Gene transfer by viral vectors

To initiate our clinical trial for chemotherapy protection, I established the retroviral vector system for human MDR1 cDNA gene transfer. The human MDR1 cDNA continued to be expressed in the transduced bone marrow cells after four cohorts of serial transplants, 17 months after the initial transduction and transplant. In addition, we used this retroviral vector pVMDR1 to transduce human bone marrow and peripheral blood CD34$\sp+$ cells on stromal monolayer in the presence of hematopoietic growth factors. These data suggest that the retroviral vector pVMDR1 could modify hematopoietic precursor cells with a capacity for long-term self renewal. Thus, it may be possible to use the MDR1 retroviruses to confer chemotherapeutic protection on human normal hematopoietic precursor cells of ovarian and breast cancer patients in whom high doses of MDR drugs may be required to control the diseases.^ Another promising vector system is recombinant adeno-associated virus (rAAV) vector. An impediment to use rAAV vectors is that production of rAAV vectors for clinical use is extremely cumbersome and labor intensive. First I set up the rAAV vector system in our laboratory and then, I focused on studies related to the production of rAAV vectors for clinical use. By using a self-inactivating retroviral vector carrying a selection marker under the control of the CMV immediate early promoter and an AAV genome with the deletion of both ITRs, I have developed either a transient or a stable method to produce rAAV vectors. These methods involve infection only and can generate high-titer rAAV vectors (up to 2 x 10$\sp5$ cfu/ml of CVL) with much less work.^ Although recombinant adenoviral vectors hardly infect early hematopoietic precursor cells lacking $\alpha\sb v\beta\sb5$ or $\alpha\sb v\beta\sb3$ integrin on their surface, but efficiently infect other cells, we can use these properties of adenoviral vectors for bone marrow purging as well as for development of new viral vectors such as pseudotyped retroviral vectors and rAAV vectors. Replacement of self-inactivating retroviral vectors by recombinant adenoviral vectors will facilitate the above strategies for production of new viral vectors. In order to accomplish these goals, I developed a new method which is much more efficient than the current methods to construct adenoviral vectors. This method involves a cosmid vector system which is utilized to construct the full-length recombinant adenoviral vectors in vitro.^ First, I developed an efficient and flexible method for in vitro construction of the full-length recombinant adenoviral vectors in the cosmid vector system by use of a three-DNA fragment ligation. Then, this system was improved by use of a two-DNA fragment ligation. The cloning capacity of recombinant adenoviral vectors constructed by this method to develop recombinant adenoviral vectors depends on the efficiency of transfection only. No homologous recombination is required for development of infectious adenoviral vectors. Thus, the efficiency of generating the recombinant adenoviral vectors by the cosmid method reported here was much higher than that by the in vitro direct ligation method or the in vivo homologous recombination method reported before. This method of the in vitro construction of recombinant adenoviral vectors in the cosmid vector system may facilitate the development of adenoviral vector for human gene therapy. (Abstract shortened by UMI.) ^

Evaluation of the accuracy of multilevel analysis parameter estimates for intra-cluster correlation in correlated binary data

Many studies in biostatistics deal with binary data. Some of these studies involve correlated observations, which can complicate the analysis of the resulting data. Studies of this kind typically arise when a high degree of commonality exists between test subjects. If there exists a natural hierarchy in the data, multilevel analysis is an appropriate tool for the analysis. Two examples are the measurements on identical twins, or the study of symmetrical organs or appendages such as in the case of ophthalmic studies. Although this type of matching appears ideal for the purposes of comparison, analysis of the resulting data while ignoring the effect of intra-cluster correlation has been shown to produce biased results.^ This paper will explore the use of multilevel modeling of simulated binary data with predetermined levels of correlation. Data will be generated using the Beta-Binomial method with varying degrees of correlation between the lower level observations. The data will be analyzed using the multilevel software package MlwiN (Woodhouse, et al, 1995). Comparisons between the specified intra-cluster correlation of these data and the estimated correlations, using multilevel analysis, will be used to examine the accuracy of this technique in analyzing this type of data. ^

Sparse computation for large-scale binary classification

Well-known data mining algorithms rely on inputs in the form of pairwise similarities between objects. For large datasets it is computationally impossible to perform all pairwise comparisons. We therefore propose a novel approach that uses approximate Principal Component Analysis to efficiently identify groups of similar objects. The effectiveness of the approach is demonstrated in the context of binary classification using the supervised normalized cut as a classifier. For large datasets from the UCI repository, the approach significantly improves run times with minimal loss in accuracy.

