LY2109761, a novel transforming growth factor beta receptor type I and type II dual inhibitor, as a therapeutic approach to suppressing pancreatic cancer metastasis.

Most pancreatic cancer patients present with inoperable disease or develop metastases after surgery. Conventional therapies are usually ineffective in treating metastatic disease. It is evident that novel therapies remain to be developed. Transforming growth factor beta (TGF-beta) plays a key role in cancer metastasis, signaling through the TGF-beta type I/II receptors (TbetaRI/II). We hypothesized that targeting TbetaRI/II kinase activity with the novel inhibitor LY2109761 would suppress pancreatic cancer metastatic processes. The effect of LY2109761 has been evaluated on soft agar growth, migration, invasion using a fibroblast coculture model, and detachment-induced apoptosis (anoikis) by Annexin V flow cytometric analysis. The efficacy of LY2109761 on tumor growth, survival, and reduction of spontaneous metastasis have been evaluated in an orthotopic murine model of metastatic pancreatic cancer expressing both luciferase and green fluorescence proteins (L3.6pl/GLT). To determine whether pancreatic cancer cells or the cells in the liver microenvironment were involved in LY2109761-mediated reduction of liver metastasis, we used a model of experimental liver metastasis. LY2109761 significantly inhibited the L3.6pl/GLT soft agar growth, suppressed both basal and TGF-beta1-induced cell migration and invasion, and induced anoikis. In vivo, LY2109761, in combination with gemcitabine, significantly reduced the tumor burden, prolonged survival, and reduced spontaneous abdominal metastases. Results from the experimental liver metastasis models indicate an important role for targeting TbetaRI/II kinase activity on tumor and liver microenvironment cells in suppressing liver metastasis. Targeting TbetaRI/II kinase activity on pancreatic cancer cells or the cells of the liver microenvironment represents a novel therapeutic approach to prevent pancreatic cancer metastasis.

SOCIAL AND BIOLOGICAL DETERMINANTS OF SHORT-TERM GROWTH VELOCITY AMONG PRESCHOOLERS OF RURAL GUATEMALA: A PATH ANALYSIS APPROACH (WEIGHT-GAIN, IRON DEFICIENCY, GASTROINTESTINAL SYMPTOMS)

This dissertation develops and tests through path analysis a theoretical model to explain how socioeconomic, socioenvironmental, and biologic risk factors simultaneously influence each other to further produce short-term, depressed growth in preschoolers. Three areas of risk factors were identified: child's proximal environment, maturational stage, and biological vulnerability. The theoretical model represented both the conceptual framework and the nature and direction of the hypotheses. Original research completed in 1978-80 and in 1982 provided the background data. It was analyzed first by nested-analysis of variance, followed by path analysis. The study provided evidence of mild iron deficiency and gastrointestinal symptomatology in the etiology of depressed, short-term weight gain. Also, there was evidence suggesting that family resources for material and social survival significantly contribute to the variability of short-term, age-adjusted growth velocity. These results challenge current views of unifocal intervention, whether for prevention or control. For policy formulations, though, the mechanisms underlying any set of interlaced relationships must be decoded. Theoretical formulations here proposed should be reassessed under a more extensive research design. It is suggested that studies should be undertaken where social changes are actually in progress; otherwise, nutritional epidemiology in developing countries operates somewhere between social reality and research concepts, with little grasp of its real potential. The study stresses that there is a connection between substantive theory, empirical observation, and policy issues. ^

A BAYESIAN APPROACH TO DOSE-RESPONSE ASSESSMENT AND DRUG-DRUG INTERACTION ANALYSIS: APPLICATION TO IN VITRO STUDIES

The considerable search for synergistic agents in cancer research is motivated by the therapeutic benefits achieved by combining anti-cancer agents. Synergistic agents make it possible to reduce dosage while maintaining or enhancing a desired effect. Other favorable outcomes of synergistic agents include reduction in toxicity and minimizing or delaying drug resistance. Dose-response assessment and drug-drug interaction analysis play an important part in the drug discovery process, however analysis are often poorly done. This dissertation is an effort to notably improve dose-response assessment and drug-drug interaction analysis. The most commonly used method in published analysis is the Median-Effect Principle/Combination Index method (Chou and Talalay, 1984). The Median-Effect Principle/Combination Index method leads to inefficiency by ignoring important sources of variation inherent in dose-response data and discarding data points that do not fit the Median-Effect Principle. Previous work has shown that the conventional method yields a high rate of false positives (Boik, Boik, Newman, 2008; Hennessey, Rosner, Bast, Chen, 2010) and, in some cases, low power to detect synergy. There is a great need for improving the current methodology. We developed a Bayesian framework for dose-response modeling and drug-drug interaction analysis. First, we developed a hierarchical meta-regression dose-response model that accounts for various sources of variation and uncertainty and allows one to incorporate knowledge from prior studies into the current analysis, thus offering a more efficient and reliable inference. Second, in the case that parametric dose-response models do not fit the data, we developed a practical and flexible nonparametric regression method for meta-analysis of independently repeated dose-response experiments. Third, and lastly, we developed a method, based on Loewe additivity that allows one to quantitatively assess interaction between two agents combined at a fixed dose ratio. The proposed method makes a comprehensive and honest account of uncertainty within drug interaction assessment. Extensive simulation studies show that the novel methodology improves the screening process of effective/synergistic agents and reduces the incidence of type I error. We consider an ovarian cancer cell line study that investigates the combined effect of DNA methylation inhibitors and histone deacetylation inhibitors in human ovarian cancer cell lines. The hypothesis is that the combination of DNA methylation inhibitors and histone deacetylation inhibitors will enhance antiproliferative activity in human ovarian cancer cell lines compared to treatment with each inhibitor alone. By applying the proposed Bayesian methodology, in vitro synergy was declared for DNA methylation inhibitor, 5-AZA-2'-deoxycytidine combined with one histone deacetylation inhibitor, suberoylanilide hydroxamic acid or trichostatin A in the cell lines HEY and SKOV3. This suggests potential new epigenetic therapies in cell growth inhibition of ovarian cancer cells.

