Mapping T-cell epitopes of the rubella virus structural proteins

Rubella virus (RV) typically causes a mild childhood illness, but complications can result from both viral and immune-mediated pathogenesis. RV can persist in the presence of neutralizing antibodies, suggesting that cell-mediated immune responses may be necessary for viral clearance. However, the molecular determinants recognized by RV-specific T-cells have not been identified. Using recombinant proteins which express the entire RV structural open reading frame in proliferation assays with lymphocytes of RV-immune individuals, domains which elicit major histocompatibility complex class II-restricted helper T-cells were identified. Synthetic peptides representing these domains were used to define specific epitopes. Two immunodominant domains were mapped to the capsid protein sequence C$\sb1$-C$\sb{29}$ and the E1 glycoprotein sequence E1$\sb{202}$-E1$\sb{283}.$ RV-specific MHC class I-restricted cytotoxic T lymphocytes (CTLs) were identified using a chromium-release assay with infected fibroblasts as target cells. An infectious Sindbis virus vector expressing each of the RV structural proteins identified the capsid, E2 and E1 proteins as targets of CTLs. Specific CTL epitopes were mapped within the previously identified immunodominant domains. This study identified domains of the RV structural proteins that may be beneficial for development of a synthetic vaccine, and provides normative data on RV-specific T-cell responses that should enhance our ability to understand RV persistence and associated complications. ^

Immune responses against major histocompatibility complex and interleukin-2 gene transfected K1735 murine melanoma: Potential vaccines for the immunotherapy of cancer

Tumor specific immunity is mediated by cytotoxic T lymphocytes (CTL) that recognize peptide antigen (Ag) in the context of major histocompatibility complex (MHC) class I molecules and by helper T (Th) lymphocytes that recognize peptide Ag in the context of MHC class II molecules. The purpose of this study is (1) to induce or augment the immunogenicity of nonimmunogenic or weakly immunogenic tumors by genetic modification of tumor cells, and (2) to use these genetically altered cells in cancer immunotherapy. To study this, I transfected a highly tumorigenic murine melanoma cell line (K1735) that did not express constitutively either MHC class I or II molecules with syngeneic cloned MHC class I and/or class II genes, and then determined the tumorigenicity of transfected cells in normal C3H mice. K1735 transfectants expressing either $\rm K\sp{k}$ or $\rm A\sp{k}$ molecules alone produced tumors in normal C3H mice, whereas most transfectants that expressed both molecules were rejected in normal C3H mice but produced tumors in nude mice. The rejection of K1735 transfectants expressing $\rm K\sp{k}$ and $\rm A\sp{k}$ Ag in normal C3H mice required both $\rm CD4\sp+$ and $\rm CD8\sp+$ T cells. Interestingly, the $\rm A\sp{k}$ requirement can be substituted by IL-2 because transfection of $\rm K\sp{k}$-positive/A$\sp{\rm k}$-negative K1735 cells with the IL-2 gene also resulted in abrogation of tumorigenicity in normal C3H mice but not in nude mice. In addition, 1735 $(\rm I\sp+II\sp+)$ transfected cells can function as antigen presenting cells (APC) since they could process and present native hen egg lysozyme (HEL) to HEL specific T cell hybridomas. Furthermore, the transplantation immunity induced by K1735 transfectants expressing both $\rm K\sp{k}$ and $\rm A\sp{k}$ molecules completely cross-protected mice against challenge with $\rm K\sp{k}$-positive transfectants but weakly protected them against challenge with parental K1735 cells or $\rm A\sp{k}$-positive transfectants. Finally, I demonstrated that MHC $(\rm I\sp+II\sp+)$ or $\rm K\sp{k}$-positive/IL-2-positive cells can function as anti-cancer vaccines since they can abrogate the growth of established tumors and metastasis.^ In summary, my results indicate that expression of either MHC class I or II molecule alone is insufficient to cause the rejection of K1735 melanoma in syngeneic hosts and that both molecules are necessary. In addition, my data suggest that the failure of $\rm K\sp{k}$-positive K1735 cells to induce a primary tumor-rejection response in normal C3H mice may be due to their inability to induce the helper arm of the anti-tumor immune response. Finally, the ability of MHC $(\rm I\sp+II\sp+)$ or $\rm K\sp{k}$-positive/IL-2-positive cells to prevent growth of established tumors or metastasis suggests that these cell lines can serve as potential vaccines for the immunotherapy of cancer. (Abstract shortened by UMI.) ^

