Assessment of clinical and nonclinical characteristics associated with health-related quality of life in patients with high-grade gliomas: a feasibility study.

PURPOSE: Results from several studies suggest that there is value in evaluating the association between nonclinical characteristics of patients and quality of life (QoL), but few studies have focused on brain cancer. The primary goal of this feasibility study was to explore the relationship between clinical factors and nonclinical factors and QoL in brain cancer patients. METHODS: Participants in this cross-sectional study were drawn from two hospital sites. Eligible patients were 18-75 years old with a pathologically confirmed diagnosis of a brain cancer histology and stable disease after treatment. Data were obtained from medical chart review and a self-administered survey consisting of main study variables and two QoL standardized measures. Independent sample t test was used to determine differences between patient factors and QoL measures. RESULTS: The sample population was comprised of 26 patients with a median age at survey of 57.5 years (range 33-72). Quality of life was adversely associated with younger age, having underage children and living alone. Patients' meaning of QoL differed by gender, however most patients viewed it as affecting multiple aspects of their lives. CONCLUSIONS: Nonclinical characteristics were significantly associated with QoL more often than clinical characteristics. Identifying these factors may help improve the quality of care for these patients. This effort demonstrates the relevancy and feasibility of conducting a larger scale study to confirm or refute these findings.

Insight in glioma susceptibility through an analysis of 6p22.3, 12p13.33-12.1, 17q22-23.2 and 18q23 SNP genotypes in familial and non-familial glioma.

The risk of glioma has consistently been shown to be increased twofold in relatives of patients with primary brain tumors (PBT). A recent genome-wide linkage study of glioma families provided evidence for a disease locus on 17q12-21.32, with the possibility of four additional risk loci at 6p22.3, 12p13.33-12.1, 17q22-23.2, and 18q23. To identify the underlying genetic variants responsible for the linkage signals, we compared the genotype frequencies of 5,122 SNPs mapping to these five regions in 88 glioma cases with and 1,100 cases without a family history of PBT (discovery study). An additional series of 84 familial and 903 non-familial cases were used to replicate associations. In the discovery study, 12 SNPs showed significant associations with family history of PBT (P < 0.001). In the replication study, two of the 12 SNPs were confirmed: 12p13.33-12.1 PRMT8 rs17780102 (P = 0.031) and 17q12-21.32 SPOP rs650461 (P = 0.025). In the combined analysis of discovery and replication studies, the strongest associations were attained at four SNPs: 12p13.33-12.1 PRMT8 rs17780102 (P = 0.0001), SOX5 rs7305773 (P = 0.0001) and STKY1 rs2418087 (P = 0.0003), and 17q12-21.32 SPOP rs6504618 (P = 0.0006). Further, a significant gene-dosage effect was found for increased risk of family history of PBT with these four SNPs in the combined data set (P(trend) <1.0 × 10(-8)). The results support the linkage finding that some loci in the 12p13.33-12.1 and 17q12-q21.32 may contribute to gliomagenesis and suggest potential target genes underscoring linkage signals.

Description of selected characteristics of familial glioma patients - results from the Gliogene Consortium.

BACKGROUND: While certain inherited syndromes (e.g. Neurofibromatosis or Li-Fraumeni) are associated with an increased risk of glioma, most familial gliomas are non-syndromic. This study describes the demographic and clinical characteristics of the largest series of non-syndromic glioma families ascertained from 14 centres in the United States (US), Europe and Israel as part of the Gliogene Consortium. METHODS: Families with 2 or more verified gliomas were recruited between January 2007 and February 2011. Distributions of demographic characteristics and clinical variables of gliomas in the families were described based on information derived from personal questionnaires. FINDINGS: The study population comprised 841 glioma patients identified in 376 families (9797 individuals). There were more cases of glioma among males, with a male to female ratio of 1.25. In most families (83%), 2 gliomas were reported, with 3 and 4 gliomas in 13% and 3% of the families, respectively. For families with 2 gliomas, 57% were among 1st-degree relatives, and 31.5% among 2nd-degree relatives. Overall, the mean (±standard deviation [SD]) diagnosis age was 49.4 (±18.7) years. In 48% of families with 2 gliomas, at least one was diagnosed at <40y, and in 12% both were diagnosed under 40y of age. Most of these families (76%) had at least one grade IV glioblastoma multiforme (GBM), and in 32% both cases were grade IV gliomas. The most common glioma subtype was GBM (55%), followed by anaplastic astrocytoma (10%) and oligodendroglioma (8%). Individuals with grades I-II were on average 17y younger than those with grades III-IV. INTERPRETATION: Familial glioma cases are similar to sporadic cases in terms of gender distribution, age, morphology and grade. Most familial gliomas appear to comprise clusters of two cases suggesting low penetrance, and that the risk of developing additional gliomas is probably low. These results should be useful in the counselling and clinical management of individuals with a family history of glioma.

