HPV-related oropharyngeal cancer: a review on burden of the disease and opportunities for prevention and early detection

The incidence of oropharyngeal cancer (OPC) related to infection with human papillomavirus (HPV) is rising, making it now the most common HPV-related malignancy in the United States. These tumors present differently than traditional mucosal head and neck cancers, and those affected often lack classic risk factors such as tobacco and alcohol use. Currently, there are no approved approaches for prevention and early detection of disease, thus leading many patients to present with advanced cancers requiring intense surgical or nonsurgical therapies resulting in significant side effects and cost to the health-care system. In this review, we outline the evolving epidemiology of HPV-related OPC. We also summarize the available evidence corresponding to HPV-related OPC prevention, including efficacy and safety of the HPV vaccine in preventing oral HPV infections. Finally, we describe emerging techniques for identifying and screening those who may be at high risk for developing these tumors

Prognostic and therapeutic significance of carbohydrate antigen 19-9 as tumor marker in patients with pancreatic cancer

In pancreatic cancer ( PC) accurate determination of treatment response by imaging often remains difficult. Various efforts have been undertaken to investigate new factors which may serve as more appropriate surrogate parameters of treatment efficacy. This review focuses on the role of carbohydrate antigen 19- 9 ( CA 19- 9) as a prognostic tumor marker in PC and summarizes its contribution to monitoring treatment efficacy. We undertook a Medline/ PubMed literature search to identify relevant trials that had analyzed the prognostic impact of CA 19- 9 in patients treated with surgery, chemoradiotherapy and chemotherapy for PC. Additionally, relevant abstract publications from scientific meetings were included. In advanced PC, pretreatment CA 19- 9 levels have a prognostic impact regarding overall survival. Also a CA 19- 9 decline under chemotherapy can provide prognostic information for median survival. A 20% reduction of CA 19- 9 baseline levels within the first 8 weeks of chemotherapy appears to be sufficient to define a prognostic relevant subgroup of patients ('CA 19- 9 responder'). It still remains to be defined whether the CA 19- 9 response is a more reliable method for evaluating treatment efficacy compared to conventional imaging. Copyright (c) 2006 S. Karger AG, Basel

Proteomics analysis of serum protein profiling in pancreatic cancer patients by DIGE: up-regulation of mannose-binding lectin 2 and myosin light chain kinase 2

<p>Abstract</p> <p>Background</p> <p>Pancreatic cancer has significant morbidity and mortality worldwide. Good prognosis relies on an early diagnosis. The purpose of this study was to develop techniques for identifying cancer biomarkers in the serum of patients with pancreatic cancer.</p> <p>Methods</p> <p>Serum samples from five individuals with pancreatic cancer and five individuals without cancer were compared. Highly abundant serum proteins were depleted by immuno-affinity column. Differential protein analysis was performed using 2-dimensional differential in-gel electrophoresis (2D-DIGE).</p> <p>Results</p> <p>Among these protein spots, we found that 16 protein spots were differently expressed between the two mixtures; 8 of these were up-regulated and 8 were down-regulated in cancer. Mass spectrometry and database searching allowed the identification of the proteins corresponding to the gel spots. Up-regulation of mannose-binding lectin 2 and myosin light chain kinase 2, which have not previously been implicated in pancreatic cancer, were observed. In an independent series of serum samples from 16 patients with pancreatic cancer and 16 non-cancer-bearing controls, increased levels of mannose-binding lectin 2 and myosin light chain kinase 2 were confirmed by western blot.</p> <p>Conclusions</p> <p>These results suggest that affinity column enrichment and DIGE can be used to identify proteins differentially expressed in serum from pancreatic cancer patients. These two proteins 'mannose-binding lectin 2 and myosin light chain kinase 2' might be potential biomarkers for the diagnosis of the pancreatic cancer.</p

DeBi: Discovering Differentially Expressed Biclusters using a Frequent Itemset Approach

<p>Abstract</p> <p>Background</p> <p>The analysis of massive high throughput data via clustering algorithms is very important for elucidating gene functions in biological systems. However, traditional clustering methods have several drawbacks. Biclustering overcomes these limitations by grouping genes and samples simultaneously. It discovers subsets of genes that are co-expressed in certain samples. Recent studies showed that biclustering has a great potential in detecting marker genes that are associated with certain tissues or diseases. Several biclustering algorithms have been proposed. However, it is still a challenge to find biclusters that are significant based on biological validation measures. Besides that, there is a need for a biclustering algorithm that is capable of analyzing very large datasets in reasonable time.</p> <p>Results</p> <p>Here we present a fast biclustering algorithm called DeBi (Differentially Expressed BIclusters). The algorithm is based on a well known data mining approach called frequent itemset. It discovers maximum size homogeneous biclusters in which each gene is strongly associated with a subset of samples. We evaluate the performance of DeBi on a yeast dataset, on synthetic datasets and on human datasets.</p> <p>Conclusions</p> <p>We demonstrate that the DeBi algorithm provides functionally more coherent gene sets compared to standard clustering or biclustering algorithms using biological validation measures such as Gene Ontology term and Transcription Factor Binding Site enrichment. We show that DeBi is a computationally efficient and powerful tool in analyzing large datasets. The method is also applicable on multiple gene expression datasets coming from different labs or platforms.</p

