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Neuroblastoma is an embryonal tumor accounting for approximately 15% of childhood cancer deaths. There exists a clinical need to identify novel therapeutic targets, particularly for treatment-resistant forms of neuroblastoma. Therefore, we investigated the role of the neuronal master regulator GSK3 in controlling neuroblastoma cell fate. We identified novel GSK3-mediated regulation of MYC (c-MYC and MYCN) mRNA levels, which may have implications for numerous MYC-driven cancers. In addition, we showed that certain GSK3 inhibitors induced large-scale cell death in neuroblastoma cells, primarily through activating apoptosis. mRNA-seq of GSK3 inhibitor–treated cells was performed and subsequent pathway analysis revealed that multiple signaling pathways contributed to the loss of neuroblastoma cell viability. The contribution of two of the signaling pathways highlighted by the mRNA-seq analysis was functionally validated. Inhibition of the p53 tumor suppressor partly rescued the cell death phenotype, whereas activation of canonical Wnt signaling contributed to the loss of viability, in a p53-independent manner. Two GSK3 inhibitors (BIO-acetoxime and LiCl) and one small-molecule Wnt agonist (Wnt Agonist 1) demonstrated therapeutic potential for neuroblastoma treatment. These inhibitors reduced the viability of numerous neuroblastoma cell lines, even those derived from high-risk MYCN-amplified metastatic tumors, for which effective therapeutics are currently lacking. Furthermore, although LiCl was lethal to neuroblastoma cells, it did not reduce the viability of differentiated neurons. Taken together our data suggest that these small molecules may hold potential as effective therapeutic agents for the treatment of neuroblastoma and other MYC-driven cancers. Mol Cancer Ther; 13(2); 454–67. ©2013 AACR.

PDF - 259K, Figure S3. Additional p53 and neuroblastoma prognostic markers overlap data. A. p53 signalling pathway schematic with LiCl differentially regulated genes overlaid, key as in image and as described in Fig. 5B. B. Overlap between IPA predicted upstream regulators from inhibitor mRNA-seq (top 110) and a 157 neuroblastoma risk stratification gene signature (all 31), image generated using Venny.

PDF - 61K, Table S3. Alignment scores of GSK3 inhibitor mRNA-seq reads in IMR32 cells with TopHat.

PDF - 122K, Figure S1. Additional expression regulation data. A. Comparison of mRNA-seq and RT-qPCR measurements for the same differentially expressed mRNAs in IMR32 cells upon 24h GSK3 inhibitor treatments. B. Time course of azakenpaullone mediated ectopic MYCN mRNA reduction in SY5Y-MYCN, measured by RT-qPCR. C. RT-qPCR results of GSK3 inhibitor regulation of c-MYC in CRC cell lines. D. pSMAD levels in IMR32 upon GSK3 inhibitor treatment. E. MDM2 mRNA expression level in response to GSK3 inhibition, mRNA-seq data. F. Wnt Agonist 1 and BIO treatment regulated the mRNA (top) and protein (bottom) of MYC genes in a similar manner.

PDF - 186K, Figure S2. Additional Retinoic Acid (RA) and cell viability. A. The effect of increased RA concentration on MYC mRNA expression in 48h doxycycline and 24h RA treated SY5Y-MYCN cells, assayed by qPCR. B. Time course of NVP-BEZ235 (dual PI3K/mTOR inhibitor) treatment on viability across the different cell lines, as measured by MTS assays. C. The effect of various inhibitors on viability across the different cell lines, as measured by MTS assays after 48h of inhibitor treatment. D. Viability results for SY5Y-MYCN cells with and without doxycycline induction (72h) of ectopic MYCN expression, treated with inhibitors for 48h as measured by MTS assays.

PDF - 98K, Table S2. Flow Cytometry full details in accordance with the MIFlowCyt Standard.

PDF - 219K, Table S1. CAS (Chemical Abstracts Service) numbers, molecular formulas, structures and references for each of the compounds used in the study.

