A novel literature-based approach to identify genetic and molecular predictors of survival in glioblastoma multiforme: Analysis of 14, 678 patients using systematic review and meta-analytical tools. Issue 5 (May 2015) - Thuy, Matthew N.T.; Kam, Jeremy K.T.; Lee, Geoffrey C.Y.; Tao, Peter L.; Ling, Dorothy Q.; Cheng, Melissa; Goh, Su Kah; Papachristos, Alexander J.; Shukla, Lipi; Wall, Krystal-Leigh; Smoll, Nicolas R.; Jones, Jordan J.; et al.
Abstract Glioblastoma multiforme (GBM) has a poor prognosis despite maximal multimodal therapy. Biomarkers of relevance to prognosis which may also identify treatment targets are needed. A few hundred genetic and molecular predictors have been implicated in the literature, however with the exception of IDH1 and O6-MGMT, there is uncertainty regarding their true prognostic relevance. This study analyses reported genetic and molecular predictors of prognosis in GBM. For each, its relationship with univariate overall survival in adults with GBM is described. A systematic search of MEDLINE (1998–July 2010) was performed. Eligible papers studied the effect of any genetic or molecular marker on univariate overall survival in adult patients with histologically diagnosed GBM. Primary outcomes were median survival difference in months and univariate hazard ratios. Analyses included converting 126 Kaplan–Meier curves and 27 raw data sets into primary outcomes. Seventy-four random effects meta-analyses were performed on 39 unique genetic or molecular factors. Objective criteria were designed to classify factors into the categories of clearly prognostic, weakly prognostic, non-prognostic and promising. Included were 304 publications and 174 studies involving 14, 678 unique patients from 33 countries. We identified 422 reported genetic and molecular predictors, of which 52 had ⩾2 studies. IDH1 mutation and O6-MGMT were classified as clearly prognostic, validating the methodology. High Ki-67/MIB-1 and loss of heterozygosity of chromosome 10/10q were classified as weakly prognostic. Four factors were classified as non-prognostic and 13 factors were classified as promising and worthy of additional investigation. Funnel plot analysis did not identify any evidence of publication bias. This study demonstrates a novel literature and meta-analytical based approach to maximise the value that can be derived from the plethora of literature reports of molecular and genetic factors in GBM. Caution is advised in over-interpreting the results due to study limitations. Further research to develop this methodology and improvements in study reporting are suggested.