Dynamic Chemical Shift Imaging for Image-Guided Thermal Therapy

Magnetic resonance temperature imaging (MRTI) is recognized as a noninvasive means to provide temperature imaging for guidance in thermal therapies. The most common method of estimating temperature changes in the body using MR is by measuring the water proton resonant frequency (PRF) shift. Calculation of the complex phase difference (CPD) is the method of choice for measuring the PRF indirectly since it facilitates temperature mapping with high spatiotemporal resolution. Chemical shift imaging (CSI) techniques can provide the PRF directly with high sensitivity to temperature changes while minimizing artifacts commonly seen in CPD techniques. However, CSI techniques are currently limited by poor spatiotemporal resolution. This research intends to develop and validate a CSI-based MRTI technique with intentional spectral undersampling which allows relaxed parameters to improve spatiotemporal resolution. An algorithm based on autoregressive moving average (ARMA) modeling is developed and validated to help overcome limitations of Fourier-based analysis allowing highly accurate and precise PRF estimates. From the determined acquisition parameters and ARMA modeling, robust maps of temperature using the k-means algorithm are generated and validated in laser treatments in ex vivo tissue. The use of non-PRF based measurements provided by the technique is also investigated to aid in the validation of thermal damage predicted by an Arrhenius rate dose model.

Differential dynamic properties of scleroderma fibroblasts in response to perturbation of environmental stimuli.

Diseases are believed to arise from dysregulation of biological systems (pathways) perturbed by environmental triggers. Biological systems as a whole are not just the sum of their components, rather ever-changing, complex and dynamic systems over time in response to internal and external perturbation. In the past, biologists have mainly focused on studying either functions of isolated genes or steady-states of small biological pathways. However, it is systems dynamics that play an essential role in giving rise to cellular function/dysfunction which cause diseases, such as growth, differentiation, division and apoptosis. Biological phenomena of the entire organism are not only determined by steady-state characteristics of the biological systems, but also by intrinsic dynamic properties of biological systems, including stability, transient-response, and controllability, which determine how the systems maintain their functions and performance under a broad range of random internal and external perturbations. As a proof of principle, we examine signal transduction pathways and genetic regulatory pathways as biological systems. We employ widely used state-space equations in systems science to model biological systems, and use expectation-maximization (EM) algorithms and Kalman filter to estimate the parameters in the models. We apply the developed state-space models to human fibroblasts obtained from the autoimmune fibrosing disease, scleroderma, and then perform dynamic analysis of partial TGF-beta pathway in both normal and scleroderma fibroblasts stimulated by silica. We find that TGF-beta pathway under perturbation of silica shows significant differences in dynamic properties between normal and scleroderma fibroblasts. Our findings may open a new avenue in exploring the functions of cells and mechanism operative in disease development.

Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.

Differential dynamic properties of scleroderma fibroblasts in response to perturbation of environmental stimuli.

Diseases are believed to arise from dysregulation of biological systems (pathways) perturbed by environmental triggers. Biological systems as a whole are not just the sum of their components, rather ever-changing, complex and dynamic systems over time in response to internal and external perturbation. In the past, biologists have mainly focused on studying either functions of isolated genes or steady-states of small biological pathways. However, it is systems dynamics that play an essential role in giving rise to cellular function/dysfunction which cause diseases, such as growth, differentiation, division and apoptosis. Biological phenomena of the entire organism are not only determined by steady-state characteristics of the biological systems, but also by intrinsic dynamic properties of biological systems, including stability, transient-response, and controllability, which determine how the systems maintain their functions and performance under a broad range of random internal and external perturbations. As a proof of principle, we examine signal transduction pathways and genetic regulatory pathways as biological systems. We employ widely used state-space equations in systems science to model biological systems, and use expectation-maximization (EM) algorithms and Kalman filter to estimate the parameters in the models. We apply the developed state-space models to human fibroblasts obtained from the autoimmune fibrosing disease, scleroderma, and then perform dynamic analysis of partial TGF-beta pathway in both normal and scleroderma fibroblasts stimulated by silica. We find that TGF-beta pathway under perturbation of silica shows significant differences in dynamic properties between normal and scleroderma fibroblasts. Our findings may open a new avenue in exploring the functions of cells and mechanism operative in disease development.

