Analysis of life expectancy using linear and nonlinear models

Life expectancy has consistently increased over the last 150 years due to improvements in nutrition, medicine, and public health. Several studies found that in many developed countries, life expectancy continued to rise following a nearly linear trend, which was contrary to a common belief that the rate of improvement in life expectancy would decelerate and was fit with an S-shaped curve. Using samples of countries that exhibited a wide range of economic development levels, we explored the change in life expectancy over time by employing both nonlinear and linear models. We then observed if there were any significant differences in estimates between linear models, assuming an auto-correlated error structure. When data did not have a sigmoidal shape, nonlinear growth models sometimes failed to provide meaningful parameter estimates. The existence of an inflection point and asymptotes in the growth models made them inflexible with life expectancy data. In linear models, there was no significant difference in the life expectancy growth rate and future estimates between ordinary least squares (OLS) and generalized least squares (GLS). However, the generalized least squares model was more robust because the data involved time-series variables and residuals were positively correlated. ^

Bayesian and survival model for tumor relapse status and disease-specific survival, with applications to breast cancer

Breast cancer is the most common non-skin cancer and the second leading cause of cancer-related death in women in the United States. Studies on ipsilateral breast tumor relapse (IBTR) status and disease-specific survival will help guide clinic treatment and predict patient prognosis.^ After breast conservation therapy, patients with breast cancer may experience breast tumor relapse. This relapse is classified into two distinct types: true local recurrence (TR) and new ipsilateral primary tumor (NP). However, the methods used to classify the relapse types are imperfect and are prone to misclassification. In addition, some observed survival data (e.g., time to relapse and time from relapse to death)are strongly correlated with relapse types. The first part of this dissertation presents a Bayesian approach to (1) modeling the potentially misclassified relapse status and the correlated survival information, (2) estimating the sensitivity and specificity of the diagnostic methods, and (3) quantify the covariate effects on event probabilities. A shared frailty was used to account for the within-subject correlation between survival times. The inference was conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in softwareWinBUGS. Simulation was used to validate the Bayesian method and assess its frequentist properties. The new model has two important innovations: (1) it utilizes the additional survival times correlated with the relapse status to improve the parameter estimation, and (2) it provides tools to address the correlation between the two diagnostic methods conditional to the true relapse types.^ Prediction of patients at highest risk for IBTR after local excision of ductal carcinoma in situ (DCIS) remains a clinical concern. The goals of the second part of this dissertation were to evaluate a published nomogram from Memorial Sloan-Kettering Cancer Center, to determine the risk of IBTR in patients with DCIS treated with local excision, and to determine whether there is a subset of patients at low risk of IBTR. Patients who had undergone local excision from 1990 through 2007 at MD Anderson Cancer Center with a final diagnosis of DCIS (n=794) were included in this part. Clinicopathologic factors and the performance of the Memorial Sloan-Kettering Cancer Center nomogram for prediction of IBTR were assessed for 734 patients with complete data. Nomogram for prediction of 5- and 10-year IBTR probabilities were found to demonstrate imperfect calibration and discrimination, with an area under the receiver operating characteristic curve of .63 and a concordance index of .63. In conclusion, predictive models for IBTR in DCIS patients treated with local excision are imperfect. Our current ability to accurately predict recurrence based on clinical parameters is limited.^ The American Joint Committee on Cancer (AJCC) staging of breast cancer is widely used to determine prognosis, yet survival within each AJCC stage shows wide variation and remains unpredictable. For the third part of this dissertation, biologic markers were hypothesized to be responsible for some of this variation, and the addition of biologic markers to current AJCC staging were examined for possibly provide improved prognostication. The initial cohort included patients treated with surgery as first intervention at MDACC from 1997 to 2006. Cox proportional hazards models were used to create prognostic scoring systems. AJCC pathologic staging parameters and biologic tumor markers were investigated to devise the scoring systems. Surveillance Epidemiology and End Results (SEER) data was used as the external cohort to validate the scoring systems. Binary indicators for pathologic stage (PS), estrogen receptor status (E), and tumor grade (G) were summed to create PS+EG scoring systems devised to predict 5-year patient outcomes. These scoring systems facilitated separation of the study population into more refined subgroups than the current AJCC staging system. The ability of the PS+EG score to stratify outcomes was confirmed in both internal and external validation cohorts. The current study proposes and validates a new staging system by incorporating tumor grade and ER status into current AJCC staging. We recommend that biologic markers be incorporating into revised versions of the AJCC staging system for patients receiving surgery as the first intervention.^ Chapter 1 focuses on developing a Bayesian method to solve misclassified relapse status and application to breast cancer data. Chapter 2 focuses on evaluation of a breast cancer nomogram for predicting risk of IBTR in patients with DCIS after local excision gives the statement of the problem in the clinical research. Chapter 3 focuses on validation of a novel staging system for disease-specific survival in patients with breast cancer treated with surgery as the first intervention. ^

