HPV16 oncogene expression levels during early cervical carcinogenesis are determined by the balance of epigenetic chromatin modifications at the integrated virus genome.

In cervical squamous cell carcinomas, high-risk human papillomavirus (HRHPV) DNA is usually integrated into host chromosomes. Multiple integration events are thought to be present within the cells of a polyclonal premalignant lesion and the features that underpin clonal selection of one particular integrant remain poorly understood. We previously used the W12 model system to generate a panel of cervical keratinocyte clones, derived from cells of a low-grade premalignant lesion naturally infected with the major HRHPV type, HPV16. The cells were isolated regardless of their selective advantage and differed only by the site of HPV16 integration into the host genome. We used this resource to test the hypothesis that levels of HPV16 E6/E7 oncogene expression in premalignant cells are regulated epigenetically. We performed a comprehensive analysis of the epigenetic landscape of the integrated HPV16 DNA in selected clones, in which levels of virus oncogene expression per DNA template varied ~6.6-fold. Across the cells examined, higher levels of virus expression per template were associated with more open chromatin at the HPV16 long control region, together with greater loading of chromatin remodelling enzymes and lower nucleosome occupancy. There were higher levels of histone post-translational modification hallmarks of transcriptionally active chromatin and lower levels of repressive hallmarks. There was greater abundance of the active/elongating form of the RNA polymerase-II enzyme (RNAPII-Ser2P), together with CDK9, the component of positive transcription elongation factor b complex responsible for Ser2 phosphorylation. The changes observed were functionally significant, as cells with higher HPV16 expression per template showed greater sensitivity to depletion and/or inhibition of histone acetyltransferases and CDK9 and less sensitivity to histone deacetylase inhibition. We conclude that virus gene expression per template following HPV16 integration is determined through multiple layers of epigenetic regulation, which are likely to contribute to selection of individual cells during cervical carcinogenesis.This work was supported by Cancer Research UK (Programme Grant A13080); the Medical Research Council; The Pathological Society of Great Britain and Ireland (E.L.A.K.); and the Agency for Science, Technology and Research, Singapore (Q.Y.A).This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/onc.2016.

A quantitative LumiFluo assay to test inhibitory compounds blocking p53 degradation induced by human papillomavirus oncoprotein E6 in living cells

High-risk human papillomaviruses (HR-HPVs) are the causative agents for the onset of several epithelial cancers in humans. The deregulated expression of the viral oncoproteins E6 and E7 is the driving force sustaining the progression of malignant transformation in pre-neoplastic lesions. Targeting the viral E6 oncoprotein through inhibitory compounds can counteract the survival of cancer cells due to the reactivation of p53-mediated pathways and represents an intriguing strategy to treat HPV-associated neoplasias. Here, we describe the development of a quantitative and easy-to-perform assay to monitor the E6-mediated degradation of p53 in living cells to be used for small-molecule testing. This assay allows to unbiasedly determine whether a compound can protect p53 from the E6-mediated degradation in cells, through a simple 3-step protocol. We validated the assay by testing two small molecules, SAHA and RITA, reported to impair the E6-mediated p53 degradation. Interestingly, we observed that only SAHA efficiently rescued p53, while RITA could not provide the same degree of protection. The possibility to specifically and quantitatively monitor the ability of a selected compound to rescue p53 in a cellular context through our LumiFluo assay could represent an important step towards the successful development of anti-HPV drugs

Overexpression of the oncostatin-M receptor in cervical squamous cell carcinoma is associated with epithelial-mesenchymal transition and poor overall survival.

BACKGROUND: Copy-number gain of the oncostatin-M receptor (OSMR) occurs frequently in cervical squamous cell carcinoma (SCC) and is associated with adverse clinical outcome. We previously showed that OSMR overexpression renders cervical SCC cells more sensitive to the major ligand oncostatin-M (OSM), which increases migration and invasion in vitro. We hypothesised that a major contribution to this phenotype would come from epithelial-mesenchymal transition (EMT). METHODS: We performed a comprehensive integrated study, involving in vitro cell line studies, in vivo animal models and numerous clinical samples from a variety of anatomical sites. RESULTS: In independent sets of cervical, head/neck and lung SCC tissues, OSMR expression levels correlated with multiple EMT-associated phenotypic markers and transcription factors. OSM treatment of OSMR overexpressing cervical SCC cells produced consistent EMT changes and increased tumour sphere formation in suspension culture. In a mouse model, OSMR overexpressing SCC cells treated with OSM showed significant increases in lung colonisation. The biological effects of exogenous OSM were mirrored by highly significant adverse overall survival in cervical SCCs with OSMR overexpression (N=251). CONCLUSIONS: OSM:OSMR interactions are able to induce EMT, increased cancer stem cell-like properties and enhanced lung colonisation in SCC cells. These changes are likely to contribute to the highly significant adverse outcome associated with OSMR overexpression in cervical SCCs.This work was supported by Cancer Research UK (Programme Grant A13080).This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by Nature Publishing Group

Developing the Questionnaire

AbstractThis chapter outlines the essential topics for developing and testing a questionnaire for a discrete choice experiment survey. It addresses issues such as the description of the environmental good, pretesting of the survey, incentive compatibility, consequentiality or mitigation of hypothetical bias. For the latter, cheap talk scripts, opt-out reminders or an oath script are discussed. Moreover, the use of instructional choice sets, the identification of protest responses and strategic bidders are considered. Finally, issues related to the payment vehicle and the cost vector design are the subject of this section

Probabilistic inference of regularisation in non-rigid registration.

A long-standing issue in non-rigid image registration is the choice of the level of regularisation. Regularisation is necessary to preserve the smoothness of the registration and penalise against unnecessary complexity. The vast majority of existing registration methods use a fixed level of regularisation, which is typically hand-tuned by a user to provide "nice" results. However, the optimal level of regularisation will depend on the data which is being processed; lower signal-to-noise ratios require higher regularisation to avoid registering image noise as well as features, and different pairs of images require registrations of varying complexity depending on their anatomical similarity. In this paper we present a probabilistic registration framework that infers the level of regularisation from the data. An additional benefit of this proposed probabilistic framework is that estimates of the registration uncertainty are obtained. This framework has been implemented using a free-form deformation transformation model, although it would be generically applicable to a range of transformation models. We demonstrate our registration framework on the application of inter-subject brain registration of healthy control subjects from the NIREP database. In our results we show that our framework appropriately adapts the level of regularisation in the presence of noise, and that inferring regularisation on an individual basis leads to a reduction in model over-fitting as measured by image folding while providing a similar level of overlap

Ensemble learning incorporating uncertain registration.

This paper proposes a novel approach for improving the accuracy of statistical prediction methods in spatially normalized analysis. This is achieved by incorporating registration uncertainty into an ensemble learning scheme. A probabilistic registration method is used to estimate a distribution of probable mappings between subject and atlas space. This allows the estimation of the distribution of spatially normalized feature data, e.g., grey matter probability maps. From this distribution, samples are drawn for use as training examples. This allows the creation of multiple predictors, which are subsequently combined using an ensemble learning approach. Furthermore, extra testing samples can be generated to measure the uncertainty of prediction. This is applied to separating subjects with Alzheimer's disease from normal controls using a linear support vector machine on a region of interest in magnetic resonance images of the brain. We show that our proposed method leads to an improvement in discrimination using voxel-based morphometry and deformation tensor-based morphometry over bootstrap aggregating, a common ensemble learning framework. The proposed approach also generates more reasonable soft-classification predictions than bootstrap aggregating. We expect that this approach could be applied to other statistical prediction tasks where registration is important