Background: The aggressiveness of metastatic neuroblastomas that lack MYCN gene amplification varies with age--they are least aggressive when diagnosed in patients younger than 12 months and most aggressive when diagnosed in patients older than 24 months. However, age at diagnosis is not always associated with patient survival. We examined whether molecular classification of metastatic neuroblastomas without MYCN gene amplification at diagnosis using gene expression profiling could improve the prediction of risk of disease progression.
Methods: We used Affymetrix microarrays to determine the gene expression profiles of 102 untreated primary neuroblastomas without MYCN gene amplification obtained from children whose ages at diagnosis ranged from 0.1 to 151 months. A supervised method using diagonal linear discriminant analysis was devised to build a multigene model for predicting risk of disease progression. The accuracy of the model was evaluated using nested cross-validations, permutation analyses, and gene expression data from 15 additional tumors obtained at disease progression.
Results: An expression profile model using 55 genes defined a tumor signature that distinguished two groups of patients from among those older than 12 months at diagnosis and clinically classified as having high-risk disease, those with a progression-free survival (PFS) rate of 16% (95% confidence interval [CI] = 8% to 28%), and those with a PFS rate of 79% (95% CI = 57% to 91%) (P<.01). These tumor signatures also identified two groups of patients with PFS of 15% (95% CI = 7% to 27%) and 69% (95% CI = 40% to 86%) (P<.01) from among patients who were older than 18 months at diagnosis. The gene expression signature of untreated molecular high-risk tumors was also present in progressively growing tumors.
Conclusion: Gene expression signatures of tumors obtained at diagnosis from patients with clinically indistinguishable high-risk, metastatic neuroblastomas identify subgroups with different outcomes. Accurate identification of these subgroups with gene expression profiles may facilitate development, implementation, and analysis of clinical trials aimed at improving outcome.