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Familiality in brain tumors.

ABSTRACT: Familiality in brain tumors is not definitively substantiated.We used the Utah Population Data Base (UPDB), a genealogy representing the Utah pioneers and their descendants, record-linked to statewide cancer records, to describe the familial nature of primary brain cancer. We examined the familial clustering of primary brain tumors, including subgroups defined by histologic type and age at diagnosis. The UPDB includes 1,401 primary brain tumor cases defined as astrocytoma or glioblastoma, all with at least three generations of genealogy data. We tested the hypothesis of excess relatedness of brain tumor cases using the Genealogical Index of Familiality method. We estimated relative risks for brain tumors in relatives using rates of brain tumors estimated internally.Significant excess relatedness was observed for astrocytomas and glioblastomas considered as a group (n = 1,401), for astrocytomas considered separately (n = 744), but not for glioblastomas considered separately (n = 658). Significantly increased risks to first- and second-degree relatives for astrocytomas were identified for relatives of astrocytomas considered separately. Significantly increased risks to first-degree relatives, but not second degree, were observed for astrocytoma and glioblastoma cases considered together, and for glioblastoma cases considered separately.This study provides strong evidence for a familial contribution to primary brain cancer risk. There is evidence that this familial aspect includes not only shared environment, but also a heritable component. Extended high-risk brain tumor pedigrees identified in the UPDB may provide the opportunity to identify predisposition genes responsible for familial brain tumors.

SUBMITTER: Blumenthal DT

PROVIDER: S-EPMC2676956 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Phenotype definition of psychotic disorders has a strong impact on the degree of familial aggregation. Nevertheless, the extent to which distinct classification systems affect familial aggregation (ie, familiality) remains an open question. This study was aimed at examining the familiality associated with 4 nosologic systems of psychotic disorders (DSM-IV, ICD-10, Leonhard's classification and a data-driven approach) and their constituting diagnoses in a sample of multiplex families with psychotic disorders.Participants were probands with a psychotic disorder, their parents and at least one first-degree relative with a psychotic disorder. The sample was made of 441 families comprising 2703 individuals, of whom 1094 were affected and 1709 unaffected.The Leonhard classification system had the highest familiality (h (2) = 0.64), followed by the empirical (h (2) = 0.55), DSM-IV (h (2) = 0.50), and ICD-10 (h (2) = 0.48). Familiality estimates for individual diagnoses varied considerably (h (2) = 0.25-0.79). Regarding schizophrenia diagnoses, Leonhard's systematic schizophrenia (h (2) = 0.78) had the highest familiality, followed by latent class core schizophrenia (h (2) = 0.74), DSM-IV schizophrenia (h (2) = 0.48), and ICD-10 schizophrenia (h (2) = 0.41). Psychotic mood disorders showed substantial familiality across nosologic systems (h (2) = 0.60-0.77). Domains of psychopathology other than reality-distortion symptoms showed moderate familiality irrespective of diagnosis (h (2) = 0.22-0.52) with the deficit syndrome of schizophrenia showing the highest familiality (h (2) = 0.66).While affective psychoses showed relatively high familiality estimates across classification schemes, those of nonaffective psychoses varied markedly as a function of the diagnostic scheme with a narrow schizophrenia phenotype maximizing its familial aggregation. Leonhard's classification of psychotic disorders may be better suited for molecular genetic studies than the official diagnostic systems.

Project description:MRI offers a tremendous armamentarium of different methods that can be employed in brain tumor characterization. MR diffusion imaging has become a widely accepted method to probe for the presence of fluid pools and molecular tissue water mobility. For most clinical applications of diffusion imaging, it is assumed that the diffusion signal vs diffusion weighting factor b decays monoexponentially. Within this framework, the measurement of a single diffusion coefficient in brain tumors permits an approximate categorization of tumor type and, for some tumors, definitive diagnosis. In most brain tumors, when compared with normal brain tissue, the diffusion coefficient is elevated. The presence of peritumoral edema, which also exhibits an elevated diffusion coefficient, often precludes the delineation of the tumor on the basis of diffusion information alone. Serially obtained diffusion data are useful to document and even predict the cellular response to drug or radiation therapy. Diffusion measurements in tissues over an extended range of b factors have clearly shown that the monoparametric description of the MR diffusion signal decay is incomplete. Very high diffusion weighting on clinical systems requires substantial compromise in spatial resolution. However, after suitable analysis, superior separation of malignant brain tumors, peritumoral edema and normal brain tissue can be achieved. These findings are also discussed in the light of tissue-specific differences in membrane structure and the restrictions exerted by membranes on diffusion. Finally, measurement of the directional dependence of diffusion permits the assessment of white matter integrity and dislocation. Such information, particularly in conjunction with advanced post-processing, is considered to be immensely useful for therapy planning. Diffusion imaging, which permits monoexponential analysis and provides directional diffusion information, is performed routinely in brain tumor patients. More advanced methods require improvement in acquisition speed and spatial resolution to gain clinical acceptance.

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Familiality in brain tumors.

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