Abstract 4760: Molecular classification and risk-stratification in medulloblastoma using an integrative genomic approach

Abstract

Abstract Purpose. Medulloblastomas are heterogeneous tumors that collectively represent the most common malignant brain tumor in children. To understand the molecular characteristics underlying their heterogeneity and to identify whether such characteristics represent risk factors for patients with this disease, we performed an integrated genomic analysis of a large series of primary tumors. Patients and Methods. We profiled the mRNA transcriptome of 194 medulloblastomas and performed high-density SNP array and miRNA analysis on 115 and 98 of these, respectively. Unsupervised clustering analysis of mRNA expression data was used to identify molecular subgroups of medulloblastoma and DNA copy number, miRNA profiles, and clinical outcomes were analyzed for each. We then developed a Bayesian cumulative log odds model for predicting outcome, starting with ‘evidence’ provided by clinical features (metastasis and histology), then incrementally adding genomic evidence representing disease-subtype independent and dependent features. We then validated our results on independent test cohorts. Results. We identify 6 molecular subgroups of medulloblastoma, each with a unique combination of numerical and structural chromosomal aberrations that globally influence mRNA and miRNA expression. We reveal the relative contribution of each subgroup to clinical outcome as a whole and show that a previously unidentified molecular subgroup, characterized genetically by c-MYC copy number gains and transcriptionally by enrichment of photoreceptor pathways and increased miR-183∼96∼182 expression, is associated with significantly lower rates of event free and overall survival. Moreover, we show that integration of high-level clinical and genomic risk factors, including molecular subtype, can yield more comprehensive, accurate and biologically interpretable prediction models for outcome in medulloblastoma. We introduce a novel Bayesian nomogram indicating the amount of evidence that each feature contributes on a patient-by-patient basis. Conclusion. Our results detail the complex genomic heterogeneity of medulloblastomas, identify a previously unrecognized molecular subgroup with particularly poor clinical outcome and propose a Bayesian outcomes prediction model that outperforms the current clinical classification schema and previously published molecular markers proposed for medulloblastoma risk stratification. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4760. doi:10.1158/1538-7445.AM2011-4760