Molecular analysis of the human $\gamma$-chain T cell receptor genes

In our studies we have focused on the issue of variability and diversity of the $\gamma$ (or $\delta)$ chain T cell receptor (TCR) genes by studying cDNA transcripts in peripheral blood mononuclear cells or $\gamma\delta$ TCR+ T cell clones. The significance of these studies lies in the better understanding of the molecular biology of the $\gamma\delta$ T cell receptor as well as in answering the question whether certain molecular forms predominate in $\gamma\delta$ T cells exhibiting specific immunologic functions. We establish that certain $\gamma$-chain TCR genes exhibit particular patterns of rearrangements in cDNA transcripts in normal individuals. V$\gamma$I subgroup were shown to preferentially rearrange to J$\gamma$2C$\gamma$2 gene segments. These preferential VJC rearrangements, may have implications regarding the potential for diversity and polymorphism of the $\gamma$-chain TCR gene. In addition, the preferential association of V$\gamma$I genes with J$\gamma$2C$\gamma$2, which encode a non-disulfide-linked $\gamma\delta$ TCR, suggests that $\gamma$ chains utilizing V$\gamma$I are predominantly expressed as non-disulfide-linked $\gamma\delta$ TCR heterodimers. The implications of this type of expression remain to be determined. We identified two alternative splicing events of the $\gamma$-chain TCR genes occurring in high frequency in all the normal individuals examined. These events may suggest additional mechanisms of regulation and control as well as diversification of $\gamma\delta$ TCR gene expression. The question whether particular forms of $\gamma$ or $\delta$-chain TCR genes are involved in HLA Class I recognition by specific $\gamma\delta$ cytotoxic T cell clones was addressed. Our results indicated that the T cell clones expressed identical $\gamma$ but distinct $\delta$-chains suggesting that the specificity for recognition of HLA-A2 or HLA-A3 may be conferred by the $\delta$-chain TCR. The issue of the degree of diversity and polymorphism of the $\delta$-chain TCR genes in a patient with a primary immunodeficiency (Omenn's syndrome) was addressed. A limited pattern of rearrangements in peripheral blood transcripts was found, suggesting that a limited $\gamma\delta$ TCR repertoire may be expressed in this particular primary immunodeficiency syndrome. Overall, our findings suggest that $\delta$-chain TCR genes exhibit the potential for significant diversity and that there are certain preferential patterns of expression that may be associated with particular immunologic functions. ^

Single nucleotide polymorphism (SNP) selection for genotype-phenotype association studies

With hundreds of single nucleotide polymorphisms (SNPs) in a candidate gene and millions of SNPs across the genome, selecting an informative subset of SNPs to maximize the ability to detect genotype-phenotype association is of great interest and importance. In addition, with a large number of SNPs, analytic methods are needed that allow investigators to control the false positive rate resulting from large numbers of SNP genotype-phenotype analyses. This dissertation uses simulated data to explore methods for selecting SNPs for genotype-phenotype association studies. I examined the pattern of linkage disequilibrium (LD) across a candidate gene region and used this pattern to aid in localizing a disease-influencing mutation. The results indicate that the r2 measure of linkage disequilibrium is preferred over the common D′ measure for use in genotype-phenotype association studies. Using step-wise linear regression, the best predictor of the quantitative trait was not usually the single functional mutation. Rather it was a SNP that was in high linkage disequilibrium with the functional mutation. Next, I compared three strategies for selecting SNPs for application to phenotype association studies: based on measures of linkage disequilibrium, based on a measure of haplotype diversity, and random selection. The results demonstrate that SNPs selected based on maximum haplotype diversity are more informative and yield higher power than randomly selected SNPs or SNPs selected based on low pair-wise LD. The data also indicate that for genes with small contribution to the phenotype, it is more prudent for investigators to increase their sample size than to continuously increase the number of SNPs in order to improve statistical power. When typing large numbers of SNPs, researchers are faced with the challenge of utilizing an appropriate statistical method that controls the type I error rate while maintaining adequate power. We show that an empirical genotype based multi-locus global test that uses permutation testing to investigate the null distribution of the maximum test statistic maintains a desired overall type I error rate while not overly sacrificing statistical power. The results also show that when the penetrance model is simple the multi-locus global test does as well or better than the haplotype analysis. However, for more complex models, haplotype analyses offer advantages. The results of this dissertation will be of utility to human geneticists designing large-scale multi-locus genotype-phenotype association studies. ^

