Browsing by Author "Zang, Yong"
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Item A Bayesian phase I/II biomarker-based design for identifying subgroup-specific optimal dose for immunotherapy(Sage, 2022) Guo, Beibei; Zang, Yong; Biostatistics and Health Data Science, School of MedicineImmunotherapy is an innovative treatment that enlists the patient’s immune system to battle tumors. The optimal dose for treating patients with an immunotherapeutic agent may differ according to their biomarker status. In this article, we propose a biomarker-based phase I/II dose-finding design for identifying subgroup-specific optimal dose for immunotherapy (BSOI) that jointly models the immune response, toxicity, and efficacy outcomes. We propose parsimonious yet flexible models to borrow information across different types of outcomes and subgroups. We quantify the desirability of the dose using a utility function and adopt a two-stage dose-finding algorithm to find the optimal dose for each subgroup. Simulation studies show that the BSOI design has desirable operating characteristics in selecting the subgroup-specific optimal doses and allocating patients to those optimal doses, and outperforms conventional designs.Item A Bayesian Phase I/II Design to Determine Subgroup-Specific Optimal Dose for Immunotherapy Sequentially Combined with Radiotherapy(Wiley, 2023) Guo, Beibei; Zang, Yong; Lin, Li-Hsiang; Zhang, Rui; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthSequential administration of immunotherapy following radiotherapy (immunoRT) has attracted much attention in cancer research. Due to its unique feature that radiotherapy upregulates the expression of a predictive biomarker for immunotherapy, novel clinical trial designs are needed for immunoRT to identify patient subgroups and the optimal dose for each subgroup. In this article, we propose a Bayesian phase I/II design for immunotherapy administered after standard-dose radiotherapy for this purpose. We construct a latent subgroup membership variable and model it as a function of the baseline and pre-post radiotherapy change in the predictive biomarker measurements. Conditional on the latent subgroup membership of each patient, we jointly model the continuous immune response and the binary efficacy outcome using plateau models, and model toxicity using the equivalent toxicity score approach to account for toxicity grades. During the trial, based on accumulating data, we continuously update model estimates and adaptively randomize patients to admissible doses. Simulation studies and an illustrative trial application show that our design has good operating characteristics in terms of identifying both patient subgroups and the optimal dose for each subgroup.Item A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data(Cold Spring Harbor Laboratory, 2021) Alghamdi, Norah; Chang, Wennan; Dang, Pengtao; Lu, Xiaoyu; Wan, Changlin; Gampala, Silpa; Huang, Zhi; Wang, Jiashi; Ma, Qin; Zang, Yong; Fishel, Melissa; Cao, Sha; Zhang, Chi; Medical and Molecular Genetics, School of MedicineThe metabolic heterogeneity and metabolic interplay between cells are known as significant contributors to disease treatment resistance. However, with the lack of a mature high-throughput single-cell metabolomics technology, we are yet to establish systematic understanding of the intra-tissue metabolic heterogeneity and cooperative mechanisms. To mitigate this knowledge gap, we developed a novel computational method, namely, single-cell flux estimation analysis (scFEA), to infer the cell-wise fluxome from single-cell RNA-sequencing (scRNA-seq) data. scFEA is empowered by a systematically reconstructed human metabolic map as a factor graph, a novel probabilistic model to leverage the flux balance constraints on scRNA-seq data, and a novel graph neural network-based optimization solver. The intricate information cascade from transcriptome to metabolome was captured using multilayer neural networks to capitulate the nonlinear dependency between enzymatic gene expressions and reaction rates. We experimentally validated scFEA by generating an scRNA-seq data set with matched metabolomics data on cells of perturbed oxygen and genetic conditions. Application of scFEA on this data set showed the consistency between predicted flux and the observed variation of metabolite abundance in the matched metabolomics data. We also applied scFEA on five publicly available scRNA-seq and spatial transcriptomics data sets and identified context- and cell group-specific metabolic variations. The cell-wise fluxome predicted by scFEA empowers a series of downstream analyses including identification of metabolic modules or cell groups that share common metabolic variations, sensitivity evaluation of enzymes with regards to their impact on the whole metabolic flux, and inference of cell-tissue and cell-cell metabolic communications.Item A Novel Perioperative Multidose Methadone-Based Multimodal Analgesic Strategy in Children Achieved Safe and Low Analgesic Blood Methadone Levels Enabling Opioid-Sparing Sustained Analgesia With Minimal Adverse Effects(Wolters Kluwer, 2021) Sadhasivam, Senthilkumar; Aruldhas, Blessed W.; Packiasabapathy, Senthil; Overholser, Brian R.; Zhang, Pengyue; Zang, Yong; Renschler, Janelle S.; Fitzgerald, Ryan E.; Quinney, Sara