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.

SURVIVAL PREDICTION FOR BRAIN TUMOR PATIENTS USING GENE EXPRESSION DATA

Brain tumor is one of the most aggressive types of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports indicating that molecular profiling of gliomas can reveal subgroups of patients with distinct survival rates. We hypothesize that coupling molecular profiling of brain tumors with clinical information might improve predictions of patient survival time and, consequently, better guide future treatment decisions. In order to evaluate this hypothesis, the general goal of this research is to build models for survival prediction of glioma patients using DNA molecular profiles (U133 Affymetrix gene expression microarrays) along with clinical information. First, a predictive Random Forest model is built for binary outcomes (i.e. short vs. long-term survival) and a small subset of genes whose expression values can be used to predict survival time is selected. Following, a new statistical methodology is developed for predicting time-to-death outcomes using Bayesian ensemble trees. Due to a large heterogeneity observed within prognostic classes obtained by the Random Forest model, prediction can be improved by relating time-to-death with gene expression profile directly. We propose a Bayesian ensemble model for survival prediction which is appropriate for high-dimensional data such as gene expression data. Our approach is based on the ensemble "sum-of-trees" model which is flexible to incorporate additive and interaction effects between genes. We specify a fully Bayesian hierarchical approach and illustrate our methodology for the CPH, Weibull, and AFT survival models. We overcome the lack of conjugacy using a latent variable formulation to model the covariate effects which decreases computation time for model fitting. Also, our proposed models provides a model-free way to select important predictive prognostic markers based on controlling false discovery rates. We compare the performance of our methods with baseline reference survival methods and apply our methodology to an unpublished data set of brain tumor survival times and gene expression data, selecting genes potentially related to the development of the disease under study. A closing discussion compares results obtained by Random Forest and Bayesian ensemble methods under the biological/clinical perspectives and highlights the statistical advantages and disadvantages of the new methodology in the context of DNA microarray data analysis.

Adolescent Sexual Behavior: Examining Data from Texas and the US

Background: The US has higher rates of teen births and sexually transmitted infections (STI) than other developed countries. Texas youth are disproportionately impacted. Purpose: To review local, state, and national data on teens’ engagement in sexual risk behaviors to inform policy and practice related to teen sexual health. Methods: 2009 middle school and high school Youth Risk Behavior Survey (YRBS) data, and data from All About Youth, a middle school study conducted in a large urban school district in Texas, were analyzed to assess the prevalence of sexual initiation, including the initiation of non-coital sex, and the prevalence of sexual risk behaviors among Texas and US youth. Results: A substantial proportion of middle and high school students are having sex. Sexual initiation begins as early as 6th grade and increases steadily through 12th grade with almost two-thirds of high school seniors being sexually experienced. Many teens are not protecting themselves from unintended pregnancy or STIs – nationally, 80% and 39% of high school students did not use birth control pills or a condom respectively the last time they had sex. Many middle and high school students are engaging in oral and anal sex, two behaviors which increase the risk of contracting an STI and HIV. In Texas, an estimated 689,512 out of 1,327,815 public high school students are sexually experienced – over half (52%) of the total high school population. Texas students surpass their US peers in several sexual risk behaviors including number of lifetime sexual partners, being currently sexually active, and not using effective methods of birth control or dual protection when having sex. They are also less likely to receive HIV/AIDS education in school. Conclusion: Changes in policy and practice, including implementation of evidence-based sex education programs in middle and high schools and increased access to integrated, teen-friendly sexual and reproductive health services, are urgently needed at the state and national levels to address these issues effectively.

