Pediatric Brain Tumors: Descriptive Epidemiology, Risk Factors, and Future Directions

Brain tumors are the most common solid tumors in children and remain a significant contributor to death by disease in this population. Pediatric brain tumors (PBT) are broadly classified into two major categories: glial and neuronal tumors. Various factors, including tumor histology, tumor location, and demographics, influence the incidence and prognosis of this heterogeneous group of neoplasms. Numerous epidemiologic studies have been conducted to identify genetic and environmental risk factors for these malignancies. Thus far, the only established risk factors for PBTs are exposure to ionizing radiation and some rare genetic syndromes. However, relatively consistent evidence of positive associations for birth defects, markers of fetal growth, advanced parental age, maternal dietary N-nitroso compounds, and exposure to pesticides have been reported. The genetic variants associated with susceptibility to PBTs were predominantly identified by a candidate-gene approach. The identified genetic variants belong to four main pathways, including xenobiotic detoxification, inflammation, DNA repair, and cell-cycle regulation. Conducting large and multi-institutional studies is warranted to systematically detect genetic and environmental risk factors for different histologic subtypes of PBTs. This, in turn, might lead to a better understanding of etiology of PBTs and eventually developing risk prediction models to prevent these clinically significate malignancies.

Automated summary

Brain tumors are the most common solid tumors in children, with factors such as tumor histology and demographics influencing their incidence and prognosis. Known risk factors for pediatric brain tumors include exposure to ionizing radiation and certain genetic syndromes, but there is also evidence of associations with factors like birth defects and maternal dietary compounds. Identifying genetic variants associated with susceptibility to brain tumors may help in developing risk prediction models for prevention.