Camp For All Connection: a community health information outreach project

Purpose: The purpose of the Camp For All Connection project is to facilitate access to electronic health information resources at the Camp For All facility. Setting/Participants/Resources: Camp For All is a barrier-free camp working in partnership with organizations to enrich the lives of children and adults with chronic illnesses and disabilities and their families by providing camping and retreat experiences. The camp facility is located on 206 acres in Burton, Texas. The project partners are Texas Woman's University, Houston Academy of Medicine-Texas Medical Center Library, and Camp For All. Brief Description: The Camp For All Connection project placed Internet-connected workstations at the camp's health center in the main lodge and provided training in the use of electronic health information resources. A train-the-trainer approach was used to provide training to Camp For All staff. Results/Outcome: Project workstations are being used by health care providers and camp staff for communication purposes and to make better informed health care decisions for Camp For All campers. Evaluation Method: A post-training evaluation was administered at the end of the train-the-trainer session. In addition, a series of site visits and interviews was conducted with camp staff members involved in the project. The site visits and interviews allowed for ongoing dialog between project staff and project participants.

NF-kappaB and AP-1 connection: mechanism of NF-kappaB-dependent regulation of AP-1 activity.

Nuclear factor kappaB (NF-kappaB) and activator protein 1 (AP-1) transcription factors regulate many important biological and pathological processes. Activation of NF-kappaB is regulated by the inducible phosphorylation of NF-kappaB inhibitor IkappaB by IkappaB kinase. In contrast, Fos, a key component of AP-1, is primarily transcriptionally regulated by serum responsive factors (SRFs) and ternary complex factors (TCFs). Despite these different regulatory mechanisms, there is an intriguing possibility that NF-kappaB and AP-1 may modulate each other, thus expanding the scope of these two rapidly inducible transcription factors. To determine whether NF-kappaB activity is involved in the regulation of fos expression in response to various stimuli, we analyzed activity of AP-1 and expression of fos, fosB, fra-1, fra-2, jun, junB, and junD, as well as AP-1 downstream target gene VEGF, using MDAPanc-28 and MDAPanc-28/IkappaBalphaM pancreatic tumor cells and wild-type, IKK1-/-, and IKK2-/- murine embryonic fibroblast cells. Our results show that elk-1, a member of TCFs, is one of the NF-kappaB downstream target genes. Inhibition of NF-kappaB activity greatly decreased expression of elk-1. Consequently, the reduced level of activated Elk-1 protein by extracellular signal-regulated kinase impeded constitutive, serum-, and superoxide-inducible c-fos expression. Thus, our study revealed a distinct and essential role of NF-kappaB in participating in the regulation of elk-1, c-fos, and VEGF expression.

Bayesian doubly optimal group sequential designs for randomized clinical trials

When conducting a randomized comparative clinical trial, ethical, scientific or economic considerations often motivate the use of interim decision rules after successive groups of patients have been treated. These decisions may pertain to the comparative efficacy or safety of the treatments under study, cost considerations, the desire to accelerate the drug evaluation process, or the likelihood of therapeutic benefit for future patients. At the time of each interim decision, an important question is whether patient enrollment should continue or be terminated; either due to a high probability that one treatment is superior to the other, or a low probability that the experimental treatment will ultimately prove to be superior. The use of frequentist group sequential decision rules has become routine in the conduct of phase III clinical trials. In this dissertation, we will present a new Bayesian decision-theoretic approach to the problem of designing a randomized group sequential clinical trial, focusing on two-arm trials with time-to-failure outcomes. Forward simulation is used to obtain optimal decision boundaries for each of a set of possible models. At each interim analysis, we use Bayesian model selection to adaptively choose the model having the largest posterior probability of being correct, and we then make the interim decision based on the boundaries that are optimal under the chosen model. We provide a simulation study to compare this method, which we call Bayesian Doubly Optimal Group Sequential (BDOGS), to corresponding frequentist designs using either O'Brien-Fleming (OF) or Pocock boundaries, as obtained from EaSt 2000. Our simulation results show that, over a wide variety of different cases, BDOGS either performs at least as well as both OF and Pocock, or on average provides a much smaller trial. ^