K.; Anesthesia, School of MedicineBackground: Intraoperative methadone, a long-acting opioid, is increasingly used for postoperative analgesia, although the optimal methadone dosing strategy in children is still unknown. The use of a single large dose of intraoperative methadone is controversial due to inconsistent reductions in total opioid use in children and adverse effects. We recently demonstrated that small, repeated doses of methadone intraoperatively and postoperatively provided sustained analgesia and reduced opioid use without respiratory depression. The aim of this study was to characterize pharmacokinetics, efficacy, and safety of a multiple small-dose methadone strategy. Methods: Adolescents undergoing posterior spinal fusion (PSF) for idiopathic scoliosis or pectus excavatum (PE) repair received methadone intraoperatively (0.1 mg/kg, maximum 5 mg) and postoperatively every 12 hours for 3-5 doses in a multimodal analgesic protocol. Blood samples were collected up to 72 hours postoperatively and analyzed for R-methadone and S-methadone, 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidene (EDDP) metabolites, and alpha-1 acid glycoprotein (AAG), the primary methadone-binding protein. Peak and trough concentrations of enantiomers, total methadone, and AAG levels were correlated with clinical outcomes including pain scores, postoperative nausea and vomiting (PONV), respiratory depression, and QT interval prolongation. Results: The study population included 38 children (10.8-17.9 years): 25 PSF and 13 PE patients. Median total methadone peak plasma concentration was 24.7 (interquartile range [IQR], 19.2-40.8) ng/mL and the median trough was 4.09 (IQR, 2.74-6.4) ng/mL. AAG concentration almost doubled at 48 hours after surgery (median = 193.9, IQR = 86.3-279.5 µg/mL) from intraoperative levels (median = 87.4, IQR = 70.6-115.8 µg/mL; P < .001), and change of AAG from intraoperative period to 48 hours postoperatively correlated with R-EDDP (P < .001) levels, S-EDDP (P < .001) levels, and pain scores (P = .008). Median opioid usage was minimal, 0.66 (IQR, 0.59-0.75) mg/kg morphine equivalents/d. No respiratory depression (95% Wilson binomial confidence, 0-0.09) or clinically significant QT prolongation (median = 9, IQR = -10 to 28 milliseconds) occurred. PONV occurred in 12 patients and was correlated with morphine equivalent dose (P = .005). Conclusions: Novel multiple small perioperative methadone doses resulted in safe and lower blood methadone levels, <100 ng/mL, a threshold previously associated with respiratory depression. This methadone dosing in a multimodal regimen resulted in lower blood methadone analgesia concentrations than the historically described minimum analgesic concentrations of methadone from an era before multimodal postoperative analgesia without postoperative respiratory depression and prolonged corrected QT (QTc). Larger studies are needed to further study the safety and efficacy of this methadone dosing strategy.Item Adaptive phase I-II clinical trial designs identifying optimal biological doses for targeted agents and immunotherapies(Sage, 2024) Zang, Yong; Guo, Beibei; Qiu, Yingjie; Liu, Hao; Opyrchal, Mateusz; Lu, Xiongbin; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthTargeted agents and immunotherapies have revolutionized cancer treatment, offering promising options for various cancer types. Unlike traditional therapies the principle of "more is better" is not always applicable to these new therapies due to their unique biomedical mechanisms. As a result, various phase I-II clinical trial designs have been proposed to identify the optimal biological dose that maximizes the therapeutic effect of targeted therapies and immunotherapies by jointly monitoring both efficacy and toxicity outcomes. This review article examines several innovative phase I-II clinical trial designs that utilize accumulated efficacy and toxicity outcomes to adaptively determine doses for subsequent patients and identify the optimal biological dose, maximizing the overall therapeutic effect. Specifically, we highlight three categories of phase I-II designs: efficacy-driven, utility-based, and designs incorporating multiple efficacy endpoints. For each design, we review the dose-outcome model, the definition of the optimal biological dose, the dose-finding algorithm, and the software for trial implementation. To illustrate the concepts, we also present two real phase I-II trial examples utilizing the EffTox and ISO designs. Finally, we provide a classification tree to summarize the designs discussed in this article.Item Applications of Time to Event Analysis in Clinical Data(2021-12) Xu, Chenjia; Gao, Sujuan; Liu, Hao; Zang, Yong; Zhang, Jianjun; Zhao, YiSurvival analysis has broad applications in diverse research areas. In this dissertation, we consider an innovative application of survival analysis approach to phase I dose-finding design and the modeling of multivariate survival data. In the first part of the dissertation, we apply time to event analysis in an innovative dose-finding design. To account for the unique feature of a new class of oncology drugs, T-cell engagers, we propose a phase I dose-finding method incorporating systematic intra-subject dose escalation. We utilize survival analysis approach to analyze