Proteomics and ovarian cancer: Integrating proteomics information into clinical care

The power of proteomics allows unparalleled opportunity to query the molecular mechanisms of a malignant cell and the tumor microenvironment in patients with ovarian cancer and other solid tumors. This information has given us insight into the perturbations of signaling pathways within tumor cells and has aided the discovery of new drug targets for the tumor and possible prognostic indicators of outcome and disease response to therapy. Proteomics analysis of serum and ascites has also given us sources with which to discover possible early markers for the presence of new disease and for the progression of established cancer throughout the course of treatment. Unfortunately, this wealth of information has yielded little to date in changing the clinical care of these patients from a diagnostic, prognostic, or treatment perspective. The rational examination and translation of proteomics data in the context of past clinical trials and the design of future clinical trials must occur before we can march forward into the future of personalized medicine.     

Enhancing endothelial progenitor cell for clinical use

Circulating endothelial progenitor cells (EPCs) have been demonstrated to correlate negatively with vascular endothelial dysfunction and cardiovascular risk factors. However, translation of basic research into the clinical practice has been limited by the lack of unambiguous and consistent definitions of EPCs and reduced EPC cell number and function in subjects requiring them for clinical use. This article critically reviews the definition of EPCs based on commonly used protocols, their value as a biomarker of cardiovascular risk factor in subjects with cardiovascular disease, and strategies to enhance EPCs for treatment of ischemic diseases.     

Hierarchical commensurate and power prior models for adaptive incorporation of historical information in clinical trials

Bayesian clinical trial designs offer the possibility of a substantially reduced sample size, increased statistical power, and reductions in cost and ethical hazard. However when prior and current information conflict, Bayesian methods can lead to higher than expected type I error, as well as the possibility of a costlier and lengthier trial. This motivates an investigation of the feasibility of hierarchical Bayesian methods for incorporating historical data that are adaptively robust to prior information that reveals itself to be inconsistent with the accumulating experimental data. In this article, we present several models that allow for the commensurability of the information in the historical and current data to determine how much historical information is used. A primary tool is elaborating the traditional power prior approach based upon a measure of commensurability for Gaussian data. We compare the frequentist performance of several methods using simulations, and close with an example of a colon cancer trial that illustrates a linear models extension of our adaptive borrowing approach. Our proposed methods produce more precise estimates of the model parameters, in particular, conferring statistical significance to the observed reduction in tumor size for the experimental regimen as compared to the control regimen.     

Continual reassessment method for partial ordering

Summary Much of the statistical methodology underlying the experimental design of phase 1 trials in oncology is intended for studies involving a single cytotoxic agent. The goal of these studies is to estimate the maximally tolerated dose, the highest dose that can be administered with an acceptable level of toxicity. A fundamental assumption of these methods is monotonicity of the dose–toxicity curve. This is a reasonable assumption for single‐agent trials in which the administration of greater doses of the agent can be expected to produce dose‐limiting toxicities in increasing proportions of patients. When studying multiple agents, the assumption may not hold because the ordering of the toxicity probabilities could possibly be unknown for several of the available drug combinations. At the same time, some of the orderings are known and so we describe the whole situation as that of a partial ordering. In this article, we propose a new two‐dimensional dose‐finding method for multiple‐agent trials that simplifies to the continual reassessment method (CRM), introduced by O'Quigley, Pepe, and Fisher (1990, Biometrics 46 , 33–48), when the ordering is fully known. This design enables us to relax the assumption of a monotonic dose–toxicity curve. We compare our approach and some simulation results to a CRM design in which the ordering is known as well as to other suggestions for partial orders.     

Rapid evolution and range expansion of an invasive plant are driven by provenance–environment interactions

To improve our ability to prevent and manage biological invasions, we must understand their ecological and evolutionary drivers. We are often able to explain invasions after they happen, but our predictive ability is limited. Here, we show that range expansions of introduced Pinus taeda result from an interaction between genetic provenance and climate and that temperature and precipitation clines predict the invasive performance of particular provenances. Furthermore, we show that genotypes can occupy climate niche spaces different from those observed in their native ranges and, at least in our case, that admixture is not a main driver of invasion. Genotypes respond to climate in distinct ways, and these interactions affect the ability of populations to expand their ranges. While rapid evolution in introduced ranges is a mechanism at later stages of the invasion process, the introduction of adapted genotypes is a key driver of naturalisation of populations of introduced species.     

