Genetic variants associated with metastatic prostate cancer

Introduction. Distant metastasis remains the leading cause of death among prostate cancer patients. Several genetic susceptibility loci associated with Prostate cancer have been identified by the Genome Wide Association Studies (GWAS). To date, few studies have explored the ability of these SNPs to identify metastatic prostate cancer. Based on the identification of genetic variants as predictors of aggressive disease, a case comparison study of prostate cancer patients was designed to explore the association of 96 GWAS single nucleotide polymorphisms (SNPs) with metastatic disease. ^ Method. 1242 histologically confirmed prostate cancer patients, with and without metastatic disease, were enrolled into the study. Data were collected from personal interviews, hospital database and abstraction of medical records. Ninety six SNPs identified from GWAS studies based on their associations with prostate cancer risk were genotyped in the study population. Univariate and multivariate logistic regression analyses were used to explore the relationships of the variants with metastatic prostate cancer in Whites and African American men. ^ Results. Four SNPs showed independent associations with metastatic prostate cancer (rs721048 in EHBP1 (2p15), rs3025039 in VEGF (6p12), rs11228565 in Intergenic(11q13.2) and rs2735839 in KLK3(19q13.33)) in the White population. For SNP rs2735839 in KLK3, genotype GA was 1.71 times as likely to be associated with metastatic prostate cancer diagnosis as genotype AA after adjusting for other significant SNPs and covariates (95% CI, 1.12-2.60; p=0.012). In men of African descent, three SNPs: rs1512268 in NKX3-1(8p21.2), rs12155172 in intergenic (7p15.3) & rs10486567 in JAZF1 (7p15.2) were positively associated with metastatic disease in the multivariate analysis. The strongest SNP was rs1512268 heterozygous genotype AG in NKX3-1(8p21.2) which was associated with 3.97-fold increased risk of metastatic prostate cancer diagnosis (95% CI, 1.69-9.34; p =0.002). ^ Conclusion. Genetic variants associated with metastatic prostate cancer were different in Whites and African American men. Given the high mortality rate recorded in men diagnosed with metastatic prostate tumor, further studies are needed to validate associations and establish their clinical application.^

Association of ALLHAT investigator characteristics with participant recruitment

Clinical trials are often not successful because of the inability to recruit a sufficient number of patients. The Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT), the largest antihypertensive trial ever conducted, provided highly generalized results and successful recruitment of over 42,000 participants. The overall purpose of this study was to examine the association of investigator characteristics with anti-hypertensive (AHT) participant recruitment in ALLHAT. This secondary data analyses collected data from the ALLHAT investigator profile survey and related investigator characteristics to recruitment success. The sample size was 502 investigators, with recruitment data from 37,947AHT participants. Recruitment was dichotomized by categorizing all sites with recruitment numbers at or above the overall median recruitment number of 46 as "Successful Recruitment". Frequency distributions and univariate and multivariate logistic regression were conducted. When adjusting for all other factors, Hispanic ethnicity, suburban setting, Department of Veterans Affairs Medical Centers (VAMC) site type, number of clinical site staff working on the trial, study coordinator hours per week, medical conference sessions attended, the investigator's primary goal and the likelihood that a physician will convince a patient to continue on randomized treatment, have significant impacts on the recruitment success of ALLHAT investigators. Most of the ALLHAT investigators described their primary commitment as being towards their patients and not to scientific knowledge alone. However, investigators that distinguished themselves as leaders in research had greater recruitment success than investigators who were leaders in clinical practice. ALLHAT was a highly successful trial that proved that community based cardiovascular trials can be implemented on a large scale. Exploring characteristics of ALLHAT investigators provides data that can be generalized to sponsors, sites, and others interested in maximizing clinical trial recruitment numbers. Future studies should further evaluate investigator and study coordinator factors that impact cardiovascular clinical trial recruitment success.^

