Mouse models of glioblastoma: lessons learned and questions to be answered

J Neurooncol (2014) 118:18 DOI 10.1007/s11060-014-1401-x

TOPIC REVIEW

Mouse models of glioblastoma: lessons learned and questions to be answered

Loury Janbazian Jason Karamchandani

Sunit Das

Received: 7 November 2013 / Accepted: 31 January 2014 / Published online: 13 February 2014 Springer Science+Business Media New York 2014

Abstract Glioblastoma is the most common primary brain tumour in adults. While many patients achieve disease remission following treatment with surgical resection, radiation therapy and chemotherapy, this remission is brief and invariably followed by tumour recurrence and progression. Recent work using mouse models of the disease, coupled with data generated by The Cancer Genome Atlas, have given us new insights into the mechanisms that underlie gliomagenesis and result in glioblastoma heterogeneity. These ndings suggest that the treatment of glioblastoma will require a more nuanced understanding of their biology and the employment of targeted therapeutic approaches. In this review, we will summarize the current state of mouse modeling in glioma, with a focus on how these models may inform our understanding of this disease and its treatment.

Keywords Glioblastoma Mouse model Preclinical

Gene expression TCGA

Introduction

Glioblastoma is the most common primary brain tumour in adults. While many patients achieve disease remission following treatment with surgical resection, radiation therapy and chemotherapy, this remission is brief and invariably followed by tumour recurrence and progression [1].

In part, our failure to offer durable therapies to patients with glioblastoma reects the complex nature of this cancer [2]. Tumors that have been considered by histology to be equivalent have been found through molecular and genetic studies to be teleologically and ontologically diverse, suggesting that their treatment will require a more nuanced understanding of their biology and the employment of a targeted therapeutic approach. Our ability to identify driver mutations and critical targets for therapeutic intervention in glioblastoma is complicated by the fact that, unlike in breast and colon cancers, discovery in the clinical setting occurs at a fulminant stage of the disease. That said, recent work using mouse models of the disease, coupled with data generated by The Cancer Genome Atlas (TCGA), have given us new insights into the mechanisms that underlie gliomagenesis and result in glioblastoma heterogeneity. In this review, we will summarize the current state of mouse modeling in glioma, and argue...