A viral effector blocks the turnover of a plant NLR receptor to trigger a robust immune response.

Plant intracellular nucleotide-binding and leucine-rich repeat immune receptors (NLRs) play a key role in activating a strong pathogen defense response. Plant NLR proteins are tightly regulated and accumulate at very low levels in the absence of pathogen effectors. However, little is known about how this low level of NLR proteins is able to induce robust immune responses upon recognition of pathogen effectors. Here, we report that, in the absence of effector, the inactive form of the tomato NLR Sw-5b is targeted for ubiquitination by the E3 ligase SBP1. Interaction of SBP1 with Sw-5b via only its N-terminal domain leads to slow turnover. In contrast, in its auto-active state, Sw-5b is rapidly turned over as SBP1 is upregulated and interacts with both its N-terminal and NB-LRR domains. During infection with the tomato spotted wilt virus, the viral effector NSm interacts with Sw-5b and disrupts the interaction of Sw-5b with SBP1, thereby stabilizing the active Sw-5b and allowing it to induce a robust immune response.

Statistical and practical considerations in planning and conduct of dose-optimization trials.

The U.S. Food and Drug Administration launched Project Optimus with the aim of shifting the paradigm of dose-finding and selection toward identifying the optimal biological dose that offers the best balance between benefit and risk, rather than the maximum tolerated dose. However, achieving dose optimization is a challenging task that involves a variety of factors and is considerably more complicated than identifying the maximum tolerated dose, both in terms of design and implementation. This article provides a comprehensive review of various design strategies for dose-optimization trials, including phase 1/2 and 2/3 designs, and highlights their respective advantages and disadvantages. In addition, practical considerations for selecting an appropriate design and planning and executing the trial are discussed. The article also presents freely available software tools that can be utilized for designing and implementing dose-optimization trials. The approaches and their implementation are illustrated through real-world examples.

Supportive care for the prevention of nausea, vomiting and anorexia in a phase 1B study of selinexor in advanced cancer patients: an exploratory study.

INTRODUCTION: Clinical observations of cancer patients treated with selinexor have reported high incidence of nausea and anorexia. The study objective was to investigate the adoption of prophylactic olanzapine for the prevention of nausea, vomiting and anorexia in cancer patients receiving selinexor and standard chemotherapy. METHODS: We retrospectively reviewed supportive care interventions in patients receiving selinexor and recorded frequency of adverse events (NCI-CTAE). Association between categorical variables were analyzed using Fisher's exact tests; repeated measures analysis was performed to assess weight changes over time. RESULTS: Of 124 evaluable patients, 83 (66.9%) were female, 93 were white (75.0%), and the most common cancer was ovarian (N = 30, 24.2%). One hundred and four patients (83.9%) received olanzapine, of which 93 (89.4%) were prophylactically treated, the majority (86.5%) receiving low 2.5 mg daily dose. Other anti-emetics included ondansetron in 90 patients (72.6%), dexamethasone prescribed in 50 patients (40.3%) and metoclopramide in 49 patients (39.5%), while aprepitant/fosaprepitant (N = 2, 1.6%) were prescribed infrequently. Cancer patients receiving prophylactic olanzapine (N = 93) compared to patients who never received olanzapine (N = 20) had more Grade 1 + anorexia (31.2% vs 20.0%), less nausea (53.8% vs 70.0%), less vomiting (33.3% vs 40.0%), and increased hyperglycemia (29.0% vs 10.0%), but differences were non-statistically significant. In addition, there was minimal weight loss over time in both groups and no statistically significant differences in weight loss between groups. CONCLUSION: Prophylactic olanzapine decreased nausea, vomiting and maintained weight over 3 months but did not prevent anorexia in patients receiving selinexor and chemotherapy. Low dose olanzapine was well tolerated but associated with hyperglycemia.

PIONEER-Panc: a platform trial for phase II randomized investigations of new and emerging therapies for localized pancreatic cancer.

