Meta-analysis of prostate cancer gene expression data identifies a novel discriminatory signature enriched for glycosylating enzymes

BACKGROUND: Tumorigenesis is characterised by changes in transcriptional control. Extensive transcript expression data have been acquired over the last decade and used to classify prostate cancers. Prostate cancer is, however, a heterogeneous multifocal cancer and this poses challenges in identifying robust transcript biomarkers.

METHODS: In this study, we have undertaken a meta-analysis of publicly available transcriptomic data spanning datasets and technologies from the last decade and encompassing laser capture microdissected and macrodissected sample sets.

RESULTS: We identified a 33 gene signature that can discriminate between benign tissue controls and localised prostate cancers irrespective of detection platform or dissection status. These genes were significantly overexpressed in localised prostate cancer versus benign tissue in at least three datasets within the Oncomine Compendium of Expression Array Data. In addition, they were also overexpressed in a recent exon-array dataset as well a prostate cancer RNA-seq dataset generated as part of the The Cancer Genomics Atlas (TCGA) initiative. Biologically, glycosylation was the single enriched process associated with this 33 gene signature, encompassing four glycosylating enzymes. We went on to evaluate the performance of this signature against three individual markers of prostate cancer, v-ets avian erythroblastosis virus E26 oncogene homolog (ERG) expression, prostate specific antigen (PSA) expression and androgen receptor (AR) expression in an additional independent dataset. Our signature had greater discriminatory power than these markers both for localised cancer and metastatic disease relative to benign tissue, or in the case of metastasis, also localised prostate cancer.

CONCLUSION: In conclusion, robust transcript biomarkers are present within datasets assembled over many years and cohorts and our study provides both examples and a strategy for refining and comparing datasets to obtain additional markers as more data are generated.

New androgen receptor genomic targets show an interaction with the ETS1 transcription factor

The androgen receptor (AR) initiates important developmental and oncogenic transcriptional pathways. The AR is known to bind as a homodimer to 15-base pair bipartite palindromic androgen-response elements; however, few direct AR gene targets are known. To identify AR promoter targets, we used chromatin immunoprecipitation with on-chip detection of genomic fragments. We identified 1,532 potential AR-binding sites, including previously known AR gene targets. Many of the new AR target genes show altered expression in prostate cancer. Analysis of sequences underlying AR-binding sites showed that more than 50% of AR-binding sites did not contain the established 15 bp AR-binding element. Unbiased sequence analysis showed 6-bp motifs, which were significantly enriched and were bound directly by the AR in vitro. Binding sequences for the avian erythroblastosis virus E26 homologue (ETS) transcription factor family were also highly enriched, and we uncovered an interaction between the AR and ETS1 at a subset of AR promoter targets.

Vaccinations and childhood type 1 diabetes mellitus: a meta-analysis of observational studies

Aims/hypothesis The aim of this study was to investigate the association between routine vaccinations and the risk of childhood type 1 diabetes mellitus by systematically reviewing the published literature and performing meta-analyses where possible.

Methods A comprehensive literature search was performed of MEDLINE and EMBASE to identify all studies that compared vaccination rates in children who subsequently developed type 1 diabetes mellitus and in control children. ORs and 95% CIs were obtained from published reports or derived from individual patient data and then combined using a random effects meta-analysis.

Results In total, 23 studies investigating 16 vaccinations met the inclusion criteria. Eleven of these contributed to meta-analyses which included data from between 359 and 11,828 childhood diabetes cases. Overall, there was no evidence to suggest an association between any of the childhood vaccinations investigated and type 1 diabetes mellitus. The pooled ORs ranged from 0.58 (95% CI 0.24, 1.40) for the measles, mumps and rubella (MMR) vaccination in five studies up to 1.04 (95% CI 0.94, 1.14) for the haemophilus influenza B (HiB) vaccination in 11 studies. Significant heterogeneity was present in most of the pooled analyses, but was markedly reduced when analyses were restricted to study reports with high methodology quality scores. Neither this restriction by quality nor the original authors’ adjustments for potential confounding made a substantial difference to the pooled ORs.

Conclusions/interpretation This study provides no evidence of an association between routine vaccinations and childhood type 1 diabetes.

Application of the pMHC Array to Characterise Tumour Antigen Specific T Cell Populations in Leukaemia Patients at Disease Diagnosis

Immunotherapy treatments for cancer are becoming increasingly successful, however to further improve our understanding of the T-cell recognition involved in effective responses and to encourage moves towards the development of personalised treatments for leukaemia immunotherapy, precise antigenic targets in individual patients have been identified. Cellular arrays using peptide-MHC (pMHC) tetramers allow the simultaneous detection of different antigen specific T-cell populations naturally circulating in patients and normal donors. We have developed the pMHC array to detect CD8+ T-cell populations in leukaemia patients that recognise epitopes within viral antigens (cytomegalovirus (CMV) and influenza (Flu)) and leukaemia antigens (including Per Arnt Sim domain 1 (PASD1), MelanA, Wilms' Tumour (WT1) and tyrosinase). We show that the pMHC array is at least as sensitive as flow cytometry and has the potential to rapidly identify more than 40 specific T-cell populations in a small sample of T-cells (0.8-1.4 x 106). Fourteen of the twenty-six acute myeloid leukaemia (AML) patients analysed had T cells that recognised tumour antigen epitopes, and eight of these recognised PASD1 epitopes. Other tumour epitopes recognised were MelanA (n = 3), tyrosinase (n = 3) and WT1126-134 (n = 1). One of the seven acute lymphocytic leukaemia (ALL) patients analysed had T cells that recognised the MUC1950-958 epitope. In the future the pMHC array may be used provide point of care T-cell analyses, predict patient response to conventional therapy and direct personalised immunotherapy for patients.

TELL ME Design: Protective Behaviour During an Epidemic

The TELL ME agent based model simulates the connections between health agency communication, personal decisions to adopt protective behaviour during an influenza epidemic, and the effect of those decisions on epidemic progress. The behaviour decisions are modelled with a combination of personal attitude, behaviour adoption by neighbours, and the local recent incidence of influenza. This paper sets out and justifies the model design, including how these decision factors have been operationalised. By exploring the effects of different communication strategies, the model is intended to assist health authorities with their influenza epidemic communication plans. It can both assist users to understand the complex interactions between communication, personal behaviour and epidemic progress, and guide future data collection to improve communication planning.