Electrical & Computer Engineering, Department of


Date of this Version



J Urol. 2008 February ; 179(2)


© 2009 American Urological Association



Introduction—To detect a predictive protein profile that distinguishes between IL-2 therapy responders and non-responders among metastatic RCC patients we used surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI TOF-MS).

Materials and Methods—Protein extracts of 56 metastatic clear cell RCC patients obtained from radical nephrectomy specimens and prior to IL-2 therapy were applied to protein chip arrays of different chromatographic properties and analyzed using SELDI TOF-MS. A class prediction algorithm was applied to identify a subset of protein peaks whose expression values were associated with IL-2 response status. Multivariate analysis was performed to assess the association between the proteomic profile and the IL-2 response status controlling for the effect of lymphadenopathy.

Results—From a total of 513 protein peaks we discovered a predictor set of 11 peaks that performed optimally for predicting IL-2 response status (86 % accuracy, Fisher’s p<0.004, permutation p<0.01). The results were validated on an independent data set with an overall accuracy of 72% (p < 0.05, permutation p<0.01). On multivariate analysis the proteomic profile was significantly associated with IL-2 response when corrected for lymph node status (p< 0.04).

Conclusions—We have identified and validated a proteomic pattern that is an independent predictor of IL-2 response. The ability to predict the probability of IL-2 response could permit targeted selection of patients most likely to respond to IL-2, while avoiding unwanted toxicities in patients less likely to respond. This proteomic predictor has the potential to significantly aid clinicians in the decision making of appropriate therapy for metastatic RCC patients.