Molecular classification of human diffuse gliomas by multidimensional scaling analysis of gene expression profiles parallels morphology-based classification, correlates with survival, and reveals clinically-relevant novel glioma subsets.
ABSTRACT There are several currently employed classification systems for diffuse gliomas that sort tumors based on histological features. Contemporary molecular techniques, however, offer the promise of improved tumor classification and resultant patient stratification for treatment and prognosis. In particular, gene expression profiling has shown exceptional promise for providing an alternative and more objective molecular approach to glioma classification. In this study, we used cDNA array technology to profile the gene expression of 30 primary human glioma tissue samples comprising 4 different glioma subtypes as defined by current World Health Organization (WHO 2000) criteria: glioblastoma (GM, WHO grade IV), anaplastic astrocytoma (AA, WHO grade III), anaplastic oligodendroglioma (AO, WHO grade III), and oligodendroglioma (OL, WHO grade II). Gene expression data alone were used to group the tumors using multidimensional scaling, which is an unsupervised statistical method. Results show that impressive separation of the 4 glioma subtypes can be achieved solely on the basis of molecular data. In addition, a subcluster of 3 glioblastomas was identified as distinct from other GMs and from the oligodendroglial tumors. These 3 patients have shown extended survival compared to other GMs in the study. Survival analysis of the full data set revealed a good correlation with the molecular classification. Results of this proof-of-principle study demonstrate that molecular profiling alone can recapitulate conventional histologic classification and grading with high fidelity. In addition, results show that the molecular approach to tumor classification can generate clinically meaningful patient stratification, and, more importantly, is an efficient class-discovery tool for human gliomas, permitting the identification of previously unrecognized, clinically relevant tumor subsets.
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ABSTRACT: The invasive nature of cancers in general, and malignant gliomas in particular, is a major clinical problem rendering tumors incurable by conventional therapies. Using a novel invasive glioma mouse model established by serial in vivo selection, we identified the p75 neurotrophin receptor (p75(NTR)) as a critical regulator of glioma invasion. Through a series of functional, biochemical, and clinical studies, we found that p75(NTR) dramatically enhanced migration and invasion of genetically distinct glioma and frequently exhibited robust expression in highly invasive glioblastoma patient specimens. Moreover, we found that p75(NTR)-mediated invasion was neurotrophin dependent, resulting in the activation of downstream pathways and producing striking cytoskeletal changes of the invading cells. These results provide the first evidence for p75(NTR) as a major contributor to the highly invasive nature of malignant gliomas and identify a novel therapeutic target.PLoS Biology 09/2007; 5(8):e212. · 11.45 Impact Factor
Article: Molecular voting for glioma classification reflecting heterogeneity in the continuum of cancer progression.[show abstract] [hide abstract]
ABSTRACT: Gliomas, the most common brain tumors, are generally categorized into two lineages (astrocytic and oligodendrocytic) and further classified as low-grade (astrocytoma and oligodendroglioma), mid-grade (anaplastic astrocytoma and anaplastic oligodendroglioma), and high-grade (glioblastoma multiforme) based on morphological features. A strict classification scheme has limitations because a specific glioma can be at any stage of the continuum of cancer progression and may contain mixed features. Thus, a more comprehensive classification based on molecular signatures may reflect the biological nature of specific tumors more accurately. In this study, we used microarray technology to profile the gene expression of 49 human brain tumors and applied the k-nearest neighbor algorithm for classification. We first trained the classification gene set with 19 of the most typical glioma cases and selected a set of genes that provide the lowest cross-validation classification error with k=5. We then applied this gene set to the 30 remaining cases, including several that do not belong to gliomas such as atypical meningioma. The results showed that not only does the algorithm correctly classify most of the gliomas, but the detailed voting results also provide more subtle information regarding the molecular similarities to neighboring classes. For atypical meningioma, the voting was equally split among the four classes, indicating a difficulty in placement of meningioma into the four classes of gliomas. Thus, the actual voting results, which are typically used only to decide the winning class label in k-nearest neighbor algorithms, provide a useful method for gaining deeper insight into the stage of a tumor in the continuum of cancer development.Oncology Reports 10/2005; 14(3):651-6. · 1.84 Impact Factor
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ABSTRACT: Therapeutic efforts for human glial tumors have over the past years been redirected towards a compartmental treatment concept. The diffusely infiltrative nature of the disease calls for therapeutic agents to reach single cells far beyond the focus of attention which present therapies like surgery and radiation are able to treat. Specific drug discovery approaches which seek to define targets which are specific for gliomas have generated therapeutic options which allow for a highly selective development of new reagents. Combined with new modalities for compartmental drug delivery, systemic complications might be reduced and advantage taken of a compartmental specificity of a target which otherwise in the context of systemic application would not be as specific or burdened with side effects. From the present status of therapeutic developments in neuro-oncology it can be expected that a sufficient number of drug targets emerge which can be exploited by means of interstitial or intracavitary delivery, which are not neurotoxic and which may even be imaged in their action with the new metabolic imaging modalities. Convection enhanced delivery, conditionally replicating oncolytic viruses and motile, genetically engineered neural stem cells all seem to fulfill the distribution requirements which an effective therapeutic for gliomas will need to overcome the very limited efficacy which surgery, conventional chemotherapy and radiation have to offer. Whereas the genomics based discovery approaches are not specific for neuro-oncology, the development of delivery strategies is highly specific for the central nervous system, thus creating a unique set of organ and disease specific therapies.Journal of Neuro-Oncology 12/2004; 70(2):255-69. · 3.21 Impact Factor