A comparison of methods for constructing confidence intervals after phase II/III clinical trials

Recently, in order to accelerate drug development, trials that use adaptive seamless designs such as phase II/III clinical trials have been proposed. Phase II/III clinical trials combine traditional phases II and III into a single trial that is conducted in two stages. Using stage 1 data, an interim analysis is performed to answer phase II objectives and after collection of stage 2 data, a final confirmatory analysis is performed to answer phase III objectives. In this paper we consider phase II/III clinical trials in which, at stage 1, several experimental treatments are compared to a control and the apparently most effective experimental treatment is selected to continue to stage 2. Although these trials are attractive because the confirmatory analysis includes phase II data from stage 1, the inference methods used for trials that compare a single experimental treatment to a control and do not have an interim analysis are no longer appropriate. Several methods for analysing phase II/III clinical trials have been developed. These methods are recent and so there is little literature on extensive comparisons of their characteristics. In this paper we review and compare the various methods available for constructing confidence intervals after phase II/III clinical trials.     

Robust Prediction of t‐Year Survival with Data from Multiple Studies

Summary Recently meta‐analysis has been widely utilized to combine information across multiple studies to evaluate a common effect. Integrating data from similar studies is particularly useful in genomic studies where the individual study sample sizes are not large relative to the number of parameters of interest. In this article, we are interested in developing robust prognostic rules for the prediction of t ‐year survival based on multiple studies. We propose to construct a composite score for prediction by fitting a stratified semiparametric transformation model that allows the studies to have related but not identical outcomes. To evaluate the accuracy of the resulting score, we provide point and interval estimators for the commonly used accuracy measures including the time‐specific receiver operating characteristic curves, and positive and negative predictive values. We apply the proposed procedures to develop prognostic rules for the 5‐year survival of breast cancer patients based on five breast cancer genomic studies.     

Sensitivity analysis for principal stratum direct effects, with an application to a study of physical activity and coronary heart disease

Summary .  In many studies, the aim is to learn about the direct exposure effect, that is, the effect not mediated through an intermediate variable. For example, in circulation disease studies it may be of interest to assess whether a suitable level of physical activity can prevent disease, even if it fails to prevent obesity. It is well known that stratification on the intermediate may introduce a so-called posttreatment selection bias. To handle this problem, we use the framework of principal stratification ( Frangakis and Rubin, 2002 , Biometrics 58, 21–29) to define a causally relevant estimand—the principal stratum direct effect (PSDE). The PSDE is not identified in our setting. We propose a method of sensitivity analysis that yields a range of plausible values for the causal estimand. We compare our work to similar methods proposed in the literature for handling the related problem of "truncation by death."     

Genome‐wide methylomic analyses identify prognostic epigenetic signature in lower grade glioma

Glioma is the most malignant and aggressive type of brain tumour with high heterogeneity and mortality. Although some clinicopathological factors have been identified as prognostic biomarkers, the individual variants and risk stratification in patients with lower grade glioma (LGG) have not been fully elucidated. The primary aim of this study was to identify an efficient DNA methylation combination biomarker for risk stratification and prognosis in LGG. We conducted a retrospective cohort study by analysing whole genome DNA methylation data of 646 patients with LGG from the TCGA and GEO database. Cox proportional hazard analysis was carried out to screen and construct biomarker model that predicted overall survival (OS). The Kaplan‐Meier survival curves and time‐dependent ROC were constructed to prove the efficiency of the signature. Then, another independent cohort was used to further validate the finding. A two‐CpG site DNA methylation signature was identified by multivariate Cox proportional hazard analysis. Further analysis indicated that the signature was an independent survival predictor from other clinical factors and exhibited higher predictive accuracy compared with known biomarkers. This signature was significantly correlated with immune‐checkpoint blockade, immunotherapy‐related signatures and ferroptosis regulator genes. The expression pattern and functional analysis showed that these two genes corresponding with two methylation sites contained in the model were correlated with immune infiltration level, and involved in MAPK and Rap1 signalling pathway. The signature may contribute to improve the risk stratification of patients and provide a more accurate assessment for precision medicine in the clinic.     

