Cytotoxic synergy between alisertib and carboplatin versus alisertib and irinotecan are inversely dependent on MGMT levels in glioblastoma cells.

INTRODUCTION: Glioblastoma remains difficult to treat and patients whose tumors express high levels of O6-methylguanine DNA methyltransferase (MGMT) usually respond poorly to standard temozolomide chemotherapy. We have previously shown that the selective AURKA inhibitor alisertib potently inhibits growth of glioblastoma cells. METHODS: We used colony formation assays, annexin V binding, and western blotting to examine the effects of alisertib on the antiproliferative capabilities of carboplatin and irinotecan in glioblastoma cells. RESULTS: In colony formation assays, alisertib potentiated the antiproliferative effects of both carboplatin and irinotecan, often synergistically, including against glioblastoma tumor stem-like cells, as demonstrated by Chou-Talalay and Bliss statistical analyses. Western blotting showed that high MGMT expression in cell lines correlated with more pronounced potentiation of carboplatin's growth inhibitory effects by alisertib, while low MGMT expression correlated with stronger potentiation of irinotecan by alisertib. This pattern was also observed when these drug combinations were tested for their ability to induce apoptosis via annexin V binding assays. MGMT knockdown increased apoptosis caused by combined alisertib and irinotecan, while exogenous MGMT overexpression increased apoptosis from alisertib and carboplatin combination treatment. CONCLUSIONS: These results suggest that tumor MGMT expression levels may be predictive of patient response to these drug combinations, and importantly that the combination of alisertib and carboplatin may be selectively effective in glioblastoma patients with high tumor MGMT who are resistant to standard therapy. Since clinical experience with alisertib, carboplatin and irinotecan as single agents already exists, these findings may provide rationale for the design of clinical trials for their use in combination treatment regimens.

The CNS penetrating taxane TPI 287 and the AURKA inhibitor alisertib induce synergistic apoptosis in glioblastoma cells.

Glioblastoma is a highly malignant disease in critical need of expanded treatment options. The AURKA inhibitor alisertib exhibits antiproliferative activity against glioblastoma in vitro and in vivo. Unlike current clinically used taxane drugs, the novel taxane TPI 287 penetrates the CNS. We tested for interactions between three selective AURKA inhibitors and TPI 287 against standard U87 and U1242 cells and primary glioblastoma neurospheres using colony formation assays. Bliss and Chou-Talalay analyses were utilized to statistically test for synergism. Morphological analysis, flow cytometry and annexin V binding were employed to examine cell cycle and apoptotic effects of these drug combinations. TPI 287 not only potentiated the cytotoxicity of the AURKA inhibitors alisertib, MLN8054 and TC-A2317, but was often potently synergistic. Morphologic and biochemical analysis of the combined effects of alisertib and TPI 287 consistently revealed synergistic induction of apoptosis. While each agent alone induces a mitotic block, slippage occurs allowing some tumor cells to avoid apoptosis. Combination treatment greatly attenuated mitotic slippage, committing the majority of cells to apoptosis. Alisertib and TPI 287 demonstrate significant synergism against glioblastoma cells largely attributable to a synergistic effect in inducing apoptosis. These results provide compelling rationale for clinical testing of alisertib and/or other AURKA inhibitors for potential combination use with TPI 287 against glioblastoma and other CNS neoplasms.

Inhibitory interneurons decorrelate excitatory cells to drive sparse code formation in a spiking model of V1.

Sparse coding models of natural scenes can account for several physiological properties of primary visual cortex (V1), including the shapes of simple cell receptive fields (RFs) and the highly kurtotic firing rates of V1 neurons. Current spiking network models of pattern learning and sparse coding require direct inhibitory connections between the excitatory simple cells, in conflict with the physiological distinction between excitatory (glutamatergic) and inhibitory (GABAergic) neurons (Dale's Law). At the same time, the computational role of inhibitory neurons in cortical microcircuit function has yet to be fully explained. Here we show that adding a separate population of inhibitory neurons to a spiking model of V1 provides conformance to Dale's Law, proposes a computational role for at least one class of interneurons, and accounts for certain observed physiological properties in V1. When trained on natural images, this excitatory-inhibitory spiking circuit learns a sparse code with Gabor-like RFs as found in V1 using only local synaptic plasticity rules. The inhibitory neurons enable sparse code formation by suppressing predictable spikes, which actively decorrelates the excitatory population. The model predicts that only a small number of inhibitory cells is required relative to excitatory cells and that excitatory and inhibitory input should be correlated, in agreement with experimental findings in visual cortex. We also introduce a novel local learning rule that measures stimulus-dependent correlations between neurons to support "explaining away" mechanisms in neural coding.