Index Tracking Using Data-Mining Techniques and Mixed-Binary Linear Programming

Index tracking has become one of the most common strategies in asset management. The index-tracking problem consists of constructing a portfolio that replicates the future performance of an index by including only a subset of the index constituents in the portfolio. Finding the most representative subset is challenging when the number of stocks in the index is large. We introduce a new three-stage approach that at first identifies promising subsets by employing data-mining techniques, then determines the stock weights in the subsets using mixed-binary linear programming, and finally evaluates the subsets based on cross validation. The best subset is returned as the tracking portfolio. Our approach outperforms state-of-the-art methods in terms of out-of-sample performance and running times.

Binary recombinase systems for high-resolution conditional mutagenesis.

Conditional mutagenesis using Cre recombinase expressed from tissue specific promoters facilitates analyses of gene function and cell lineage tracing. Here, we describe two novel dual-promoter-driven conditional mutagenesis systems designed for greater accuracy and optimal efficiency of recombination. Co-Driver employs a recombinase cascade of Dre and Dre-respondent Cre, which processes loxP-flanked alleles only when both recombinases are expressed in a predetermined temporal sequence. This unique property makes Co-Driver ideal for sequential lineage tracing studies aimed at unraveling the relationships between cellular precursors and mature cell types. Co-InCre was designed for highly efficient intersectional conditional transgenesis. It relies on highly active trans-splicing inteins and promoters with simultaneous transcriptional activity to reconstitute Cre recombinase from two inactive precursor fragments. By generating native Cre, Co-InCre attains recombination rates that exceed all other binary SSR systems evaluated in this study. Both Co-Driver and Co-InCre significantly extend the utility of existing Cre-responsive alleles.

Variables affecting the stability of multiple cloning sites and hairpin structures in retroviral vectors

Retroviruses are RNA viruses that replicate through a double-stranded DNA intermediate. The viral enzyme reverse transcriptase copies the retroviral genomic RNA into this DNA intermediate through the process of reverse transcription. Many variables can affect the fidelity of reverse transcriptase during reverse transcription, including specific sequences within the retroviral genome. ^ Previous studies have observed that multiple cloning sites (MCS) and sequences predicted to form stable hairpin structures are hotspots for deletion during retroviral replication. The studies described in this dissertation were performed to elucidate the variables that affect the stability of MCS and hairpin structures in retroviral vectors. Two series of retroviral vectors were constructed and characterized in these studies. ^ Spleen necrosis virus-based vectors were constructed containing separate MCS insertions of varying length, orientation, and symmetry. The only MCS that was a hotspot for deletion formed a stable hairpin structure. Upon more detailed study, the MCS previously reported as a hotspot for deletion was found to contain a tandem linker insertion that formed a hairpin structure. Murine leukemia virus-based vectors were constructed containing separate sequence insertions of either inverted repeat symmetry (122IR) that could form a hairpin structure, or little symmetry (122c) that would form a less stable structure. These insertions were made into either the neomycin resistance marker ( neo) or the hygromycin resistance marker (hyg) of the vector. 122c was stable in both neo and hyg, while 122IR was preferentially deleted in neo and was remarkably unstable in hyg. ^ These results suggest that MCS are hotspots for deletion in retroviral vectors if they can form hairpin structures, and that hairpin structures can be highly unstable at certain locations in retroviral vectors. This information may contribute to improved design of retroviral vectors for such uses as human gene therapy, and will contribute to a greater understanding of the basic science of retroviral reverse transcription. ^

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. ^

Logistic regression with incompletely observed binary

Logistic regression is one of the most important tools in the analysis of epidemiological and clinical data. Such data often contain missing values for one or more variables. Common practice is to eliminate all individuals for whom any information is missing. This deletion approach does not make efficient use of available information and often introduces bias.^ Two methods were developed to estimate logistic regression coefficients for mixed dichotomous and continuous covariates including partially observed binary covariates. The data were assumed missing at random (MAR). One method (PD) used predictive distribution as weight to calculate the average of the logistic regressions performing on all possible values of missing observations, and the second method (RS) used a variant of resampling technique. Additional seven methods were compared with these two approaches in a simulation study. They are: (1) Analysis based on only the complete cases, (2) Substituting the mean of the observed values for the missing value, (3) An imputation technique based on the proportions of observed data, (4) Regressing the partially observed covariates on the remaining continuous covariates, (5) Regressing the partially observed covariates on the remaining continuous covariates conditional on response variable, (6) Regressing the partially observed covariates on the remaining continuous covariates and response variable, and (7) EM algorithm. Both proposed methods showed smaller standard errors (s.e.) for the coefficient involving the partially observed covariate and for the other coefficients as well. However, both methods, especially PD, are computationally demanding; thus for analysis of large data sets with partially observed covariates, further refinement of these approaches is needed. ^