A multigene predictor of outcome in glioblastoma - PubMed (original) (raw)

doi: 10.1093/neuonc/nop007. Epub 2009 Oct 20.

Li Zhang, Erik P Sulman, J Matthew McDonald, Nasrin Latif Shooshtari, Andreana Rivera, Sonya Popoff, Catherine L Nutt, David N Louis, J Gregory Cairncross, Mark R Gilbert, Heidi S Phillips, Minesh P Mehta, Arnab Chakravarti, Christopher E Pelloski, Krishna Bhat, Burt G Feuerstein, Robert B Jenkins, Ken Aldape

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A multigene predictor of outcome in glioblastoma

Howard Colman et al. Neuro Oncol. 2010 Jan.

Abstract

Only a subset of patients with newly diagnosed glioblastoma (GBM) exhibit a response to standard therapy. To date, a biomarker panel with predictive power to distinguish treatment sensitive from treatment refractory GBM tumors does not exist. An analysis was performed using GBM microarray data from 4 independent data sets. An examination of the genes consistently associated with patient outcome, revealed a consensus 38-gene survival set. Worse outcome was associated with increased expression of genes associated with mesenchymal differentiation and angiogenesis. Application to formalin fixed-paraffin embedded (FFPE) samples using real-time reverse-transcriptase polymerase chain reaction assays resulted in a 9-gene subset which appeared robust in these samples. This 9-gene set was then validated in an additional independent sample set. Multivariate analysis confirmed that the 9-gene set was an independent predictor of outcome after adjusting for clinical factors and methylation of the methyl-guanine methyltransferase promoter. The 9-gene profile was also positively associated with markers of glioma stem-like cells, including CD133 and nestin. In sum, a multigene predictor of outcome in glioblastoma was identified which appears applicable to routinely processed FFPE samples. The profile has potential clinical application both for optimization of therapy in GBM and for the identification of novel therapies targeting tumors refractory to standard therapy.

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Figures

Fig. 1.

Fig. 1.

Identification of robust outcome-associated genes from microarray data. (A) Overlap of survival genes among 4 microarray data sets. The top 200 genes were identified for each data set individually and the overlap of the 4 lists is shown in a Venn diagram. (B) Estimation of false discovery rate. The survival data were scrambled among the samples and a list of 200 genes was generated from each data set using the scrambled survival data. (C) Survival according to metagene score. The 38 survival-associated genes common to all 4 data sets were used to calculate a metagene score for each sample. The metagene score was calculated by subtracting the sum of the values of the good-prognosis genes from the sum of the values of the poor-prognosis genes. The samples were ranked by metagene score and divided into 2 groups based on results from recursive partitioning analysis. Survival according to metagene score is shown for the group with the lower ranking metagene scores (red) vs samples with higher metagene scores (blue).

Fig. 2.

Fig. 2.

Validation of multigene predictor for overall survival in an independent sample set. A set of 68 formalin-fixed, paraffin-embedded glioblastoma samples was subject to qRT-PCR for the 38 gene set identified in Fig. 1. A metagene score was calculated as in Fig. 1 and the samples were ranked by metagene score. Patients were dichotomized into 2 groups based on metagene score using proportions identical to those in Fig. 1. Survival is shown for the lower metagene scores (red) vs the higher metagene scores (blue). Analyses were performed for the entire 38-gene set as well as a smaller 9-gene profile composed of those genes that had the highest individual survival association in the tumors and showed high technical feasibility in paraffin tissues. (A) and (B) Progression-free survival (PFS) according to the entire 38-gene set (A) as well as the 9 gene-profile (B). (C) and (D) Overall survival (OS) according to the 38-gene set (C) and 9-gene set (D). NR, median not reached.

Fig. 3.

Fig. 3.

Validation of 9-gene profile in temozolomide (TMZ)-treated GBM and comparison with MGMT status. Glioblastoma samples from temozolomide-treated patients (n = 101) were tested for MGMT methylation as well as the 9-gene predictor. For all Kaplan–Meier curves, red indicates low score and blue indicates high score. Tumors were ranked by metagene score and divided into distinct metagene groups using the same cutoffs as in Fig. 1. Progression-free survival (PFS) according to the entire MGMT status (A) as well as the 9-gene profile. (C) and (D) Overall survival (OS) according to MGMT status (C) and 9-gene set (D). (E) Cox proportional hazards multivariate analysis showing that the 9-gene profile is an independent predictor of outcome in TMZ-treated patients after adjusting for MGMT status. NR, median not reached.

Fig. 4.

Fig. 4.

Comparison of 9-gene molecular profile with clinical variables. Cases were stratified into favorable vs unfavorable clinical groups as descrbed in the text. (A) and (B) Progression-free survival (PFS) according to clinical factors (A), as well as the 9 gene-profile. Median PFS is as shown. (C) and (D) Overall survival (OS) according to MGMT status (C) and 9-gene set (D). Median OS is as shown. (E) Cox proportional hazards multivariate analyses on all (n = 169) validation cases. To generate comparable hazard ratios, patient age was coded as above/below the median and KPS was coded as 70 or above vs <70.

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