title: Proteomic identification of interleukin-2 therapy response in metastatic renal cell cancer.
abstract: OBJECTIVE
  To detect a predictive protein profile that distinguishes interleukin-2 therapy responders and nonresponders among patients with metastatic renal cell carcinoma we used surface-enhanced laser desorption/ionization time-of-flight mass spectrometry.
  
  
  METHODS
  Protein extracts from 56 patients with metastatic clear cell patients renal cell carcinoma obtained from radical nephrectomy specimens before interleukin-2 therapy were applied to protein chip arrays of different chromatographic properties and analyzed using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. A class prediction algorithm was applied to identify a subset of protein peaks with expression values associated with interleukin-2 response status. Multivariate analysis was performed to assess the association between the proteomic profile and interleukin-2 response status, controlling for the effect of lymphadenopathy.
  
  
  RESULTS
  From 513 protein peaks we discovered a predictor set of 11 that performed optimally for predicting interleukin-2 response status with 86% accuracy (Fisher's p <0.004, permutation p <0.01). Results were validated in an independent data set with 72% overall accuracy (p <0.05, permutation p <0.01). On multivariate analysis the proteomic profile was significantly associated with the interleukin-2 response when corrected for lymph node status (p <0.04).
  
  
  CONCLUSIONS
  We identified and validated a proteomic pattern that is an independent predictor of the interleukin-2 response. The ability to predict the probability of the interleukin-2 response could permit targeted selection of the patients most likely to respond to interleukin-2, while avoiding unwanted toxicity in patients less likely to respond. This proteomic predictor has the potential to significantly aid clinicians in the decision making of appropriate therapy for patients with metastatic renal cell carcinoma.
authors: Beth Israel Deaconess Medical Center Genomics Center and Dana Farber/Harvard Cancer Center Cancer Proteomics Core, Boston, Massachusetts, USA.
authors: Jon
authors: J
authors: Jones
authors: Hasan H
authors: HH
authors: Otu
authors: Franck
authors: F
authors: Grall
authors: Dimitrios
authors: D
authors: Spentzos
authors: Handan
authors: H
authors: Can
authors: Manuel
authors: M
authors: Aivado
authors: Arie S
authors: AS
authors: Belldegrun
authors: Allan J
authors: AJ
authors: Pantuck
authors: Towia A
authors: TA
authors: Libermann
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D000970:Antineoplastic Agents / Q000627:therapeutic use*; D002292:Carcinoma, Renal Cell / Q000188:drug therapy / Q000378:metabolism* / Q000556:secondary; D015331:Cohort Studies; D005260:Female; D006801:Humans; D007376:Interleukin-2 / Q000627:therapeutic use*; D007680:Kidney Neoplasms / Q000188:drug therapy / Q000378:metabolism* / Q000473:pathology; D008297:Male; D008875:Middle Aged; D009363:Neoplasm Proteins / Q000378:metabolism*; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; D016896:Treatment Outcome
chemical_list: D000970:Antineoplastic Agents; D007376:Interleukin-2; D009363:Neoplasm Proteins