Correlation of blood type with thrombosis in pancreatic cancer patients

Objective: The study aimed to identify the risk factors involved in initiating thromboembolism (TE) in pancreatic cancer (PC) patients, with focus on ABO blood type. ^ Methods and Patients: There were 35.7% confirmed cases of TE and 64.3% cases remained free of TE (n=687). There were 12.7% only Pulmonary embolism (PE), 9% only Deep vein thrombosis (DVT), 53.5% only other sites, 3.3% combined PE and DVT, 8.6% combined PE and other sites, 9.8% combined DVT and other sites, and 3.3% all three combined cases. ^ Results: The risk factors for thrombosis identified by multivariate logistic regression were: history of previous anti-thrombotic treatment, tumor site in pancreatic body or tail, large tumor size, maximum glucose category more than 126 and 200 mg/dL. ^ The factors with worse overall survival by multivariate Cox regression and Kaplan Meier analyses were: locally advanced or metastatic stage, worsening performance status, high CA 19-9 levels, and HbA1C levels more than 6 %, at diagnosis. ^ There were 29.1% and 39.1% of the patients with thrombosis in the O and non-O blood type groups respectively. Both Non-O blood type (P=0.02) and the A, B and AB blood types (P= 0.007) were associated with thrombosis as compared to O type. The odds of thrombosis were nearly half in O blood type patients as compared to non-O blood type [OR-0.54 (95% C.I.- 0.37-0.79), P<0.001]. ^ Conclusion: A better understanding of the TE and PC relationship and involved risk factors may provide insights on tumor biology and patient response to prophylactic anticoagulation therapy.^

Shifting the focus: Addressing breast cancer disparities at the health care organization level

The purpose of this study was to understand the scope of breast cancer disparities within the Texas Medical Center. The goal was to increase the awareness of breast cancer disparities at the health care organization level, and to foster the development of organizational interventions to reduce breast cancer disparities. The study seeks to answer the following questions: 1. Are hospitals in the Texas Medical Center implementing interventions to reduce breast cancer disparities? 2. What are their interventions for reducing the effects of non clinical factors on breast cancer treatment disparities? 3. What are their measures for monitoring, continuously improving, and evaluating the success of their interventions? ^ This research project was designed as a mixed methods case study. Quantitative breast cancer data for the years 2000-2009 was obtained from the Texas Cancer Registry (TCR). Qualitative data collection and analysis was done by conducting a total of 20 semi-structured interviews of administrators, physicians and nurses at five hospitals (A, B, C, D and E) in the Texas Medical Center (TMC). For quantitative analysis, the study was limited to early stage breast cancer patients: local and regional. The dependent variable was receipt of standard treatment: Surgery (Yes/No), BCS vs Mastectomy, Chemotherapy (Yes/No) and Radiation after BCS (Yes/No). The main independent variable was race: non-Hispanic White (NHW) , non-Hispanic Black (NHB), and Hispanic. Other covariates included age at diagnosis, diagnosis date, percent poverty, grade, stage, and regional nodes. Multivariate logistic regression was used to test the adjusted association between receipt of standard care and race. Qualitative data was analyzed with the Atlas.ti7 software (ATLAS.ti GmbH, Berlin). ^ Though there were significant differences by race for all dependent variables when the data was analyzed as a single group of all hospitals; at the level of the individual hospitals the results were not consistent by race/ethnicity across all dependent variables for hospitals A, B, and E. There were no racial differences in adjusted analysis for receipt of chemotherapy for the individual hospitals of interest in this study. For hospitals C and D, no racial disparities in treatment was observed in adjusted multivariable analysis. All organizations in this study were aware of the body of research which shows that there are disparities in breast cancer outcomes for patient population groups. However, qualitative data analysis found that there were differences in interest among hospitals in addressing breast cancer disparities in their patient population groups. Some organizations were actively implementing directed measures to reduce the breast cancer disparity gap in outcomes for patients, and others were not. Despite the differences in levels of interest, quantitative data analysis showed that organizations in the Texas Medical Center were making progress in reducing the burden of breast cancer disparities in the patient populations being served.^

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. ^

Assessing time-by-covariate interactions in Cox proportional hazards regression models using cubic spline functions

This dissertation develops and explores the methodology for the use of cubic spline functions in assessing time-by-covariate interactions in Cox proportional hazards regression models. These interactions indicate violations of the proportional hazards assumption of the Cox model. Use of cubic spline functions allows for the investigation of the shape of a possible covariate time-dependence without having to specify a particular functional form. Cubic spline functions yield both a graphical method and a formal test for the proportional hazards assumption as well as a test of the nonlinearity of the time-by-covariate interaction. Five existing methods for assessing violations of the proportional hazards assumption are reviewed and applied along with cubic splines to three well known two-sample datasets. An additional dataset with three covariates is used to explore the use of cubic spline functions in a more general setting. ^