title: Systems biology modeling of the radiation sensitivity network: a biomarker discovery platform.
abstract: OBJECTIVE
  The discovery of effective biomarkers is a fundamental goal of molecular medicine. Developing a systems-biology understanding of radiosensitivity can enhance our ability of identifying radiation-specific biomarkers.
  
  
  METHODS
  Radiosensitivity, as represented by the survival fraction at 2 Gy was modeled in 48 human cancer cell lines. We applied a linear regression algorithm that integrates gene expression with biological variables, including ras status (mut/wt), tissue of origin and p53 status (mut/wt).
  
  
  RESULTS
  The biomarker discovery platform is a network representation of the top 500 genes identified by linear regression analysis. This network was reduced to a 10-hub network that includes c-Jun, HDAC1, RELA (p65 subunit of NFKB), PKC-beta, SUMO-1, c-Abl, STAT1, AR, CDK1, and IRF1. Nine targets associated with radiosensitization drugs are linked to the network, demonstrating clinical relevance. Furthermore, the model identified four significant radiosensitivity clusters of terms and genes. Ras was a dominant variable in the analysis, as was the tissue of origin, and their interaction with gene expression but not p53. Overrepresented biological pathways differed between clusters but included DNA repair, cell cycle, apoptosis, and metabolism. The c-Jun network hub was validated using a knockdown approach in 8 human cell lines representing lung, colon, and breast cancers.
  
  
  CONCLUSIONS
  We have developed a novel radiation-biomarker discovery platform using a systems biology modeling approach. We believe this platform will play a central role in the integration of biology into clinical radiation oncology practice.
authors.affiliation: Division of Biomedical Informatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA.
authors.forename: Steven
authors.initials: S
authors.lastname: Eschrich
authors.forename: Hongling
authors.initials: H
authors.lastname: Zhang
authors.forename: Haiyan
authors.initials: H
authors.lastname: Zhao
authors.forename: David
authors.initials: D
authors.lastname: Boulware
authors.forename: Ji-Hyun
authors.initials: JH
authors.lastname: Lee
authors.forename: Gregory
authors.initials: G
authors.lastname: Bloom
authors.forename: Javier F
authors.initials: JF
authors.lastname: Torres-Roca
mesh_terms: D000465:Algorithms; D045744:Cell Line, Tumor / Q000473:pathology / Q000528:radiation effects; D002470:Cell Survival / Q000235:genetics / Q000528:radiation effects; D016000:Cluster Analysis; D016208:Databases, Factual; D015870:Gene Expression / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods; D055785:Gene Knockdown Techniques / Q000379:methods; D053263:Gene Regulatory Networks / Q000235:genetics*; D016758:Genes, jun / Q000235:genetics; D016158:Genes, p53 / Q000235:genetics; D011905:Genes, ras / Q000235:genetics; D005819:Genetic Markers; D006801:Humans; D016014:Linear Models; D008957:Models, Genetic; D011829:Radiation Dosage; D011836:Radiation Tolerance / Q000235:genetics*
chemical_list: D005819:Genetic Markers