Sequential updating via dynamic hierarchical models: A Bayesian approach to longitudinal data analysis

Most statistical analysis, theory and practice, is concerned with static models; models with a proposed set of parameters whose values are fixed across observational units. Static models implicitly assume that the quantified relationships remain the same across the design space of the data. While this is reasonable under many circumstances this can be a dangerous assumption when dealing with sequentially ordered data. The mere passage of time always brings fresh considerations and the interrelationships among parameters, or subsets of parameters, may need to be continually revised. ^ When data are gathered sequentially dynamic interim monitoring may be useful as new subject-specific parameters are introduced with each new observational unit. Sequential imputation via dynamic hierarchical models is an efficient strategy for handling missing data and analyzing longitudinal studies. Dynamic conditional independence models offers a flexible framework that exploits the Bayesian updating scheme for capturing the evolution of both the population and individual effects over time. While static models often describe aggregate information well they often do not reflect conflicts in the information at the individual level. Dynamic models prove advantageous over static models in capturing both individual and aggregate trends. Computations for such models can be carried out via the Gibbs sampler. An application using a small sample repeated measures normally distributed growth curve data is presented. ^

Identification and analysis of novel mitotic inhibitors of the Caenorhabditis elegans Aurora B kinase

Proper execution of mitosis requires the accurate segregation of replicated DNA into each daughter cell. The highly conserved mitotic kinase AIR-2/Aurora B is a dynamic protein that interacts with subsets of cofactors and substrates to coordinate chromosome segregation and cytokinesis in Caenorhabdiris elegans. To identify components of the AIR-2 regulatory pathway, a genome-wide RNAi-based screen for suppressors of air-2 temperature-sensitive mutant lethality was conducted. Here, I present evidence that two classes of suppressors identified in this screen are bona fide regulators of the AIR-2 kinase. The strongest suppressor cdc-48.3, encodes an Afg2/Spaf-related Cdc48-like AAA+ ATPase that regulates AIR-2 kinase activity and stability during C. elegans embryogenesis. Loss of CDC-48.3 suppresses the lethality of air-2 mutant embryos, marked by the restoration of the dynamic behavior of AIR-2 and rescue of chromosome segregation and cytokinesis defects. Loss of CDC-48.3 leads to mitotic delays and abnormal accumulation of AIR-2 during late telophase/mitotic exit. In addition, AIR-2 kinase activity is significantly upregulated from metaphase through mitotic exit in CDC-48.3 depleted embryos. Inhibition of the AIR-2 kinase is dependent on (1) a direct physical interaction between CDC-48.3 and AIR-2, and (2) CDC-48.3 ATPase activity. Importantly, the increase in AIR-2 kinase activity does not correlate with the stabilization of AIR-2 in late mitosis. Hence, CDC-48.3 is a bi-functional inhibitor of AIR-2 that is likely to act via distinct mechanisms. The second class of suppressors consists of psy-2/smk-1 and pph-4.1, which encode two components of the conserved PP4 phosphatase complex that is essential for spindle assembly, chromosome segregation, and overall mitotic progression. AIR-2 and its substrates are likely to be targets of this complex since mitotic AIR-2 kinase activity is significantly increased during mitosis when either PSY-2/SMK-1 or PPH-4.l is depleted. Altogether, this study demonstrates that during the C. elegans embryonic cell cycle, regulators including the CDC-48.3 ATPase and PP4 phosphatase complex interact with and control the kinase activity, targeting behavior and protein stability of the Aurora B kinase to ensure accurate and timely progression of mitosis. ^

Evaluating the Utility of Clinical Criteria for the Identification of Lynch Syndrome among Endometrial Cancer Patients

