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Recurrent chromosomal rearrangements have not been well characterized in common carcinomas. We used a bioinformatics approach to discover candidate oncogenic chromosomal aberrations on the basis of outlier gene expression. Two ETS transcription factors, ERG and ETV1, were identified as outliers in prostate cancer. We identified recurrent gene fusions of the(More)
Prostate cancer is the most frequently diagnosed cancer in American men. Screening for prostate-specific antigen (PSA) has led to earlier detection of prostate cancer, but elevated serum PSA levels may be present in non-malignant conditions such as benign prostatic hyperlasia (BPH). Characterization of gene-expression profiles that molecularly distinguish(More)
Androgen receptor (AR) is a ligand-dependent transcription factor that plays a key role in prostate cancer. Little is known about the nature of AR cis-regulatory sites in the human genome. We have mapped the AR binding regions on two chromosomes in human prostate cancer cells by combining chromatin immunoprecipitation (ChIP) with tiled oligonucleotide(More)
Despite efforts to profile prostate cancer, the genetic alterations and biological processes that correlate with the observed histological progression are unclear. Using laser-capture microdissection to isolate 101 cell populations, we have profiled prostate cancer progression from benign epithelium to metastatic disease. By analyzing expression signatures(More)
Characterization of the prostate cancer transcriptome and genome has identified chromosomal rearrangements and copy number gains and losses, including ETS gene family fusions, PTEN loss and androgen receptor (AR) amplification, which drive prostate cancer development and progression to lethal, metastatic castration-resistant prostate cancer (CRPC). However,(More)
DNA microarray technology has led to an explosion of oncogenomic analyses, generating a wealth of data and uncovering the complex gene expression patterns of cancer. Unfortunately, due to the lack of a unifying bioinformatic resource, the majority of these data sit stagnant and disjointed following publication, massively underutilized by the cancer research(More)
Many studies have used DNA microarrays to identify the gene expression signatures of human cancer, yet the critical features of these often unmanageably large signatures remain elusive. To address this, we developed a statistical method, comparative metaprofiling, which identifies and assesses the intersection of multiple gene expression signatures from a(More)
A catalog of all human protein-protein interactions would provide scientists with a framework to study protein deregulation in complex diseases such as cancer. Here we demonstrate that a probabilistic analysis integrating model organism interactome data, protein domain data, genome-wide gene expression data and functional annotation data predicts nearly(More)
The evolution of prostate cancer from an androgen-dependent state to one that is androgen-independent marks its lethal progression. The androgen receptor (AR) is essential in both, though its function in androgen-independent cancers is poorly understood. We have defined the direct AR-dependent target genes in both androgen-dependent and -independent cancer(More)
SUMMARY Next generation sequencing (NGS) technologies have enabled de novo gene fusion discovery that could reveal candidates with therapeutic significance in cancer. Here we present an open-source software package, ChimeraScan, for the discovery of chimeric transcription between two independent transcripts in high-throughput transcriptome sequencing data.(More)