Measuring respiratory symptoms of COPD: performance of the EXACT- Respiratory Symptoms Tool (E-RS) in three clinical trials

Background

Symptomatic relief is an important treatment goal for patients with COPD. To date, no diary for evaluating respiratory symptoms in clinical trials has been developed and scientifically-validated according to FDA and EMA guidelines. The EXACT – Respiratory Symptoms (E-RS) scale is a patient-reported outcome (PRO) measure designed to address this need. The E-RS utilizes 11 respiratory symptom items from the existing and validated 14-item EXACT, which measures symptoms of exacerbation. The E-RS total score quantifies respiratory symptom severity, and 3 domains assess breathlessness, cough and sputum, and chest symptoms.

Methods

This study examined the performance of the E-RS in each of 3 controlled trials with common and unique validation variables: one 6-month (N = 235, US) and two 3-month (N = 749; N = 597; international). Subjects completed the E-RS as part of a daily eDiary. Tests of reliability, validity, and responsiveness were conducted in each dataset.

Results

In each study, RS-Total score was internally consistent (Cronbach α) (0.88, 0.92, 0.92) and reproducible (intra-class correlation) in stable patients (2 days apart: 0.91; 7 days apart: 0.71, 0.74). RS-Total scores correlated significantly with the following criterion variables (Spearman’s rho; p < 0.01, all comparisons listed here): FEV₁% predicted (−0.19, −0.14, −0.15); St. George’s Respiratory Questionnaire (SGRQ) (0.65, 0.52, 0.51); Breathlessness, Cough, and Sputum Scale (BCSS) (0.89, 0.89); modified Medical Research Council dyspnoea scale (mMRC) (0.40); rescue medication use (0.43, 0.42); Functional Performance Inventory Short-Form (FPI-SF) (0.43); 6-minute walk distance (6-MWT) (−0.30, −0.14) and incremental shuttle walk (ISWT) (−0.18) tests. Correlations between these variables and RS-Breathlessness, RS-Cough and Sputum, RS-Chest Symptoms scores supported subscale validity. RS-Total, RS-Breathlessness, and RS-Chest Symptoms differentiated mMRC levels of breathlessness severity (p < 0.0001). RS-Total and domain scores differentiated subjects with no rescue medication use and 3 or more puffs (p < 0.0001). Sensitivity to changes in health status (SGRQ), symptoms (BCSS), and exercise capacity (6MWT, ISWT) were also shown and responder definitions using criterion- and distribution-based methods are proposed.

Conclusions

Results suggest the E-RS is a reliable, valid, and responsive measure of respiratory symptoms of COPD suitable for use in natural history studies and clinical trials.

Trial registration

MPEX: {"type":"clinical-trial","attrs":{"text":"NCT00739648","term_id":"NCT00739648"}}NCT00739648; AZ1: {"type":"clinical-trial","attrs":{"text":"NCT00949975","term_id":"NCT00949975"}}NCT00949975; AZ 2: {"type":"clinical-trial","attrs":{"text":"NCT01023516","term_id":"NCT01023516"}}NCT01023516

Electronic supplementary material

The online version of this article (doi:10.1186/s12931-014-0124-z) contains supplementary material, which is available to authorized users.     

