Abstract
Glioblastoma multiforme (GBM) is a genomically complex and aggressive primary adult brain tumor, with a median survival time of 12-14 months. The heterogeneous nature of this disease has made the identification and validation of prognostic biomarkers difficult. Using reverse phase protein array data from 203 primary untreated GBM patients, we have identified a set of 13 proteins with prognostic significance. Our protein signature predictive of glioblastoma (PROTGLIO) patient survival model was constructed and validated on independent data sets and was shown to significantly predict survival in GBM patients (log-rank test: p = 0.0009). Using a multivariate Cox proportional hazards, we have shown that our PROTGLIO model is distinct from other known GBM prognostic factors (age at diagnosis, extent of surgical resection, postoperative Karnofsky performance score (KPS), treatment with temozolomide (TMZ) chemoradiation, and methylation of the MGMT gene). Tenfold cross-validation repetition of our model generation procedure confirmed validation of PROTGLIO. The model was further validated on an independent set of isocitrate dehydrogenase wild-type (IDHwt) lower grade gliomas (LGG)-a portion of these tumors progress rapidly to GBM. The PROTGLIO model contains proteins, such as Cox-2 and Annexin 1, involved in inflammatory response, pointing to potential therapeutic interventions. The PROTGLIO model is a simple and effective predictor of overall survival in glioblastoma patients, making it potentially useful in clinical practice of glioblastoma multiforme.
Highlights
Glioblastoma multiforme (GBM)1 is the most common and aggressive malignant primary brain tumor in adults, with a
Even more limiting than methylguanine DNA methyltransferase gene (MGMT) methylation is the combination of genomic aberrations, recently described by The Cancer Genome Atlas (TCGA) [10], which showed that patients with the glioma CpG island hypermethylator phenotype (GCIMP) and an isocitrate dehydrogenase 1 (IDH1) mutation, have improved survival [10]
A targeted therapeutic approach to glioblastoma treatment based on the immunohistochemistry of a small number of proteins presents a practical and promising approach to treatment of GBM patients
Summary
TCGA Patient Samples—Glioblastoma (GBM) and lower grade glioma (LGG) tumor samples were collected and snap-frozen at Tissue Source Sites on behalf of the TCGA and processed through the Biospecimens Core Resource at the International Genomics Consortium (Phoenix, AZ) for standardized pathology quality control as previously described [10, 23]. The following two submatrices (n Ͻ N) were used to train the algorithm [1] a survival matrix, Y ⑀ Pn, 2, where n is the number of TCGA GBM or LGG patients and column values are patients’ overall survival time in months and event indicator (vital status: living or deceased). We used the  coefficients generated from the training set to calculate PROTGLIO scores for the TCGA test samples in the validation set. We assessed the reproducibility of the approach described above by generating a PROTGLIO model on ten random pairs of training and validation sets from the TCGA GBM data, i.e. 10-repeated twofold cross-validations (Supplemental Table S3). The elastic net regression model was implemented on each training set to generate ten separate PROTGLIO patient survival models. We utilize the IDHwt subset of TCGA LGG samples to validate our prognostic GBM signature in the absence of available proteomics GBM validation datasets
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