title: A gene expression model of intrinsic tumor radiosensitivity: prediction of response and prognosis after chemoradiation.
abstract: OBJECTIVE
  Development of a radiosensitivity predictive assay is a central goal of radiation oncology. We reasoned a gene expression model could be developed to predict intrinsic radiosensitivity and treatment response in patients.
  
  
  METHODS
  Radiosensitivity (determined by survival fraction at 2 Gy) was modeled as a function of gene expression, tissue of origin, ras status (mut/wt), and p53 status (mut/wt) in 48 human cancer cell lines. Ten genes were identified and used to build a rank-based linear regression algorithm to predict an intrinsic radiosensitivity index (RSI, high index = radioresistance). This model was applied to three independent cohorts treated with concurrent chemoradiation: head-and-neck cancer (HNC, n = 92); rectal cancer (n = 14); and esophageal cancer (n = 12).
  
  
  RESULTS
  Predicted RSI was significantly different in responders (R) vs. nonresponders (NR) in the rectal (RSI R vs. NR 0.32 vs. 0.46, p = 0.03), esophageal (RSI R vs. NR 0.37 vs. 0.50, p = 0.05) and combined rectal/esophageal (RSI R vs. NR 0.34 vs. 0.48, p = 0.001511) cohorts. Using a threshold RSI of 0.46, the model has a sensitivity of 80%, specificity of 82%, and positive predictive value of 86%. Finally, we evaluated the model as a prognostic marker in HNC. There was an improved 2-year locoregional control (LRC) in the predicted radiosensitive group (2-year LRC 86% vs. 61%, p = 0.05).
  
  
  CONCLUSIONS
  We validate a robust multigene expression model of intrinsic tumor radiosensitivity in three independent cohorts totaling 118 patients. To our knowledge, this is the first time that a systems biology-based radiosensitivity model is validated in multiple independent clinical datasets.
authors.affiliation: H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
authors.forename: Steven A
authors.initials: SA
authors.lastname: Eschrich
authors.forename: Jimmy
authors.initials: J
authors.lastname: Pramana
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: Caio
authors.initials: C
authors.lastname: Rocha-Lima
authors.forename: Scott
authors.initials: S
authors.lastname: Kelley
authors.forename: Douglas P
authors.initials: DP
authors.lastname: Calvin
authors.forename: Timothy J
authors.initials: TJ
authors.lastname: Yeatman
authors.forename: Adrian C
authors.initials: AC
authors.lastname: Begg
authors.forename: Javier F
authors.initials: JF
authors.lastname: Torres-Roca
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000970:Antineoplastic Agents / Q000008:administration & dosage; D045744:Cell Line, Tumor; D004938:Esophageal Neoplasms / Q000188:drug therapy / Q000235:genetics / Q000532:radiotherapy; D005260:Female; D015870:Gene Expression / Q000235:genetics*; D006258:Head and Neck Neoplasms / Q000188:drug therapy / Q000235:genetics / Q000532:radiotherapy; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D011379:Prognosis; D011446:Prospective Studies; D011836:Radiation Tolerance / Q000235:genetics*; D012004:Rectal Neoplasms / Q000188:drug therapy / Q000235:genetics / Q000532:radiotherapy; D019772:Topotecan / Q000008:administration & dosage
chemical_list: D000970:Antineoplastic Agents; D019772:Topotecan