Expression of the Arabidopsis vacuolar H+‐pyrophosphatase gene (AVP1) improves the shoot biomass of transgenic barley and increases grain yield in a saline field

Cereal varieties with improved salinity tolerance are needed to achieve profitable grain yields in saline soils. The expression of AVP1, an Arabidopsis gene encoding a vacuolar proton pumping pyrophosphatase (H⁺‐PPase), has been shown to improve the salinity tolerance of transgenic plants in greenhouse conditions. However, the potential for this gene to improve the grain yield of cereal crops in a saline field has yet to be evaluated. Recent advances in high‐throughput nondestructive phenotyping technologies also offer an opportunity to quantitatively evaluate the growth of transgenic plants under abiotic stress through time. In this study, the growth of transgenic barley expressing AVP1 was evaluated under saline conditions in a pot experiment using nondestructive plant imaging and in a saline field trial. Greenhouse‐grown transgenic barley expressing AVP1 produced a larger shoot biomass compared to null segregants, as determined by an increase in projected shoot area, when grown in soil with 150 mm NaCl. This increase in shoot biomass of transgenic AVP1 barley occurred from an early growth stage and also in nonsaline conditions. In a saline field, the transgenic barley expressing AVP1 also showed an increase in shoot biomass and, importantly, produced a greater grain yield per plant compared to wild‐type plants. Interestingly, the expression of AVP1 did not alter barley leaf sodium concentrations in either greenhouse‐ or field‐grown plants. This study validates our greenhouse‐based experiments and indicates that transgenic barley expressing AVP1 is a promising option for increasing cereal crop productivity in saline fields.     

天门冬多糖对人工感染鸡传染性支气管炎的治疗试验

旨在评价复方天门冬多糖注射液对鸡传染性支气管炎的临床治疗效果,为鸡传染性支气管炎临床治疗提供依据。以复方天门冬多糖注射液临床给药剂量筛选试验为基础,分别设置高剂量组、中剂量组、低剂量组、药物对照组、阳性对照组及空白对照组,运用t检验和方差分析法对攻毒前后试验动物的相对体质量及试验结束后免疫器官指数进行统计分析,通过比较各组数据的显著性差异来评价复方天门冬多糖注射液对鸡传染性支气管炎的治疗效果。结果显示,复方天门冬多糖注射液能提高发病鸡的相对增量率,并促进发病鸡的生长发育。说明,复方天门冬多糖注射液对患病鸡的临床治疗效果显著,在实际生产中可以推广应用。     

0Adipose-derived stem cells: Implications in tissue regeneration

Adipose-derived stem cells(ASCs) are mesenchymal stem cells(MSCs) that are obtained from abundant adipose tissue, adherent on plastic culture flasks, can be expanded in vitro, and have the capacity to differ-entiate into multiple cell lineages. Unlike bone marrow-derived MSCs, ASCs can be obtained from abundant adipose tissue by a minimally invasive procedure, which results in a high number of cells. Therefore, ASCs are promising for regenerating tissues and organs dam-aged by injury and diseases. This article reviews the implications of ASCs in tissue regeneration.     

A statistical simulation model for field testing of non‐target organisms in environmental risk assessment of genetically modified plants

Genetic modification of plants may result in unintended effects causing potentially adverse effects on the environment. A comparative safety assessment is therefore required by authorities, such as the European Food Safety Authority, in which the genetically modified plant is compared with its conventional counterpart. Part of the environmental risk assessment is a comparative field experiment in which the effect on non‐target organisms is compared. Statistical analysis of such trials come in two flavors: difference testing and equivalence testing. It is important to know the statistical properties of these, for example, the power to detect environmental change of a given magnitude, before the start of an experiment. Such prospective power analysis can best be studied by means of a statistical simulation model. This paper describes a general framework for simulating data typically encountered in environmental risk assessment of genetically modified plants. The simulation model, available as Supplementary Material, can be used to generate count data having different statistical distributions possibly with excess‐zeros. In addition the model employs completely randomized or randomized block experiments, can be used to simulate single or multiple trials across environments, enables genotype by environment interaction by adding random variety effects, and finally includes repeated measures in time following a constant, linear or quadratic pattern in time possibly with some form of autocorrelation. The model also allows to add a set of reference varieties to the GM plants and its comparator to assess the natural variation which can then be used to set limits of concern for equivalence testing. The different count distributions are described in some detail and some examples of how to use the simulation model to study various aspects, including a prospective power analysis, are provided.