Bayesian dual threshold design with Dirichlet distribution: An alternative way to frequentist multi-stage phase II designs in oncology

Many phase II clinical studies in oncology use two-stage frequentist design such as Simon's optimal design. However, they have a common logistical problem regarding the patient accrual at the interim. Strictly speaking, patient accrual at the end of the first stage may have to be suspended until all patients have events, success or failure. For example, when the study endpoint is six-month progression free survival, patient accrual has to be stopped until all outcomes from stage I is observed. However, study investigators may have concern when accrual is suspended after the first stage due to the loss of accrual momentum during this hiatus. We propose a two-stage phase II design that resolves the patient accrual problem due to an interim analysis, and it can be used as an alternative way to frequentist two-stage phase II studies in oncology. ^

The connection between E2F and ATM in p53-dependent apoptosis

Programmed cell death is an anticancer mechanism utilized by p53 that when disrupted can accelerate tumor development in response to oncogenic stress. Defects in the RB tumor suppressor cause aberrant cell proliferation as well as apoptosis. The combinatorial loss of the p53 and RB pathways is observed in a large percentage of human tumors. The E2F family of transcription factors primarily mediates the phenotype of Rb loss, since RB is a negative regulator of E2F. Contrary to early expectations, it has now been shown that the ARF (alternative reading frame) tumor suppressor is not required for p53-dependent apoptosis in response to deregulation of the RB/E2F pathway. In this study, we demonstrate that ATM, known as a DNA double-strand break (DSB) sensor, is responsible for ARF-independent apoptosis and p53 activation induced by deregulated E2F1. Moreover, NBS1, a component of the MRN DNA repair complex, is also required for E2F1-induced apoptosis and apparently works in the same pathway as ATM. We further found that endogenous E2F1 and E2F3 both play a role in apoptosis and ATM activation in response to inhibition of RB by the adenoviral E1A oncoprotein. We demonstrate that, unlike deregulated E2F3 and Myc, ATM activation by deregulated E2F1 does not involve the induction of DNA damage, autophosphorylation of ATM on Ser 1981, a marker of ATM activation by DSB, but does depend on the presence of NBS1, suggesting that E2F1 activates ATM in a different manner from E2F3 and Myc. Results from domain mapping studies show that the DNA binding, dimerization, and marked box domains of E2F1 are required to activate ATM and stimulate apoptosis but the transactivation domain is not. This implies that E2F1's DNA binding and interaction with other proteins through the marked box domain are necessary to induce ATM activation leading to apoptosis but transcriptional activation by E2F1 is dispensable. Together these data suggest a model in which E2F1 activates ATM to phosphorylate p53 through a novel mechanism that is independent of DNA damage and transcriptional activation by E2F1.^

Bayesian adaptive designs for drug combinations in early phase clinical trials

Treating patients with combined agents is a growing trend in cancer clinical trials. Evaluating the synergism of multiple drugs is often the primary motivation for such drug-combination studies. Focusing on the drug combination study in the early phase clinical trials, our research is composed of three parts: (1) We conduct a comprehensive comparison of four dose-finding designs in the two-dimensional toxicity probability space and propose using the Bayesian model averaging method to overcome the arbitrariness of the model specification and enhance the robustness of the design; (2) Motivated by a recent drug-combination trial at MD Anderson Cancer Center with a continuous-dose standard of care agent and a discrete-dose investigational agent, we propose a two-stage Bayesian adaptive dose-finding design based on an extended continual reassessment method; (3) By combining phase I and phase II clinical trials, we propose an extension of a single agent dose-finding design. We model the time-to-event toxicity and efficacy to direct dose finding in two-dimensional drug-combination studies. We conduct extensive simulation studies to examine the operating characteristics of the aforementioned designs and demonstrate the designs' good performances in various practical scenarios.^

