Individuals are exposed to a large number of diverse environmental chemicals simultaneously and the evaluation of multiple chemical exposures is important for identifying cancer risk factors. The measurement of a large number of chemicals (the exposome) in epidemiologic studies is allowing for a more comprehensive assessment of cancer risk factors than was done in earlier studies that focused on only a few chemicals. Empirical evidence from epidemiologic studies shows that chemicals from different chemical classes have different magnitudes and directions of association with cancers. Given increasing data availability, there is a need for the development and assessment of statistical methods to model environmental cancer risk that considers a large number of diverse chemicals with different effects for different chemical classes. The method of grouped weighted quantile sum (GWQS) regression allows for multiple groups of chemicals to be considered in the model such that different magnitudes and directions of associations are possible for each group of chemicals. In this paper, we assessed the ability of GWQS regression to estimate exposure effects for multiple chemical groups and correctly identify important chemicals in each group using a simulation study. We compared the performance of GWQS regression with WQS regression, the least absolute shrinkage and selection operator (lasso), and the group lasso in estimating exposure effects and identifying important chemicals. The simulation study results demonstrate that GWQS is an effective method for modeling exposure to multiple groups of chemicals and compares favorably with other methods used in mixture analysis. As an application, we used GWQS regression in the California Childhood Leukemia Study (CCLS), a population-based case-control study of childhood leukemia in California to estimate exposure effects for many chemical classes while also adjusting for demographic factors. The CCLS analysis found evidence of a positive association between exposure to the herbicide dacthal and an increased risk of childhood leukemia.

There has been a growing interest in the literature on multiple environmental risk factors for diseases and an increasing emphasis on assessing multiple environmental exposures simultaneously in epidemiologic studies of cancer. One method used to analyze exposure to multiple chemical exposures is weighted quantile sum (WQS) regression. While WQS regression has been demonstrated to have good sensitivity and specificity when identifying important exposures, it has limitations including a two-step model fitting process that decreases power and model stability and a requirement that all exposures in the weighted index have associations in the same direction with the outcome, which is not realistic when chemicals in different classes have different directions and magnitude of association with a health outcome. Grouped WQS (GWQS) was proposed to allow for multiple groups of chemicals in the model where different magnitude and direction of associations are possible for each group. However, GWQS shares the limitation of WQS of a two-step estimation process and splitting of data into training and validation sets. In this paper, we propose a Bayesian group index model to avoid the estimation limitation of GWQS while having multiple exposure indices in the model. To evaluate the performance of the Bayesian group index model, we conducted a simulation study with several different exposure scenarios. We also applied the Bayesian group index method to analyze childhood leukemia risk in the California Childhood Leukemia Study (CCLS). The results showed that the Bayesian group index model had slightly better power for exposure effects and specificity and sensitivity in identifying important chemical exposure components compared with the existing frequentist method, particularly for small sample sizes. In the application to the CCLS, we found a significant negative association for insecticides, with the most important chemical being carbaryl. In addition, for children who were born and raised in the home where dust samples were taken, there was a significant positive association for herbicides with dacthal being the most important exposure. In conclusion, our approach of the Bayesian group index model appears able to make a substantial contribution to the field of environmental epidemiology.

There is growing scientific interest in identifying the multitude of chemical exposures related to human diseases through mixture analysis. In this paper, we address the issue of below detection limit (BDL) missing data in mixture analysis using Bayesian group index regression by treating both regression effects and missing BDL observations as parameters in a model estimated through a Markov chain Monte Carlo algorithm that we refer to as pseudo-Gibbs imputation. We compare this with other Bayesian imputation methods found in the literature (Multiple Imputation by Chained Equations and Sequential Full Bayes imputation) as well as with a non-Bayesian single-imputation method. To evaluate our proposed method, we conduct simulation studies with varying percentages of BDL missingness and strengths of association. We apply our method to the California Childhood Leukemia Study (CCLS) to estimate concentrations of chemicals in house dust in a mixture analysis of potential environmental risk factors for childhood leukemia. Our results indicate that pseudo-Gibbs imputation has superior power for exposure effects and sensitivity for identifying individual chemicals at high percentages of BDL missing data. In the CCLS, we found a significant positive association between concentrations of polycyclic aromatic hydrocarbons (PAHs) in homes and childhood leukemia as well as significant positive associations for polychlorinated biphenyls (PCBs) and herbicides among children from the highest quartile of household income. In conclusion, pseudo-Gibbs imputation addresses a commonly encountered problem in environmental epidemiology, providing practitioners the ability to jointly estimate the effects of multiple chemical exposures with high levels of BDL missingness.

