PURPOSE: Glioblastoma (GBM) is the most common form of brain tumor and has a uniformly poor prognosis. Development of prognostic biomarkers in easily accessible serum samples have the potential to improve the outcomes of patients with GBM through personalized therapy planning. MATERIAL/ METHODS: In this study pre-treatment serum samples from 30 patients newly diagnosed with GBM were evaluated using a 40-protein multiplex ELISA platform. Analysis of potentially relevant gene targets using The Cancer Genome Atlas database was done using the Glioblastoma Bio Discovery Portal (GBM-BioDP). A ten-biomarker subgroup of clinically relevant molecules was selected using a functional grouping analysis of the 40 plex genes with two genes selected from each group on the basis of degree of variance, lack of co-linearity with other biomarkers and clinical interest. A Multivariate Cox proportional hazard approach was used to analyze the relationship between overall survival (OS), gene expression, and resection status as covariates. RESULTS: Thirty of 40 of the MSD molecules mapped to known genes within TCGA and separated the patient cohort into two main clusters centered predominantly around a grouping of classical and proneural versus the mesenchymal subtype as classified by Verhaak. Using the values for the 30 proteins in a prognostic index (PI) demonstrated that patients in the entire cohort with a PI below the median lived longer than those patients with a PI above the median (HR 1.8, p=0.001) even when stratified by both age and MGMT status. This finding was also consistent within each Verhaak subclass and highly significant (range p=0.0001-0.011). Additionally, a subset of ten proteins including, CRP, SAA, VCAM1, VEGF, MDC, TNFA, IL7, IL8, IL10, IL16 were found to have prognostic value within the TCGA database and a positive correlation with overall survival in GBM patients who had received gross tumor resection followed by conventional radiation therapy and temozolomide treatment concurrent with the addition of valproic acid. CONCLUSION: These findings demonstrate that proteomic approaches to the development of prognostic assays for treatment of GBM may hold potential clinical value.
PURPOSE: Glioblastoma (GBM) is the most common form of brain tumor and has a uniformly poor prognosis. Development of prognostic biomarkers in easily accessible serum samples have the potential to improve the outcomes of patients with GBM through personalized therapy planning. MATERIAL/ METHODS: In this study pre-treatment serum samples from 30 patients newly diagnosed with GBM were evaluated using a 40-protein multiplex ELISA platform. Analysis of potentially relevant gene targets using The Cancer Genome Atlas database was done using the Glioblastoma Bio Discovery Portal (GBM-BioDP). A ten-biomarker subgroup of clinically relevant molecules was selected using a functional grouping analysis of the 40 plex genes with two genes selected from each group on the basis of degree of variance, lack of co-linearity with other biomarkers and clinical interest. A Multivariate Cox proportional hazard approach was used to analyze the relationship between overall survival (OS), gene expression, and resection status as covariates. RESULTS: Thirty of 40 of the MSD molecules mapped to known genes within TCGA and separated the patient cohort into two main clusters centered predominantly around a grouping of classical and proneural versus the mesenchymal subtype as classified by Verhaak. Using the values for the 30 proteins in a prognostic index (PI) demonstrated that patients in the entire cohort with a PI below the median lived longer than those patients with a PI above the median (HR 1.8, p=0.001) even when stratified by both age and MGMT status. This finding was also consistent within each Verhaak subclass and highly significant (range p=0.0001-0.011). Additionally, a subset of ten proteins including, CRP, SAA, VCAM1, VEGF, MDC, TNFA, IL7, IL8, IL10, IL16 were found to have prognostic value within the TCGA database and a positive correlation with overall survival in GBM patients who had received gross tumor resection followed by conventional radiation therapy and temozolomide treatment concurrent with the addition of valproic acid. CONCLUSION: These findings demonstrate that proteomic approaches to the development of prognostic assays for treatment of GBM may hold potential clinical value.
Authors: Patrick Y Wen; David R Macdonald; David A Reardon; Timothy F Cloughesy; A Gregory Sorensen; Evanthia Galanis; John Degroot; Wolfgang Wick; Mark R Gilbert; Andrew B Lassman; Christina Tsien; Tom Mikkelsen; Eric T Wong; Marc C Chamberlain; Roger Stupp; Kathleen R Lamborn; Michael A Vogelbaum; Martin J van den Bent; Susan M Chang Journal: J Clin Oncol Date: 2010-03-15 Impact factor: 44.544
Authors: Dhimankrishna Ghosh; Cory C Funk; Juan Caballero; Nameeta Shah; Katherine Rouleau; John C Earls; Liliana Soroceanu; Greg Foltz; Charles S Cobbs; Nathan D Price; Leroy Hood Journal: Cell Syst Date: 2017-03-29 Impact factor: 10.304
Authors: Roel G W Verhaak; Katherine A Hoadley; Elizabeth Purdom; Victoria Wang; Yuan Qi; Matthew D Wilkerson; C Ryan Miller; Li Ding; Todd Golub; Jill P Mesirov; Gabriele Alexe; Michael Lawrence; Michael O'Kelly; Pablo Tamayo; Barbara A Weir; Stacey Gabriel; Wendy Winckler; Supriya Gupta; Lakshmi Jakkula; Heidi S Feiler; J Graeme Hodgson; C David James; Jann N Sarkaria; Cameron Brennan; Ari Kahn; Paul T Spellman; Richard K Wilson; Terence P Speed; Joe W Gray; Matthew Meyerson; Gad Getz; Charles M Perou; D Neil Hayes Journal: Cancer Cell Date: 2010-01-19 Impact factor: 31.743
Authors: Monika E Hegi; Annie-Claire Diserens; Thierry Gorlia; Marie-France Hamou; Nicolas de Tribolet; Michael Weller; Johan M Kros; Johannes A Hainfellner; Warren Mason; Luigi Mariani; Jacoline E C Bromberg; Peter Hau; René O Mirimanoff; J Gregory Cairncross; Robert C Janzer; Roger Stupp Journal: N Engl J Med Date: 2005-03-10 Impact factor: 91.245