Higher Glioma Risk Not Seen for Regular Cell Phone Users

Roxanne Nelson

March 21, 2012 — The controversy surrounding the health risks related to cell phones continues to evolve.

A new study, published online March 8 in BMJ, suggests that there is little evidence linking cell phone use to a higher risk for glioma. The researchers note that the higher risks seen previously are not consistent with the incidence trends they found in the American population.

"Our study looked at actual rates of glioma in the United States, in comparison to projected or expected rates of glioma based on observations from the INTERPHONE study [Int J Epidemiol. 2010;39:675-694] and a study in Sweden [Int J Oncol. 2011;38:1465-1474]," explained lead author Mark Little, DPhil, senior scientist at the National Cancer Institute (NCI) in Rockville, Maryland. "We found that actual glioma rates declined over the study period."

The researchers used population-based data from 1992 to 2008, reported in 12 Surveillance, Epidemiology, and End Results (SEER) registries, to determine glioma incidence.

When they modeled the incidence rates projected in the INTERPHONE study, which showed slight increases in glioma risk among a small number of heavy users, they found that they were not statistically distinguishable from the actual rates in the United States.

However, "when we modeled incidence rates projected from the Swedish study, we found that the actual rates would need to be at least 40% higher than what was observed," he told Medscape Medical News. "Thus, actual glioma rates in the United States are consistent with the INTERPHONE study, but not with the rates based on the Swedish study."

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Genetic Signature of Adult Gliomas and Correlation with MRI Features

Maria Grazia Bruzzone; Marica Eoli; Valeria Cuccarini; Marina Grisoli; Lorella Valletta; Gaetano Finocchiaro

In recent years the amount of information concerning the genetics and the biology of gliomas, and particularly of glioblastoma multiforme, increased steadily. Such an increase has been paralleled by the technological progress of MRI. The merging of these scientific areas, as summarized in this review, is helping the stratification of glioma patients for clinical trials and their clinical follow-up. Although available therapeutic options appear limited in number, it is likely that in the next 5 years, both as a consequence of the increased knowledge due to genomic sequencing of hundreds of glioblastoma specimens and to continuous improvements of MRI, new perspectives will be available for these patients, with a sizable impact on their prognosis.

Introduction

Gliomas, the most frequent tumors occurring in the CNS, are defined and graded on the basis of histological features, and pathology is fundamental to predict prognosis and guide the correct patient management.^[1] However, pathological diagnosis can be rather subjective and allows considerable interobserver variability, especially in the case of gliomas with mixed histological features.^[2] In addition, gliomas of identical histology may be associated with different genetic alterations. Therefore, owing to biological heterogeneity, the histological diagnosis and expected clinical outcome do not match in a significant number of patients and the histological examination does not distinguish tumors responding or not responding to the therapy. Throwing light upon individual biological alterations, molecular analyses may detect subsets of morphologically identical tumors with different clinical behavior (diagnostic markers), describing their prognosis more effectively (prognostic markers).^[3] Moreover, molecular biological studies may lead to the discovery of gene-based predictors of therapeutic response, helping to guide more rationally currently available therapies (predictive markers).^[4] At present few tumor biomarkers are available for gliomas and it is sometimes unclear how to incorporate molecular genetic information into clinical practice. Differences in study design, patient and specimen characteristics, assay methods and statistical analysis make different studies poorly comparable and also make it difficult to understand the context in which the conclusion should be applied.^[5]

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