BACKGROUND: Personalized and effective treatments for pancreatic ductal adenocarcinoma (PDAC) continue to remain elusive. Novel clinical trial designs that enable continual and rapid evaluation of novel therapeutics are needed. Here, we describe a platform clinical trial to address this unmet need. METHODS: This is a phase II study using a Bayesian platform design to evaluate multiple experimental arms against a control arm in patients with PDAC. We first separate patients into three clinical stage groups of localized PDAC (resectable, borderline resectable, and locally advanced disease), and further divide each stage group based on treatment history (treatment naïve or previously treated). The clinical stage and treatment history therefore define 6 different cohorts, and each cohort has one control arm but may have one or more experimental arms running simultaneously. Within each cohort, adaptive randomization rules are applied and patients will be randomized to either an experimental arm or the control arm accordingly. The experimental arm(s) of each cohort are only compared to the applicable cohort specific control arm. Experimental arms may be added independently to one or more cohorts during the study. Multiple correlative studies for tissue, blood, and imaging are also incorporated. DISCUSSION: To date, PDAC has been treated as a single disease, despite knowledge that there is substantial heterogeneity in disease presentation and biology. It is recognized that the current approach of single arm phase II trials and traditional phase III randomized studies are not well-suited for more personalized treatment strategies in PDAC. The PIONEER Panc platform clinical trial is designed to overcome these challenges and help advance our treatment strategies for this deadly disease. TRIAL REGISTRATION: This study is approved by the Institutional Review Board (IRB) of MD Anderson Cancer Center, IRB-approved protocol 2020-0075. The PIONEER trial is registered at the US National Institutes of Health (ClinicalTrials.gov) NCT04481204 .

Bayesian group sequential enrichment designs based on adaptive regression of response and survival time on baseline biomarkers.

Precision medicine relies on the idea that, for a particular targeted agent, only a subpopulation of patients is sensitive to it and thus may benefit from it therapeutically. In practice, it is often assumed based on preclinical data that a treatment-sensitive subpopulation is known, and moreover that the agent is substantively efficacious in that subpopulation. Due to important differences between preclinical settings and human biology, however, data from patients treated with a new targeted agent often show that one or both of these assumptions are false. This paper provides a Bayesian randomized group sequential enrichment design that compares an experimental treatment to a control based on survival time and uses early response as an ancillary outcome to assist with adaptive variable selection and enrichment. Initially, the design enrolls patients under broad eligibility criteria. At each interim decision, submodels for regression of response and survival time on a baseline covariate vector and treatment are fit; variable selection is used to identify a covariate subvector that characterizes treatment-sensitive patients and determines a personalized benefit index, and comparative superiority and futility decisions are made. Enrollment of each cohort is restricted to the most recent adaptively identified treatment-sensitive patients. Group sequential decision cutoffs are calibrated to control overall type I error and account for the adaptive enrollment restriction. The design provides a basis for precision medicine by identifying a treatment-sensitive subpopulation, if it exists, and determining whether the experimental treatment is superior to the control in that subpopulation. A simulation study shows that the proposed design reliably identifies a sensitive subpopulation, yields much higher generalized power compared to several existing enrichment designs and a conventional all-comers group sequential design, and is robust.

Chinese universities' special programs supporting talents to seek a United Nations career: a center-periphery-model analysis.

In recent years, China's higher education sector has started to establish special programs to train and support talents to seek career opportunities in the United Nations (UN). To explore these special programs and understand their relationship with China's internationalization strategies and its higher education, we used the center-periphery model as the theoretical framework. We analyzed 53 institutional documents and conducted semi-structured interviews among 5 university staff members and 21 students/recent graduates who were involved in these special programs. The analysis on the special programs implied Chinese higher education's peripheral position in supporting talents to work in the UN. This was reflected by the conforming practice, including accepting current UN recruitment regulations and English's dominance in the UN recruitment practice. However, we also identified alternative dynamics that China and its higher education do not simply obey the center-periphery model and accept their peripheral status. Instead, special programs were established to achieve China's global strategy of moving to the center of international arena via multilateralism and international organizations such as the UN. This study sheds light on further explorations of the state-university relationship in China in the globalization era, especially from the perspective of cultural diplomacy and soft power.

Hybrid design evaluating new biomarkers when there is an existing screening test.

Development of cancer screening biomarkers usually follows the Early Detection Research Network 5-Phase guideline in Pepe et al. A key feature of this guide is that the phased development follows a sequential order, moving to the next phase only when the current phase study is complete and has met its target performance. Motivated by a newly funded Newly onset Diabetes cohort study, we propose a design evaluating new biomarkers to discriminate between cases and controls in the presence of an existing screening test. The proposed design achieves two goals: (1) avoiding bias in estimating sensitivity or specificity in predicting cancer at a given time period prior to clinical diagnosis, using data from both screening detected cancers in Phase IV study and clinically diagnosed cancers in Phase III study; and (2) building a panel with biomarkers for Phase III and IV studies based on all data. A simulation study shows that the proposed design outperforms both a conventional method using data in Phase III arm only and a naive method using data in Phase III and IV arms ignoring the difference between the time of screening the detected cancer and the time of clinical diagnosis. The proposed design yields a smaller standard error of the estimation and increases the statistical power to confirm biomarker performance. This proposed method has the potential to shorten the cancer screening biomarker development process, use resources more effectively, and bring benefits to patients quickly.

Glycopeptide Biomarkers in Serum Haptoglobin for Hepatocellular Carcinoma Detection in Patients with Nonalcoholic Steatohepatitis.

Nonalcoholic steatohepatitis (NASH) is rising in prevalence in the United States and is a growing cause of hepatocellular carcinomas (HCCs). Site-specific glycan heterogeneity on glycoproteins has been shown as a potential diagnostic biomarker for HCC. Herein, we have performed a comprehensive screening of site-specific N-glycopeptides in serum haptoglobin (Hp), a reporter molecule for aberrant glycosylation in HCC, to characterize glycopeptide markers for NASH-related HCCs. In total, 70 NASH patients (22 early HCC, 15 advanced HCC, and 33 cirrhosis cases) were analyzed, with Hp purified from 20 µL of serum in each patient, and 140 sets of mass spectrometry (MS) data were collected using liquid chromatography coupled with electron-transfer high-energy collisional dissociation tandem MS (LC-EThcD-MS/MS) for quantitative analysis on a novel software platform, Byos. Differential quantitation analysis revealed that five N-glycopeptides at sites N184 and N241 were significantly elevated during the progression from NASH cirrhosis to HCC (p < 0.05). Receiver operating characteristic (ROC) curve analysis demonstrated that the N-glycopeptides at sites N184 and N241 bearing a monofucosylated triantennary glycan A3G3F1S3 had the best diagnostic performance in detection of early NASH HCC, area under the curve (AUC) = 0.733 and 0.775, respectively, whereas α-fetoprotein (AFP) had an AUC of 0.692. When combined with AFP, the two panels improved the sensitivity for early NASH HCC from 59% (AFP alone) to 73% while maintaining a specificity of 70%, based on the optimal cutoff. Two-dimensional (2-D) scatter plots of the AFP value and N-glycopeptides showed that these N-glycopeptide markers detected 58% of AFP-negative HCC patients as distinct from cirrhosis. These site-specific N-glycopeptides could serve as potential markers for early detection of HCC in patients with NASH-related cirrhosis.

Noisy consensus dynamics in windmill-type graphs.

In the present study, two types of consensus algorithms, including the leaderless coherence and the leader-follower coherence quantified by the Laplacian spectrum, are applied to noisy windmill graphs. Based on the graph construction, exact solutions are obtained for the leader-follower coherence with freely assigned leaders. In order to compare consensus dynamics of two nonisomorphic graphs with the same number of nodes and edges, two generalized windmill graphs are selected as the network models and then explicit expressions of the network coherence are obtained. Then, coherences of models are compared. The obtained results reveal distinct coherence behaviors originating from intrinsic structures of models. Finally, the robustness of the coherence is analyzed. Accordingly, it is found that graph parameters and the number of leaders have a profound impact on the studied consensus algorithms.

An optimal Bayesian predictive probability design for phase II clinical trials with simple and complicated endpoints.

Most existing phase II clinical trial designs focus on conventional chemotherapy with binary tumor response as the endpoint. The advent of novel therapies, such as molecularly targeted agents and immunotherapy, has made the endpoint of phase II trials more complicated, often involving ordinal, nested, and coprimary endpoints. We propose a simple and flexible Bayesian optimal phase II predictive probability (OPP) design that handles binary and complex endpoints in a unified way. The Dirichlet-multinomial model is employed to accommodate different types of endpoints. At each interim, given the observed interim data, we calculate the Bayesian predictive probability of success, should the trial continue to the maximum planned sample size, and use it to make the go/no-go decision. The OPP design controls the type I error rate, maximizes power or minimizes the expected sample size, and is easy to implement, because the go/no-go decision boundaries can be enumerated and included in the protocol before the onset of the trial. Simulation studies show that the OPP design has satisfactory operating characteristics.

Sample size determination for mediation analysis of longitudinal data.

BACKGROUND: Sample size planning for longitudinal data is crucial when designing mediation studies because sufficient statistical power is not only required in grant applications and peer-reviewed publications, but is essential to reliable research results. However, sample size determination is not straightforward for mediation analysis of longitudinal design. METHODS: To facilitate planning the sample size for longitudinal mediation studies with a multilevel mediation model, this article provides the sample size required to achieve 80% power by simulations under various sizes of the mediation effect, within-subject correlations and numbers of repeated measures. The sample size calculation is based on three commonly used mediation tests: Sobel's method, distribution of product method and the bootstrap method. RESULTS: Among the three methods of testing the mediation effects, Sobel's method required the largest sample size to achieve 80% power. Bootstrapping and the distribution of the product method performed similarly and were more powerful than Sobel's method, as reflected by the relatively smaller sample sizes. For all three methods, the sample size required to achieve 80% power depended on the value of the ICC (i.e., within-subject correlation). A larger value of ICC typically required a larger sample size to achieve 80% power. Simulation results also illustrated the advantage of the longitudinal study design. The sample size tables for most encountered scenarios in practice have also been published for convenient use. CONCLUSIONS: Extensive simulations study showed that the distribution of the product method and bootstrapping method have superior performance to the Sobel's method, but the product method was recommended to use in practice in terms of less computation time load compared to the bootstrapping method. A R package has been developed for the product method of sample size determination in mediation longitudinal study design.

Width Based Quantitation of Chromatographic Peaks: Principles and Principal Characteristics.

Height- and area-based quantitation reduce two-dimensional data to a single value. For a calibration set, there is a single height- or area-based quantitation equation. High-speed high-resolution data acquisition now permits rapid measurement of the width of a peak (Wh), at any height h (a fixed height, not a fixed fraction of the peak maximum) leading to any number of calibration curves. We propose a width-based quantitation (WBQ) paradigm complementing height or area based approaches. When the analyte response across the measurement range is not strictly linear, WBQ can offer superior overall performance (lower root-mean-square relative error over the entire range) compared to area- or height-based linear regression methods, rivaling weighted linear regression, provided that response is uniform near the height used for width measurement. To express concentration as an explicit function of width, chromatographic peaks are modeled as two different independent generalized Gaussian distribution functions, representing, respectively, the leading/trailing halves of the peak. The simple generalized equation can be expressed as Wh = p(ln h̅)q, where h̅ is hmax/h, hmax being the peak amplitude, and p and q being constants. This fits actual chromatographic peaks well, allowing explicit expressions for Wh. We consider the optimum height for quantitation. The width-concentration relationship is given as ln C = aWhn + b, where a, b, and n are constants. WBQ ultimately performs quantitation by projecting hmax from the width, provided that width is measured at a fixed height in the linear response domain. A companion paper discusses several other utilitarian attributes of width measurement.

Optimal marker-strategy clinical trial design to detect predictive markers for targeted therapy.

In developing targeted therapy, the marker-strategy design (MSD) provides an important approach to evaluate the predictive marker effect. This design first randomizes patients into non-marker-based or marker-based strategies. Patients allocated to the non-marker-based strategy are then further randomized to receive either the standard or targeted treatments, while patients allocated to the marker-based strategy receive treatments based on their marker statuses. Little research has been done on the statistical properties of the MSD, which has led to some widespread misconceptions and placed clinical researchers at high risk of using inefficient designs. In this article, we show that the commonly used between-strategy comparison has low power to detect the predictive effect and is valid only under a restrictive condition that the randomization ratio within the non-marker-based strategy matches the marker prevalence. We propose a Wald test that is generally valid and also uniformly more powerful than the between-strategy comparison. Based on that, we derive an optimal MSD that maximizes the power to detect the predictive marker effect by choosing the optimal randomization ratios between the two strategies and treatments. Our numerical study shows that using the proposed optimal designs can substantially improve the power of the MSD to detect the predictive marker effect. We use a lung cancer trial to illustrate the proposed optimal designs.

A robust Bayesian dose-finding design for phase I/II clinical trials.

We propose a Bayesian phase I/II dose-finding trial design that simultaneously accounts for toxicity and efficacy. We model the toxicity and efficacy of investigational doses using a flexible Bayesian dynamic model, which borrows information across doses without imposing stringent parametric assumptions on the shape of the dose-toxicity and dose-efficacy curves. An intuitive utility function that reflects the desirability trade-offs between efficacy and toxicity is used to guide the dose assignment and selection. We also discuss the extension of this design to handle delayed toxicity and efficacy. We conduct extensive simulation studies to examine the operating characteristics of the proposed method under various practical scenarios. The results show that the proposed design possesses good operating characteristics and is robust to the shape of the dose-toxicity and dose-efficacy curves.

Bridging continual reassessment method for phase I clinical trials in different ethnic populations.

Accumulating evidence shows that the conventional one-size-fits-all dose-finding paradigm is problematic when applied to different ethnic populations. Because of inter-ethnic heterogeneity, the dosage established in a landmark trial for a certain population may not be generalizable to a different ethnic population, and a follow-up bridge trial is often needed to find the maximum tolerated dose for the new population. We propose the bridging continual reassessment method (B-CRM) to facilitate dose finding for such follow-up bridge trials. The B-CRM borrows information from the landmark trial through a novel estimate of the dose-toxicity curve and accommodates the inter-ethnic heterogeneity using the Bayesian model averaging approach. Extensive simulation studies show that the B-CRM has desirable operating characteristics with a high probability to select the target dose. This article focuses on ethnic heterogeneity, but the proposed method can be directly used to handle other types of patient heterogeneity, for example, patient subgroups defined by prognostic factors or biomarkers. The software to implement the B-CRM design is available for free download at http://odin.mdacc.tmc.edu/~yyuan/.

Controlling contagion processes in activity driven networks.

The vast majority of strategies aimed at controlling contagion processes on networks consider the connectivity pattern of the system either quenched or annealed. However, in the real world, many networks are highly dynamical and evolve, in time, concurrently with the contagion process. Here, we derive an analytical framework for the study of control strategies specifically devised for a class of time-varying networks, namely activity-driven networks. We develop a block variable mean-field approach that allows the derivation of the equations describing the coevolution of the contagion process and the network dynamic. We derive the critical immunization threshold and assess the effectiveness of three different control strategies. Finally, we validate the theoretical picture by simulating numerically the spreading process and control strategies in both synthetic networks and a large-scale, real-world, mobile telephone call data set.

Evaluation of image registration spatial accuracy using a Bayesian hierarchical model.

To evaluate the utility of automated deformable image registration (DIR) algorithms, it is necessary to evaluate both the registration accuracy of the DIR algorithm itself, as well as the registration accuracy of the human readers from whom the "gold standard" is obtained. We propose a Bayesian hierarchical model to evaluate the spatial accuracy of human readers and automatic DIR methods based on multiple image registration data generated by human readers and automatic DIR methods. To fully account for the locations of landmarks in all images, we treat the true locations of landmarks as latent variables and impose a hierarchical structure on the magnitude of registration errors observed across image pairs. DIR registration errors are modeled using Gaussian processes with reference prior densities on prior parameters that determine the associated covariance matrices. We develop a Gibbs sampling algorithm to efficiently fit our models to high-dimensional data, and apply the proposed method to analyze an image dataset obtained from a 4D thoracic CT study.

A Bayesian design for phase II clinical trials with delayed responses based on multiple imputation.

Interim monitoring is routinely conducted in phase II clinical trials to terminate the trial early if the experimental treatment is futile. Interim monitoring requires that patients' responses be ascertained shortly after the initiation of treatment so that the outcomes are known by the time the interim decision must be made. However, in some cases, response outcomes require a long time to be assessed, which causes difficulties for interim monitoring. To address this issue, we propose a Bayesian trial design to allow for continuously monitoring phase II clinical trials in the presence of delayed responses. We treat the delayed responses as missing data and handle them using a multiple imputation approach. Extensive simulations show that the proposed design yields desirable operating characteristics under various settings and dramatically reduces the trial duration.

Computational study of associations between the synaptic conductance of STN and GPe and the development of Parkinson's disease.

There is evidence that the subthalamic nucleus (STN) and globus pallidus pars externa (GPe) involve in the development of Parkinson's disease, a neurodegenerative disorder characterized by motor and non-motor symptoms and loss of dopaminergic neurons in which the error index (EI) in firing patterns is widely used to address the related issues. Whether and how this interaction mechanism of STN and GPe affects EI in Parkinson's disease is uncertain. To account for this, we propose a kind of basal ganglia-thalamic network model associated with Parkinson's disease coupled with neurons, and investigate the effect of synaptic conductance of STN and GPe on EI in this network, as well as their internal relationship under EI as an index. The results show a relationship like a piecewise function between the error index and the slope of the state transition function of synaptic conductance from STN to GPe ( g snge ) and from GPe to STN ( g gesn ). And there is an approximate negative correlation between EI and g gesn . Increasing g snge and decreasing g gesn can improve the fidelity of thalamus information transmission and alleviate Parkinson's disease effectively. These obtained results can give some theoretical evidence that the abnormal synaptic releases of STN and GPe may be the symptoms of the development of Parkinson's disease, and further enrich the understanding of the pathogenesis and treatment mechanism of Parkinson's disease.

Efficacy and Safety of Atezolizumab and Bevacizumab in Appendiceal Adenocarcinoma.

PURPOSE: Appendiceal adenocarcinoma (AA) remains an orphan disease with limited treatment options for patients unable to undergo surgical resection. Evidence supporting the efficacy of combined VEGF and PD-1 inhibition in other tumor types provided a compelling rationale for investigating this combination in AA, where immune checkpoint inhibitors have not been explored previously. EXPERIMENTAL DESIGN: We conducted a prospective, single-arm phase II study evaluating efficacy and safety of atezolizumab in conjunction with bevacizumab (Atezo+Bev) in advanced, unresectable AA. RESULTS: Patients treated with the Atezo+Bev combination had 100% disease control rate (1 partial response, 15 stable disease) with progression-free survival (PFS) of 18.3 months and overall survival not-yet-reached with median duration of follow-up of 40 months. These survival intervals were significantly longer relative to a clinically and molecularly matched synthetic control cohort treated with cytotoxic chemotherapy designed for colorectal cancer (PFS of 4.4 months, P = 0.041). CONCLUSIONS: In light of recent data demonstrating a lack of efficacy of 5-fluorouracil-based chemotherapy, Atezo+Bev is a promising treatment option for patients with low-grade unresectable AA; further study is warranted. SIGNIFICANCE: AA remains an orphan disease with limited systemic therapy options for patients who are not candidates for surgical resection. These data suggest activity from combined VEGF and PD-L1 inhibition that warrants further study.