intra-subject dose-escalation data and to identify the maximum tolerated dose. We evaluate the operating characteristics of the proposed design through simulation studies and compare it to existing methodologies. The second part of the dissertation focuses on multivariate survival data with semi-competing risks. Time-to-event data from the same subject are often correlated. In addition, semi-competing risks are sometimes present with correlated events when a terminal event can censor other non-terminal events but not vice versa. We use a semiparametric frailty model to account for the dependence between correlated survival events and semi-competing risks and adopt penalized partial likelihood (PPL) approach for parameter estimation. In addition, we investigate methods for variable selection in semi-parametric frailty models and propose a double penalized partial likelihood (DPPL) procedure for variable selection of fixed effects in frailty models. We consider two penalty functions, least absolute shrinkage and selection operator (LASSO) and smoothly clipped absolute deviation (SCAD) penalty. The proposed methods are evaluated in simulation studies and illustrated using data from Indianapolis-Ibadan Dementia Project.Item Aspartyl-tRNA synthetase 2 orchestrates iron-sulfur metabolism in hematopoietic stem cells via fine-tuning alternative RNA splicing(Elsevier, 2023-10) Gu, Xuan; Li, Kailing; Zhang, Meng; Chen, Yandan; Zhou, Jingchao; Yao, Chunxu; Zang, Yong; He, Jiefeng; Wan, Jun; Guo, Bin; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthAspartyl-tRNA synthetase 2 (Dars2) is involved in the regulation of mitochondrial protein synthesis and tissue-specific mitochondrial unfolded protein response (UPRmt). The role of Dars2 in the self-renewal and differentiation of hematopoietic stem cells (HSCs) is unknown. Here, we show that knockout (KO) of Dars2 significantly impairs the maintenance of hematopoietic stem and progenitor cells (HSPCs) without involving its tRNA synthetase activity. Dars2 KO results in significantly reduced expression of Srsf2/3/6 and impairs multiple events of mRNA alternative splicing (AS). Dars2 directly localizes to Srsf3-labeled spliceosomes in HSPCs and regulates the stability of Srsf3. Dars2-deficient HSPCs exhibit aberrant AS of mTOR and Slc22a17. Dars2 KO greatly suppresses the levels of labile ferrous iron and iron-sulfur cluster-containing proteins, which dampens mitochondrial metabolic activity and DNA damage repair pathways in HSPCs. Our study reveals that Dars2 plays a crucial role in the iron-sulfur metabolism and maintenance of HSPCs by modulating RNA splicing.Item Associations of parent–adolescent closeness with P3 amplitude, frontal theta, and binge drinking among offspring with high risk for alcohol use disorder(Wiley, 2023) Pandey, Gayathri; Kuo, Sally I-Chun; Horne-Osipenko, Kristina A.; Pandey, Ashwini K.; Kamarajan, Chella; Saenz de Viteri, Stacey; Kinreich, Sivan; Chorlian, David B.; Kuang, Weipeng; Stephenson, Mallory; Kramer, John; Anokhin, Andrey; Zang, Yong; Kuperman, Samuel; Hesselbrock, Victor; Schuckit, Marc; Dick, Danielle; Chan, Grace; McCutcheon, Vivia V.; Edenberg, Howard; Bucholz, Kathleen K.; Meyers, Jacquelyn L.; Porjesz, Bernice; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthBackground: Parents impact their offspring's brain development, neurocognitive function, risk, and resilience for alcohol use disorder (AUD) via both genetic and socio-environmental factors. Individuals with AUD and their unaffected children manifest low parietal P3 amplitude and low frontal theta (FT) power, reflecting heritable neurocognitive deficits associated with AUD. Likewise, children who experience poor parenting tend to have atypical brain development and greater rates of alcohol problems. Conversely, positive parenting can be protective and critical for normative development of self-regulation, neurocognitive functioning and the neurobiological systems subserving them. Yet, the role of positive parenting in resiliency toward AUD is understudied and its association with neurocognitive functioning and behavioral vulnerability to AUD among high-risk offspring is less known. Using data from the Collaborative Study on the Genetics of Alcoholism prospective cohort (N = 1256, mean age [SD] = 19.25 [1.88]), we investigated the associations of closeness with mother and father during adolescence with offspring P3 amplitude, FT power, and binge drinking among high-risk offspring. Methods: Self-reported closeness with mother and father between ages 12 and 17 and binge drinking were assessed using the Semi-Structured Assessment for the Genetics of Alcoholism. P3 amplitude and FT power were assessed in response to target stimuli using a Visual Oddball Task. Results: Multivariate multiple regression analyses showed that closeness with father was associated with larger P3 amplitude (p = 0.002) and higher FT power (p = 0.01). Closeness with mother was associated with less binge drinking (p = 0.003). Among male offspring, closeness with father was associated with larger P3 amplitude, but among female offspring, closeness with mother was associated with less binge drinking. These associations remained statistically significant with father's and mothers' AUD symptoms, socioeconomic status, and offspring impulsivity in