PDF - 78K, Figure S4. Additional PFTalpha and GSK3 inhibitor co-treatment viability data. A. Attempted p53 mediated rescue of GSK3 inhibitor cell death phenotype across three cell lines as measured by MTS viability assay after 24h and 48h of treatment. Only BIO treatment in IMR32 and KCNR showed partial rescue upon PFTalpha co-treatment.

XLSX - 5959K, Table S4. Gene expression tables and differentially expressed gene lists from GSK3 inhibitor mRNA-seq. A. Gene expression values (CPMkb) for all genes in each sample obtained by mRNA-seq.

PDF file - 53K, Recurrence free survival and miR187 expression in cohort 2

PDF file - 73K, Association of clinicopathological characteristics of tumours with miR-187 expression in Cohort 1 (n=117) using Pearson's chi-squared test

PDF file - 757K, Ectopic expression of miR187 in SBKR3 cells, phenotypic effects

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PDF file - 85K, qRT-PCR, standard in vitro functional assays and Western blotting description

PDF file - 414K, miR187 ectopic expression in MCF7, effects on growth, quantification of invasion

Supplementary Data from Altered Cytoplasmic-to-Nuclear Ratio of Survivin Is a Prognostic Indicator in Breast Cancer

PDF file - 73K, Association of clinicopathological characteristics of tumours with miR-187 expression in Cohort 2 (n=470) using Pearson's chi-squared test

PDF file - 68K, Clinicopathological charactistics of tumours in Cohorts 1 and 2

PDF file - 65K, miRNAs associated with poor outcome in breast cancer patient cohorts

PDF file - 47K, miR187 expression and patient outcome stratified according to ER status

Purpose: Survivin (BIRC5) is a promising tumor biomarker. Conflicting data exist on its prognostic effect in breast cancer. These data may at least be partly due to the manual interpretation of immunohistochemical staining, especially as survivin can be located in both the nucleus and cytoplasm. Quantitative determination of survivin expression using image analysis offers the opportunity to develop alternative scoring models for survivin immunohistochemistry. Here, we present such a model.Experimental Design: A breast cancer tissue microarray containing 102 tumors was stained with an anti-survivin antibody. Whole-slide scanning was used to capture high-resolution images. These images were analyzed using automated algorithms to quantify the staining.Results: Increased nuclear, but not cytoplasmic, survivin was associated with a reduced overall survival (OS; P = 0.038) and disease-specific survival (P = 0.0015). A high cytoplasmic-to-nuclear ratio (CNR) of survivin was associated with improved OS (P = 0.005) and disease-specific survival (P = 0.05). Multivariate analysis revealed that the survivin CNR was an independent predictor of OS (hazard ratio, 0.09; 95% confidence interval, 0.01-0.76; P = 0.027). A survivin CNR of >5 correlated positively with estrogen receptor (P = 0.019) and progesterone receptor (P = 0.033) levels, whereas it was negatively associated with Ki-67 expression (P = 0.04), p53 status (P = 0.005), and c-myc amplification (P = 0.016).Conclusion: Different prognostic information is supplied by nuclear and cytoplasmic survivin in breast cancer. Nuclear survivin is a poor prognostic marker in breast cancer. Moreover, CNR of survivin, as determined by image analysis, is an independent prognostic factor.

Articular cartilage has only very limited regenerative capacities in humans. Tissue engineering techniques for cartilage damage repair are limited in the production of hyaline cartilage. Mesenchymal stem/stromal cells (MSCs) are multipotent stem cells and can be differentiated into mature cartilage cells, chondrocytes, which could be used for repairing damaged cartilage. Chondrogenesis is a highly complex, relatively inefficient process lasting over 3 weeks in vitro. In order to better understand chondrogenic differentiation, especially the commitment phase, we have performed transcriptional profiling of MSC differentiation into chondrocytes from early timepoints starting 15 minutes after induction to 16 hours and fully differentiated chondrocytes at 21 days. Transcriptional gene regulatory networks (GRN) were identified using time-course clustering and upstream-regulator predictions which revealed that cells start epigenetic reprogramming as early as 2 hours after induction and commit to differentiation within 4-6 hours. After that they adapt their gene expression to cater for differentiation specific protein production. These results suggest that interventions to improve the frequency and efficiency of differentiation should target early processes.