Tracking of plasma lipids and lipoproteins, within-person and year-to-year variability in plasma cholesterol levels among participants from age 8 to 18 in Project HeartBeat!

Coronary heart disease remains the leading cause of death in the United States and increased blood cholesterol level has been found to be a major risk factor with roots in childhood. Tracking of cholesterol, i.e., the tendency to maintain a particular cholesterol level relative to the rest of the population, and variability in blood lipid levels with increase in age have implications for cholesterol screening and assessment of lipid levels in children for possible prevention of further rise to prevent adulthood heart disease. In this study the pattern of change in plasma lipids, over time, and their tracking were investigated. Also, within-person variance and retest reliability defined as the square root of within-person variance for plasma total cholesterol, HDL-cholesterol, LDL-cholesterol, and triglycerides and their relation to age, sex and body mass index among participants from age 8 to 18 years were investigated. ^ In Project HeartBeat!, 678 healthy children aged 8, 11 and 14 years at baseline were enrolled and examined at 4-monthly intervals for up to 4 years. We examined the relationship between repeated observations by Pearson's correlations. Age- and sex-specific quintiles were calculated and the probability of participants to remain in the uppermost quintile of their respective distribution was evaluated with life table methods. Plasma total cholesterol, HDL-C and LDL-C at baseline were strongly and significantly correlated with measurements at subsequent visits across the sex and age groups. Plasma triglyceride at baseline was also significantly correlated with subsequent measurements but less strongly than was the case for other plasma lipids. The probability to remain in the upper quintile was also high (60 to 70%) for plasma total cholesterol, HDL-C and LDL-C. ^ We used a mixed longitudinal, or synthetic cohort design with continuous observations from age 8 to 18 years to estimate within person variance of plasma total cholesterol, HDL-C, LDL-C and triglycerides. A total of 5809 measurements were available for both cholesterol and triglycerides. A multilevel linear model was used. Within-person variance among repeated measures over up to four years of follow-up was estimated for total cholesterol, HDL-C, LDL-C and triglycerides separately. The relationship of within-person and inter-individual variance with age, sex, and body mass index was evaluated. Likelihood ratio tests were conducted by calculating the deviation of −2log (likelihood) within the basic model and alternative models. The square root of within-person variance provided the retest reliability (within person standard deviation) for plasma total cholesterol, HDL-C, LDL-C and triglycerides. We found 13.6 percent retest reliability for plasma cholesterol, 6.1 percent for HDL-cholesterol, 11.9 percent for LDL-cholesterol and 32.4 percent for triglycerides. Retest reliability of plasma lipids was significantly related with age and body mass index. It increased with increase in body mass index and age. These findings have implications for screening guidelines, as participants in the uppermost quintile tended to maintain their status in each of the age groups during a four-year follow-up. The magnitude of within-person variability of plasma lipids influences the ability to classify children into risk categories recommended by the National Cholesterol Education Program. ^

Identification and characterization of a molecular marker in epithelial ovarian cancer