the model. Conclusions: Among high-risk offspring, closeness with parents during adolescence may promote resilience for developing AUD and related neurocognitive deficits albeit with important sex differences.Item Bayesian Adaptive Designs for Early Phase Clinical Trials(2023-07) Guo, Jiaying; Zang, Yong; Han, Jiali; Zhao, Yi; Ren, JieDelayed toxicity outcomes are common in phase I clinical trials, especially in oncology studies. It causes logistic difficulty, wastes resources, and prolongs the trial duration. We propose the time-to-event 3+3 (T-3+3) design to solve the delayed outcome issue for the 3+3 design. We convert the dose decision rules of the 3+3 design into a series of events. A transparent yet efficient Bayesian probability model is applied to calculate the event happening probabilities in the presence of delayed outcomes, which incorporates the informative pending patients' remaining follow-up time into consideration. The T-3+3 design only models the information for the pending patients and seamlessly reduces to the conventional 3+3 design in the absence of delayed outcomes. We further extend the proposed method to interval 3+3 (i3+3) design, an algorithm-based phase I dose-finding design which is based on simple but more comprehensive rules that account for the variabilities in the observed data. Similarly, the dose escalation/deescalation decision is recommended by comparing the event happening probabilities which are calculated by considering the ratio between the averaged follow-up time for at-risk patients and the total assessment window. We evaluate the operating characteristics of the proposed designs through simulation studies and compare them to existing methods. The umbrella trial is a clinical trial strategy that accommodates the paradigm shift towards personalized medicine, which evaluates multiple investigational drugs in different subgroups of patients with the same disease. A Bayesian adaptive umbrella trial design is proposed to select effective targeted agents for different biomarker-based subgroups of patients. To facilitate treatment evaluation, the design uses a mixture regression model that jointly models short-term and long-term response outcomes. In addition, a data-driven latent class model is employed to adaptively combine subgroups into induced latent classes based on overall data heterogeneities, which improves the statistical power of the umbrella trial. To enhance individual ethics, the design includes a response-adaptive randomization scheme with early stopping rules for futility and superiority. Bayesian posterior probabilities are used to make these decisions. Simulation studies demonstrate that the proposed design outperforms two conventional designs across a range of practical treatment-outcome scenarios.Item Bayesian Adaptive Designs for Phase II Clinical Trials Evaluating Subgroup-Specific Treatment Effect(2024-12) Shan, Mu; Zang, Yong; Han, Jiali; Tu, Wanzhu; Zhang, PengyueIn Phase II clinical trials, particularly for molecularly targeted agents (MTAs) and biotherapies, there is a critical need to evaluate subgroup-specific treatment effects due to the heterogeneous nature of these therapies. This dissertation introduces two innovative Bayesian adaptive designs for biomarker-guided clinical trials: the Bayesian Order Constrained Adaptive (BOCA) design and the Bayesian Adaptive Marker-Stratified Design Using Calibrated Spike-and-Slab priors (SSS). The BOCA design addresses the limitations of the "one-size-fits-all" approach in non-randomized Phase II trials by efficiently detecting subgroup-specific treatment effects. It combines elements of enrichment and sequential designs, starting with an "all-comers" stage and transitioning to an enrichment stage based on interim analysis results. The decision to continue with either the marker-positive or marker-negative subgroup is guided by two posterior probabilities utilizing inherent ordering constraints. This adaptive approach enhances trial efficiency and cost-effectiveness while managing missing biomarker data. Comprehensive simulation studies show that the BOCA design outperforms conventional designs in detecting subgroup-specific treatment effects, making it a robust tool for Phase II trials. The SSS design improves the efficiency of marker-stratified designs (MSD) by leveraging clinical features of biomarkers and treatments. Patients are classified into marker-positive and marker-negative subgroups and randomized to receive either the MTA or a control treatment. The SSS design uses spike-and-slab priors to dynamically share information on response rates across subgroups, governed by two posterior probabilities that assess similarities in response rates. Additionally, it incorporates a Bayesian multiple imputation method to address missing biomarker profiles. Simulation studies confirm that the SSS design exhibits favorable operational characteristics, surpassing conventional designs in evaluating subgroup-specific treatment effects. Both the BOCA and SSS designs represent significant advancements in Bayesian adaptive methodologies for Phase II trials. By addressing traditional approach limitations, these designs enhance the evaluation of subgroup-specific treatment effects, contributing valuable methodologies to the field of personalized medicine.