Myogenin modulates exercise endurance by altering skeletal muscle metabolism

The function of myogenic regulatory factors (MRFs) during adult life is not well understood. The requirement of one of these MRFs, myogenin (Myog), during embryonic muscle development suggests an equally important role in adult muscle. In this study, we have determined the function of myogenin during adult life using a conditional allele of Myog. In contrast to embryonic development, myogenin is not required for adult viability, and Myog-deleted mice exhibited no remarkable phenotypic changes during sedentary life. Remarkably, sedentary Myog-deleted mice demonstrated enhanced exercise endurance during involuntary treadmill running. Altered blood glucose and lactate levels in sedentary Myog-deleted mice after exhaustion suggest an enhanced glycolytic metabolism and an ability to excessively deplete muscle and liver glycogen stores. Traditional changes associated with enhanced exercise endurance, such as fiber type switching, and increased oxidative potential, were not detected in sedentary Myog-deleted mice. After long-term voluntary exercise, trained Myog-deleted mice demonstrated an enhanced adaptive response to exercise. Trained Myog-deleted mice exhibited superior exercise endurance associated with an increased proportion of slow-twitch fibers and increased oxidative capacity. In a parallel experiment, dystrophin-deficient young adult mice showed attenuated muscle fatigue following the deletion of Myog. These results demonstrate a novel and unexpected role for myogenin in modulating skeletal muscle metabolism.

Apply response adaptive randomization to group sequential with early stopping

Group sequential methods and response adaptive randomization (RAR) procedures have been applied in clinical trials due to economical and ethical considerations. Group sequential methods are able to reduce the average sample size by inducing early stopping, but patients are equally allocated with half of chance to inferior arm. RAR procedures incline to allocate more patients to better arm; however it requires more sample size to obtain a certain power. This study intended to combine these two procedures. We applied the Bayesian decision theory approach to define our group sequential stopping rules and evaluated the operating characteristics under RAR setting. The results showed that Bayesian decision theory method was able to preserve the type I error rate as well as achieve a favorable power; further by comparing with the error spending function method, we concluded that Bayesian decision theory approach was more effective on reducing average sample size.^

Outcome-based adaptive randomization for binary data in clinical trials---Compare and contrast

Monte Carlo simulation has been conducted to investigate parameter estimation and hypothesis testing in some well known adaptive randomization procedures. The four urn models studied are Randomized Play-the-Winner (RPW), Randomized Pôlya Urn (RPU), Birth and Death Urn with Immigration (BDUI), and Drop-the-Loses Urn (DL). Two sequential estimation methods, the sequential maximum likelihood estimation (SMLE) and the doubly adaptive biased coin design (DABC), are simulated at three optimal allocation targets that minimize the expected number of failures under the assumption of constant variance of simple difference (RSIHR), relative risk (ORR), and odds ratio (OOR) respectively. Log likelihood ratio test and three Wald-type tests (simple difference, log of relative risk, log of odds ratio) are compared in different adaptive procedures. ^ Simulation results indicates that although RPW is slightly better in assigning more patients to the superior treatment, the DL method is considerably less variable and the test statistics have better normality. When compared with SMLE, DABC has slightly higher overall response rate with lower variance, but has larger bias and variance in parameter estimation. Additionally, the test statistics in SMLE have better normality and lower type I error rate, and the power of hypothesis testing is more comparable with the equal randomization. Usually, RSIHR has the highest power among the 3 optimal allocation ratios. However, the ORR allocation has better power and lower type I error rate when the log of relative risk is the test statistics. The number of expected failures in ORR is smaller than RSIHR. It is also shown that the simple difference of response rates has the worst normality among all 4 test statistics. The power of hypothesis test is always inflated when simple difference is used. On the other hand, the normality of the log likelihood ratio test statistics is robust against the change of adaptive randomization procedures. ^

Performance evaluation in Bayesian adaptive randomization

Bayesian adaptive randomization (BAR) is an attractive approach to allocate more patients to the putatively superior arm based on the interim data while maintains good statistical properties attributed to randomization. Under this approach, patients are adaptively assigned to a treatment group based on the probability that the treatment is better. The basic randomization scheme can be modified by introducing a tuning parameter, replacing the posterior estimated response probability, setting a boundary to randomization probabilities. Under randomization settings comprised of the above modifications, operating characteristics, including type I error, power, sample size, imbalance of sample size, interim success rate, and overall success rate, were evaluated through simulation. All randomization settings have low and comparable type I errors. Increasing tuning parameter decreases power, but increases imbalance of sample size and interim success rate. Compared with settings using the posterior probability, settings using the estimated response rates have higher power and overall success rate, but less imbalance of sample size and lower interim success rate. Bounded settings have higher power but less imbalance of sample size than unbounded settings. All settings have better performance in the Bayesian design than in the frequentist design. This simulation study provided practical guidance on the choice of how to implement the adaptive design. ^

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.

Cellular Uptake of Neutrohpil Elastase Links Inflammation to Adaptive Immunity

Many tumors arise from sites of inflammation providing evidence that innate immunity is a critical component in the development and progression of cancer. Neutrophils are primary mediators of the innate immune response. Upon activation, an important function of neutrophils is release of an assortment of proteins from their granules including the serine protease neutrophil elastase (NE). The effect of NE on cancer has been attributed primarily to its ability to degrade the extracellular matrix thereby promoting invasion and metastasis. Recently, it was shown that NE could be taken up by lung cancer cells leading to degradation of insulin receptor substrate-1 thereby promoting hyperactivity of the phosphatidylinositol-3 kinase (PI3K) pathway and tumor cell proliferation. To our knowledge, nobody has investigated uptake of NE by other tumor types. In addition, NE has broad substrate specificity suggesting that uptake of NE by tumor cells could impact processes regulating tumorigenensis other than activation of the PI3K pathway. Neutrophil elastase has been identified in breast cancer specimens where high levels of NE have prognostic significance. These studies have assessed NE levels in whole tumor lysates. Because the major source of NE is from activated neutrophils, we hypothesized that breast cancer cells do not have endogenous NE but may take up NE released by tumor associated neutrophils in the tumor microenvironment and that this could provide a link between the innate immune response to tumors and specific adaptive immune responses. In this thesis, we show that breast cancer cells lack endogenous NE expression and that they are able to take up NE resulting in increased generation of low molecular weight cyclin E (CCNE) and enhanced susceptibility to lysis by CCNE-specific cytotoxic T lymphocytes. We also show that after taking up NE and proteinase 3 (PR3), a second primary granule protease with significant homology to NE, breast cancer cells cross-present the NE- and PR3-derived peptide PR1 rendering them susceptible to PR1-targeted therapies. Taken together, our data support a role for NE uptake in modulating adaptive immune responses against breast cancer.