Genome wide DNA copy number analysis of serous type ovarian carcinomas identifies genetic markers predictive of clinical outcome

Ovarian cancer is the fifth leading cause of cancer death in women. Ovarian cancers display a high degree of complex genetic alterations involving many oncogenes and tumor suppressor genes. Analysis of the association between genetic alterations and clinical endpoints such as survival will lead to improved patient management via genetic stratification of patients into clinically relevant subgroups. In this study, we aim to define subgroups of high-grade serous ovarian carcinomas that differ with respect to prognosis and overall survival. Genome-wide DNA copy number alterations (CNAs) were measured in 72 clinically annotated, high-grade serous tumors using high-resolution oligonucleotide arrays. Two clinically annotated, independent cohorts were used for validation. Unsupervised hierarchical clustering of copy number data derived from the 72 patient cohort resulted in two clusters with significant difference in progression free survival (PFS) and a marginal difference in overall survival (OS). GISTIC analysis of the two clusters identified altered regions unique to each cluster. Supervised clustering of two independent large cohorts of high-grade serous tumors using the classification scheme derived from the two initial clusters validated our results and identified 8 genomic regions that are distinctly different among the subgroups. These 8 regions map to 8p21.3, 8p23.2, 12p12.1, 17p11.2, 17p12, 19q12, 20q11.21 and 20q13.12; and harbor potential oncogenes and tumor suppressor genes that are likely to be involved in the pathogenesis of ovarian carcinoma. We have identified a set of genetic alterations that could be used for stratification of high-grade serous tumors into clinically relevant treatment subgroups.     

Causal inference with outcomes truncated by death in multiarm studies

It is challenging to evaluate causal effects when the outcomes of interest suffer from truncation-by-death in many clinical studies; that is, outcomes cannot be observed if patients die before the time of measurement. To address this problem, it is common to consider average treatment effects by principal stratification, for which, the identifiability results and estimation methods with a binary treatment have been established in previous literature. However, in multiarm studies with more than two treatment options, estimation of causal effects becomes more complicated and requires additional techniques. In this article, we consider identification, estimation, and bounds of causal effects with multivalued ordinal treatments and the outcomes subject to truncation-by-death. We define causal parameters of interest in this setting and show that they are identifiable either using some auxiliary variable or based on linear model assumption. We then propose a semiparametric method for estimating the causal parameters and derive their asymptotic results. When the identification conditions are invalid, we derive sharp bounds of the causal effects by use of covariates adjustment. Simulation studies show good performance of the proposed estimator. We use the estimator to analyze the effects of a four-level chronic toxin on fetal developmental outcomes such as birth weight in rats and mice, with data from a developmental toxicity trial conducted by the National Toxicology Program. Data analyses demonstrate that a high dose of the toxin significantly reduces the weights of pups.     

Climate change scenarios experiments predict a future reduction in small pelagic fish recruitment in the Humboldt Current system

The Humboldt Current System (HCS) sustains the world′s largest small pelagic fishery. While a cooling of this system has been observed during recent decades, there is debate about the potential impacts of rising atmospheric CO₂ concentrations on upwelling dynamics and productivity. Recent studies suggest that under increased atmospheric CO₂ scenarios the oceanic stratification may strongly increase and upwelling‐favorable winds may remain nearly constant off Peru and increase off Chile. Here we investigate the impact of such climatic conditions on egg and larval dispersal phases, a key stage of small pelagic fish reproduction. We used larval retention rate in a predefined nursery area to provide a proxy for the recruitment level. Numerical experiments are based on hydrodynamics downscaled to the HCS from global simulations forced by pre‐industrial (PI), 2 × CO₂ and 4 × CO₂ scenarios. A biogeochemical model is applied to the PI and 4 × CO₂ scenarios to define a time‐variable nursery area where larval survival is optimum. We test two distinct values of the oxycline depth that limits larval vertical distribution: One corresponding to the present‐day situation and the other corresponding to a shallower oxycline potentially produced by climate change. It appeared that larval retention over the continental shelf increases with enhanced stratification due to regional warming. However, this increase in retention is largely compensated for by a decrease of the nursery area and the shoaling of the oxycline. The underlying dynamics are explained by a combination of stratification effects and mesoscale activity changes. Our results therefore show that future climate change may significantly reduce fish capacity in the HCS with strong ecological, economic and social consequences.     

