No abstract

Renewable neurosphere formation in culture is a defining characteristic of certain brain tumor initiating cells. This retrospective study was designed to assess the relationship between neurosphere formation in cultured human glioma, tumorigenic capacity, and patient clinical outcome. Tumor samples were cultured in neurosphere conditions from 32 patients with glioma, including a subpopulation of 15 patients with primary glioblastoma. A subsample of renewable neurosphere cultures was xenografted into mouse brain to determine if they were tumorigenic. Our study shows that both renewable neurosphere formation and tumorigenic capacity are significantly associated with clinical outcome measures. Renewable neurosphere formation in cultured human glioma significantly predicted an increased hazard of patient death and more rapid tumor progression. These results pertained to both the full population of glioma and the subpopulation of primary glioblastoma. Similarly, there was a significant hazard of progression for patients whose glioma had tumorigenic capacity. Multivariate analysis demonstrated that neurosphere formation remained a significant predictor of clinical outcome independent of Ki67 proliferation index. In addition, multivariate analysis of neurosphere formation, tumor grade and patient age, demonstrated that neurosphere formation was a robust, independent predictor of glioma tumor progression. While the lengthy duration of this assay may preclude direct clinical application, these results exemplify how neurosphere culture serves as a clinically relevant model for the study of malignant glioma. Furthermore, this study suggests that the ability to propagate brain tumor stem cells in vitro is associated with clinical outcome.

Emerging evidence suggests that neural stem cells and brain tumors regulate their proliferation via similar pathways. In a previous study, we demonstrated that maternal embryonic leucine zipper kinase (Melk) is highly expressed in murine neural stem cells and regulates their proliferation. Here we describe how MELK expression is correlated with pathologic grade of brain tumors, and its expression levels are significantly correlated with shorter survival, particularly in younger glioblastoma patients. In normal human astrocytes, MELK is only faintly expressed, and MELK knockdown does not significantly influence their growth, whereas Ras and Akt overexpressing astrocytes have up-regulated MELK expression, and the effect of MELK knockdown is more prominent in these transformed astrocytes. In primary cultures from human glioblastoma and medulloblastoma, MELK knockdown by siRNA results in inhibition of the proliferation and survival of these tumors. Furthermore, we show that MELK siRNA dramatically inhibits proliferation and, to some extent, survival of stem cells isolated from glioblastoma in vitro. These results demonstrate a critical role for MELK in the proliferation of brain tumors, including their stem cells, and suggest that MELK may be a compelling molecular target for treatment of high-grade brain tumors.

The clinical practice of oncology is being transformed by molecular diagnostics that will enable predictive and personalized medicine. Current technologies for quantitation of the cancer proteome are either qualitative (e.g., immunohistochemistry) or require large sample sizes (e.g., flow cytometry). Here, we report a microfluidic platform, Microfluidic Image Cytometry (MIC), capable of quantitative, single-cell proteomic analysis of multiple signaling molecules using only 1,000-2,800 cells. Using cultured cell lines, we demonstrate simultaneous measurement of four critical signaling proteins (EGFR, PTEN, phospho-Akt and phospho-S6) within the oncogenic PI3K/Akt/mTOR signaling pathway. To demonstrate the clinical application of the MIC platform to solid tumors, we analyzed a panel of 19 human brain tumor biopsies, including glioblastomas. Our MIC measurements were validated by clinical immunohistochemistry and confirmed the striking inter- and intra-tumoral heterogeneity characteristic of glioblastoma. To interpret the multiparameter, single-cell MIC measurements, we adapted bioinformatic methods including self-organizing maps that stratify patients into clusters which predict tumor progression and patient survival. Together with bioinformatic analysis, the MIC platform represents a robust, enabling in vitro molecular diagnostic technology for systems pathology analysis and personalized medicine.