Vector assembly of colloids on monolayer substrates

The key to spontaneous and directed assembly is to encode the desired assembly information to building blocks in a programmable and efficient way. In computer graphics, raster graphics encodes images on a single-pixel level, conferring fine details at the expense of large file sizes, whereas vector graphics encrypts shape information into vectors that allow small file sizes and operational transformations. Here, we adapt this raster/vector concept to a 2D colloidal system and realize &apos;vector assembly&apos; by manipulating particles on a colloidal monolayer substrate with optical tweezers. In contrast to raster assembly that assigns optical tweezers to each particle, vector assembly requires a minimal number of optical tweezers that allow operations like chain elongation and shortening. This vector approach enables simple uniform particles to form a vast collection of colloidal arenes and colloidenes, the spontaneous dissociation of which is achieved with precision and stage-by-stage complexity by simply removing the optical tweezers

Persistent Photoconductivity Studies in Nanostructured ZnO UV Sensors

The phenomenon of persistent photoconductivity is elusive and has not been addressed to an extent to attract attention both in micro and nanoscale devices due to unavailability of clear material systems and device configurations capable of providing comprehensive information. In this work, we have employed a nanostructured (nanowire diameter 30–65 nm and 5 μm in length) ZnO-based metal–semiconductor–metal photoconductor device in order to study the origin of persistent photoconductivity. The current–voltage measurements were carried with and without UV illumination under different oxygen levels. The photoresponse measurements indicated a persistent conductivity trend for depleted oxygen conditions. The persistent conductivity phenomenon is explained on the theoretical model that proposes the change of a neutral anion vacancy to a charged state

Modelling prognostic factors in advanced pancreatic cancer

Pancreatic cancer is the fifth most common cause of cancer death. Identification of defined patient groups based on a prognostic index may improve the prediction of survival and selection of therapy. Many prognostic factors have been identified often based on retrospective, underpowered studies with unclear analyses. Data from 653 patients were analysed. Continuous variables are often simplified assuming a linear relationship with log hazard or introducing a step function (dichotomising). Misspecification may lead to inappropriate conclusions but has not been previously investigated in pancreatic cancer studies. Models based on standard assumptions were compared with a novel approach using nonlinear fractional polynomial (FP) transformations. The model based on FP-transformed covariates was most appropriate and confirmed five previously reported prognostic factors: albumin, CA19-9, alkaline phosphatase, LDH and metastases, and identified three additional factors not previously reported: WBC, AST and BUN. The effects of CA19-9, alkaline phosphatase, AST and BUN may go unrecognised due to simplistic assumptions made in statistical modelling. We advocate a multivariable approach that uses information contained within continuous variables appropriately. The functional form of the relationship between continuous covariates and survival should always be assessed. Our model should aid individual patient risk stratification and the design and analysis of future trials in pancreatic cancer

Proteostasis Dysregulation in Pancreatic Cancer

The most common form of pancreatic cancer, pancreatic ductal adenocarcinoma (PDAC), has a dismal 5-year survival rate of less than 5%. Radical surgical resection, in combination with adjuvant chemotherapy, provides the best option for long-term patient survival. However, only approximately 20% of patients are resectable at the time of diagnosis, due to locally advanced or metastatic disease. There is an urgent need for the identification of new, specific, and more sensitive biomarkers for diagnosis, prognosis, and prediction to improve the treatment options for pancreatic cancer patients. Dysregulation of proteostasis is linked to many pathophysiological conditions, including various types of cancer. In this review, we report on findings relating to the main cellular protein degradation systems, the ubiquitin-proteasome system (UPS) and autophagy, in pancreatic cancer. The expression of several components of the proteolytic network, including E3 ubiquitinligases and deubiquitinating enzymes, are dysregulated in PDAC, which accounts for approximately 90% of all pancreatic malignancies. In the future, a deeper understanding of the emerging role of proteostasis in pancreatic cancer has the potential to provide clinically relevant biomarkers and new strategies for combinatorial therapeutic options to better help treat the patients.Peer reviewe

A Survey of Bayesian Statistical Approaches for Big Data

The modern era is characterised as an era of information or Big Data. This has motivated a huge literature on new methods for extracting information and insights from these data. A natural question is how these approaches differ from those that were available prior to the advent of Big Data. We present a review of published studies that present Bayesian statistical approaches specifically for Big Data and discuss the reported and perceived benefits of these approaches. We conclude by addressing the question of whether focusing only on improving computational algorithms and infrastructure will be enough to face the challenges of Big Data