Subclassification and individual survival time prediction from gene expression data of neuroblastoma patients by using CASPAR.

PURPOSE To predict individual survival times for neuroblastoma patients from gene expression data using the cancer survival prediction using automatic relevance determination (CASPAR) algorithm. EXPERIMENTAL DESIGN A first set of oligonucleotide microarray gene expression profiles comprising 256 neuroblastoma patients was generated. Then, CASPAR was combined with a leave-one-out cross-validation to predict individual times for both the whole cohort and subgroups of patients with unfavorable markers, including stage 4 disease (n = 67), unfavorable genetic alterations, intermediate-risk or high-risk stratification by the German neuroblastoma trial, and patients predicted as unfavorable by a recently described gene expression classifier (n = 83). Prediction accuracy of individual survival times was assessed by Kaplan-Meier analyses and time-dependent receiver operator characteristics curve analyses. Subsequently, classification results were validated in an independent cohort (n = 120). RESULTS CASPAR separated patients with divergent outcome in both the initial and the validation cohort [initial set, 5y-OS 0.94 +/- 0.04 (predicted long survival) versus 0.38 +/- 0.17 (predicted short survival), P < 0.0001; validation cohort, 5y-OS 0.94 +/- 0.07 (long) versus 0.40 +/- 0.13 (short), P < 0.0001]. Time-dependent receiver operator characteristics analyses showed that CASPAR-predicted individual survival times were highly accurate (initial set, mean area under the curve for first 10 years of overall survival prediction 0.92 +/- 0.04; validation set, 0.81 +/- 0.05). Furthermore, CASPAR significantly discriminated short (<5 years) from long survivors (>5 years) in subgroups of patients with unfavorable markers with the exception of MYCN-amplified patients (initial set). Confirmatory results with high significance were observed in the validation cohort [stage 4 disease (P = 0.0049), NB2004 intermediate-risk or high-risk stratification (P = 0.0017), and unfavorable gene expression prediction (P = 0.0017)]. CONCLUSIONS CASPAR accurately forecasts individual survival times for neuroblastoma patients from gene expression data.