Bayesian Adaptive Designs for Early Phase Clinical Trials

My dissertation focuses mainly on Bayesian adaptive designs for phase I and phase II clinical trials. It includes three specific topics: (1) proposing a novel two-dimensional dose-finding algorithm for biological agents, (2) developing Bayesian adaptive screening designs to provide more efficient and ethical clinical trials, and (3) incorporating missing late-onset responses to make an early stopping decision. Treating patients with novel biological agents is becoming a leading trend in oncology. Unlike cytotoxic agents, for which toxicity and efficacy monotonically increase with dose, biological agents may exhibit non-monotonic patterns in their dose-response relationships. Using a trial with two biological agents as an example, we propose a phase I/II trial design to identify the biologically optimal dose combination (BODC), which is defined as the dose combination of the two agents with the highest efficacy and tolerable toxicity. A change-point model is used to reflect the fact that the dose-toxicity surface of the combinational agents may plateau at higher dose levels, and a flexible logistic model is proposed to accommodate the possible non-monotonic pattern for the dose-efficacy relationship. During the trial, we continuously update the posterior estimates of toxicity and efficacy and assign patients to the most appropriate dose combination. We propose a novel dose-finding algorithm to encourage sufficient exploration of untried dose combinations in the two-dimensional space. Extensive simulation studies show that the proposed design has desirable operating characteristics in identifying the BODC under various patterns of dose-toxicity and dose-efficacy relationships. Trials of combination therapies for the treatment of cancer are playing an increasingly important role in the battle against this disease. To more efficiently handle the large number of combination therapies that must be tested, we propose a novel Bayesian phase II adaptive screening design to simultaneously select among possible treatment combinations involving multiple agents. Our design is based on formulating the selection procedure as a Bayesian hypothesis testing problem in which the superiority of each treatment combination is equated to a single hypothesis. During the trial conduct, we use the current values of the posterior probabilities of all hypotheses to adaptively allocate patients to treatment combinations. Simulation studies show that the proposed design substantially outperforms the conventional multi-arm balanced factorial trial design. The proposed design yields a significantly higher probability for selecting the best treatment while at the same time allocating substantially more patients to efficacious treatments. The proposed design is most appropriate for the trials combining multiple agents and screening out the efficacious combination to be further investigated. The proposed Bayesian adaptive phase II screening design substantially outperformed the conventional complete factorial design. Our design allocates more patients to better treatments while at the same time providing higher power to identify the best treatment at the end of the trial. Phase II trial studies usually are single-arm trials which are conducted to test the efficacy of experimental agents and decide whether agents are promising to be sent to phase III trials. Interim monitoring is employed to stop the trial early for futility to avoid assigning unacceptable number of patients to inferior treatments. We propose a Bayesian single-arm phase II design with continuous monitoring for estimating the response rate of the experimental drug. To address the issue of late-onset responses, we use a piece-wise exponential model to estimate the hazard function of time to response data and handle the missing responses using the multiple imputation approach. We evaluate the operating characteristics of the proposed method through extensive simulation studies. We show that the proposed method reduces the total length of the trial duration and yields desirable operating characteristics for different physician-specified lower bounds of response rate with different true response rates.

DETERMINING THE GENOTYPE-PHENOTYPE CONNECTION IN SYNTHETIC INDUCIBLE GENE EXPRESSION SYSTEMS

Introduction Gene expression is an important process whereby the genotype controls an individual cell’s phenotype. However, even genetically identical cells display a variety of phenotypes, which may be attributed to differences in their environment. Yet, even after controlling for these two factors, individual phenotypes still diverge due to noisy gene expression. Synthetic gene expression systems allow investigators to isolate, control, and measure the effects of noise on cell phenotypes. I used mathematical and computational methods to design, study, and predict the behavior of synthetic gene expression systems in S. cerevisiae, which were affected by noise. Methods I created probabilistic biochemical reaction models from known behaviors of the tetR and rtTA genes, gene products, and their gene architectures. I then simplified these models to account for essential behaviors of gene expression systems. Finally, I used these models to predict behaviors of modified gene expression systems, which were experimentally verified. Results Cell growth, which is often ignored when formulating chemical kinetics models, was essential for understanding gene expression behavior. Models incorporating growth effects were used to explain unexpected reductions in gene expression noise, design a set of gene expression systems with “linear” dose-responses, and quantify the speed with which cells explored their fitness landscapes due to noisy gene expression. Conclusions Models incorporating noisy gene expression and cell division were necessary to design, understand, and predict the behaviors of synthetic gene expression systems. The methods and models developed here will allow investigators to more efficiently design new gene expression systems, and infer gene expression properties of TetR based systems.

