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 We compare the performance of our methods with baseline reference survival methods and apply our methodology to an unpublished data set of brain tumor survival times and gene expression data, selecting genes potentially related to the development of the disease under study.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.

Is a 3-mm intrafractional margin sufficient for daily image-guided intensity-modulated radiation therapy of prostate cancer?

PURPOSE: To determine whether a 3-mm isotropic target margin adequately covers the prostate and seminal vesicles (SVs) during administration of an intensity-modulated radiation therapy (IMRT) treatment fraction, assuming that daily image-guided setup is performed just before each fraction. MATERIALS AND METHODS: In-room computed tomographic (CT) scans were acquired immediately before and after a daily treatment fraction in 46 patients with prostate cancer. An eight-field IMRT plan was designed using the pre-fraction CT with a 3-mm margin and subsequently recalculated on the post-fraction CT. For convenience of comparison, dose plans were scaled to full course of treatment (75.6 Gy). Dose coverage was assessed on the post-treatment CT image set. RESULTS: During one treatment fraction (21.4+/-5.5 min), there were reductions in the volumes of the prostate and SVs receiving the prescribed dose (median reduction 0.1% and 1.0%, respectively, p<0.001) and in the minimum dose to 0.1 cm(3) of their volumes (median reduction 0.5 and 1.5 Gy, p<0.001). Of the 46 patients, three patients' prostates and eight patients' SVs did not maintain dose coverage above 70 Gy. Rectal filling correlated with decreased percentage-volume of SV receiving 75.6, 70, and 60 Gy (p<0.02). CONCLUSIONS: The 3-mm intrafractional margin was adequate for prostate dose coverage. However, a significant subset of patients lost SV dose coverage. The rectal volume change significantly affected SV dose coverage. For advanced-stage prostate cancers, we recommend to use larger margins or improve organ immobilization (such as with a rectal balloon) to ensure SV coverage.

Preclinical modeling of multi-drug cancer therapies

Anticancer drugs typically are administered in the clinic in the form of mixtures, sometimes called combinations. Only in rare cases, however, are mixtures approved as drugs. Rather, research on mixtures tends to occur after single drugs have been approved. The goal of this research project was to develop modeling approaches that would encourage rational preclinical mixture design. To this end, a series of models were developed. First, several QSAR classification models were constructed to predict the cytotoxicity, oral clearance, and acute systemic toxicity of drugs. The QSAR models were applied to a set of over 115,000 natural compounds in order to identify promising ones for testing in mixtures. Second, an improved method was developed to assess synergistic, antagonistic, and additive effects between drugs in a mixture. This method, dubbed the MixLow method, is similar to the Median-Effect method, the de facto standard for assessing drug interactions. The primary difference between the two is that the MixLow method uses a nonlinear mixed-effects model to estimate parameters of concentration-effect curves, rather than an ordinary least squares procedure. Parameter estimators produced by the MixLow method were more precise than those produced by the Median-Effect Method, and coverage of Loewe index confidence intervals was superior. Third, a model was developed to predict drug interactions based on scores obtained from virtual docking experiments. This represents a novel approach for modeling drug mixtures and was more useful for the data modeled here than competing approaches. The model was applied to cytotoxicity data for 45 mixtures, each composed of up to 10 selected drugs. One drug, doxorubicin, was a standard chemotherapy agent and the others were well-known natural compounds including curcumin, EGCG, quercetin, and rhein. Predictions of synergism/antagonism were made for all possible fixed-ratio mixtures, cytotoxicities of the 10 best-scoring mixtures were tested, and drug interactions were assessed. Predicted and observed responses were highly correlated (r2 = 0.83). Results suggested that some mixtures allowed up to an 11-fold reduction of doxorubicin concentrations without sacrificing efficacy. Taken together, the models developed in this project present a general approach to rational design of mixtures during preclinical drug development. ^

