Rory Stark

Bio: Rory Stark is an academic researcher from University of Cambridge. The author has contributed to research in topics: Prostate cancer & Androgen receptor. The author has an hindex of 20, co-authored 27 publications receiving 4957 citations.

Differential oestrogen receptor binding is associated with clinical outcome in breast cancer

Abstract: This paper, which maps ERα binding via ChIP-seq in tumour tissue from twenty ER+ breast cancer patients, relies on a concurrently developed Bioconductor package, DiffBind, which provides a framework for quantitative differential analysis of protein/DNA binding events. Here we use DiffBind to identify ERα sites significantly differentially bound between those found in tumours from patients with good prognosis vs. those with poor prognosis and metastases. Gene signatures that predict clinical outcome in ER+ disease, validated in publically available breast cancer gene expression datasets, are derived from these sites. These signatures are enriched for genes with relevant proximal cis-regulatory events. Statistical characterization of differentially bound ERα sites enables further downstream analysis, including identification of a differentially enriched motif for the transcription factor FoxA1. Further differential analysis in five ER+ breast cancer cell lines shows how ERα binding is extensively shifted in tamoxifen-resistance, with the FoxA1 motif enriched proximal to ERα binding sites differentially bound in cells resistant to treatment. Analysis of FoxA1 binding at mitogen-induced ERα sites demonstrates that the observed differential ER binding program is not due to the selection of a rare subpopulation of cells, but rather to the FoxA1-mediated reprogramming of ER binding on a rapid time scale. Focusing our analysis on differential binding in primary tumour material allows us to show the plasticity of ERα binding capacity, with distinct combinations of cis-regulatory elements linked with the different clinical outcomes. These techniques are applicable to other cancers (and indeed other diseases) where master transcription factor regulators are known.

RNA sequencing: the teenage years

Abstract: Over the past decade, RNA sequencing (RNA-seq) has become an indispensable tool for transcriptome-wide analysis of differential gene expression and differential splicing of mRNAs. However, as next-generation sequencing technologies have developed, so too has RNA-seq. Now, RNA-seq methods are available for studying many different aspects of RNA biology, including single-cell gene expression, translation (the translatome) and RNA structure (the structurome). Exciting new applications are being explored, such as spatial transcriptomics (spatialomics). Together with new long-read and direct RNA-seq technologies and better computational tools for data analysis, innovations in RNA-seq are contributing to a fuller understanding of RNA biology, from questions such as when and where transcription occurs to the folding and intermolecular interactions that govern RNA function.

The androgen receptor fuels prostate cancer by regulating central metabolism and biosynthesis

Abstract: The androgen receptor (AR) is a key regulator of prostate growth and the principal drug target for the treatment of prostate cancer. Previous studies have mapped AR targets and identified some candidates which may contribute to cancer progression, but did not characterize AR biology in an integrated manner. In this study, we took an interdisciplinary approach, integrating detailed genomic studies with metabolomic profiling and identify an anabolic transcriptional network involving AR as the core regulator. Restricting flux through anabolic pathways is an attractive approach to deprive tumours of the building blocks needed to sustain tumour growth. Therefore, we searched for targets of the AR that may contribute to these anabolic processes and could be amenable to therapeutic intervention by virtue of differential expression in prostate tumours. This highlighted calcium/calmodulin-dependent protein kinase kinase 2, which we show is overexpressed in prostate cancer and regulates cancer cell growth via its unexpected role as a hormone-dependent modulator of anabolic metabolism. In conclusion, it is possible to progress from transcriptional studies to a promising therapeutic target by taking an unbiased interdisciplinary approach.

Progesterone receptor modulates ERα action in breast cancer

Abstract: Progesterone receptor (PR) expression is used as a biomarker of oestrogen receptor-α (ERα) function and breast cancer prognosis. Here we show that PR is not merely an ERα-induced gene target, but is also an ERα-associated protein that modulates its behaviour. In the presence of agonist ligands, PR associates with ERα to direct ERα chromatin binding events within breast cancer cells, resulting in a unique gene expression programme that is associated with good clinical outcome. Progesterone inhibited oestrogen-mediated growth of ERα(+) cell line xenografts and primary ERα(+) breast tumour explants, and had increased anti-proliferative effects when coupled with an ERα antagonist. Copy number loss of PGR, the gene coding for PR, is a common feature in ERα(+) breast cancers, explaining lower PR levels in a subset of cases. Our findings indicate that PR functions as a molecular rheostat to control ERα chromatin binding and transcriptional activity, which has important implications for prognosis and therapeutic interventions.

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Abstract: In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html .

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Abstract: In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-Seq data, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data. DESeq2 uses shrinkage estimation for dispersions and fold changes to improve stability and interpretability of the estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression and facilitates downstream tasks such as gene ranking and visualization. DESeq2 is available as an R/Bioconductor package.

featureCounts: an efficient general-purpose program for assigning sequence reads to genomic features

Abstract: MOTIVATION: Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. RESULTS: We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. AVAILABILITY AND IMPLEMENTATION: featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.

2016 update of the PRIDE database and its related tools

Abstract: The PRoteomics IDEntifications (PRIDE) database is one of the world-leading data repositories of mass spectrometry (MS)-based proteomics data Since the beginning of 2014, PRIDE Archive (http://wwwebiacuk/pride/archive/) is the new PRIDE archival system, replacing the original PRIDE database Here we summarize the developments in PRIDE resources and related tools since the previous update manuscript in the Database Issue in 2013 PRIDE Archive constitutes a complete redevelopment of the original PRIDE, comprising a new storage backend, data submission system and web interface, among other components PRIDE Archive supports the most-widely used PSI (Proteomics Standards Initiative) data standard formats (mzML and mzIdentML) and implements the data requirements and guidelines of the ProteomeXchange Consortium The wide adoption of ProteomeXchange within the community has triggered an unprecedented increase in the number of submitted data sets (around 150 data sets per month) We outline some statistics on the current PRIDE Archive data contents We also report on the status of the PRIDE related stand-alone tools: PRIDE Inspector, PRIDE Converter 2 and the ProteomeXchange submission tool Finally, we will give a brief update on the resources under development 'PRIDE Cluster' and 'PRIDE Proteomes', which provide a complementary view and quality-scored information of the peptide and protein identification data available in PRIDE Archive

Small silencing RNAs: an expanding universe

Abstract: Since the discovery in 1993 of the first small silencing RNA, a dizzying number of small RNA classes have been identified, including microRNAs (miRNAs), small interfering RNAs (siRNAs) and Piwi-interacting RNAs (piRNAs). These classes differ in their biogenesis, their modes of target regulation and in the biological pathways they regulate. There is a growing realization that, despite their differences, these distinct small RNA pathways are interconnected, and that small RNA pathways compete and collaborate as they regulate genes and protect the genome from external and internal threats.