A variable age of onset segregation model for linkage analysis, with correction for ascertainment, applied to glioma.

BACKGROUND: We propose a 2-step model-based approach, with correction for ascertainment, to linkage analysis of a binary trait with variable age of onset and apply it to a set of multiplex pedigrees segregating for adult glioma. METHODS: First, we fit segregation models by formulating the likelihood for a person to have a bivariate phenotype, affection status and age of onset, along with other covariates, and from these we estimate population trait allele frequencies and penetrance parameters as a function of age (N = 281 multiplex glioma pedigrees). Second, the best fitting models are used as trait models in multipoint linkage analysis (N = 74 informative multiplex glioma pedigrees). To correct for ascertainment, a prevalence constraint is used in the likelihood of the segregation models for all 281 pedigrees. Then the trait allele frequencies are reestimated for the pedigree founders of the subset of 74 pedigrees chosen for linkage analysis. RESULTS: Using the best-fitting segregation models in model-based multipoint linkage analysis, we identified 2 separate peaks on chromosome 17; the first agreed with a region identified by Shete and colleagues who used model-free affected-only linkage analysis, but with a narrowed peak: and the second agreed with a second region they found but had a larger maximum log of the odds (LOD). CONCLUSIONS: Our approach was able to narrow the linkage peak previously published for glioma. IMPACT: We provide a practical solution to model-based linkage analysis for disease affection status with variable age of onset for the kinds of pedigree data often collected for linkage analysis.

Genome-wide high-density SNP linkage search for glioma susceptibility loci: results from the Gliogene Consortium.

Gliomas, which generally have a poor prognosis, are the most common primary malignant brain tumors in adults. Recent genome-wide association studies have shown that inherited susceptibility plays a role in the development of glioma. Although first-degree relatives of patients exhibit a two-fold increased risk of glioma, the search for susceptibility loci in familial forms of the disease has been challenging because the disease is relatively rare, fatal, and heterogeneous, making it difficult to collect sufficient biosamples from families for statistical power. To address this challenge, the Genetic Epidemiology of Glioma International Consortium (Gliogene) was formed to collect DNA samples from families with two or more cases of histologically confirmed glioma. In this study, we present results obtained from 46 U.S. families in which multipoint linkage analyses were undertaken using nonparametric (model-free) methods. After removal of high linkage disequilibrium single-nucleotide polymorphism, we obtained a maximum nonparametric linkage score (NPL) of 3.39 (P = 0.0005) at 17q12-21.32 and the Z-score of 4.20 (P = 0.000007). To replicate our findings, we genotyped 29 independent U.S. families and obtained a maximum NPL score of 1.26 (P = 0.008) and the Z-score of 1.47 (P = 0.035). Accounting for the genetic heterogeneity using the ordered subset analysis approach, the combined analyses of 75 families resulted in a maximum NPL score of 3.81 (P = 0.00001). The genomic regions we have implicated in this study may offer novel insights into glioma susceptibility, focusing future work to identify genes that cause familial glioma.

Survey of familial glioma and role of germline p16INK4A/p14ARF and p53 mutation.

There is increasing recognition of familial propensity to glioma as a distinct clinical entity beyond a few rare syndromes; however its genetic basis is poorly understood. The role of p16(INK4A)/p14(ARF) and p53 mutations in sporadic glioma provides a strong rationale for investigating germline mutations in these genes as a cause of familial glioma. To survey the familial glioma phenotype and examine the contribution of germline mutation in p16(INK4A)/p14(ARF) and p53 to the disease we have analyzed a series of 101 index familial cases collected through the GLIOGENE Consortium (http://braintumor.epigenetic.org/). There was little evidence for within family correlations for tumour histology, suggesting generic susceptibility to glial tumors. We did not detect any functional mutations in p16(INK4A) or p14(ARF). One index case with glioblastoma multiforme (GBM) diagnosed at age 54 and had a family history comprised of a paternal aunt with GBM at age 55, carried the p53 R158H mutation, which is predicted to be functional and has previously been implicated as a cause of Li-Fraumeni syndrome. Our findings provide no evidence that p16(INK4A)/p14(ARF) and p53 mutations contribute significantly to familial glioma.