Mesenchymal Stromal Cell Secretome Up-Regulates 47 kDa CXCR4 Expression,and Induce Invasiveness in Neuroblastoma Cell Lines

Neuroblastoma accounts for 15% of childhood cancer deaths and presents with metastatic disease of the bone and the bone marrow at diagnosis in 70% of the cases. Previous studies have shown that the Mesenchymal Stromal Cell (MSC) secretome, triggers metastases in several cancer types such as breast and prostate cancer, but the specific role of the MSC factors in neuroblastoma metastasis is unclear. To better understand the effect of MSC secretome on chemokine receptors in neuroblastoma, and its role in metastasis, we studied a panel of 20 neuroblastoma cell lines, and compared their invasive potential towards MSC-conditioned-RPMI (mRPMI) and their cytokine receptor expression profiles. Western blot analysis revealed the expression of multiple CXCR4 isoforms in neuroblastoma cells. Among the five major isoforms, the expression of the 47 kDa isoform showed significant correlation with high invasiveness. Pretreatment with mRPMI up-regulated the expression of the 47 kDa CXCR4 isoform and also increased MMP-9 secretion, expression of integrin α3 and integrin β1, and the invasive potential of the cell; while blocking CXCR4 either with AMD 3100, a CXCR4 antagonist, or with an anti-47 kDa CXCR4 neutralizing antibody decreased the secretion of MMP-9, the expression of integrin α3 and integrin β1, and the invasive potential of the cell. Pretreatment with mRPMI also protected the 47 kDa CXCR4 isoform from ubiquitination and subsequent degradation. Our data suggest a modulatory role of the MSC secretome on the expression of the 47 kDa CXCR4 isoform and invasion potential of the neuroblastoma cells to the bone marrow.     

Accelerating Neuroimage Registration through Parallel Computation of Similarity Metric

Neuroimage registration is crucial for brain morphometric analysis and treatment efficacy evaluation. However, existing advanced registration algorithms such as FLIRT and ANTs are not efficient enough for clinical use. In this paper, a GPU implementation of FLIRT with the correlation ratio (CR) as the similarity metric and a GPU accelerated correlation coefficient (CC) calculation for the symmetric diffeomorphic registration of ANTs have been developed. The comparison with their corresponding original tools shows that our accelerated algorithms can greatly outperform the original algorithm in terms of computational efficiency. This paper demonstrates the great potential of applying these registration tools in clinical applications.