Carcinomas that arise from the ovarian surface epithelium represent a great challenge in gynecologic oncology. Although the prognosis of ovarian cancer is influenced by many factors capable of predicting clinical outcome, including tumor stage, pathological grade, and amount of residual disease following primary surgery, the biological aspects of ovarian cancer are not completely understood, thus implying that there may be other predictive indicators that could be used independently or in conjunction with these factors to provide a clearer clinical picture. The identification of additional markers with biological relevance is desirable. To identify disease-associated peptides, a phage display random peptide library was used to screen immunoglobulins derived from a patient with ovarian cancer. One peptide was markedly enriched following three rounds of affinity selection. The presence of autoantibodies against the peptide was examined in a panel of ovarian cancer patients. Stage IV patients exhibited a high percentage of positive reactivity (59%). This was in contrast to stage III patients, who only displayed 7% positive reactivity. Antibodies against the peptide were affinity purified, and heat-shock protein 90 (Hsp90) was identified as the corresponding autoantigen. The expression profile of the identified antigen was determined. Hsp90 was expressed in all sections examined regardless of degree of anaplasia. This thesis shows that utilizing the humoral response to ovarian cancer can be used to identify a tumor antigen in ovarian cancer. The data show that certain antigens may be expressed in ovarian tumors independent of the disease stage or grade, whereas circulating antibodies against such epitopes are only found in a subset of patients. ^

Maternal ingestion of radon-222 in drinking water and the absorbed radiation dose to the embryo

Maternal ingestion of high concentrations of radon-222 (Rn-222) in drinking during pregnancy may pose a significant radiation hazard to the developing embryo. The effects of ionizing radiation to the embryo and fetus have been the subject of research, analyses, and the development of a number of radiation dosimetric models for a variety of radionuclides. Currently, essentially all of the biokinetic and dosimetric models that have been developed by national and international radiation protection agencies and organizations recommend calculating the dose to the mother's uterus as a surrogate for estimating the dose to the embryo. Heretofore, the traditional radiation dosimetry models have neither considered the embryo a distinct and rapidly developing entity, the fact that it is implanted in the endometrial layer of the uterus, nor the physiological interchanges that take place between maternal and embryonic cells following the implantation of the blastocyst in the endometrium. The purpose of this research was to propose a new approach and mathematical model for calculating the absorbed radiation dose to the embryo by utilizing a semiclassical treatment of alpha particle decay and subsequent scattering of energy deposition in uterine and embryonic tissue. The new approach and model were compared and contrasted with the currently recommended biokinetic and dosimetric models for estimating the radiation dose to the embryo. The results obtained in this research demonstrate that the estimated absorbed dose for an embryo implanted in the endometrial layer of the uterus during the fifth week of embryonic development is greater than the estimated absorbed dose for an embryo implanted in the uterine muscle on the last day of the eighth week of gestation. This research provides compelling evidence that the recommended methodologies and dosimetric models of the Nuclear Regulatory Commission and International Commission on Radiological Protection employed for calculating the radiation dose to the embryo from maternal intakes of radionuclides, including maternal ingestion of Rn-222 in drinking water would result in an underestimation of dose. ^