A full pedigree based method for the statistical assessment of genetic anticipation

Genetic anticipation is defined as a decrease in age of onset or increase in severity as the disorder is transmitted through subsequent generations. Anticipation has been noted in the literature for over a century. Recently, anticipation in several diseases including Huntington's Disease, Myotonic Dystrophy and Fragile X Syndrome were shown to be caused by expansion of triplet repeats. Anticipation effects have also been observed in numerous mental disorders (e.g. Schizophrenia, Bipolar Disorder), cancers (Li-Fraumeni Syndrome, Leukemia) and other complex diseases. ^ Several statistical methods have been applied to determine whether anticipation is a true phenomenon in a particular disorder, including standard statistical tests and newly developed affected parent/affected child pair methods. These methods have been shown to be inappropriate for assessing anticipation for a variety of reasons, including familial correlation and low power. Therefore, we have developed family-based likelihood modeling approaches to model the underlying transmission of the disease gene and penetrance function and hence detect anticipation. These methods can be applied in extended families, thus improving the power to detect anticipation compared with existing methods based only upon parents and children. The first method we have proposed is based on the regressive logistic hazard model. This approach models anticipation by a generational covariate. The second method allows alleles to mutate as they are transmitted from parents to offspring and is appropriate for modeling the known triplet repeat diseases in which the disease alleles can become more deleterious as they are transmitted across generations. ^ To evaluate the new methods, we performed extensive simulation studies for data simulated under different conditions to evaluate the effectiveness of the algorithms to detect genetic anticipation. Results from analysis by the first method yielded empirical power greater than 87% based on the 5% type I error critical value identified in each simulation depending on the method of data generation and current age criteria. Analysis by the second method was not possible due to the current formulation of the software. The application of this method to Huntington's Disease and Li-Fraumeni Syndrome data sets revealed evidence for a generation effect in both cases. ^

The genetic contribution of the major histocompatibility complex to bleomycin-induced lung injury

Pulmonary fibrosis (PF) is the result of a variety of environmental and cancer treatment related insults and is characterized by excessive deposition of collagen. Gas exchange in the alveoli is impaired as the normal lung becomes dense and collapsed leading to a loss of lung volume. It is now accepted that lung injury and fibrosis are in part genetically regulated. ^ Bleomycin is a chemotherapeutic agent used for testicular cancer and lymphomas that induces significant pulmonary toxicity. We delivered bleomycin to mice subcutaneously via a miniosmotic pump in order to elicit lung injury (LI) and quantified the %LI morphometrically using video imaging software. We previously identified a quantitative trait loci, Blmpf-1(LOD=17.4), in the Major Histocompatibility Complex (MHC), but the exact genetic components involved have remained unknown. ^ In the current studies, Blmpf-1 was narrowed to an interval spanning 31.9-32.9Mb on Chromosome 17 using MHC Congenic mice. This region includes the MHC Class II and III genes, and is flanked by the TNF-alpha super locus and MHC Class I genes. Knockout mice of MHC Class I genes (B2mko), MHC Class II genes (Cl2ko), and TNF-alpha (TNF-/-) and its receptors (p55-/-, p75-/-, and p55/p75-/-) were treated with bleomycin in order to ascertain the role of these genes in the pathogenesis of lung injury. ^ Cl2ko mice had significantly better survival and %LI when compared to treated background BL/6 (B6, P<.05). In contrast, B2mko showed no differences in survival or %LI compared to B6. This suggests that the MHC Class II locus contains susceptibility genes for bleomycin-induced lung injury. ^ TNF-alpha, a Class III gene, was examined and it was found that TNF-/- and p55-/- mice had higher %LI and lower survival when compared to B6 (P<.05). In contrast, p75-/- mice had significantly reduced %LI when compared to TNF-/-, p55-/-, and B6 mice as well as higher survival (P<.01). These data contradict the current paradigm that TNF-alpha is a profibrotic mediator of lung injury and suggest a novel and distinct role for the p55 and p75 receptors in mediating lung injury. ^

