Placing the university: thinking in and beyond globalization

In some respects, the impact of globalization on universities is well rehearsed (competition for international students; the drive for status in global rankings; the opening of overseas campuses; the dream of massive open online courses and other forms of digital education), but the relationship between universities as place-based institutions and globalization is less well understood. It is on that this chapter focuses. Drawing on work undertaken as part of an Economic and Social Research Council project (“Higher Education and Regional Social Transformation”) the author sets the arguments in a wider context. He explores the extent to which and ways in which universities have become key players in the reimagination of their city regions in a (neoliberal) global context. As well as reflecting on the wider public (and local) role of universities, he also considers how universities use the tools available to them to position themselves effectively as successful businesses within the new world in which they find themselves

Meta-analysis confirms BCL2 is an independent prognostic marker in breast cancer.

BACKGROUND: A number of protein markers have been investigated as prognostic adjuncts in breast cancer but their translation into clinical practice has been impeded by a lack of appropriate validation. Recently, we showed that BCL2 protein expression had prognostic power independent of current used standards. Here, we present the results of a meta-analysis of the association between BCL2 expression and both disease free survival (DFS) and overall survival (OS) in female breast cancer. METHODS: Reports published in 1994-2006 were selected for the meta-analysis using a search of PubMed. Studies that investigated the role of BCL2 expression by immunohistochemistry with a sample size greater than 100 were included. Seventeen papers reported the results of 18 different series including 5,892 cases with an average median follow-up of 92.1 months. RESULTS: Eight studies investigated DFS unadjusted for other variables in 2,285 cases. The relative hazard estimates ranged from 0.85 - 3.03 with a combined random effects estimate of 1.66 (95%CI 1.25 - 2.22). The effect of BCL2 on DFS adjusted for other prognostic factors was reported in 11 studies and the pooled random effects hazard ratio estimate was 1.58 (95%CI 1.29-1.94). OS was investigated unadjusted for other variables in eight studies incorporating 3,910 cases. The hazard estimates ranged from 0.99-4.31 with a pooled estimate of risk of 1.64 (95%CI 1.36-2.0). OS adjusted for other parameters was evaluated in nine series comprising 3,624 cases and the estimates for these studies ranged from 1.10 to 2.49 with a pooled estimate of 1.37 (95%CI 1.19-1.58). CONCLUSION: The meta-analysis strongly supports the prognostic role of BCL2 as assessed by immunohistochemistry in breast cancer and shows that this effect is independent of lymph node status, tumour size and tumour grade as well as a range of other biological variables on multi-variate analysis. Large prospective studies are now needed to establish the clinical utility of BCL2 as an independent prognostic marker

The effect of rare variants on inflation of the test statistics in case-control analyses.

BACKGROUND: The detection of bias due to cryptic population structure is an important step in the evaluation of findings of genetic association studies. The standard method of measuring this bias in a genetic association study is to compare the observed median association test statistic to the expected median test statistic. This ratio is inflated in the presence of cryptic population structure. However, inflation may also be caused by the properties of the association test itself particularly in the analysis of rare variants. We compared the properties of the three most commonly used association tests: the likelihood ratio test, the Wald test and the score test when testing rare variants for association using simulated data. RESULTS: We found evidence of inflation in the median test statistics of the likelihood ratio and score tests for tests of variants with less than 20 heterozygotes across the sample, regardless of the total sample size. The test statistics for the Wald test were under-inflated at the median for variants below the same minor allele frequency. CONCLUSIONS: In a genetic association study, if a substantial proportion of the genetic variants tested have rare minor allele frequencies, the properties of the association test may mask the presence or absence of bias due to population structure. The use of either the likelihood ratio test or the score test is likely to lead to inflation in the median test statistic in the absence of population structure. In contrast, the use of the Wald test is likely to result in under-inflation of the median test statistic which may mask the presence of population structure.This work was supported by a grant from Cancer Research UK (C490/A16561). AP is funded by a Medical Research Council studentship.This is the final published version. It first appeared at http://dx.doi.org/10.1186%2Fs12859-015-0496-1

The admixture maximum likelihood test to test for association between rare variants and disease phenotypes.

BACKGROUND: The development of genotyping arrays containing hundreds of thousands of rare variants across the genome and advances in high-throughput sequencing technologies have made feasible empirical genetic association studies to search for rare disease susceptibility alleles. As single variant testing is underpowered to detect associations, the development of statistical methods to combine analysis across variants - so-called "burden tests" - is an area of active research interest. We previously developed a method, the admixture maximum likelihood test, to test multiple, common variants for association with a trait of interest. We have extended this method, called the rare admixture maximum likelihood test (RAML), for the analysis of rare variants. In this paper we compare the performance of RAML with six other burden tests designed to test for association of rare variants. RESULTS: We used simulation testing over a range of scenarios to test the power of RAML compared to the other rare variant association testing methods. These scenarios modelled differences in effect variability, the average direction of effect and the proportion of associated variants. We evaluated the power for all the different scenarios. RAML tended to have the greatest power for most scenarios where the proportion of associated variants was small, whereas SKAT-O performed a little better for the scenarios with a higher proportion of associated variants. CONCLUSIONS: The RAML method makes no assumptions about the proportion of variants that are associated with the phenotype of interest or the magnitude and direction of their effect. The method is flexible and can be applied to both dichotomous and quantitative traits and allows for the inclusion of covariates in the underlying regression model. The RAML method performed well compared to the other methods over a wide range of scenarios. Generally power was moderate in most of the scenarios, underlying the need for large sample sizes in any form of association testing.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancer.