THE NEW ROLE OF PROPROTEIN CONVERTASE SUBTILISIN/KEXIN TYPE 9: A CONNECTION OF PROPROTEIN CONVERTASE SUBTILISIN/KEXIN TYPE 9, APOLIPOPROTEIN B, and AUTOPHAGY

Plasma low-density lipoprotein (LDL) levels are positively correlated with the incidence of coronary artery disease. In the circulation, the plasma LDL clearance is mainly achieved by the uptake via LDL receptor (LDLR). Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a newly discovered gene, playing an important role in LDL metabolism. Gain-of-function mutations of PCSK9 lead to hypercholesterolemia and loss-of-function mutations of PCSK9 are associated with decrease of LDL cholesterol. The effects of PCSK9 on cholesterol levels are the consequence of a strong interaction between the catalytic domain of PCSK9 and epidermal growth factor-like repeat A (EGF-A) domain of LDLR on the cell surface of hepatocytes. This PCSK9/LDLR complex enters the cell via endocytosis, where both PCSK9 and LDLR are removed via the lysosome pathway, resulting in decreased levels of LDLR and accumulation of LDL in the plasma. However, whether this is the exclusive function of PCSK9 on LDL metabolism was challenged by us; we observed PCSK9 interacted with apolipoprotein B (apoB) and increased apoB production, irrespective of the LDLR. ApoB is the primary structure protein of LDL particle and it also serves as the ligand for the LDL receptor. There is ample evidence showing that the levels of apoB are a better indicator for heart disease than either total cholesterol or LDL cholesterol levels. We used a second-generation adenoviral vector to overexpress PCSK9 (Ad-PCSK9) in wild-type C57BL/6 and LDLR deficient mice (Ldlr-/- and Ldlr-/-Apobec1-/-). Our study revealed that overexpression of PCSK9 promoted the production and secretion of apoB in the form of very-low density lipoprotein (VLDL), which is the precursor of LDL, in the 3 mouse models studied (C57BL/6J, Ldlr-/-, and Ldlr-/-Apobec1-/-). The increased apoB production in mice was regulated at post-transcriptional levels, since there was no difference in apoB mRNA levels between mice treated with Ad-PCSK9 and control vector Ad-Null. By using pulse-chase experiment on primary hepatocytes, we showed that overexpression of PCSK9 increased the secretion of apoB, independent of LDLR. In the circulation, we showed that PCSK9 was associated with LDL particles. By using 3 different protein–protein interaction assays of co-immunoprecipitation, mammalian two-hybrid system, and in situ proximity ligation assay, we demonstrated a direct protein–protein interaction between PCSK9 and apoB. The impact of this interaction inhibited the physiological removal process of apoB via autophagosome/lysosome pathway in an LDLR-independent fashion, resulting in increased production and secretion of apoB-containing lipoproteins. The significance of this process was shown in the Pcsk9 knockout mice in the background of Ldlr-/-Apobec1-/- mice (triple knockout mice); in the absence of Pcsk9 (triple knockout mice) the levels of cholesterol, triacylglycerol, and apoB decreased significantly in comparison to that of Ldlr-/-Apobec1-/- mice. Taken together, our study demonstrated a direct intracellular interaction of PCSK9 with apoB, resulting in the inhibition of apoB degradation via the autophagosome/lysosome pathway independent of LDLR. This discovery provides a new concept of the importance of PCSK9 and suggests new approaches for the therapeutic intervention of hyperlipidemia.

