Using a Bayesian hierarchical model for identifying single nucleotide polymorphisms associated with childhood acute lymphoblastic leukemia risk in case-parent triads

Intracellular Signaling Pathways Involved in Childhood Acute Lymphoblastic Leukemia; Molecular Targets

Acute lymphoblastic leukemia (ALL) is a malignant disease characterized by an uncontrolled proliferation of immature lymphoid cells. ALL is the most common hematologic malignancy in early childhood, and it reaches peak incidence between the ages of 2 and 3 years. The prognosis of ALL is associated with aberrant gene expression, in addition to the presence of numerical or structural chromosomal alterations, age, race, and immunophenotype. The Relapse rate with regard to pharmacological treatment rises in childhood; thus, the expression of biomarkers associated with the activation of cell signaling pathways is crucial to establish the disease prognosis. Intracellular pathways involved in ALL are diverse, including Janus kinase/Signal transducers and transcription activators (JAK-STAT), Phosphoinositide-3-kinase-protein kinase B (PI3K-AKT), Ras mitogen-activated protein kinase (Ras-MAPK), Glycogen synthase kinase-3β (GSK-3β), Nuclear factor-kappa beta (NF-κB), and Hypoxia-inducible transcription factor 1α (HIF-1α), among others. In this review, we present several therapeutic targets, intracellular pathways, and molecular markers that are being studied extensively at present.

Air toxics and birth defects: a Bayesian hierarchical approach to evaluate multiple pollutants and spina bifida

BACKGROUND

While there is evidence that maternal exposure to benzene is associated with spina bifida in offspring, to our knowledge there have been no assessments to evaluate the role of multiple hazardous air pollutants (HAPs) simultaneously on the risk of this relatively common birth defect. In the current study, we evaluated the association between maternal exposure to HAPs identified by the United States Environmental Protection Agency (U.S. EPA) and spina bifida in offspring using hierarchical Bayesian modeling that includes Stochastic Search Variable Selection (SSVS).

METHODS

The Texas Birth Defects Registry provided data on spina bifida cases delivered between 1999 and 2004. The control group was a random sample of unaffected live births, frequency matched to cases on year of birth. Census tract-level estimates of annual HAP levels were obtained from the U.S. EPA's 1999 Assessment System for Population Exposure Nationwide. Using the distribution among controls, exposure was categorized as high exposure (>95(th) percentile), medium exposure (5(th)-95(th) percentile), and low exposure (<5(th) percentile, reference). We used hierarchical Bayesian logistic regression models with SSVS to evaluate the association between HAPs and spina bifida by computing an odds ratio (OR) for each HAP using the posterior mean, and a 95% credible interval (CI) using the 2.5(th) and 97.5(th) quantiles of the posterior samples. Based on previous assessments, any pollutant with a Bayes factor greater than 1 was selected for inclusion in a final model.

RESULTS

Twenty-five HAPs were selected in the final analysis to represent "bins" of highly correlated HAPs (ρ > 0.80). We identified two out of 25 HAPs with a Bayes factor greater than 1: quinoline (ORhigh = 2.06, 95% CI: 1.11-3.87, Bayes factor = 1.01) and trichloroethylene (ORmedium = 2.00, 95% CI: 1.14-3.61, Bayes factor = 3.79).

CONCLUSIONS

Overall there is evidence that quinoline and trichloroethylene may be significant contributors to the risk of spina bifida. Additionally, the use of Bayesian hierarchical models with SSVS is an alternative approach in the evaluation of multiple environmental pollutants on disease risk. This approach can be easily extended to environmental exposures, where novel approaches are needed in the context of multi-pollutant modeling.