INTRODUCTION: The aim of this study was to develop and validate a prognostication model to predict overall and breast cancer specific survival for women treated for early breast cancer in the UK. METHODS: Using the Eastern Cancer Registration and Information Centre (ECRIC) dataset, information was collated for 5,694 women who had surgery for invasive breast cancer in East Anglia from 1999 to 2003. Breast cancer mortality models for oestrogen receptor (ER) positive and ER negative tumours were derived from these data using Cox proportional hazards, adjusting for prognostic factors and mode of cancer detection (symptomatic versus screen-detected). An external dataset of 5,468 patients from the West Midlands Cancer Intelligence Unit (WMCIU) was used for validation. RESULTS: Differences in overall actual and predicted mortality were <1% at eight years for ECRIC (18.9% vs. 19.0%) and WMCIU (17.5% vs. 18.3%) with area under receiver-operator-characteristic curves (AUC) of 0.81 and 0.79 respectively. Differences in breast cancer specific actual and predicted mortality were <1% at eight years for ECRIC (12.9% vs. 13.5%) and <1.5% at eight years for WMCIU (12.2% vs. 13.6%) with AUC of 0.84 and 0.82 respectively. Model calibration was good for both ER positive and negative models although the ER positive model provided better discrimination (AUC 0.82) than ER negative (AUC 0.75). CONCLUSIONS: We have developed a prognostication model for early breast cancer based on UK cancer registry data that predicts breast cancer survival following surgery for invasive breast cancer and includes mode of detection for the first time. The model is well calibrated, provides a high degree of discrimination and has been validated in a second UK patient cohort.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Seq4SNPs: new software for retrieval of multiple, accurately annotated DNA sequences, ready formatted for SNP assay design.

BACKGROUND: In moderate-throughput SNP genotyping there was a gap in the workflow, between choosing a set of SNPs and submitting their sequences to proprietary assay design software, which was not met by existing software. Retrieval and formatting of sequences flanking each SNP, prior to assay design, becomes rate-limiting for more than about ten SNPs, especially if annotated for repetitive regions and adjacent variations. We routinely process up to 50 SNPs at once. IMPLEMENTATION: We created Seq4SNPs, a web-based, walk-away software that can process one to several hundred SNPs given rs numbers as input. It outputs a file of fully annotated sequences formatted for one of three proprietary design softwares: TaqMan's Primer-By-Design FileBuilder, Sequenom's iPLEX or SNPstream's Autoprimer, as well as unannotated fasta sequences. We found genotyping assays to be inhibited by repetitive sequences or the presence of additional variations flanking the SNP under test, and in multiplexes, repetitive sequence flanking one SNP adversely affects multiple assays. Assay design software programs avoid such regions if the input sequences are appropriately annotated, so we used Seq4SNPs to provide suitably annotated input sequences, and improved our genotyping success rate. Adjacent SNPs can also be avoided, by annotating sequences used as input for primer design. CONCLUSION: The accuracy of annotation by Seq4SNPs is significantly better than manual annotation (P < 1e-5).Using Seq4SNPs to incorporate all annotation for additional SNPs and repetitive elements into sequences, for genotyping assay designer software, minimizes assay failure at the design stage, reducing the cost of genotyping. Seq4SNPs provides a rapid route for replacement of poor test SNP sequences. We routinely use this software for assay sequence preparation. Seq4SNPs is available as a service at (http://moya.srl.cam.ac.uk/oncology/bio/s4shome.html) and (http://moya.srl.cam.ac.uk/cgi-bin/oncology/srl/ncbi/seq4snp1.pl), currently for human SNPs, but easily extended to include any species in dbSNP.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Inclusion of KI67 significantly improves performance of the PREDICT prognostication and prediction model for early breast cancer.

BACKGROUND: PREDICT (http://www.predict.nhs.uk) is a prognostication and treatment benefit tool for early breast cancer (EBC). The aim of this study was to incorporate the prognostic effect of KI67 status in a new version (v3), and compare performance with the Predict model that includes HER2 status (v2). METHODS: The validation study was based on 1,726 patients with EBC treated in Nottingham between 1989 and 1998. KI67 positivity for PREDICT is defined as >10% of tumour cells staining positive. ROC curves were constructed for Predict models with (v3) and without (v2) KI67 input. Comparison was made using the method of DeLong. RESULTS: In 1274 ER+ patients the predicted number of events at 10 years increased from 196 for v2 to 204 for v3 compared to 221 observed. The area under the ROC curve (AUC) improved from 0.7611 to 0.7676 (p=0.005) in ER+ patients and from 0.7546 to 0.7595 (p=0.0008) in all 1726 patients (ER+ and ER-). CONCLUSION: Addition of KI67 to PREDICT has led to a statistically significant improvement in the model performance for ER+ patients and will aid clinical decision making in these patients. Further studies should determine whether other markers including gene expression profiling provide additional prognostic information to that provided by PREDICT.SEARCH was funded through a programme grant from Cancer Research UK (C490/A10124) and this work is supported by the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge.This is the final version of the article. It first appeared from BioMed Central via http://dx.doi.org/10.1186/1471-2407-14-90

Decline in Antigenicity of Tumor Markers by Storage Time Using Pathology Sections Cut From Tissue Microarrays.