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. ^

Ambulatory blood pressure and physical activity in heart failure

Background. Ambulatory blood pressure (ABP) measurement is a means of monitoring cardiac function in a noninvasive way, but little is known about ABP in heart failure (HF) patients. Blood pressure (BP) declines during sleep as protection from consistent BP load, a phenomenon termed "dipping." The aims of this study were (1) to compare BP dipping and physical activity between two groups of HF patients with different functional statuses and (2) to determine whether the strength of the association between ambulatory BP and PA is different between these two different functional statuses of HF. ^ Methods. This observational study used repeated measures of ABP and PA over a 24-hour period to investigate the profiles of BP and PA in community-based individuals with HF. ABP was measured every 30 minutes by using a SpaceLabs 90207, and a Basic Motionlogger actigraph was used to measure PA minute by minute. Fifty-six participants completed both BP and physical activity for a 24-hour monitoring period. Functional status was based on New York Heart Association (NYHA) ratings. There were 27 patients with no limitation of PA (NYHA class I HF) and 29 with some limitation of PA but no discomfort at rest (NYHA class II or III HF). The sample consisted of 26 men and 30 women, aged 45 to 91 years (66.96 ± 12.35). ^ Results. Patients with NYHA class I HF had significantly greater dipping percent than those with NYHA class II/III HF after controlling their left ventricular ejection fraction (LVEF). In a mixed model analysis (PROC MIXED, SAS Institute, v 9.1), PA was significantly related to ambulatory systolic and diastolic BP and mean arterial pressure. The strength of the association between PA and ABP readings was not significantly different for the two groups of patients. ^ Conclusions. These preliminary findings demonstrate differences between NYHA class I and class II/III of HF in BP dipping status and ABP but not PA. Longitudinal research is recommended to improve understanding of the influence of disease progression on changes in 24-hour physical activity and BP profiles of this patient population. ^ Key Words. Ambulatory Blood Pressure; Blood Pressure Dipping; Heart Failure; Physical Activity. ^

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. ^

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.^

Functional data analysis approaches for genotype-phenotype association studies from next-generation sequencing

Next-generation DNA sequencing platforms can effectively detect the entire spectrum of genomic variation and is emerging to be a major tool for systematic exploration of the universe of variants and interactions in the entire genome. However, the data produced by next-generation sequencing technologies will suffer from three basic problems: sequence errors, assembly errors, and missing data. Current statistical methods for genetic analysis are well suited for detecting the association of common variants, but are less suitable to rare variants. This raises great challenge for sequence-based genetic studies of complex diseases.^ This research dissertation utilized genome continuum model as a general principle, and stochastic calculus and functional data analysis as tools for developing novel and powerful statistical methods for next generation of association studies of both qualitative and quantitative traits in the context of sequencing data, which finally lead to shifting the paradigm of association analysis from the current locus-by-locus analysis to collectively analyzing genome regions.^ In this project, the functional principal component (FPC) methods coupled with high-dimensional data reduction techniques will be used to develop novel and powerful methods for testing the associations of the entire spectrum of genetic variation within a segment of genome or a gene regardless of whether the variants are common or rare.^ The classical quantitative genetics suffer from high type I error rates and low power for rare variants. To overcome these limitations for resequencing data, this project used functional linear models with scalar response to develop statistics for identifying quantitative trait loci (QTLs) for both common and rare variants. To illustrate their applications, the functional linear models were applied to five quantitative traits in Framingham heart studies. ^ This project proposed a novel concept of gene-gene co-association in which a gene or a genomic region is taken as a unit of association analysis and used stochastic calculus to develop a unified framework for testing the association of multiple genes or genomic regions for both common and rare alleles. The proposed methods were applied to gene-gene co-association analysis of psoriasis in two independent GWAS datasets which led to discovery of networks significantly associated with psoriasis.^

A hybrid method in combining treatment effects from matched and unmatched studies

