Regulation of angiogenesis by interferon-beta

The purpose of these studies was to investigate the role of interferon-beta (IFN-$\beta$) in angiogenesis. IFN-$\alpha/\beta$ have been implicated in inhibiting a number of steps in the angiogenic pathway. We examined the balance of angiogenesis-regulating molecules in several systems including human infantile hemangiomas, UV-B irradiated mice, and dorsal incisional wound healing in mice. In each system, epidermal hyperplasia and cutaneous angiogenesis were directly related to the expression of positive angiogenic factors (bFGF and VEGF) and inversely related to the expression of endogenous IFN-$\beta.$ The re-expression of IFN-$\beta$ correlated with tumor regression and/or resolution of wound healing. In contrast to control mice, UV-B-induced cutaneous angiogenesis and hyperplasia persisted in IFN-$\alpha/\beta$ receptor knock-out mice. In normal mice, endogenous IFN-$\beta$ was expressed by all differentiated epithelial cells exposed to environmental stimuli. The expression of endogenous IFN-$\beta$ was necessary but insufficient for complete differentiation of epidermal keratinocytes.^ The tumor organ microenvironment can regulate angiogenesis. Human bladder carcinoma cells growing in the bladder wall of nude mice express high levels of bFGF, VEGF, and MMP-9, have higher vascular densities, and produce metastases to lymph nodes and lungs, whereas the same cells growing subcutaneously express less bFGF, VEGF, and MMP-9, have lower vascular densities, and do not metastasize. IFN-$\alpha/\beta$ was found to inhibit bFGF and MMP-9 expression both in vitro and in vivo in human bladder carcinoma cells. Systemic therapy with human IFN-$\alpha$ of human bladder cancer cells growing orthotopically in nude mice, resulted in decreased vascularity, tumorigenicity, and metastasis as compared to saline treated mice. Human bladder cancer cells resistant to the antiproliferative effects of IFN were transfected with the human IFN-$\beta$ gene. Hu-IFN-$\beta$ transfected cells expressed significantly less bFGF protein and gelatinase activity than parental or control-transfected cells and did not grow at ectopic or orthotopic sites. Collectively the data provide direct evidence that IFN-$\alpha/\beta$ can inhibit angiogenesis via down-regulation of angiogenesis-stimulating cytokines. ^

Selection bias and the cross-validation of regression models for prediction

Strategies are compared for the development of a linear regression model with stochastic (multivariate normal) regressor variables and the subsequent assessment of its predictive ability. Bias and mean squared error of four estimators of predictive performance are evaluated in simulated samples of 32 population correlation matrices. Models including all of the available predictors are compared with those obtained using selected subsets. The subset selection procedures investigated include two stopping rules, C$\sb{\rm p}$ and S$\sb{\rm p}$, each combined with an 'all possible subsets' or 'forward selection' of variables. The estimators of performance utilized include parametric (MSEP$\sb{\rm m}$) and non-parametric (PRESS) assessments in the entire sample, and two data splitting estimates restricted to a random or balanced (Snee's DUPLEX) 'validation' half sample. The simulations were performed as a designed experiment, with population correlation matrices representing a broad range of data structures.^ The techniques examined for subset selection do not generally result in improved predictions relative to the full model. Approaches using 'forward selection' result in slightly smaller prediction errors and less biased estimators of predictive accuracy than 'all possible subsets' approaches but no differences are detected between the performances of C$\sb{\rm p}$ and S$\sb{\rm p}$. In every case, prediction errors of models obtained by subset selection in either of the half splits exceed those obtained using all predictors and the entire sample.^ Only the random split estimator is conditionally (on $\\beta$) unbiased, however MSEP$\sb{\rm m}$ is unbiased on average and PRESS is nearly so in unselected (fixed form) models. When subset selection techniques are used, MSEP$\sb{\rm m}$ and PRESS always underestimate prediction errors, by as much as 27 percent (on average) in small samples. Despite their bias, the mean squared errors (MSE) of these estimators are at least 30 percent less than that of the unbiased random split estimator. The DUPLEX split estimator suffers from large MSE as well as bias, and seems of little value within the context of stochastic regressor variables.^ To maximize predictive accuracy while retaining a reliable estimate of that accuracy, it is recommended that the entire sample be used for model development, and a leave-one-out statistic (e.g. PRESS) be used for assessment. ^

REGRESSION ANALYSIS FOR STANDARDIZED MORTALITY RATIOS WITH APPLICATION TO OCCUPATIONAL FOLLOW-UP STUDIES

Traditional comparison of standardized mortality ratios (SMRs) can be misleading if the age-specific mortality ratios are not homogeneous. For this reason, a regression model has been developed which incorporates the mortality ratio as a function of age. This model is then applied to mortality data from an occupational cohort study. The nature of the occupational data necessitates the investigation of mortality ratios which increase with age. These occupational data are used primarily to illustrate and develop the statistical methodology.^ The age-specific mortality ratio (MR) for the covariates of interest can be written as MR(,ij...m) = ((mu)(,ij...m)/(theta)(,ij...m)) = r(.)exp (Z('')(,ij...m)(beta)) where (mu)(,ij...m) and (theta)(,ij...m) denote the force of mortality in the study and chosen standard populations in the ij...m('th) stratum, respectively, r is the intercept, Z(,ij...m) is the vector of covariables associated with the i('th) age interval, and (beta) is a vector of regression coefficients associated with these covariables. A Newton-Raphson iterative procedure has been used for determining the maximum likelihood estimates of the regression coefficients.^ This model provides a statistical method for a logical and easily interpretable explanation of an occupational cohort mortality experience. Since it gives a reasonable fit to the mortality data, it can also be concluded that the model is fairly realistic. The traditional statistical method for the analysis of occupational cohort mortality data is to present a summary index such as the SMR under the assumption of constant (homogeneous) age-specific mortality ratios. Since the mortality ratios for occupational groups usually increase with age, the homogeneity assumption of the age-specific mortality ratios is often untenable. The traditional method of comparing SMRs under the homogeneity assumption is a special case of this model, without age as a covariate.^ This model also provides a statistical technique to evaluate the relative risk between two SMRs or a dose-response relationship among several SMRs. The model presented has application in the medical, demographic and epidemiologic areas. The methods developed in this thesis are suitable for future analyses of mortality or morbidity data when the age-specific mortality/morbidity experience is a function of age or when there is an interaction effect between confounding variables needs to be evaluated. ^