Sectioning a whole tissue microarrray (TMA block) and storing the sections maximizes the number of sections obtained, but may impair the antigenicity of the stored sections. We have investigated the impact of TMA section storage on antigenicity. First, we reexamined existing TMA data to determine whether antigenicity in stored sections changes over time. Component scores for each marker, based on cellular compartment of staining and score-type, were evaluated separately. Residual components scores adjusted for grade, tumor size, and node positivity, were regressed on the number of days storage to evaluate the effect of storage time. Storage time ranged from 2 to 1897 days, and the mean change in antigenicity per year ranged from -0.88 (95% confidence interval, -1.11 to -0.65) to 0.035 (95% confidence interval, 0.016-0.054). Further analysis showed no significant improvement in the fit of survival models if storage time adjusted scores were included in the models rather than unadjusted scores. We then compared 3 ways of processing TMA sections after cutting-immediate staining, staining after 1 year, and staining after 1 year coated in wax-on the immunohistochemistry results for: progesterone receptor, a routinely used, robust antibody, and MKI67, which is generally considered less robust. The progesterone receptor scores for stored sections were similar to those for unstored sections, whereas the MKI67 scores for stored sections were substantially different to those for unstored sections. Wax coating made little difference to the results. Biomarker antigenicity shows a small decline over time that is unlikely to have an important effect on studies of prognostic biomarkers.We acknowledge the SEARCH team, the National Cancer Registration Service Eastern Office and Information Centre, the Histopathology Core Facility at the CRUK Cambridge Research Institute for immunohistochemical staining and digital image acquisition and the Human Research Tissue Bank, Cambridge University Hospitals NHS Foundation Trust. This work was funded through a programme grant from Cancer Research UK (C490/A10119, C490/A10124 and C490/A16561) and funding from the NIHR Biomedical Research Centre.This is the final version of the article. It was first published by Lippincott Williams & Wilkins at http://dx.doi.org/10.1097/PAI.000000000000017

Genomic-Bioinformatic Analysis of Transcripts Enriched in the Third-Stage Larva of the Parasitic Nematode Ascaris suum

Differential transcription in Ascaris suum was investigated using a genomic-bioinformatic approach. A cDNA archive enriched for molecules in the infective third-stage larva (L3) of A. suum was constructed by suppressive-subtractive hybridization (SSH), and a subset of cDNAs from 3075 clones subjected to microarray analysis using cDNA probes derived from RNA from different developmental stages of A. suum. The cDNAs (n = 498) shown by microarray analysis to be enriched in the L3 were sequenced and subjected to bioinformatic analyses using a semi-automated pipeline (ESTExplorer). Using gene ontology (GO), 235 of these molecules were assigned to ‘biological process’ (n = 68), ‘cellular component’ (n = 50), or ‘molecular function’ (n = 117). Of the 91 clusters assembled, 56 molecules (61.5%) had homologues/orthologues in the free-living nematodes Caenorhabditis elegans and C. briggsae and/or other organisms, whereas 35 (38.5%) had no significant similarity to any sequences available in current gene databases. Transcripts encoding protein kinases, protein phosphatases (and their precursors), and enolases were abundantly represented in the L3 of A. suum, as were molecules involved in cellular processes, such as ubiquitination and proteasome function, gene transcription, protein–protein interactions, and function. In silico analyses inferred the C. elegans orthologues/homologues (n = 50) to be involved in apoptosis and insulin signaling (2%), ATP synthesis (2%), carbon metabolism (6%), fatty acid biosynthesis (2%), gap junction (2%), glucose metabolism (6%), or porphyrin metabolism (2%), although 34 (68%) of them could not be mapped to a specific metabolic pathway. Small numbers of these 50 molecules were predicted to be secreted (10%), anchored (2%), and/or transmembrane (12%) proteins. Functionally, 17 (34%) of them were predicted to be associated with (non-wild-type) RNAi phenotypes in C. elegans, the majority being embryonic lethality (Emb) (13 types; 58.8%), larval arrest (Lva) (23.5%) and larval lethality (Lvl) (47%). A genetic interaction network was predicted for these 17 C. elegans orthologues, revealing highly significant interactions for nine molecules associated with embryonic and larval development (66.9%), information storage and processing (5.1%), cellular processing and signaling (15.2%), metabolism (6.1%), and unknown function (6.7%). The potential roles of these molecules in development are discussed in relation to the known roles of their homologues/orthologues in C. elegans and some other nematodes. The results of the present study provide a basis for future functional genomic studies to elucidate molecular aspects governing larval developmental processes in A. suum and/or the transition to parasitism