In the biomedical studies, the general data structures have been the matched (paired) and unmatched designs. Recently, many researchers are interested in Meta-Analysis to obtain a better understanding from several clinical data of a medical treatment. The hybrid design, which is combined two data structures, may create the fundamental question for statistical methods and the challenges for statistical inferences. The applied methods are depending on the underlying distribution. If the outcomes are normally distributed, we would use the classic paired and two independent sample T-tests on the matched and unmatched cases. If not, we can apply Wilcoxon signed rank and rank sum test on each case. ^ To assess an overall treatment effect on a hybrid design, we can apply the inverse variance weight method used in Meta-Analysis. On the nonparametric case, we can use a test statistic which is combined on two Wilcoxon test statistics. However, these two test statistics are not in same scale. We propose the Hybrid Test Statistic based on the Hodges-Lehmann estimates of the treatment effects, which are medians in the same scale.^ To compare the proposed method, we use the classic meta-analysis T-test statistic on the combined the estimates of the treatment effects from two T-test statistics. Theoretically, the efficiency of two unbiased estimators of a parameter is the ratio of their variances. With the concept of Asymptotic Relative Efficiency (ARE) developed by Pitman, we show ARE of the hybrid test statistic relative to classic meta-analysis T-test statistic using the Hodges-Lemann estimators associated with two test statistics.^ From several simulation studies, we calculate the empirical type I error rate and power of the test statistics. The proposed statistic would provide effective tool to evaluate and understand the treatment effect in various public health studies as well as clinical trials.^

Bayesian estimation of multilevel item response model with missing data

This study proposed a novel statistical method that modeled the multiple outcomes and missing data process jointly using item response theory. This method follows the "intent-to-treat" principle in clinical trials and accounts for the correlation between outcomes and missing data process. This method may provide a good solution to chronic mental disorder study. ^ The simulation study demonstrated that if the true model is the proposed model with moderate or strong correlation, ignoring the within correlation may lead to overestimate of the treatment effect and result in more type I error than specified level. Even if the within correlation is small, the performance of proposed model is as good as naïve response model. Thus, the proposed model is robust for different correlation settings if the data is generated by the proposed model.^

Targeting Histone deacetylases (HDAC) for the treatment of soft tissue sarcoma

Targeting Histone deacetylases (HDAC) for the treatment of genetically complex soft tissue sarcoma Histone deactylase inhibitors (HDACi) are a new class of anticancer therapeutics; however, little is known about HDACi or the individual contribution of HDAC isoform activity in soft tissue sarcoma (STS). We investigated the potential efficacy of HDACi as monotherapy and in combination with chemotherapy in a panel of genetically complex STS. We found that HDACi combined with chemotherapy significantly induced anti-STS effects in vitro and in vivo. We then focused our study of HDACi in malignant peripheral nerve sheath tumor (MPNST), a subtype of highly aggressive, therapeutically resistant, and commonly fatal malignancies that occur in patients with neurofibromatosis type-1 (NF1) or sporadically. The therapeutic efficacy of HDACi was investigated in a panel of NF1-associated and sporadic MPNST cell lines. Our results demonstrate the NF1-assocaited cohort to be highly sensitive to HDACi while sporadic cell lines exhibited resistance. HDACi-induced productive autophagy was found to be a mode of resistance and inhibiting HDACi-induced autophagy significantly induced pro-apoptotic effects of HDACi in vitro and in vivo. HDACs are not a single enzyme consisting of 11 currently known isoforms. HDACis used in these studies inhibit a variety of these isoforms, namely class I HDACs which include HDAC1, 2, 3, and 8. Recently, HDAC8-specific inhibitors (HDAC8i) have been created and tested in various cancer cell lines. Lastly, the potential therapeutic efficacy of HDAC8i was investigated in human (NF1-associated and sporadic) and NF1-associated murine-derived MPNST. HDAC8i abrogated cell growth in human and murine-derived MPNST cells. Similar to the pattern noticed with pan-HDACis NF1-associated cells, especially murine-derived, were more sensitive to HDAC8i compared to human sporadic MPNST cell lines. S-phase arrest was observed in human and murine MPNST cells, independent of p53 mutational and NF1 status. HDAC8i induced apoptosis is all cell lines tested, with a more pronounced effects in human and murine-derived NF1-associated cells. Most importantly, HDAC8i abrogated murine-derived MPNST xenograft growth in vivo. Taken together, these findings support the evaluation of pan-HDACi and isoform-specific inhibitors as a novel therapy to treat MPNST, including in combination with autophagy blocking combination regimens in particular for patients with sporadic MPNST.

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.^