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

Abstract

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 F 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, CASPARsignificantly 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. © 2008 American Association for Cancer Research.