Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Serial transcriptome analysis and cross species integration identifies CENPE as novel neuroblastoma target

ABSTRACT: Cancer genomic studies that rely on analysis of diagnostic biopsies from primary tumors may not fully identify the molecular events associated with tumor progression. We hypothesized that characterizing the transcriptome during tumor progression in the TH-MYCN transgenic neuroblastoma model would identify oncogenic drivers that would be targetable therapeutically. We quantified expression of 32,381 murine genes in 9 hyperplastic ganglia harvested at 3 time points, and 4 tumor cohorts of progressively larger size (n=6 each group) in mice homozygous for the TH-MYCN transgene. We found 93 genes that showed a linearly increasing or decreasing pattern of expression from the preneoplastic ganglia to end stage tumors. Cross-species integration identified 24 genes that were highly expressed in human MYCN amplified neuroblastomas. The genes prioritized were not exclusively driven by increasing Myc transactivation or proliferative rate. We manually curated the 24 candidates and prioritized 3 targets (Cenpe, Gpr49, Impdh2) with previously determined roles in cancer. Using siRNA knockdown in human neuroblastoma cell line models we further prioritized CENPE due to potent inhibition of cellular proliferation. Targeting of CENPE with the selective small molecular inhibitor GSK923295 showed potent inhibition of in vitro proliferation of 19 neuroblastoma derived cell lines (median IC50=41 nM; range 27-266 nM), and significantly delayed tumor growth kinetics in 3 xenograft models (p-values ranged from p<0.0001 to p=0.018). Here we provide preclinical validation that serial transcriptome analysis of a transgenic mouse model followed by cross-species integration is a useful method to identify therapeutic targets, and identify CENPE as a novel therapeutic candidate in neuroblastoma. set 1: 24 tumor samples analyzed set 2: 9 hyperplastic ganglia harvested at 3 time points (day 0, day 7 and day 14) and 2 tumors "small" size were analysed

ORGANISM(S): Mus musculus

SUBMITTER: Jayanti Jagannathan

PROVIDER: E-GEOD-17740 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

Project description:Liver metastasis is one of the major causes of death in colorectal cancer (CRC) patients. To understand this process, we investigated whether the gene expression profiling of matched colorectal carcinomas and liver metastases could reveal key molecular events involved in tumor progression and metastasis. We performed experiments using a cDNA microarray containing 17,104 genes with the following tissue samples: paired tissues of 25 normal colorectal mucosa, 27 primary colorectal tumors, 13 normal liver and 27 liver metastasis, and 20 primary colorectal tumors without liver metastasis. To remove the effect of normal cell contamination, we selected 4,583 organ-specific genes with a false discovery rate (FDR) of 0.0067% by comparing normal colon and liver tissues using significant analysis of microarray, and these genes were excluded from further analysis. We then identified and validated 46 liver metastasis-specific genes with an accuracy of 83.3% by comparing the expression of paired primary colorectal tumors and liver metastases using prediction analysis of microarray. The 46 selected genes contained several known oncogenes and 2 ESTs. To confirm that the results correlated with the microarray expression patterns, we performed RT-PCR with WNT5A and carbonic anhydrase II. Additionally, we observed that 21 of the 46 genes were differentially expressed (FDR = 2.27%) in primary tumors with synchronous liver metastasis compared with primary tumors without liver metastasis. We scanned the human genome using a cDNA microarray and identified 46 genes that may play an important role in the progression of liver metastasis in CRC. Keywords: gene expression profiling using cDNA microarray We performed 17K cDNA microarray with the amplified RNAs from the following tissue samples: normal colorectal mucosa, primary colorectal tumors, normal liver and liver metastasis tumors, and primary colorectal tumors without liver metastasis. Organ-specific genes in normal colon and liver tissues were excluded from the pre-filtered genes, and then we discovered and validated liver metastasis-specific genes commonly up-regulated in the primary colorectal tumors and liver metastasis tumors. To confirm the microarray data, we performed a RT-PCR of two genes (WNT5A and carbonic anhydrase II) in the primary colorectal tumors with and without liver metastases.

Project description:TP53 mutations are a poor prognostic factor in breast cancers. This study sets out to identify the gene set that determine expression signature of the TP53 status (TP53 signature) and to correlate it with clinical outcome. Using comprehensive expression analysis and DNA sequencing of the TP53 gene in 38 Japanese breast cancer patients, we have isolated a gene set of 33 genes from differentially expressed genes in the learning set (n=26), depending on the TP53 status. Predictive accuracy of TP53 status by gene expression profile was 83.3% in the test set (n=12). As independent external datasets, two published datasets were introduced for validation of TP53 status prediction (251 Swedish samples) and survival analysis (both the Swedish and 295 Dutch samples). TP53 signature has the ability to predict recurrence-free survival (RFS) of 29 stage I and II Japanese breast cancers (log rank, P = 0.032), and RFS, overall survival of two independently published datasets (log rank, both P < 0.0001). Multivariate analysis has shown an independent and significant prognostic factor with a hazard ratio (HR) for recurrence and survival in two external datasets (P < 0.0001). The TP53 signature is also a strong prognostic factor in the subgroups: estrogen-receptor positive, lymph node (LN) positive and negative, intermediate/high risk in St. Gallen criteria, and high risk in National Cancer Institute (NCI) criteria (log rank, P < 0.0001). TP53 signature is a reliable and independent predictor of the outcome of disease in operated breast cancer. Experiment Overall Design: Microarray hybridizations (Agilent: Whole Human Genome Oligo Microarray; 41k unique probe) were carried out with 1Î¼g Cy3 labeled cRNA and 1 Î¼g Cy5 labeled cRNA, both prepared from each sample and reference pool, respectively. Experiment Overall Design: Fluorescent intensities of scanned images were quantified by ArrayVision Ver.8 rev.4 (Imaging research). Experiment Overall Design: The gene expression data was quantified and analyzed by GeneSpring 6.2 (Silicon Genetics). To identify the TP53 status predictor gene set, a Wilcoxon rank sum test along with Benjamini and Hochberg false discovery rate (FDR) was used.

Dataset Information

Serial transcriptome analysis and cross species integration identifies CENPE as novel neuroblastoma target

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets