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Wax gourd (Benincasa hispida) is highly appreciated for its nutritional and therapeutic value, especially throughout Asia. As the market for wax gourd expands, new varieties of unknown purity are being promoted. In this study, the unpublished 'GX-19' wax gourd genome elucidated by our research group was used as reference genome. Two high-generation inbred lines ('HX-1' and 'HX-6') were resequenced and 330 pairs of InDel primers were designed and synthesised. Following screening, 11 pairs of InDel markers were identified and used to determine the degree of genetic purity of wax-gourd hybrid seeds. InDel molecular markers were used to identify the purity of 17 hybrids, including 'Lvxianzi 2', 'Fenxianzi 1', 'Jinyuan 1', 'Yinyuan 4', 'Chunfeng 868', 'Xinjinyu' and 'Meihua 8'. Additionally, we showed that the amplification fragment from DNA extracted using the alkali rapid-extraction method developed in this study was identical to that of the fragment amplified from DNA extracted using a modified CTAB method. When the InDel molecular marker-based purity determination of five wax gourd hybrids was compared with that using the growing out test, the results were highly consistent. A stable, accurate, efficient and straightforward approach is provided to determine the genetic purity of hybrid wax gourd seeds.

Biomarker-guided phase II trials have become increasingly important for personalized cancer treatment. In this paper, we propose a Bayesian two-stage sequential enrichment design for such biomarker-guided trials. We assumed that all patients were dichotomized as marker positive or marker negative based on their biomarker status; the positive patients were considered more likely to respond to the targeted drug. Early stopping rules and adaptive randomization methods were embedded in the design to control the number of patients receiving inferior treatment. At the same time, a Bayesian hierarchical model was used to borrow information between the positive and negative control arms to improve efficiency. Simulation results showed that the proposed design achieved higher empirical power while controlling the type I error and assigned more patients to the superior treatment arms. The operating characteristics suggested that the design has good performance and may be useful for biomarker-guided phase II trials for evaluating anticancer therapies.

Background. There have been thousands of clinical trials for COVID-19 to target effective treatments. However, quite a few of them are traditional randomized controlled trials with low efficiency. Considering the three particularities of pandemic disease: timeliness, repurposing, and case spike, new trial designs need to be developed to accelerate drug discovery. Methods. We propose an adaptive information borrowing platform design that can sequentially test drug candidates under a unified framework with early efficacy/futility stopping. Power prior is used to borrow information from previous stages and the time trend calibration method deals with the baseline effectiveness drift. Two drug development strategies are applied: the comprehensive screening strategy and the optimal screening strategy. At the same time, we adopt adaptive randomization to set a higher allocation ratio to the experimental arms for ethical considerations, which can help more patients to receive the latest treatments and shorten the trial duration. Results. Simulation shows that in general, our method has great operating characteristics with type I error controlled and power increased, which can select effective/optimal drugs with a high probability. The early stopping rules can be successfully triggered to stop the trial when drugs are either truly effective or not optimal, and the time trend calibration performs consistently well with regard to different baseline drifts. Compared with the nonborrowing method, borrowing information in the design substantially improves the probability of screening promising drugs and saves the sample size. Sensitivity analysis shows that our design is robust to different design parameters. Conclusions. Our proposed design achieves the goal of gaining efficiency, saving sample size, meeting ethical requirements, and speeding up the trial process and is suitable and well performed for COVID-19 clinical trials to screen promising treatments or target optimal therapies.

A random delayed treatment effect is expected in a confirmatory clinical trial for an immunotherapy due to the individual heterogeneity of physiological conditions. For this reason, the delay time will be assumed to follow a continuous distribution that is difficult to estimate accurately based on the early-phase data, which hinders the specification of the most powerful weighted log-rank test. Therefore, we propose a simulation-based maximum duration design with a robustly powerful Maxcombo test for a group sequential trial for the immunotherapy with the random delayed treatment effect. The design obtains the group sequential boundaries by a simulation procedure and determines the required maximum sample size using a one-dimensional search in which another simulation procedure is used to calculate empirical power. The simulation researches proved the accuracy of the group sequential boundaries and their robustness against the misspecified maximum sample sizes for large samples and revealed their moderate sensitivity against the misspecified survival distributions under the null hypothesis of no difference. The studies investigated whether the type I error rate would inflate under the "inferior" null hypothesis and evaluated the robustness against different distributions of the delay time in terms of the empirical power among the Maxcombo tests and component weighted log-rank tests.

