The Glioma International Case-control Study: A Report From the Genetic Epidemiology of Glioma International Consortium

E. Susan Amirian; Georgina N. Armstrong; Renke Zhou; Ching C. Lau; Elizabeth B. Claus; Jill S. Barnholtz-Sloan; Dora Il'yasova; Joellen Schildkraut; Francis Ali-Osman; Siegal Sadetzki; Christoffer Johansen; Richard S. Houlston; Robert B. Jenkins; Daniel Lachance; Sara H. Olson; Jonine L. Bernstein; Ryan T. Merrell; Margaret R. Wrensch; Faith G. Davis; Rose Lai; Sanjay Shete; Christopher I. Amos; Michael E. Scheurer; Kenneth Aldape; Irina Alafuzoff; Thomas Brännström; Helle Broholm; Peter Collins; Caterina Giannini; Marc Rosenblum; Tarik Tihan; Beatrice S. Melin; Melissa L. Bondy

Disclosures

Am J Epidemiol. 2016;183(2):85-91. 

In This Article

Abstract and Introduction

Abstract

Decades of research have established only a few etiological factors for glioma, which is a rare and highly fatal brain cancer. Common methodological challenges among glioma studies include small sample sizes, heterogeneity of tumor subtypes, and retrospective exposure assessment. Here, we briefly describe the Glioma International Case-Control (GICC) Study (recruitment, 2010–2013), a study being conducted by the Genetic Epidemiology of Glioma International Consortium that integrates data from multiple data collection sites, uses a common protocol and questionnaire, and includes biospecimen collection. To our knowledge, the GICC Study is the largest glioma study to date that includes collection of blood samples, which will allow for genetic analysis and interrogation of gene-environment interactions.

Introduction

With an annual incidence rate of 2–3 cases per 100,000 population in the United States, glioma, which comprises approximately 28% of all primary brain tumors, is a rare but highly fatal disease.[1–4] Decades of research have established only a few etiological factors (family history, rare genetic cancer predisposition syndromes, ionizing radiation, and 10 independent genetic risk loci),[5–11] partly because glioma is a particularly challenging disease to study. Because it is a rare, highly fatal, and heterogeneous disease,[2] it is difficult to accrue enough cases for large-scale epidemiologic studies.[6] Due to potential etiological differences by tumor subtype, large sample sizes are needed to stratify results by histology. Additionally, it is not usually feasible to conduct a prospective study, thus necessitating the use of case-control study designs and retrospective exposure assessments. For these reasons, we opted to develop a consortium structure that integrates data from multiple sites, uses a common protocol and questionnaire, and includes biospecimen collection. Studies conducted by such consortia can help overcome some of the above obstacles and may be able to attain sufficient statistical power for identifying novel risk factors for this enigmatic disease.

To examine the genetic factors underlying familial glioma, the Genetic Epidemiology of Glioma International Consortium (GLIOGENE Consortium) was formed in 2006 to recruit families affected by ≥2 cases of glioma from 14 institutions across 5 countries.[12] Research carried out by this consortium has yielded a number of high-impact discoveries.[13,14] However, because familial glioma accounts for only about 5% of all gliomas,[12] the GLIOGENE investigators recognized the need to study sporadic glioma, which comprises the remaining 95% of gliomas. Thus, capitalizing upon the infrastructure in place from the GLIOGENE familial study, we have launched a large study of glioma that includes biospecimen collection: the Glioma International Case-Control (GICC) Study.

The main goals of the GICC Study are: 1) to identify novel genetic risk variants for glioma, as well as validate variants implicated by previous genome-wide association studies of glioma; and 2) to explore biologically relevant gene-gene and gene-environment interactions in glioma susceptibility. With 4,545 cases and 4,173 controls, the GICC Study confers the opportunity to evaluate both environmental exposures and genetic variation while accounting for tumor subtype. The study's comprehensive questionnaire data will allow for examination of putative risk factors identified from prior literature (i.e., radiation exposure, atopy, childhood infections), which can be reexamined in detail and validated. Additionally, the large study population may enable us to explore gene-environment interactions. Here, we present an overview of the study's structure and methodology, some methodological challenges and solutions, and preliminary demographic data.

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