Biological diagnosis for neuroblastoma using the combination of highly sensitive analysis of prognostic factors

Background/Purpose: To select the optimal treatment according to the degree of malignancy of neuroblastoma, it is essential to accurately and rapidly identify any genetic abnormalities associated with the prognosis. This study aims to assess the correlation between the combination of prognostic factors and the biologic findings of neuroblastoma using a highly sensitive analysis of prognostic factors. Methods: In 44 neuroblastoma primary samples, we determined the gene dosages of MYCN and Survivin (as the target of 17q gain) and the expression levels of MYCN, Survivin, and BINI using highly sensitive analysis (the quantitative polymerase chain reaction method); furthermore, we assessed the correlation between the combination of their prognostic factors and the biology of neuroblastoma. Results: The gene dosage of MYCN or Survivin was significantly associated with all known prognostic factors. The expression level of MYCN or Survivin was not significantly associated with any prognostic factors, whereas the expression level of BINI was significantly associated with 5 of 6 prognostic factors. Regarding the combination of MYCN amplification and 17q gain (the gene dosage of Survivin), and the low expression of BINI, the rates of advanced stages (stage III or IV) were 100% for the cases with 3 factors, 63% for the cases with 2 factors, 42% for the cases with I factor, and 0% for the cases with null factor. Furthermore, the survival rates were 20% for the cases with 3 factors, 50% for the cases with 2 factors, 100% for the cases with 1 factor, and 100% for the cases with null factor. Conclusion: The combination of gene dosages of MYCN and Survivin and the expression level of BINI using the quantitative polymerase chain reaction method was significantly correlated with the clinical stage and the patients' outcome. This combination of biologic factors may enhance the accuracy to the conventional criteria, but this would have to be shown in a much larger study that is adequately powered to detect such an advantage. (c) 2006 Elsevier Inc. All rights reserved.