Rajan P.,University of Glasgow |
Stockley J.,University of Glasgow |
Sudbery I.M.,MRC Functional Genomics Unit |
Fleming J.T.,CR UK Beatson Institute |
And 9 more authors.
BMC Cancer | Year: 2014
Background: Although chemotherapy for prostate cancer (PCa) can improve patient survival, some tumours are chemo-resistant. Tumour molecular profiles may help identify the mechanisms of drug action and identify potential prognostic biomarkers. We performed in vivo transcriptome profiling of pre- and post-treatment prostatic biopsies from patients with advanced hormone-naive prostate cancer treated with docetaxel chemotherapy and androgen deprivation therapy (ADT) with an aim to identify the mechanisms of drug action and identify prognostic biomarkers. Methods: RNA sequencing (RNA-Seq) was performed on biopsies from four patients before and ~22 weeks after docetaxel and ADT initiation. Gene fusion products and differentially-regulated genes between treatment pairs were identified using TopHat and pathway enrichment analyses undertaken. Publically available datasets were interrogated to perform survival analyses on the gene signatures identified using cBioportal. Results: A number of genomic rearrangements were identified including the TMPRSS2/ERG fusion and 3 novel gene fusions involving the ETS family of transcription factors in patients, both pre and post chemotherapy. In total, gene expression analyses showed differential expression of at least 2 fold in 575 genes in post-chemotherapy biopsies. Of these, pathway analyses identified a panel of 7 genes (ADAM7, FAM72B, BUB1B, CCNB1, CCNB2, TTK, CDK1), including a cell cycle-related geneset, that were differentially-regulated following treatment with docetaxel and ADT. Using cBioportal to interrogate the MSKCC-Prostate Oncogenome Project dataset we observed a statistically-significant reduction in disease-free survival of patients with tumours exhibiting alterations in gene expression of the above panel of 7 genes (p = 0.015). Conclusions: Here we report on the first "real-time" in vivo RNA-Seq-based transcriptome analysis of clinical PCa from pre- and post-treatment TRUSS-guided biopsies of patients treated with docetaxel chemotherapy plus ADT. We identify a chemotherapy-driven PCa transcriptome profile which includes the down-regulation of important positive regulators of cell cycle progression. A 7 gene signature biomarker panel has also been identified in high-risk prostate cancer patients to be of prognostic value. Future prospective study is warranted to evaluate the clinical value of this panel. © 2014 Rajan et al.
Melnyk C.W.,University of Cambridge |
Molnar A.,University of Cambridge |
Bassett A.,University of Cambridge |
Bassett A.,MRC Functional Genomics Unit |
Baulcombe D.C.,University of Cambridge
Current Biology | Year: 2011
RNA silencing in flowering plants generates a signal that moves between cells and through the phloem [1, 2]. Nucleotide sequence specificity of the signal is conferred by 21, 22, and 24 nucleotide (nt) sRNAs that are generated by Dicer-like (DCL) proteins . In the recipient cells these sRNAs bind to Argonaute (AGO) effectors of silencing and the 21 nt sRNAs mediate posttranscriptional regulation (PTGS) via mRNA cleavage  whereas the 24 nt sRNAs are associated with RNA-dependent DNA methylation (RdDM)  that may underlie transcriptional gene silencing (TGS). Intriguingly, genes involved in TGS are required for graft-transmissible gene silencing associated with PTGS . However, some of the same genes were also required for spread of a PTGS silencing signal out of the veins of Arabidopsis , and grafting tests failed to demonstrate direct transmission of TGS signals [8-10]. It seemed likely, therefore, that mobile silencing is associated only with PTGS. To address this possibility, we grafted TGS-inducing wild-type Arabidopsis and a mutant that is compromised in 24 nt sRNA production onto a wild-type reporter line. The 21-24 nt sRNAs from the TGS construct were transmitted across a graft union but only the 24 nt sRNAs directed RdDM and TGS of a transgene promoter in meristematic cells. These data extend the significance of an RNA silencing signal to embrace epigenetics and transcriptional gene silencing and support the hypothesis that these signals transmit information to meristematic cells where they initiate persistent epigenetic changes that may influence growth, development, and heritable phenotypes. © 2011 Elsevier Ltd. All rights reserved.
PubMed | Weatherall Institute of Molecular Medicine and MRC Functional Genomics Unit
Type: | Journal: Human molecular genetics | Year: 2016
Dense genotyping approaches have revealed much about the genetic architecture both of gene expression and disease susceptibility. However, assigning causality to genetic variants associated with a transcriptomic or phenotypic trait presents a far greater challenge. The development of epigenomic resources by ENCODE, the Epigenomic Roadmap and others has led to strategies that seek to infer the likely functional variants underlying these genome-wide association signals. It is known, for example, that such variants tend to be located within areas of open chromatin, as detected by techniques such as DNase-seq and FAIRE-seq. We aimed to assess what proportion of variants associated with phenotypic or transcriptomic traits in the human brain are located within transcription factor binding sites. The bioinformatic tools, Wellington and HINT, were used to infer transcription factor footprints from existing DNase-seq data derived from central nervous system tissues with high spatial resolution. This dataset was then employed to assess the likely contribution of altered transcription factor binding to both expression quantitative trait loci (eQTL) and genome-wide association study (GWAS) signals. Surprisingly, we show that most haplotypes associated with GWAS or eQTL phenotypes are located outside of DNase-seq footprints. This could imply that DNase-seq footprinting is too insensitive an approach to identify a large proportion of true transcription factor binding sites. Importantly, this suggests that prioritising variants for genome engineering studies to establish causality will continue to be frustrated by an inability of footprinting to identify the causative variant within a haplotype.