Applications of stable isotope analyses to avian ecology

In the past 20 years the use of stable isotope analysis has become increasingly common in ecological studies. In fact, in some instances these techniques have yielded remarkable insights into the foraging preferences and migrations of birds. Despite these advances and the potential of the approach, it is possibly still not as widely used as might be expected. In this paper we aim to illustrate the potential of the approach in the hope of encouraging more avian ecologists to think again about how these techniques might provide insights in the systems on which they work. We discuss some of the principles behind the approach, and review some of the more recent ornithological studies that have used stable isotope techniques to trace trophic pathways or infer migratory origins. We follow this by discussing some of the latest ideas on how stable isotopes may be used to generate community metrics and close by detailing the important assumptions and caveats that should be considered before undertaking any studies using this technique.     

Challenges and solutions for the identification of membrane proteins in non-model plants

The workhorse for proteomics in non-model plants is classical two-dimensional electrophoresis, a combination of iso-electric focusing and SDS-PAGE. However, membrane proteins with multiple membrane spanning domains are hardly detected on classical 2-DE gels because of their low abundance and poor solubility in aqueous media. In the current review, solutions that have been proposed to handle these two problems in non-model plants are discussed. An overview of alternative techniques developed for membrane proteomics is provided together with a comparison of their strong and weak points. Subsequently, strengths and weaknesses of the different techniques and methods to evaluate the identification of membrane proteins are discussed. Finally, an overview of recent plant membrane proteome studies is provided with the used separation technique and the number of identified membrane proteins listed.     

Gene expression profiling – Clusters of possibilities

Advances in qPCR technology allow studies of increasingly large systems comprising many genes and samples. The increasing data sizes allow expression profiling both in the gene and the samples dimension while also putting higher demands on sound statistical analysis and expertise to handle and interpret its results. We distinguish between exploratory and confirmatory statistical studies. In this paper we demonstrate several techniques available for exploratory studies on a system of Xenopus laevis development from egg to tadpole. Techniques include hierarchical clustering, heatmap, principal component analysis and self-organizing maps. We stress that even though exploratory studies are excellent for generating hypotheses, results have not been proven statistically significant until an independent confirmatory study has been performed. An exploratory study may certainly be valuable in its own right, and there are often not enough resources to report both an exploratory and a confirmatory study at the same time. However, exploratory and confirmatory studies are intimately connected and we would like to raise that awareness among qPCR practitioners. We suggest that scientific reports should always have a hypothesis focus. Reports are either hypothesis generating, from an exploratory study, or hypothesis validating, from a confirmatory study, or both. In either case, we suggest the generated or validated hypotheses be specifically stated.     

Time-dependent ROC curves for censored survival data and a diagnostic marker

ROC curves are a popular method for displaying sensitivity and specificity of a continuous diagnostic marker, X, for a binary disease variable, D. However, many disease outcomes are time dependent, D(t), and ROC curves that vary as a function of time may be more appropriate. A common example of a time-dependent variable is vital status, where D(t) = 1 if a patient has died prior to time t and zero otherwise. We propose summarizing the discrimination potential of a marker X, measured at baseline (t = 0), by calculating ROC curves for cumulative disease or death incidence by time t, which we denote as ROC(t). A typical complexity with survival data is that observations may be censored. Two ROC curve estimators are proposed that can accommodate censored data. A simple estimator is based on using the Kaplan-Meier estimator for each possible subset X > c. However, this estimator does not guarantee the necessary condition that sensitivity and specificity are monotone in X. An alternative estimator that does guarantee monotonicity is based on a nearest neighbor estimator for the bivariate distribution function of (X, T), where T represents survival time (Akritas, M. J., 1994, Annals of Statistics 22, 1299-1327). We present an example where ROC(t) is used to compare a standard and a modified flow cytometry measurement for predicting survival after detection of breast cancer and an example where the ROC(t) curve displays the impact of modifying eligibility criteria for sample size and power in HIV prevention trials.     