Bayesian generalized linear models for meta-analysis of diagnostic tests

With the recognition of the importance of evidence-based medicine, there is an emerging need for methods to systematically synthesize available data. Specifically, methods to provide accurate estimates of test characteristics for diagnostic tests are needed to help physicians make better clinical decisions. To provide more flexible approaches for meta-analysis of diagnostic tests, we developed three Bayesian generalized linear models. Two of these models, a bivariate normal and a binomial model, analyzed pairs of sensitivity and specificity values while incorporating the correlation between these two outcome variables. Noninformative independent uniform priors were used for the variance of sensitivity, specificity and correlation. We also applied an inverse Wishart prior to check the sensitivity of the results. The third model was a multinomial model where the test results were modeled as multinomial random variables. All three models can include specific imaging techniques as covariates in order to compare performance. Vague normal priors were assigned to the coefficients of the covariates. The computations were carried out using the 'Bayesian inference using Gibbs sampling' implementation of Markov chain Monte Carlo techniques. We investigated the properties of the three proposed models through extensive simulation studies. We also applied these models to a previously published meta-analysis dataset on cervical cancer as well as to an unpublished melanoma dataset. In general, our findings show that the point estimates of sensitivity and specificity were consistent among Bayesian and frequentist bivariate normal and binomial models. However, in the simulation studies, the estimates of the correlation coefficient from Bayesian bivariate models are not as good as those obtained from frequentist estimation regardless of which prior distribution was used for the covariance matrix. The Bayesian multinomial model consistently underestimated the sensitivity and specificity regardless of the sample size and correlation coefficient. In conclusion, the Bayesian bivariate binomial model provides the most flexible framework for future applications because of its following strengths: (1) it facilitates direct comparison between different tests; (2) it captures the variability in both sensitivity and specificity simultaneously as well as the intercorrelation between the two; and (3) it can be directly applied to sparse data without ad hoc correction. ^

Body mass index trajectories and predictors among 3rd to 12th graders using growth curve mixture modeling the Child and Adolescent Trial for Cardiovascular Health (CATCH) study

This dissertation examined body mass index (BMI) growth trajectories and the effects of gender, ethnicity, dietary intake, and physical activity (PA) on BMI growth trajectories among 3rd to 12th graders (9-18 years of age). Growth curve model analysis was performed using data from The Child and Adolescent Trial for Cardiovascular Health (CATCH) study. The study population included 2909 students who were followed up from grades 3-12. The main outcome was BMI at grades 3, 4, 5, 8, and 12. ^ The results revealed that BMI growth differed across two distinct developmental periods of childhood and adolescence. Rate of BMI growth was faster in middle childhood (9-11 years old or 3rd - 5th grades) than in adolescence (11-18 years old or 5th - 12th grades). Students with higher BMI at 3rd grade (baseline) had faster rates of BMI growth. Three groups of students with distinct BMI growth trajectories were identified: high, average, and low. ^ Black and Hispanic children were more likely to be in the groups with higher baseline BMI and faster rates of BMI growth over time. The effects of gender or ethnicity on BMI growth differed across the three groups. The effects of ethnicity on BMI growth were weakened as the children aged. The effects of gender on BMI growth were attenuated in the groups with a large proportion of black and Hispanic children, i.e., “high” or “average” BMI trajectory group. After controlling for gender, ethnicity, and age at baseline, in the “high BMI trajectory”, rate of yearly BMI growth in middle childhood increased 0.102 for every 500 Kcals increase (p=0.049). No significant effects of percentage of energy from total fat and saturated fat on BMI growth were found. Baseline BMI increased 0.041 for every 30 minutes increased in moderate-to-vigorous PA (MVPA) in the “low BMI trajectory”, while Baseline BMI decreased 0.345 for every 30 minutes increased in vigorous PA (VPA) in the “high BMI trajectory”. ^ Childhood overweight and obesity interventions should start at the earliest possible ages, prior to 3rd grade and continue through grade school. Interventions should focus on all children, but specifically black and Hispanic children, who are more likely to be highest at-risk. Promoting VPA earlier in childhood is important for preventing overweight and obesity among children and adolescents. Interventions should target total energy intake, rather than only percentage of energy from total fat or saturated fat. ^

Ethnic differences in tumor characteristics, treatment and survival in glioma patients