Predictive oncology: a review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth.

Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.

Antibodies targeting hepatoma-derived growth factor as a novel strategy in treating lung cancer.

Hepatoma-derived growth factor (HDGF) is overexpressed in lung cancer and the overexpression correlates with aggressive biological behaviors and poor clinical outcomes. We developed anti-HDGF monoclonal antibodies and tested their antitumor activity in lung cancer xenograft models. We also determined biological effects in tumors treated with the antibody alone or in combination with bevacizumab/avastin (an anti-vascular endothelial growth factor antibody) and/or gemcitabine (a chemotherapeutic agent). We found the anti-HDGF was effective to inhibit tumor growth in non-small cell lung cancer xenograft models. In the A549 model, compared with control IgG, tumor growth was substantially inhibited in animals treated with anti-HDGF antibodies, particularly HDGF-C1 (P = 0.002) and HDGF-H3 (P = 0.005). When HDGF-H3 was combined with either bevacizumab or gemcitabine, we observed enhanced tumor growth inhibition, particularly when the three agents were used together. HDGF-H3-treated tumors exhibited significant reduction of microvessel density with a pattern distinctive from the microvessel reduction pattern observed in bevacizumab-treated tumors. HDGF-H3-treated but not bevacizumab-treated tumors also showed a significant increase of apoptosis. Interestingly, many of the apoptotic cells in HDGF-H3-treated tumors are stroma cells, suggesting that the mechanism of the antitumor activity is, at least in part, through disrupting formation of tumor-stroma structures. Our results show that HDGF is a novel therapeutic target for lung cancer and can be effectively targeted by an antibody-based approach.

Studies on regulation of skeletal muscle differentiation by growth factors

Skeletal muscle differentiation involves sequential events in which proliferating undifferentiated myoblasts withdraw from the cell cycle and fuse to form multinucleated myotubes. The process of fusion is accompanied by the disappearance of proteins associated with cell proliferation and the coordinate induction of a battery of muscle-specific gene products, which includes the muscle isoenzyme of creatine kinase, nicotinic acetylcholine receptor, and contractile proteins such as alpha-actin. The molecular events associated with myogenesis are particularly amenable to experimental analysis because the events which occur in vivo can be recapitulated in vitro using established muscle cell lines. Initiation of myogenic differentiation in vitro can be achieved by removing serum from the culture medium. Myogenesis, therefore, can be considered to be regulated through a repression-type of mechanism by components in serum. The objectives of this project were to identify the components involved in regulation of myogenesis and approach the mechanism(s) whereby these components achieve their regulatory function. Initially, the effects of a series of polypeptide growth factors on myogenesis were examined. Among them TGF$\beta$ and FGF were found to be potent inhibitors of myogenic differentiation which did not affect cell proliferation. The inhibitory effects of these growth factors on differentiation requires their persistent presence in the culture medium. After myoblasts have undergone fusion, they become refractory to the inhibitory effects of TGF$\beta$, FGF, and serum. When fusion is inhibited by the presence of EGTA, a Ca$\sp{2+}$ chelator, muscle-specific genes are expressed reversibly upon removal of inhibitory growth factors. Subsequent exposure of biochemically differentiated cells to serum or TGF$\beta$ leads to down-regulation of muscle-specific genes. Stimulation with serum also leads to reentry of myocytes into the cell cycle, whereas fused myotubes are irreversibly and terminally differentiated. Measurement of levels of TGF$\beta$ receptors reveals that under non-fusing conditions, TGF$\beta$ receptor levels in biochemically differentiated myocytes remained as high as in undifferentiated myoblasts, while during terminal differentiation, TGF$\beta$ receptors decreased at least five-fold. Thus, down-regulation of TGF$\beta$ receptors is coupled to irreversible differentiation, but not reversible differentiation in the absence of fusion. The possible involvement of second messenger systems, such as cAMP and protein kinase C, in the pathway(s) by which TGF$\beta$, FGF, or serum factors transduce their signals from the cell surface to the nucleus was also examined. The results showed that myogenic differentiation is subject to negative regulation through cAMP elevation-dependent and cAMP elevation-independent pathways and that serum mitogens, TGF$\beta$ and FGF inhibit differentiation through a mechanism independent of cAMP-elevation or protein kinase C activation. ^

