A validated microRNA profile with predictive potential in glioblastoma patients treated with bevacizumab

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Abstract

Purpose:

We investigated whether microRNA expression data from glioblastoma could be used to produce a profile that defines a bevacizumab responsive group of patients.

Patients and methods:

TCGA microRNA expression data from tumors resected at first diagnosis of glioblastoma in patients treated with bevacizumab at any time during the course of their disease were randomly separated into training (n = 50) and test (n = 37) groups for model generation. MicroRNA-seq data for 51 patients whose treatment included bevacizumab in the BELOB trial were used as an independent validation cohort.

Results:

Using penalized regression we identified 8 microRNAs as potential predictors of overall survival in the training set. We dichotomized the response score based on the most prognostic minimum of a density plot of the response scores (log-rank HR = 0.16, p = 1.2e−5) and validated the profile in the test cohort (one-sided log-rank HR = 0.34, p = 0.026). Analysis of the profile using all samples in the TCGA glioblastoma dataset, regardless of treatment received, (n = 473) showed that the prediction of patient benefit was not significant (HR = 0.84, p = 0.083) suggesting the profile is specific to bevacizumab. Further independent validation of our microRNA profile in RNA-seq data from patients treated with bevacizumab (alone or in combination with CCNU) at glioblastoma recurrence in the BELOB trial confirmed that our microRNA profile predicted patient benefit from bevacizumab (HR = 0.59, p = 0.043).

Conclusion:

We have identified and validated an 8-microRNA profile that predicts overall survival in patients with glioblastoma treated with bevacizumab. This may be useful for identifying patients who are likely to benefit from this agent.

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