Introduction: With limited resources for rehabilitation, burn therapists throughout the world rely on local resources, professional creativity and community relationships to create solutions to help their patients recover. The demands of helping a burn survivor achieve an optimal outcome drive the burn therapist to create tools and strategies that effectively solve common problems after burn injury. Methods: The International Society for Burn Injury (ISBI) invited burn therapists from around the world to describe the innovations that they have created to solve burn rehabilitation problems. The submissions were provided through an online form and inclusion of photographs was encouraged. Each submission was reviewed in a blinded manner by an expert panel of three independent rehabilitation therapists from the ISBI Rehabilitation Committee. The submissions were reviewed for originality, utility, quality and safety, then edited and compiled to be shared and disseminated throughout the world. Results: A total of 77 innovations were submitted for review. 71 were accepted for publication (92%) and given the opportunity to publish in Burns Open. Clinicians from 15 countries submitted innovations. The innovations were categorized as: Orthosis, Scar Management, Techniques, Activities of Daily Living Devices, Educational Tools and Prosthesis. Nineteen submissions were translated from Spanish to English. Discussion: Creative and innovative ideas using local resources are turned into patient solutions by experienced burn therapists around the world. However, because such innovations don’t reach widespread investigation or production, the ideas remain in a silo in the local area when they could be of use to therapists in other cultures. This project resulted in a book of “Innovative Solutions in Burn Rehabilitation from Around the World,” that was printed and is now available as this Special Edition in Burns Open. Future work would include expanding on the book with more innovative solutions and translating it into multiple languages to improve access and usability throughout the world.

Soluble solids content (SSC) is an important quality trait of wax gourd, but reports about its regulatory genes are scarce. In this study, the SSC regulatory gene

A delayed treatment effect is often observed in the confirmatory trials for immunotherapies and is reflected by a delayed separation of the survival curves of the immunotherapy groups versus the control groups. This phenomenon makes the design based on the log-rank test not applicable because this design would violate the proportional hazard assumption and cause loss of power. Thus, we propose a group sequential design allowing early termination on the basis of efficacy based on a more powerful piecewise weighted log-rank test for an immunotherapy trial with a delayed treatment effect. We present an approach on the group sequential monitoring, in which the information time is defined based on the number of events occurring after the delay time. Furthermore, we developed a one-dimensional search algorithm to determine the required maximum sample size for the proposed design, which uses an analytical estimation obtained by the inflation factor as an initial value and an empirical power function calculated by a simulation-based procedure as an objective function. In the simulation, we tested the unstable accuracy of the analytical estimation, the consistent accuracy of the maximum sample size determined by the search algorithm and the advantages of the proposed design on saving sample size.

PURPOSE With deeper insight into precision medicine, more innovative oncology trial designs have been proposed to contribute to the characteristics of novel antitumor drugs. Bayesian information borrowing is an indispensable part of these designs, which shows great advantages in improving the efficiency of clinical trials. Bayesian methods provide an effective framework when incorporating information. However, the key point lies in how to choose an appropriate method for complex oncology clinical trials. METHODS We divided the borrowing information scenarios into concurrent and nonconcurrent scenarios according to whether the data to be borrowed are observed at the same time as in the current trial or not. Then, we provided an overview of the methods in each scenario. Performance comparison of different methods is carried out with regard to the type I error and power. RESULTS As demonstrated by the simulation results in each borrowing scenario, the Bayesian hierarchical model and its extensions are more appropriate for concurrent borrowing. The simulation results demonstrate that the Bayesian hierarchical model shows great advantages when the arms are homogeneous. However, such a method should be adopted with caution when heterogeneity exists. We recommend the other methods, considering heterogeneity. Borrow information from informative priors is more suggested for nonconcurrent borrowing scenarios. Multisource exchangeability models are more suitable for multiple historical trials, while meta-analytic-predictive prior should be carefully applied. CONCLUSION Bayesian information borrowing is useful and can improve the efficiency of clinical trial designs. However, we should carefully choose an appropriate information borrowing method when facing a practical innovative oncology trial, as an appropriate method is essential to provide ideal design performance.