Analysis of pp60$\sp{{\rm c}{-}src}$ and pp62$\sp{{\rm c}{-}yes}$ intracellular protein tyrosine kinase function in colon tumor cell growth regulation using herbimycin A, a tyrosine kinase specific inhibitor

Two approaches were utilized to investigate the role of pp60c-src activation in growth control of model colon tumor cell lines. The first approach involved analysis of pp60c-src activity in response to growth factor treatment to determine if transient activation of the protein was associated with ligand induced mitogenic signal transduction as occurs in non-colonic cell types. Activation of pp60c-src was detected using colon tumor cell lysates after treatment with platelet derived growth factor (PDGF). Activation of pp60c-src was also detected in response to epidermal growth factor (EGF) treatment using cellular lysates and intact cells. In contrast, down-regulation of purified pp60c-src occurred after incubation with EGF-treated EGFr immune complexes in vitro suggesting additional cellular events were potentially required for the stimulatory response observed in intact cells. The results demonstrated activation of pp60c-src in colon tumor cells in response to PDGF and EGF which is consistent with the role of the protein in mitogenic signal transduction in non-colonic cell types.^ The second approach used to study the role of pp60c-src activation in colonic cell growth control focused on analysis of the role of constitutive activation of the protein, which occurs in approximately 80% of colon tumors and cell lines, in growth control. These studies involved analysis of the effects of the tyrosine kinase specific inhibitor Herbimycin A (HA) on monolayer growth and pp60c-src enzymatic activity using model colon tumor cell lines. HA induced dose-dependent growth inhibition of all colon tumor cell lines examined possessing elevated pp60c-src activity. In HT29 cells the dose-dependent growth inhibition induced by HA correlated with dose-dependent pp60c-src inactivation. Inactivation of pp60c-src was shown to be an early event in response to treatment with HA which preceded induction of HT29 colon tumor cell growth inhibition. The growth effects of HA towards the colon tumor cells examined did not appear to be associated with induction of differentiation or a cytotoxic mechanism of action as changes in morphology were not detected in treated cells and growth inhibition (and pp60c-src inactivation) were reversible upon release from treatment with the compound. The results suggested the constitutive activation of pp60c-src functioned as a proliferative signal in colon tumor cells. Correlation between pp60c-src inactivation and growth inhibition was also observed using HA chemical derivatives confirming the role of tyrosine kinase inactivation by these compounds in inhibition of mitogenic signalling. In contrast, in AS15 cells possessing specific antisense mRNA mediated inactivation of pp60c-src, HA-induced inactivation of the related pp62c-yes tyrosine kinase, which is also activated during colon tumor progression, was not associated with induction of monolayer growth inhibition. These results suggested a function for the constitutively activated pp62c-yes protein in colon tumor cell proliferation which was different from that of activated pp60c-src. (Abstract shortened by UMI.) ^

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. ^

The role of AKT-mediated phosphorylation in suppression of MAD1 and the development of new approach in protein complex detection

MAX dimerization protein 1 (MAD1) is a basic-helix-loop-helix transcription factors that recruits transcription repressor such as HDAC to suppress target genes transcription. It antagonizes to MYC because the promoter binding sites for MYC are usually also serve as the binding sites for MAD1 so they compete for it. However, the mechanism of the switch between MYC and MAD1 in turning on and off of genes' transcription is obscure. In this study, we demonstrated that AKT-mediated MAD1 phosphorylation inhibits MAD1 transcription repression function. The association between MAD1 and its target genes' promoter is reduced after been phosphorylated by AKT; therefore, consequently, allows MYC to occupy the binding site and activates transcription. Mutation of such phosphorylation site abrogates the inhibition from AKT. In addition, functional assays demonstrated that AKT suppressed MAD1-mediated transcription repression of its target genes hTERT and ODC. Cell cycle and cell growth were also been released from inhibition by MAD1 in the presents of AKT. Taken together, our study suggests that MAD1 is a novel substrate of AKT and AKT-mediated MAD1 phosphorylation inhibits MAD1function; therefore, activates MAD1 target genes expression. ^ Furthermore, analysis of protein-protein interaction is indispensable for current molecular biology research, but multiplex protein dynamics in cells is too complicated to be analyzed by using existing biochemical methods. To overcome the disadvantage, we have developed a single molecule level detection system with nanofluidic chip. Single molecule was analyzed based on their fluorescent profile and their profiles were plotted into 2 dimensional time co-incident photon burst diagram (2DTP). From this 2DTP, protein complexes were characterized. These results demonstrate that the nanochannel protein detection system is a promising tool for future molecular biology. ^