Background: Lynch Syndrome (LS) is a familial cancer syndrome with a high prevalence of colorectal and endometrial carcinomas among affected family members. Clinical criteria, developed from information obtained from familial colorectal cancer registries, have been generated to identify individuals at elevated risk for having LS. In 2007, the Society of Gynecologic Oncology (SGO) codified criteria to assist in identifying women presenting with gynecologic cancers at elevated risk for having LS. These criteria have not been validated in a population-based setting. Materials and Methods: We retrospectively identified 412, unselected endometrial cancer cases. Clinical and pathologic information were obtained from the electronic medical record, and all tumors were tested for expression of the DNA mismatch repair proteins through immunohistochemistry. Tumors exhibiting loss of MSH2, MSH6 and PMS2 were designated as probable Lynch Syndrome (PLS). For tumors exhibiting immunohistochemical loss of MLH1, we used the PCR-based MLH1 methylation assay to delineate PLS tumors from sporadic tumors. Samples lacking methylation of the MLH1 promoter were also designated as PLS. The sensitivity and specificity for SGO criteria for detecting PLS tumors was calculated. We compared clinical and pathologic features of sporadic tumors and PLS tumors. A simplified cost-effectiveness analysis was also performed comparing the direct costs of utilizing SGO criteria vs. universal tumor testing. Results: In our cohort, 43/408 (10.5%) of endometrial carcinomas were designated as PLS. The sensitivity and specificity of SGO criteria to identify PLS cases were 32.7 and 77%, respectively. Multivariate analysis of clinical and pathologic parameters failed to identify statistically significant differences between sporadic and PLS tumors with the exception of tumors arising from the lower uterine segment. These tumors were more likely to occur in PLS tumors. Cost-effectiveness analysis showed clinical criteria and universal testing strategies cost $6,235.27/PLS case identified and $5,970.38/PLS case identified, respectively. Conclusions: SGO 5-10% criteria successfully identify PLS cases among women who are young or have significant family history of LS related tumors. However, a larger proportion of PLS cases occurring at older ages with less significant family history are not detected by this screening strategy. Compared to SGO clinical criteria, universal tumor testing is a cost effective strategy to identify women presenting with endometrial cancer who are at elevated risk for having LS.

RNA-SEQUENCING APPLICATIONS: GENE EXPRESSION QUANTIFICATION AND METHYLATOR PHENOTYPE IDENTIFICATION

My dissertation focuses on two aspects of RNA sequencing technology. The first is the methodology for modeling the overdispersion inherent in RNA-seq data for differential expression analysis. This aspect is addressed in three sections. The second aspect is the application of RNA-seq data to identify the CpG island methylator phenotype (CIMP) by integrating datasets of mRNA expression level and DNA methylation status. Section 1: The cost of DNA sequencing has reduced dramatically in the past decade. Consequently, genomic research increasingly depends on sequencing technology. However it remains elusive how the sequencing capacity influences the accuracy of mRNA expression measurement. We observe that accuracy improves along with the increasing sequencing depth. To model the overdispersion, we use the beta-binomial distribution with a new parameter indicating the dependency between overdispersion and sequencing depth. Our modified beta-binomial model performs better than the binomial or the pure beta-binomial model with a lower false discovery rate. Section 2: Although a number of methods have been proposed in order to accurately analyze differential RNA expression on the gene level, modeling on the base pair level is required. Here, we find that the overdispersion rate decreases as the sequencing depth increases on the base pair level. Also, we propose four models and compare them with each other. As expected, our beta binomial model with a dynamic overdispersion rate is shown to be superior. Section 3: We investigate biases in RNA-seq by exploring the measurement of the external control, spike-in RNA. This study is based on two datasets with spike-in controls obtained from a recent study. We observe an undiscovered bias in the measurement of the spike-in transcripts that arises from the influence of the sample transcripts in RNA-seq. Also, we find that this influence is related to the local sequence of the random hexamer that is used in priming. We suggest a model of the inequality between samples and to correct this type of bias. Section 4: The expression of a gene can be turned off when its promoter is highly methylated. Several studies have reported that a clear threshold effect exists in gene silencing that is mediated by DNA methylation. It is reasonable to assume the thresholds are specific for each gene. It is also intriguing to investigate genes that are largely controlled by DNA methylation. These genes are called “L-shaped” genes. We develop a method to determine the DNA methylation threshold and identify a new CIMP of BRCA. In conclusion, we provide a detailed understanding of the relationship between the overdispersion rate and sequencing depth. And we reveal a new bias in RNA-seq and provide a detailed understanding of the relationship between this new bias and the local sequence. Also we develop a powerful method to dichotomize methylation status and consequently we identify a new CIMP of breast cancer with a distinct classification of molecular characteristics and clinical features.

