BAZ2A (TIP5) is involved in epigenetic alterations in prostate cancer and its overexpression predicts disease recurrence

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Abstract

Prostate cancer is driven by a combination of genetic and/or epigenetic alterations. Epigenetic alterations are frequently observed in all human cancers, yet how aberrant epigenetic signatures are established is poorly understood. Here we show that the gene encoding BAZ2A (TIP5), a factor previously implicated in epigenetic rRNA gene silencing, is overexpressed in prostate cancer and is paradoxically involved in maintaining prostate cancer cell growth, a feature specific to cancer cells. BAZ2A regulates numerous protein-coding genes and directly interacts with EZH2 to maintain epigenetic silencing at genes repressed in metastasis. BAZ2A overexpression is tightly associated with a molecular subtype displaying a CpG island methylator phenotype (CIMP). Finally, high BAZ2A levels serve as an independent predictor of biochemical recurrence in a cohort of 7,682 individuals with prostate cancer. This work identifies a new aberrant role for the epigenetic regulator BAZ2A, which can also serve as a useful marker for metastatic potential in prostate cancer.

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RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

Bo Li, Colin Dewey (2011)

Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.

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Comprehensive genomic characterization defines human glioblastoma genes and core pathways

(2008)

Human cancer cells typically harbor multiple chromosomal aberrations, nucleotide substitutions and epigenetic modifications that drive malignant transformation. The Cancer Genome Atlas (TCGA) pilot project aims to assess the value of large-scale multidimensional analysis of these molecular characteristics in human cancer and to provide the data rapidly to the research community. Here, we report the interim integrative analysis of DNA copy number, gene expression and DNA methylation aberrations in 206 glioblastomas (GBM), the most common type of adult brain cancer, and nucleotide sequence aberrations in 91 of the 206 GBMs. This analysis provides new insights into the roles of ERBB2, NF1 and TP53, uncovers frequent mutations of the PI3 kinase regulatory subunit gene PIK3R1, and provides a network view of the pathways altered in the development of GBM. Furthermore, integration of mutation, DNA methylation and clinical treatment data reveals a link between MGMT promoter methylation and a hypermutator phenotype consequent to mismatch repair deficiency in treated glioblastomas, an observation with potential clinical implications. Together, these findings establish the feasibility and power of TCGA, demonstrating that it can rapidly expand knowledge of the molecular basis of cancer.

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The Mutational Landscape of Lethal Castrate Resistant Prostate Cancer

Catherine S. Grasso, Yi-Mi Wu, Dan R Robinson … (2012)

Characterization of the prostate cancer transcriptome and genome has identified chromosomal rearrangements and copy number gains/losses, including ETS gene fusions, PTEN loss and androgen receptor (AR) amplification, that drive prostate cancer development and progression to lethal, metastatic castrate resistant prostate cancer (CRPC) 1 . As less is known about the role of mutations 2–4 , here we sequenced the exomes of 50 lethal, heavily-pretreated metastatic CRPCs obtained at rapid autopsy (including three different foci from the same patient) and 11 treatment naïve, high-grade localized prostate cancers. We identified low overall mutation rates even in heavily treated CRPC (2.00/Mb) and confirmed the monoclonal origin of lethal CRPC. Integrating exome copy number analysis identified disruptions of CHD1, which define a subtype of ETS fusionnegative prostate cancer. Similarly, we demonstrate that ETS2, which is deleted in ~1/3 of CRPCs (commonly through TMPRSS2:ERG fusions), is also deregulated through mutation. Further, we identified recurrent mutations in multiple chromatin/histone modifying genes, including MLL2 (mutated in 8.6% of prostate cancers), and demonstrate interaction of the MLL complex with AR, which is required for AR-mediated signaling. We also identified novel recurrent mutations in the AR collaborating factor FOXA1, which is mutated in 5 of 147 (3.4%) prostate cancers (both untreated localized prostate cancer and CRPC), and showed that mutated FOXA1 represses androgen signaling and increases tumour growth. Proteins that physically interact with AR, such as the ERG gene fusion product, FOXA1, MLL2, UTX, and ASXL1 were found to be mutated in CRPC. In summary, we describe the mutational landscape of a heavily treated metastatic cancer, identify novel mechanisms of AR signaling deregulated in prostate cancer, and prioritize candidates for future study.