# Predictive modeling of glioblastoma recurrence for therapeutic target identification

## TL;DR

This paper introduces a new model for studying glioblastoma recurrence to find better treatment options.

## Contribution

The study presents a novel model of glioblastoma recurrence for identifying therapeutic vulnerabilities.

## Key findings

- A new model of glioblastoma recurrence was generated and characterized.
- The model helps identify potential therapeutic targets for glioblastoma.
- The model preserves the original tumor genotype, unlike traditional cell culture methods.

## Abstract

Patient-derived xenograft (PDX) models are an essential resource in cancer research, enabling the discovery of novel molecular mechanisms and therapeutic targets, while providing a preclinical platform for the evaluation of experimental treatments. For glioblastoma (GB), the most malignant primary brain tumor, PDX models have long remained artificial, being derived from serum-cultured and long-term cultured cell lines such as U87 and U251 (Huszthy et al, 2012). A major step forward was made in 2006 by Lee et al, who discovered that serum-free cultures in neural stem cell medium recapitulated patient tumors at the genotypic and phenotypic level much better than serum-derived cultures (Lee et al, 2006). This discovery set a new standard for the culture of patient-derived GB cells and their corresponding PDX models which are still used today. An alternative to cell culture in neural stem cell media is the development of PDX models through serial in vivo passaging of patient-derived 3D spheroids in immunodeficient animals. This model preserves the original genotype including EGFR amplification, which typically disappears in cell culture-derived models (Talasila et al, 2013).

H Miletic and T Daubon discuss the study by S Lucchini et al, in this issue of EMBO Mol. Med., which describes the generation and characterization of a novel model of glioblastoma recurrence to identify therapeutic vulnerabilities.