The identification and regulation of a novel interaction occurring between $\beta$-catenin and the actin -bundling protein fascin

Catenins were first characterized as linking the cytoplasmic domains of cadherin cell-cell adhesion molecules to the cortical actin cytoskeleton. In addition to their essential role in modulating cadherin adhesion, catenins have more recently been indicated to participate in cell and developmental signaling pathways. $\beta$-catenin, for example, associates directly with receptor tyrosine kinases and transcription factors such as LEF-1/TCF, and tranduces developmental signals within the Wnt pathway. $\beta$-catenin also appear to a role in regulating cell proliferation via its interaction with the tumor supressor protein APC. I have employed the yeast two-hybrid method to reveal that fascin, a bundler of actin filaments, binds to $\beta$-catenin's central Armadillo-repeat domain. The $\beta$-catenin-fascin interaction exists in cell lines as well as in animal brain tissues as revealed by immunoprecipitation analysis, and substantiated in vitro with purified proteins. Fascin additionally binds to plakoglobin, which contains a more divergent Armadillo-repeat domain. Fascin and E-cadherin utilize a similar binding-site within $\beta$-catenin, such that they form mutually exclusive complexes with $\beta$-catenin. Fascin and $\beta$-catenin co-localize at cell-cell borders and dynamic cell leading edges of epithelial and endothelial cells. Total immunoprecipitable b-catein has several isoforms, only the hyperphosphorylated isoform 1 associated with fascin. An increased $\beta$-catenin-fascin interaction was observed in HGF stimulated cells, and in Xenopus embryos injected with src kinase RNAs. The increased $\beta$-catenin association with fascin is correlated with increased levels of $\beta$-catenin phosphorylation. $\beta$-catenin, but not fascin, can be readily phosphorylated on tyrosine in vivo following src injection of embryos, or in vitro following v-src addition to purified protein components. These observations suggest a role of $\beta$-catenin phosphorylation in regulating its interaction with fascin, and src kinase may be an important regulator of the $\beta$-catenin-fascin association in vivo. The $\beta$-catenin-fascin interaction represents a novel catenin complex, that may conceivably regulate actin cytoskeletal structures, cell adhesion, and cellular motility, perhaps in a coordinate manner with its functions in cadherin and APC complexes. ^

Dynamic contrast agent -enhanced magnetic resonance imaging assessment of tumor microvasculature

Dynamic contrast agent-enhanced magnetic resonance imaging (DCE MRI) data, when analyzed with the appropriate pharmacokinetic models, have been shown to provide quantitative estimates of microvascular parameters important in characterizing the angiogenic activity of malignant tissue. These parameters consist of the whole blood volume per unit volume of tissue, v b, transport constant from the plasma to the extravascular, extracellular space (EES), k1 and the transport constant from the EES to the plasma, k2. Parameters vb and k1 are expected to correlate with microvascular density (MVD) and vascular permeability, respectively, which have been suggested to serve as surrogate markers for angiogenesis. In addition to being a marker for angiogenesis, vascular permeability is also useful in estimating tumor penetration potential of chemotherapeutic agents. ^ Histological measurements of the intratumoral microvascular environment are limited by their invasiveness and susceptibility to sampling errors. Also, MVD and vascular permeability, while useful for characterizing tumors at a single time point, have shown less utility in longitudinal studies, particularly when used to monitor the efficacy of antiangiogenic and traditional chemotherapeutic agents. These limitations led to a search for a non-invasive means of characterizing the microvascular environment of an entire tumor. ^ The overall goal of this project was to determine the utility of DCE MRI for monitoring the effect of antiangiogenic agents. Further applications of a validated DCE MRI technique include in vivo measurements of tumor microvascular characteristics to aid in determining prognosis at presentation and in estimating drug penetration. DCE MRI data were generated using single- and dual-tracer pharmacokinetic models with different molecular-weight contrast agents. The resulting pharmacokinetic parameters were compared to immunohistochemical measurements. The model and contrast agent combination yielding the best correlation between the pharmacokinetic parameters and histological measures was further evaluated in a longitudinal study to evaluate the efficacy of DCE MRI in monitoring the intratumoral microvascular environment following antiangiogenic treatment. ^