chinnaiyan
mesh_terms: D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling; D006801:Humans; D008297:Male; D009362:Neoplasm Metastasis; D011471:Prostatic Neoplasms / Q000235:genetics / Q000378:metabolism / Q000473:pathology*; D040901:Proteomics / Q000379:methods*