Principal stratification in causal inference

Many scientific problems require that treatment comparisons be adjusted for posttreatment variables, but the estimands underlying standard methods are not causal effects. To address this deficiency, we propose a general framework for comparing treatments adjusting for posttreatment variables that yields principal effects based on principal stratification. Principal stratification with respect to a posttreatment variable is a cross-classification of subjects defined by the joint potential values of that posttreatment variable tinder each of the treatments being compared. Principal effects are causal effects within a principal stratum. The key property of principal strata is that they are not affected by treatment assignment and therefore can be used just as any pretreatment covariate. such as age category. As a result, the central property of our principal effects is that they are always causal effects and do not suffer from the complications of standard posttreatment-adjusted estimands. We discuss briefly that such principal causal effects are the link between three recent applications with adjustment for posttreatment variables: (i) treatment noncompliance, (ii) missing outcomes (dropout) following treatment noncompliance. and (iii) censoring by death. We then attack the problem of surrogate or biomarker endpoints, where we show, using principal causal effects, that all current definitions of surrogacy, even when perfectly true, do not generally have the desired interpretation as causal effects of treatment on outcome. We go on to forrmulate estimands based on principal stratification and principal causal effects and show their superiority.     

The study of plant growth: A link between the physiological ecology and population biology of plants

As the fields of plant physiological ecology and plant population biology mature, it becomes apparent that answers to many key ecological questions require the approaches of both subdisciplines. Historically, the two approaches have developed along separate lines and the links between them have not been clearly defined. In the present paper, we suggest that the study of plant growth may provide one necessary link. This suggestion is motivated by recent trends toward evaluation of physiological responses in terms of growth, and the use of plant size as a predictor of life history traits. In the future, studies which combine these techniques will accrue two important advantages: (1) the significance of plant adaptations (physiological or morphological) may be understood in demographic or Darwinian terms of survivorship and fecundity schedules of the individuals possessing them, and conversely, (2) variation in survivorship and fecundity may be attributed to alternative plant adaptations.     

Phase separation by the SARS-CoV-2 nucleocapsid protein: Consensus and open questions

In response to the recent SARS-CoV-2 pandemic, a number of labs across the world have reallocated their time and resources to better our understanding of the virus. For some viruses, including SARS-CoV-2, viral proteins can undergo phase separation: a biophysical process often related to the partitioning of protein and RNA into membraneless organelles in vivo. In this review, we discuss emerging observations of phase separation by the SARS-CoV-2 nucleocapsid (N) protein—an essential viral protein required for viral replication—and the possible in vivo functions that have been proposed for N-protein phase separation, including viral replication, viral genomic RNA packaging, and modulation of host-cell response to infection. Additionally, since a relatively large number of studies examining SARS-CoV-2 N-protein phase separation have been published in a short span of time, we take advantage of this situation to compare results from similar experiments across studies. Our evaluation highlights potential strengths and pitfalls of drawing conclusions from a single set of experiments, as well as the value of publishing overlapping scientific observations performed simultaneously by multiple labs.     

Variable selection in penalized model-based clustering via regularization on grouped parameters

Summary .   Penalized model-based clustering has been proposed for high-dimensional but small sample-sized data, such as arising from genomic studies; in particular, it can be used for variable selection. A new regularization scheme is proposed to group together multiple parameters of the same variable across clusters, which is shown both analytically and numerically to be more effective than the conventional L ₁ penalty for variable selection. In addition, we develop a strategy to combine this grouping scheme with grouping structured variables. Simulation studies and applications to microarray gene expression data for cancer subtype discovery demonstrate the advantage of the new proposal over several existing approaches.     