Leukemia is the most common childhood cancer in industrialized countries, and the increasing incidence trends in the US suggest that environmental exposures play a role in its etiology. Neighborhood socioeconomic status (SES) has been found to be associated with many health outcomes, including childhood leukemia. In this paper, we used a Bayesian index model approach to estimate a neighborhood deprivation index (NDI) in the analysis of childhood leukemia in a population-based case-control study (diagnosed 1999 to 2006) in northern and central California, with direct indoor measurements of many chemicals for 277 cases and 306 controls <8 years of age. We considered spatial random effects in the Bayesian index model approach to identify any areas of significantly elevated risk not explained by neighborhood deprivation or individual covariates, and assessed if groups of indoor chemicals would explain any elevated spatial risk areas. Due to not all eligible cases and controls participating in the study, we conducted a simulation study to add non-participants to evaluate the impact of potential selection bias when estimating NDI effects and spatial risk. The results in the crude model showed an odds ratio (OR) of 1.06 and 95% credible interval (CI) of (0.98, 1.15) for a one unit increase in the NDI, but the association became slightly inverse when adjusting for individual level covariates in the observed data (OR = 0.97 and 95% CI: 0.87, 1.07), as well as when using simulated data (average OR = 0.98 and 95% CI: 0.91, 1.05). We found a significant spatial risk of childhood leukemia after adjusting for NDI and individual-level covariates in two counties, but the area of elevated risk was partly explained by selection bias in simulation studies that included more participating controls in areas of lower SES. The area of elevated risk was explained when including chemicals measured inside the home, and insecticides and herbicides had greater effects for the risk area than the overall study. In summary, the consideration of exposures and variables at different levels from multiple sources, as well as potential selection bias, are important for explaining the observed spatial areas of elevated risk and effect estimates.

Our primary objectives are to empirically identify distinct childhood groups based on trajectories of waist circumference (WC) and waist circumference index measurements, and then to estimate associations between these groups and adult diabetes incidence, as well as other outcomes, including blood pressure, body size, body composition, and hemoglobin levels. Childhood WC and height measurements as well as various adult measurements are taken from participants in the Fels Longitudinal Study. Childhood groups are identified using group-based trajectory modeling. Associations between the resulting group probabilities and adult outcomes are examined using mixed models. Our results show that distinct childhood groups are identifiable for both waist size measurements, with growth curves exhibited by these groups becoming distinguishably separate at around 4 years of age. Higher probabilities for groups exhibiting the larger waist size for either measurement were estimated to have higher risks of developing diabetes in adulthood. Associations were also observed between group probabilities and systolic blood pressure, diastolic blood pressure, and various anthropomorphic measurements, with most associations consistently occurring in early adulthood. These findings expand upon the existing literature, showing that childhood trends in waist size, distinguishable at ages as early as 4 years, are associated with adult Type-2 diabetes.

Glioblastoma Multiforme (GBM) is an aggressive type of astrocytoma and the most common malignant brain tumor in adults. Bevacizumab is a recombinant monoclonal antibody that targets VEGF-A. VEGF is regulated by the hypoxia-inducible-factor (HIF-1α) pathway, with the tumor suppressor gene TP53 as one of the key regulating factors. Our study examines the relevance of various genetic mutations of the HIF-1α pathway and their predictive significance in patients diagnosed with recurrent GBM and treated with bevacizumab. A total of 41 patients diagnosed with recurrent GBM and treated with Bevacizumab at Virginia Commonwealth University from January 1990 through November 2018 were included in our study. Our study cohort consists of 56.1% males and 43.9% females, with an average age of 59.9 (SD 12.1), and 68.3% were Caucasian. The majority (90%) of the cohort were classified as de novo cases of GBM. The overall survival (OS) was 172.5 days (95% CI: 122.0, 218.5), with a minimum of 11 days and maximum of 904 days measured from the date of the first bevacizumab treatment to the date of death. The majority of patients (70.7%) responded to treatment, with 61% exhibiting a partial response and 9.8% exhibiting a complete response. The median first response to treatment (FRT) was 34 days (95% CI: 21, 40), with a minimum of 2 days and maximum of 76. All patients died during the follow-up period. Survival analyses for OS, along with FRT, were both performed with Kaplan-Meier curves generated individually. We have examined the clinical characteristics of our study cohort and are currently examining the genetic characteristics of the tumors in our cohort. At the time of the conference we will have updated genomic and radiographic data in addition to the current detailed clinical data. Citation Format: Marcin Dragan, Matthew Carli, Mark Malkin, Victor Yazbeck. Predictive biomarkers for Bevacizumab activity in recurrent glioblastoma: A single institution study [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 807.