Purpose. A descriptive analysis of glioma patients by race was carried out in order to better elucidate potential differences between races in demographics, treatment, characteristics, prognosis and survival. ^ Patients and Methods. Among 1,967 patients ≥ 18 years diagnosed with glioma seen between July 2000 and September 2006 at The University of Texas M.D. Anderson Cancer Center (UTMDACC). Data were collated from the UTMDACC Patient History Database (PHDB) and the UTMDACC Tumor Registry Database (TRDB). Chi-square analysis, uni- /multivariate Cox proportional hazards modeling and survival analysis were used to analyze differences by race. ^ Results. Demographic, treatment and histologic differences exist between races. Though risk differences were seen between races, race was not found to be a significant predictor in multivariate regression analysis after accounting for age, surgery, chemotherapy, radiation, tumor type as stratified by WHO tumor grade. Age was the most consistent predictor in risk for death. Overall survival by race was significantly different (p=0.0049) only in low-grade gliomas after adjustment for age although survival differences were very slight. ^ Conclusion. Among this cohort of glioma patients, age was the strongest predictor for survival. It is likely that survival is more influenced by age, time to treatment, tumor grade and surgical expertise rather than racial differences. However, age at diagnosis, gender ratios, histology and history of cancer differed significantly between race and genetic differences to this effect cannot be excluded. ^

Involvement of lipocalin 2 in leukemia and breast cancer

Human lipocalin 2 is described as the neutrophil gelatinase-associated lipocalin (NGAL). The lipocalin 2 gene encodes a small, secreted glycoprotein that possesses a variety of functions, of which the best characterized function is organic iron binding activity. Elevated NGAL expression has been observed in many human cancers including breast, colorectal, pancreatic and ovarian cancers. I focused on the characterization of NGAL function in chronic myelogenous leukemia (CML) and breast cancer. Using the leukemic xenograft mouse model, we demonstrated that over-expression of NGAL in K562 cells, a leukemic cell line, led to a higher apoptotic rate and an atrophy phenotype in the spleen of inoculated mice compared to K562 cells alone. These results indicate that NGAL plays a primary role in suppressing hematopoiesis by inducing apoptosis within normal hematopoietic cells. In the breast cancer project, we analyzed two microarray data sets of breast cancer cell lines ( n = 54) and primary breast cancer samples (n = 318), and demonstrated that high NGAL expression is significantly correlated with several tumor characteristics, including negative estrogen receptor (ER) status, positive HER2 status, high tumor grade, and lymph node metastasis. Ectopic NGAL expression in non-aggressive (ZR75.1 and MCF7) cells led to aggressive tumor phenotypes in vitro and in vivo. Conversely, knockdown of NGAL expression in various breast cancer cell lines by shRNA lentiviral infection significantly decreased migration, invasion, and metastasis activities of tumor cells both in vitro and in vivo . It has been previously reported that transgenic mice with a mutation in the region of trans-membrane domain (V664E) of HER2 develop mammary tumors that progress to lung metastasis. However, we observed that genetic deletion of the 24p3 gene, a mouse homolog of NGAL, in HER2 transgenic mice by breeding with 24p3-null mice resulted in a significant delay of mammary tumor formation and reduction of lung metastasis. Strikingly, we also found that treatment with affinity purified 24p3 antibodies in the 4T1 breast cancer mice strongly reduced lung metastasis. Our studies provide evidence that NGAL plays a critical role in breast cancer development and progression, and thus NGAL has potential as a new therapeutic target in breast cancer.^

Proliferation index in ductal carcinoma in situ of the breast: Relation to estrogen, progesterone and HER2/neu receptors

