There is substantial evidence that genetic events are associated with the etiology of brain tumors. The purpose of this study is to characterize the cancer risk in large number of kindreds of glioma patients using combined epidemiologic and statistical techniques and incorporating molecular genetic markers. The investigators will continue to accrue families to obtain a sufficiently large sample size to have precise estimates of the heritable component of gliomas. Several types of analyses will be performed on the data set. First, a descriptive analysis to detect overall cancer excess or excess of specific sites by computing Standardized Incidence Ratios (SIRs). The SIRs will determine whether excess cancer exists in first-degree relatives of glioma patients, by specific site, or by specific proband or relative characteristics. The next type of analysis is hypothesis testing using segregation analysis (with the computer program, Statistical Analysis for Genetic Epidemiology - SAGE) to determine the genetic model and parameter estimates that characterize the mode of inheritance of gliomas and identify specific kindreds showing evidence for a major gene to include in linkage analysis. Through linkage analysis the investigators will determine if mutations at distinct cancer predisposing loci are etiologically relevant, and inherited from an antecedent relative. The candidate gene analysis will be conducted by Dr. Peter O'Connell from The University of Texas Health Science Center at San Antonio. With a larger sample the investigators will be able to confirm their finding that patients with multifocal glioma, gliomas as a secondary malignancy, and a family history, constitute a genetically predisposed subgroup. This study will be the first comprehensive evaluation of familial aggregation in families of unselected glioma patients. In addition, they are collecting blood from each new patient and their specimen repository will be available to study future candidate genes to integrate with the family data.
| Mostovenko, Ekaterina; Liu, Yanhong; Amirian, E Susan et al. (2018) Combined Proteomic-Molecular Epidemiology Approach to Identify Precision Targets in Brain Cancer. ACS Chem Neurosci 9:80-84 |
| Jacobs, Daniel I; Liu, Yanhong; Gabrusiewicz, Konrad et al. (2018) Germline polymorphisms in myeloid-associated genes are not associated with survival in glioma patients. J Neurooncol 136:33-39 |
| Krishnamachari, Bhuma; Il'yasova, Dora; Scheurer, Michael E et al. (2015) A pooled multisite analysis of the effects of atopic medical conditions in glioma risk in different ethnic groups. Ann Epidemiol 25:270-4 |
| Kinnersley, Ben; Mitchell, Jonathan S; Gousias, Konstantinos et al. (2015) Quantifying the heritability of glioma using genome-wide complex trait analysis. Sci Rep 5:17267 |
| Liu, Yanhong; Zhou, Renke; Sulman, Erik P et al. (2015) Genetic Modulation of Neurocognitive Function in Glioma Patients. Clin Cancer Res 21:3340-6 |
| Bainbridge, Matthew N; Armstrong, Georgina N; Gramatges, M Monica et al. (2015) Germline mutations in shelterin complex genes are associated with familial glioma. J Natl Cancer Inst 107:384 |
| Jalali, Ali; Amirian, E Susan; Bainbridge, Matthew N et al. (2015) Targeted sequencing in chromosome 17q linkage region identifies familial glioma candidates in the Gliogene Consortium. Sci Rep 5:8278 |
| Krishnamachari, Bhuma; Il'yasova, Dora; Scheurer, Michael E et al. (2014) A pooled multisite analysis of the effects of female reproductive hormones on glioma risk. Cancer Causes Control 25:1007-13 |
| Andersson, Ulrika; Wibom, Carl; Cederquist, Kristina et al. (2014) Germline rearrangements in families with strong family history of glioma and malignant melanoma, colon, and breast cancer. Neuro Oncol 16:1333-40 |
| Enciso-Mora, Victor; Hosking, Fay J; Kinnersley, Ben et al. (2013) Deciphering the 8q24.21 association for glioma. Hum Mol Genet 22:2293-302 |
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