In:
Clinical Cancer Research, American Association for Cancer Research (AACR), Vol. 14, No. 20 ( 2008-10-15), p. 6590-6601
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 & lt; 0.0001; validation cohort, 5y-OS 0.94 ± 0.07 (long) versus 0.40 ± 0.13 (short), P & lt; 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 ( & lt;5 years) from long survivors ( & gt;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.
Type of Medium:
Online Resource
ISSN:
1078-0432
,
1557-3265
DOI:
10.1158/1078-0432.CCR-07-4377
Language:
English
Publisher:
American Association for Cancer Research (AACR)
Publication Date:
2008
detail.hit.zdb_id:
1225457-5
detail.hit.zdb_id:
2036787-9
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