Background. Ductal carcinoma in situ (DCIS) is the most prevalent precursor to invasive breast cancer (IBC), the second leading cause of death in women in the United States. The three most important prognostic markers for IBC are Estrogen receptor (ER), Progesterone receptor (PR) and HER2/neu. The four groups (IBC) defined as (1) ER and/or PR positive and HER2/neu negative, (2) ER and/or PR positive and HER2/neu positive (3) ER and/or PR negative and HER2/neu positive and (4) negative for all three of these receptors (Triple negative). However, they have not been well studied in DCIS. This is an exploratory study with a primary objective to examine the prevalence of ER, PR, and HER2/neu in DCIS, to explore if the defined groups of IBC occur in DCIS and to consider the biological relationship between these four groups and the proliferative activity of the tumor. A secondary goal of this study is to examine the relationship between grade and proliferative activity. Methods. Using immunohistochemistry, I have measured Ki-67, ER, PR and HER2/neu positivity for a series of cases of DCIS. Results. 20 ER and/or PR positive and HER2/neu negative (50%) with average PI of 0.05, 7 ER and/or PR positive and HER2/neu positive (17.5%) with average PI of 0.14, 10 ER and/or PR negative and HER2/neu positive (25%) with average PI of 0.18, and three triple negative (7.5%) with average PI of 0.18. ER and/or PR positive and HER2/neu positive group has the highest PI (p<0.001). Further, the ER and/or PR positive and HER2/neu positive group show a linear relationship between PI and average ER/PR positivity (R=0.6). PI increases with higher grades. Conclusion. PI appears to depend upon the average fraction of positive ER/PR tumor cells, possibly with a synergistic dependence when HER2/neu is positive. If ER/PR is negative, then both HER2/neu positive and the triple negative cases appear to cluster around an average PI that is higher than the average PI in HER2/neu negative ER/PR positive negative cases. In the triple negative tumors there must be another driver of proliferation.^

Is sexual violence associated with symptoms of depression and suicide attempts

Sexual assault, depression, and suicide are all very serious issues among youth today. This study sought to quantify the association between sexual violence, symptoms of depression, and suicide attempts through the use of 2007 Youth Risk Behavior Survey (YRBS) data. The YRBS is a nationally representative dataset of United States high school students, grades 9-12. It was hypothesized that sexual violence is significantly associated with symptoms of depression and suicide attempts. Through multivariate logistic regression, it was determined that students who had ever experienced forced sex were 3.10 (2.7-3.6) times as likely to be depressed, in the past 12 months, and 4.22 (3.5-5.1) times as likely to have attempted suicide. Female victims were 3.43 (2.9-4.0) times as likely to be depressed; male victims were 5.40 (3.7-7.9) times as likely to have attempted suicide. Sexual violence is significantly associated with both symptoms of depression and suicide attempt when stratified by gender, grade, and race/ethnicity. These results indicate that further study of the association between sexual violence and symptoms of depression and suicide attempts need to be conducted in order to establish temporality.^

Comparison of Bayesian and frequentist approaches in clinical outcomes between hospitals

Many public health agencies and researchers are interested in comparing hospital outcomes, for example, morbidity, mortality, and hospitalization across areas and hospitals. However, since there is variation of rates in clinical trials among hospitals because of several biases, we are interested in controlling for the bias and assessing real differences in clinical practices. In this study, we compared the variations between hospitals in rates of severe Intraventricular Haemorrhage (IVH) infant using Frequentist statistical approach vs. Bayesian hierarchical model through simulation study. The template data set for simulation study was included the number of severe IVH infants of 24 intensive care units in Australian and New Zealand Neonatal Network from 1995 to 1997 in severe IVH rate in preterm babies. We evaluated the rates of severe IVH for 24 hospitals with two hierarchical models in Bayesian approach comparing their performances with the shrunken rates in Frequentist method. Gamma-Poisson (BGP) and Beta-Binomial (BBB) were introduced into Bayesian model and the shrunken estimator of Gamma-Poisson (FGP) hierarchical model using maximum likelihood method were calculated as Frequentist approach. To simulate data, the total number of infants in each hospital was kept and we analyzed the simulated data for both Bayesian and Frequentist models with two true parameters for severe IVH rate. One was the observed rate and the other was the expected severe IVH rate by adjusting for five predictors variables for the template data. The bias in the rate of severe IVH infant estimated by both models showed that Bayesian models gave less variable estimates than Frequentist model. We also discussed and compared the results from three models to examine the variation in rate of severe IVH by 20th centile rates and avoidable number of severe IVH cases. ^