Molecular risk stratification of medulloblastoma patients based on immunohistochemical analysis of MYC, LDHB, and CCNB1 expression

Talitha De Haas, Nancy Hasselt, Dirk Troost, Huib Caron, Mara Popovic, Lorna Zadravec-Zaletel, Wieslawa Grajkowska, Marta Perek, Maria Chiara Osterheld, David Ellison, Frank Baas, Rogier Versteeg, Marcel Kool

Research output: Contribution to journalArticlepeer-review

53 Citations (Scopus)


Purpose: Medulloblastoma is the most common malignant embryonal brain tumor in children. The current clinical risk stratification to select treatment modalities is not optimal because it does not identify the standard-risk patients with resistant disease or the unknown number of high-risk patients who might be overtreated with current protocols. The aim of this study is to improve the risk stratification of medulloblastoma patients by using the expression of multiple prognostic markers in combination with current clinical parameters. Experimental Design: Candidate prognostic markers were selected from literature or from medulloblastoma expression data. Selected genes were immunohistochemically analyzed for their prognostic value using medulloblastoma tissue arrays containing 124 well-characterized patient samples. Results: Protein expression analyses showed that the combined expression of three genes was able to predict survival in medulloblastoma patients. Low MYC expression identified medulloblastoma patients with a very good outcome. In contrast, concomitant expression of LDHB and CCNB1 characterized patients with a very poor outcome. Multivariate analyses showed that both expression of MYC and the LDHB/CCNB1 gene signature were strong prognostic markers independent of the clinical parameters metastasis and residual disease. Combined analysis of clinical and molecular markers enabled greater resolution of disease risk than clinical factors alone. Conclusions: A molecular risk stratification model for medulloblastoma patients is proposed based on the signature of MYC, LDHB, and CCNB1 expression. Combined with clinical variables, the model may provide a more accurate basis for targeting therapy in children with this disease.

Original languageEnglish
Pages (from-to)4154-4160
Number of pages7
JournalClinical Cancer Research
Issue number13
Publication statusPublished - 1 Jul 2008
Externally publishedYes


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