Risk factors for spinal surgical site infection

Background. Surgical site infections (SSI) are one of the most common nosocomial infections in the United States. This study was conducted following an increase in the rate of SSI following spinal procedures at the study hospital. ^ Methods. This study examined patient and hospital associated risk factors for SSI using existing data on patients who had spinal surgery performed at the study hospital between December 2003 and August 2005. There were 59 patients with SSI identified as cases; controls were randomly selected from patients who had spinal procedures performed at the study hospital during the study period, but did not develop infection. Of the 245 original records reviewed, 5% were missing more than half the variables and were eliminated from the data set. A total of 234 patients were included in the final analysis, representing 55 cases and 179 controls. Multivariable analysis was conducted using logistic regression to control for confounding variables. ^ Results. Three variables were found to be significant risk factors for SSI in the study population: presence of comorbidities (odds ratio 3.15, 95% confidence interval 1.20 to 8.26), cut time above the population median of 100 minutes (odds ratio 2.98, 95% confidence interval 1.12 to 5.49), and use of iodine only for preoperative skin antisepsis (odds ratio 0.16, 95% confidence interval 0.06 to 0.45). Several risk factors of specific concern to the study hospital, such as operating room, hospital staff involved in the procedures and workers' compensation status, were not shown to be statistically significant. In addition, multiple factors that have been identified in prior studies, such as method of hair removal, smoking status, or incontinence, were not shown to be statistically significant in this population. ^ Conclusions. This study confirms that increased cut time is a risk for post-operative infection. Use of iodine only was found to decrease risk of infection; further study is recommended in a population with higher usage of chlorhexadine gluconate. Presence of comorbidities at the time of surgery was also found to be a risk factor for infection; however, specific comorbidities were not studied. ^

Racial disparities in CD4 counts at initial HIV-1 diagnosis: Analysis of the Adult Spectrum of HIV disease dataset and public health implications

Introduction. The HIV/AIDS disease burden disproportionately affects minority populations, specifically African Americans. While sexual risk behaviors play a role in the observed HIV burden, other factors including gender, age, socioeconomics, and barriers to healthcare access may also be contributory. The goal of this study was to determine how far down the HIV/AIDS disease process people of different ethnicities first present for healthcare. The study specifically analyzed the differences in CD4 cell counts at the initial HIV-1 diagnosis with respect to ethnicity. The study also analyzed racial differences in HIV/AIDS risk factors. ^ Methods. This is a retrospective study using data from the Adult Spectrum of HIV Disease (ASD), collected by the City of Houston Department of Health. The ASD database contains information on newly reported HIV cases in the Harris County District Hospitals between 1989 and 2000. Each patient had an initial and a follow-up report. The extracted variables of interest from the ASD data set were CD4 counts at the initial HIV diagnosis, race, gender, age at HIV diagnosis and behavioral risk factors. One-way ANOVA was used to examine differences in baseline CD4 counts at HIV diagnosis between racial/ethnic groups. Chi square was used to analyze racial differences in risk factors. ^ Results. The analyzed study sample was 4767. The study population was 47% Black, 37% White and 16% Hispanic [p<0.05]. The mean and median CD4 counts at diagnosis were 254 and 193 cells per ml, respectively. At the initial HIV diagnosis Blacks had the highest average CD4 counts (285), followed by Whites (233) and Hispanics (212) [p<0.001 ]. These statistical differences, however, were only observed with CD4 counts above 350 [p<0.001], even when adjusted for age at diagnosis and gender [p<0.05]. Looking at risk factors, Blacks were mostly affected by intravenous drug use (IVDU) and heterosexuality, whereas Whites and Hispanics were more affected by male homosexuality [ p<0.05]. ^ Conclusion. (1) There were statistical differences in CD4 counts with respect to ethnicity, but these differences only existed for CD4 counts above 350. These differences however do not appear to have clinical significance. Antithetically, Blacks had the highest CD4 counts followed by Whites and Hispanics. (2) 50% of this study group clinically had AIDS at their initial HIV diagnosis (median=193), irrespective of ethnicity. It was not clear from data analysis if these observations were due to failure of early HIV surveillance, HIV testing policies or healthcare access. More studies need to be done to address this question. (3) Homosexuality and bisexuality were the biggest risk factors for Whites and Hispanics, whereas for Blacks were mostly affected by heterosexuality and IVDU, implying a need for different public health intervention strategies for these racial groups. ^