Bayesian adaptive designs in early phase clinical trials

There are two practical challenges in the phase I clinical trial conduct: lack of transparency to physicians, and the late onset toxicity. In my dissertation, Bayesian approaches are used to address these two problems in clinical trial designs. The proposed simple optimal designs cast the dose finding problem as a decision making process for dose escalation and deescalation. The proposed designs minimize the incorrect decision error rate to find the maximum tolerated dose (MTD). For the late onset toxicity problem, a Bayesian adaptive dose-finding design for drug combination is proposed. The dose-toxicity relationship is modeled using the Finney model. The unobserved delayed toxicity outcomes are treated as missing data and Bayesian data augment is employed to handle the resulting missing data. Extensive simulation studies have been conducted to examine the operating characteristics of the proposed designs and demonstrated the designs' good performances in various practical scenarios.^

On Bayesian seamless phase I-II designs

Early phase clinical trial designs have long been the focus of interest for clinicians and statisticians working in oncology field. There are several standard phse I and phase II designs that have been widely-implemented in medical practice. For phase I design, the most commonly used methods are 3+3 and CRM. A newly-developed Bayesian model-based mTPI design has now been used by an increasing number of hospitals and pharmaceutical companies. The advantages and disadvantages of these three top phase I designs have been discussed in my work here and their performances were compared using simulated data. It was shown that mTPI design exhibited superior performance in most scenarios in comparison with 3+3 and CRM designs. ^ The next major part of my work is proposing an innovative seamless phase I/II design that allows clinicians to conduct phase I and phase II clinical trials simultaneously. Bayesian framework was implemented throughout the whole design. The phase I portion of the design adopts mTPI method, with the addition of futility rule which monitors the efficacy performance of the tested drugs. Dose graduation rules were proposed in this design to allow doses move forward from phase I portion of the study to phase II portion without interrupting the ongoing phase I dose-finding schema. Once a dose graduated to phase II, adaptive randomization was used to randomly allocated patients into different treatment arms, with the intention of more patients being assigned to receive more promising dose(s). Again simulations were performed to compare the performance of this innovative phase I/II design with a recently published phase I/II design, together with the conventional phase I and phase II designs. The simulation results indicated that the seamless phase I/II design outperform the other two competing methods in most scenarios, with superior trial power and the fact that it requires smaller sample size. It also significantly reduces the overall study time. ^ Similar to other early phase clinical trial designs, the proposed seamless phase I/II design requires that the efficacy and safety outcomes being able to be observed in a short time frame. This limitation can be overcome by using validated surrogate marker for the efficacy and safety endpoints.^

Optimal, minimax, and Bayesian two-stage designs in two-dose phase II clinical trials

Background: For most cytotoxic and biologic anti-cancer agents, the response rate of the drug is commonly assumed to be non-decreasing with an increasing dose. However, an increasing dose does not always result in an appreciable increase in the response rate. This may especially be true at high doses for a biologic agent. Therefore, in a phase II trial the investigators may be interested in testing the anti-tumor activity of a drug at more than one (often two) doses, instead of only at the maximum tolerated dose (MTD). This way, when the lower dose appears equally effective, this dose can be recommended for further confirmatory testing in a phase III trial under potential long-term toxicity and cost considerations. A common approach to designing such a phase II trial has been to use an independent (e.g., Simon's two-stage) design at each dose ignoring the prior knowledge about the ordering of the response probabilities at the different doses. However, failure to account for this ordering constraint in estimating the response probabilities may result in an inefficient design. In this dissertation, we developed extensions of Simon's optimal and minimax two-stage designs, including both frequentist and Bayesian methods, for two doses that assume ordered response rates between doses. ^ Methods: Optimal and minimax two-stage designs are proposed for phase II clinical trials in settings where the true response rates at two dose levels are ordered. We borrow strength between doses using isotonic regression and control the joint and/or marginal error probabilities. Bayesian two-stage designs are also proposed under a stochastic ordering constraint. ^ Results: Compared to Simon's designs, when controlling the power and type I error at the same levels, the proposed frequentist and Bayesian designs reduce the maximum and expected sample sizes. Most of the proposed designs also increase the probability of early termination when the true response rates are poor. ^ Conclusion: Proposed frequentist and Bayesian designs are superior to Simon's designs in terms of operating characteristics (expected sample size and probability of early termination, when the response rates are poor) Thus, the proposed designs lead to more cost-efficient and ethical trials, and may consequently improve and expedite the drug discovery process. The proposed designs may be extended to designs of multiple group trials and drug combination trials.^