Diagnostic and Prognostic Biomarkers in ovarian cancer and the potential roles of cancer stem cells – An updated review

Ovarian carcinomas relate to highest death rate in gynecologic malignancies as absence of symptoms shield the disease in the early stage. Current evidences have been devoted to discovering early effective screening mechanism prior to the onset of clinical symptoms. Therefore, biomarkers are the crucial tools that are capable of predicting progression, risk stratification and overall therapeutic benefit to fight against this deadly disease. Although recent studies have revealed serum protein markers, CA-125, HE4, mesothelin etc. have higher sensitivity and specificity at the early stages of the cancer; the critical questions arise regarding the applicability and reproducibility of genomic profiling across different patient groups. Hence, our hypothesis is that the panels of signature biomarkers will be much more effective to improve the diagnosis and prediction of patient survival outcome with high sensitivity and specificity. Ovarian cancer is heterogeneous in nature and contain a sub-population of stem cell-like characteristics that has the ability to grow as anchorage-independent manner and subsequently is able to metastasize. Highly tumorigenic and chemotherapy-resistant cancer stem cells (CSCs)-specific biomarkers therefore reflects the interesting possibilities to be targeted to minimize the high frequency of relapse and resistance to drugs. Several putative ovarian CSC markers such as CD24, CD44, CD133, SSEA have already been proposed in recent studies, yet, a large panel of updated biomarkers have high clinical relevance to define the prospective isolation of viable circulating CSCs. Therefore, this review highlights current evidence based updated ovarian cancer specific prognostic and diagnostic biomarkers and potential importance of CSCs in context of tumorigenicity and metastatic activity for fundamental biological and clinical implications.     

Concentration dependency of nonequilibrium thermal dissociation curves in complex target samples

The nonequilibrium thermal dissociation (NTD) methodology has been proposed to provide a superior discrimination between specific and nonspecific hybridizations than the commonly used array techniques involving hybridization followed by a single stringent wash. Multiple studies have used this method on gel-pad, planar, and nylon membrane arrays to identify specific microbial targets in complex target mixtures. A recent physicochemical study revealed several problems, particularly when the method was used to examine complex target samples. In the present study, we investigated the effect of target concentration on NTD of complex target samples obtained from an anaerobic bioreactor. Our purpose was to experimentally demonstrate that variation in the concentrations of both specific and nonspecific targets determines the course of dissociation, which was not evaluated in initial microbiological studies. We also present an approach for analyzing the dissociation curves that is less error prone compared to those used in the previous studies. Our results show that: (i) a specific target in a mixture, at a certain concentration, may have a higher dissociation temperature/time than that of the same pure target, and (ii) the concentration dependence of the dissociation precludes usage of reference curves for identifying a target. Contrary to the previous studies, an explicit calibration is required, which makes the NTD approach impractical for high throughput analysis.     

Proteomics in atherothrombosis: a future perspective

Atherothrombosis is the primary cause of death in Western countries. The cellular and molecular mechanisms underlying atherosclerosis remain widely unknown. The complex nature of atherosclerotic cardiovascular diseases demands the development of novel technologies that enable discovery of new biomarkers for early disease detection and risk stratification, which may predict clinical outcome. In this review, we outline potential sources and recent proteomic approaches that could be applied in the search of novel biomarkers of cardiovascular risk. In addition, we describe some issues raised in relation to the application of proteomics to blood samples, as well as two novel emerging concepts, such as peptidomics and population proteomics. In the future, the use of high-throughput techniques (proteomic, genomics and metabolomics) will potentially identify novel patterns of biomarkers, which, along with traditional risk factors and imaging techniques, could help to target vulnerable patients and monitor the beneficial effects of pharmacological agents.     

Proteomics in atherothrombosis: a future perspective

Atherothrombosis is the primary cause of death in Western countries. The cellular and molecular mechanisms underlying atherosclerosis remain widely unknown. The complex nature of atherosclerotic cardiovascular diseases demands the development of novel technologies that enable discovery of new biomarkers for early disease detection and risk stratification, which may predict clinical outcome. In this review, we outline potential sources and recent proteomic approaches that could be applied in the search of novel biomarkers of cardiovascular risk. In addition, we describe some issues raised in relation to the application of proteomics to blood samples, as well as two novel emerging concepts, such as peptidomics and population proteomics. In the future, the use of high-throughput techniques (proteomic, genomics and metabolomics) will potentially identify novel patterns of biomarkers, which, along with traditional risk factors and imaging techniques, could help to target vulnerable patients and monitor the beneficial effects of pharmacological agents.