BackgroundHepatitis B virus (HBV), the main risk factor for hepatocellular carcinoma (HCC) development, integrates into the host genome, causing genetic instability, which may trigger malignancies to exhibit chronic DNA replication stress, providing exploitable therapeutic vulnerabilities. Therefore, customizing prognostication approach and expanding therapeutic options are of great clinical significance to HBV-associated HCCs.MethodsA robust machine-learning framework was designed to develop a DNA replication stress-related prognostic index (PIRS) based on 606 retrospectively collected HBV-associated HCC cases. Molecular profiles and drug response of HCC cell lines were leveraged to predict therapeutic targets and agents for patients with high mortality risk.ResultsCompared with established population-based predictors, PIRS manifested superior performance for prognostic prediction in HBV-associated HCCs. Lower PIRS tightly associated with higher expression of HBV oncoproteins, activated immune/metabolism pathways and higher likelihood of responding to immunotherapy; while higher PIRS showed co-occurrence manner with elevated Ki-67 progression marker and cancer stemness, and significantly enriched in DNA replication stress, cell cycle pathways, chromatin remodeling regulons, and presented an ‘immune-cold’ phenotype with unfavorable clinical outcome. Through large-scale in silico drug screening, four potential therapeutic targets (TOP2A, PRMT1, CSNK1D, and PPIH) and five agents including three topoisomerase inhibitors (teniposide, doxorubicin, and epirubicin) and two CDK inhibitors (JNJ-7706621 and PHA-793887) were identified for patients with high PIRS.ConclusionsOverall, PIRS holds potential to improve the population-based therapeutic strategies in HCC and sheds new insight to the clinical management for those HBV carriers; current analytic framework provides a roadmap for the rational clinical development of personalized treatment.

Bitter gourd (Momordica charantia L.) is one of the most important vegetable crops in many Asian and African countries. Here, InDel markers developed by comparing whole genome re-sequencing data were used to analyze the genetic diversity of a bitter gourd germplasm sample from various geographical origins. To verify the reliability of the set of InDel markers identified, 220 pairs of InDel primers were designed. The primers were preliminarily detected by 8% polyacrylamide gel electrophoresis, and 25 pairs of primers with better polymorphism were screened. Using the 25 primer combinations, the 53 bitter gourd accessions were effectively distinguished and the InDel fingerprint of DNA was constructed. Concomitantly, the degree of purity of different crosses was determined based on the differences in specific bands among genotypes. The unweighted pair-group method with arithmetic means showed that the 53 bitter gourd materials may be divided into three groups, with a similarity coefficient of 0.645 as the threshold. Therefore, this study can provide many InDel markers for genotypic identification, genetic relationship analysis, and genetic map construction of bitter gourd.

Fruit shape is an important agronomic trait in wax gourds [Benincasa hispida (Thunb) Cogn.]. However, the candidate genes for this important trait, and their genetic mechanisms, remain unknown. In this study, we identified a candidate gene for fruit shape in wax gourds using a next-generation sequencing-based bulked segregant analysis in F2 populations derived from a cross between GX-71 (long cylindrical fruit, FSI = 4.56) and MY-1 (round fruit, FSI = 1.06) genotypes. According to bulked segregant analysis, the candidate gene is located in the 17.18 Mb region on chromosome 2, and the kompetitive allele specific polymerase chain reaction (KASP) marker was used to reduce it to the 19.6 Kb region. There is only one gene in the corresponding region of the reference genome, Bch02G016830 (designated BFS). We sequenced BFS in six wax gourd varieties with different fruit shapes. Sequence analysis showed that there were two non-synonymous mutations in the spherical wax gourd and one non-synonymous mutation in the cylindrical wax gourd. Quantitative real‑time polymerase chain reaction (qRT-PCR) analysis showed that the expression of BFS in round fruits was significantly higher than in long cylindrical fruits at the ovary formation stage. Therefore, BFS is a candidate gene for determination of the fruit shape of wax gourds. The predicted protein encoded by the BFS gene belongs to the IQD protein family, which have the structural characteristics of scaffold proteins and coordinate Ca2+ CaM signaling from the membrane to the nucleus. The BFS gene can assist with the breeding of new varieties that possess ideal fruit shapes.

