Semiparametric marginal and mixed models for longitudinal data

This thesis consists of three papers which investigate marginal models, nonparametric approaches, generalized mixed effects models and variance components estimation in longitudinal data analysis. In the first paper, a new marginal approach is introduced for high-dimensional cell-cycle microarray data with no replicates. There are two kinds of correlation for cell-cycle microarray data. Measurements within a gene are correlated and measurements between genes are also correlated since some genes may be biologically related. The proposed procedure combines a classifying method, quadratic inference function method and nonparametric techniques for complex high dimensional cell cycle microarray data. The gene classifying method is first applied to identify genes with similar cell cycle patterns into the same class. Then we use genes within the same group as pseudo-replicates to fit a nonparametric model. The quadratic inference function is applied to incorporate within-gene correlations. An asymptotic chi-squared test is also applied to test whether certain genes have cell cycles phenomena. Simulations and an example of cell-cycle microarray data are illustrated. The second paper proposes a new approach for generalized linear mixed models in longitudinal data analysis. This new approach is an extension of the quadratic inference function (Qu et al., 2000) for generalized linear mixed models. Two conditional extended scores are constructed for estimating fixed effects and random effects. This new approach involves only the first and second conditional moments. It does not require the specification of a likelihood function and also takes serial correlations of errors into account. In addition, the estimation of unknown variance components associated with random effects or nuisance parameters associated with working correlations are not required. Furthermore, it does not require the normality assumption for random effects. In the third paper, we develop a new approach to estimate variance components using the second-order quadratic inference function. This is an extension of the quadratic inference function for variance components estimation in linear mixed models. The new approach does not require the specification of a likelihood function. In addition, we propose a chi-squared test to test whether the variance components of interest are significant. This chi-squared test can also be used for testing whether the serial correlation is significant. Simulations and a real data example are provided as illustration

Preferentially Quantized Linker DNA Lengths in Saccharomyces cerevisiae

The exact lengths of linker DNAs connecting adjacent nucleosomes specify the intrinsic three-dimensional structures of eukaryotic chromatin fibers. Some studies suggest that linker DNA lengths preferentially occur at certain quantized values, differing one from another by integral multiples of the DNA helical repeat, ∼10 bp; however, studies in the literature are inconsistent. Here, we investigate linker DNA length distributions in the yeast Saccharomyces cerevisiae genome, using two novel methods: a Fourier analysis of genomic dinucleotide periodicities adjacent to experimentally mapped nucleosomes and a duration hidden Markov model applied to experimentally defined dinucleosomes. Both methods reveal that linker DNA lengths in yeast are preferentially periodic at the DNA helical repeat (∼10 bp), obeying the forms 10n+5 bp (integer n). This 10 bp periodicity implies an ordered superhelical intrinsic structure for the average chromatin fiber in yeast

A Regulatory View of Adaptive Trial Design

Developing a new medicine is an expensive and time-consuming process. Researchers are interested in applying better designs to expedite the approval of potential medicinal products. Adaptive designs, which allow for some types of prospectively planned mid-study change, can improve the efficiency of a trial and maximize the chance of success. Possible design adaptations of clinical trials include sample size re-estimation, change in primary endpoint, interim dropping of treatment arms, change in statistical hypothesis, and change in the primary analysis. In this article, the regulatory considerations of the methodological issues with respect to adaptive design are discussed. Several examples of design adaptation that the Center for Drug Evaluation has encountered during the past 3 years are presented

Up-Regulation of Hepatoma-Derived Growth Factor Facilities Tumor Progression in Malignant Melanoma

<div><p>Cutaneous malignant melanoma is the fastest increasing malignancy in humans. Hepatoma-derived growth factor (HDGF) is a novel growth factor identified from human hepatoma cell line. HDGF overexpression is correlated with poor prognosis in various types of cancer including melanoma. However, the underlying mechanism of HDGF overexpression in developing melanoma remains unclear. In this study, human melanoma cell lines (A375, A2058, MEL-RM and MM200) showed higher levels of HDGF gene expression, whereas human epidermal melanocytes (HEMn) expressed less. Exogenous application of HDGF stimulated colony formation and invasion of human melanoma cells. Moreover, HDGF overexpression stimulated the degree of invasion and colony formation of B16–F10 melanoma cells whereas HDGF knockdown exerted opposite effects <i>in vitro</i>. To evaluate the effects of HDGF on tumour growth and metastasis <i>in vivo,</i> syngeneic mouse melanoma and metastatic melanoma models were performed by manipulating the gene expression of HDGF in melanoma cells. It was found that mice injected with HDGF-overexpressing melanoma cells had greater tumour growth and higher metastatic capability. In contrast, mice implanted with HDGF-depleted melanoma cells exhibited reduced tumor burden and lung metastasis. Histological analysis of excised tumors revealed higher degree of cell proliferation and neovascularization in HDGF-overexpressing melanoma. The present study provides evidence that HDGF promotes tumor progression of melanoma and targeting HDGF may constitute a novel strategy for the treatment of melanoma.</p> </div

Expression level of HDGF and effects of HDGF on tumorgenicity of human melanoma cells <i>in vitro.</i>

<p>(A) The gene expression of HDGF in human epidermal melanocyte (HEMn) and melanoma cell lines (A375, A2058, MEL-RM and MM200) was measured by qRT-PCR. The relative gene expression level of HDGF was normalised to GAPDH. No statistical analysis was performed (melanocyte group is populated by only one type of cell line). (B) A375 and A2058 cells were infected with adenoviral vector at different MOI (100 or 200) then evaluated gene expression level of HDGF. (C) Representative images illustrating the effects of rHDGF (10 ng/mL) and Ad-HDGF shRNA (at 200 MOI) and Ad-GFP (at 200 MOI) on colony formation identified by crystal violet stains in A375 and A2058 cells. Quantitative measures of colony formation by counting the number of crystal violet positive cells. (D) Cells invaded through polycarbonate membrane (10 mm pore size) were stained with Giemsa. Representative photomicrographs of migrated cells through the Matrigel-coated filter were quantified in A375 and A2058 cells. All data are expressed as mean ± SEM from 3 experiments. *<i>P</i><0.05 compared to control, one-way ANOVA with post-doc analysis.</p

Effects of HDGF overexpression or gene silencing on tumorigenesis of melanoma cells <i>in vitro</i> and <i>in vivo.</i>

<p>(A) A representative western blot demonstrating HDGF protein expression in B16–F10 melanoma cells infected with Ad-GFP, Ad-HDGF shRNA and Ad-HDGF. The β-actin was used as controls. (B) Representative photos and quantification of colony formation ability assay (from 3 experiments). (C) Weights of tumors isolated from C57BL/6 mice. Melanoma cells infected with recombinant adenovirus for 24 hrs were subcutaneously implanted into mice over 28 days. Tumors were then excised and weighted (n = 10/each group). (D) The representative profile of Ki-67 expression in tumor sections from mice inoculated with B16–F10 melanoma cells infected with Ad-GFP, Ad-HDGF and Ad-HDGF shRNA. Bar chart shows proliferation index of Ki-67 positive cells in tumor tissues (n = 6/each group). All data are expressed as mean ± SEM. *<i>P</i><0.05 compared to Ad-GFP-treated groups, one-way ANOVA with post-doc analysis.</p