The molecular classification of muscle-invasive bladder cancer (MIBC) based on transcriptomic signatures has been extensively studied. The complementary nature of information provided by different molecular profiles motivated us to refine MIBC classification by aggregating multi-omics data. We generated a consensus ensemble through ten multi-omics integrative clustering approaches on 396 MIBCs from TCGA. A total of 701 MIBCs from different sequencing technologies were used for external validation. Associations between subtypes and prognosis, molecular profiles, the tumour microenvironment, and potential response to frontline therapies were further analyzed. Nearest template prediction and random forest classification were used to develop a predictive signature/classifier for MIBC refinement. We identified four integrative consensus subtypes of MIBC, which were further designated basal-inflamed, basal-noninflamed, luminal-excluded and luminal-desert by immune profiling. Of note, the refinement of basal-like MIBC classification adds to the literature by identifying a basal-noninflamed MIBC subtype presenting with a significantly poor outcome and a global immune-cold phenotype, which might be triggered by Chr4 deletion and high activation of the oncogenic NRF2 pathway. In contrast, basal-inflamed MIBC showed high immunocyte infiltration and high expression of potential targets for immunotherapy. Using an external metastatic MIBC cohort in which patients received anti-PD-L1 treatment, we suggested that basal-inflamed MIBC had a higher likelihood of responding to immunotherapy than other MIBCs. The R package “refineMIBC” was offered as a research tool to refine MIBC from a single-sample perspective (https://github.com/xlucpu/refineMIBC). This consensus ensemble refines the intrinsic MIBC subtypes, which provides a blueprint for the clinical development of rational targeted and immunotherapeutic strategies.

Recently, real-world study has attracted wide attention for drug development. In bioequivalence study, the reference drug often has been marketed for many years and accumulated abundant real-world data. It is therefore appealing to incorporate these data in the design to improve trial efficiency. In this paper, we propose a Bayesian method to include real-world data of the reference drug in a current bioequivalence trial, with the aim to increase the power of analysis and reduce sample size for long half-life drugs. We adopt the power prior method for incorporating real-world data and use the average bioequivalence posterior probability to evaluate the bioequivalence between the test drug and the reference drug. Simulations were conducted to investigate the performance of the proposed method in different scenarios. The simulation results show that the proposed design has higher power than the traditional design without borrowing real-world data, while controlling the type I error. Moreover, the proposed method saves sample size and reduces costs for the trial.

Supplemental material, sj-pdf-1-smm-10.1177_0962280220980780 for A group sequential design and sample size estimation for an immunotherapy trial with a delayed treatment effect by Bosheng Li, Liwen Su, Jun Gao, Liyun Jiang and Fangrong Yan in Statistical Methods in Medical Research

Supplemental material, sj-pdf-1-smm-10.1177_0962280220980780 for A group sequential design and sample size estimation for an immunotherapy trial with a delayed treatment effect by Bosheng Li, Liwen Su, Jun Gao, Liyun Jiang and Fangrong Yan in Statistical Methods in Medical Research

Ovarian epithelial cancer is a gynecological tumor with a high risk of recurrence and death. In the clinical diagnosis of ovarian epithelial cancer, CA125 has become an important indicator of disease burden. To account for patient recurrence and death, a proper method is needed to integrate information from biomarkers and recurrence simultaneously. In the past 10 years, many methods have been proposed for joint modeling of longitudinal biomarkers and survival data, but few of them are applicable to longitudinal data and disease processes, including recurrence and death. In this article, we proposed a new joint frailty model based on functional principal component analysis for dynamic prediction of survival probabilities on the total time scale, which took recurrent history and longitudinal data into account simultaneously. The estimation of the joint frailty model is achieved by maximizing the penalized log-likelihood function. The simulation results demonstrated the advantages of our method in both discrimination and accuracy under different scenarios. To indicate the method's practicality, it is applied to an actual dataset of patients with ovarian epithelial cancer to predict survival dynamically using longitudinal data of biomarker CA125 and recurrent history data.