A novel model of glioblastoma recurrence to identify therapeutic vulnerabilities

Glioblastoma remains incurable and recurs in all patients. Here we design and characterize a novel induced-recurrence model in which mice xenografted with primary patient-derived glioma initiating/stem cells (GIC) are treated with a therapeutic regimen closely recapitulating patient standard of care, followed by monitoring until tumours recur (induced recurrence patient-derived xenografts, IR-PDX). By tracking in vivo tumour growth, we confirm the patient specificity and initial efficacy of treatment prior to recurrence. Availability of longitudinally matched pairs of primary and recurrent GIC enabled patient-specific evaluation of the fidelity with which the model recapitulated phenotypes associated with the true recurrence. Through comprehensive multi-omic analyses, we show that the IR-PDX model recapitulates aspects of genomic, epigenetic, and transcriptional state heterogeneity upon recurrence in a patient-specific manner. The accuracy of the IR-PDX enabled both novel biological insights, including the positive association between glioblastoma recurrence and levels of ciliated neural stem cell-like tumour cells, and the identification of druggable patient-specific therapeutic vulnerabilities. This proof-of-concept study opens the possibility for prospective precision medicine approaches to identify target-drug candidates for treatment at glioblastoma recurrence.

Authors

Institutions

Publication Details

Journal
EMBO Molecular Medicine
Published
2025-04-28
DOI
https://doi.org/10.1038/s44321-025-00237-z
Citations
2
Primary Topic
Glioma Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
3.25

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A novel model of glioblastoma recurrence to identify therapeutic vulnerabilities

Sara Lucchini, Sebastian Brandner, Thomas O Millner, Silvia Marino et al.
2 citations
EMBO Molecular Medicine
Glioma Diagnosis and Treatment
3.25
article

A novel model of glioblastoma recurrence to identify therapeutic vulnerabilities

Sara Lucchini, Sebastian Brandner, Thomas O Millner, Silvia Marino, James Nicholson, Jacob Househam, Trevor A. Graham, Yau Mun Lim, Maximilian Mossner, Xinyu Zhang
article en
2 citations

Abstract

Glioblastoma remains incurable and recurs in all patients. Here we design and characterize a novel induced-recurrence model in which mice xenografted with primary patient-derived glioma initiating/stem cells (GIC) are treated with a therapeutic regimen closely recapitulating patient standard of care, followed by monitoring until tumours recur (induced recurrence patient-derived xenografts, IR-PDX). By tracking in vivo tumour growth, we confirm the patient specificity and initial efficacy of treatment prior to recurrence. Availability of longitudinally matched pairs of primary and recurrent GIC enabled patient-specific evaluation of the fidelity with which the model recapitulated phenotypes associated with the true recurrence. Through comprehensive multi-omic analyses, we show that the IR-PDX model recapitulates aspects of genomic, epigenetic, and transcriptional state heterogeneity upon recurrence in a patient-specific manner. The accuracy of the IR-PDX enabled both novel biological insights, including the positive association between glioblastoma recurrence and levels of ciliated neural stem cell-like tumour cells, and the identification of druggable patient-specific therapeutic vulnerabilities. This proof-of-concept study opens the possibility for prospective precision medicine approaches to identify target-drug candidates for treatment at glioblastoma recurrence.

EMBO Molecular MedicineVol. 17(6)
University College London Hospitals NHS Foundation Trust (GB), Institute of Cancer Research (GB), Queen Mary University of London (GB), National Hospital for Neurology and Neurosurgery (GB), University College London (GB)
Barts Charity, Cancer Research UK, National Institute for Health and Care Research, Brain Tumour Research
Good health and well-being
Openalex Percentile: Top 9%
Glioma Diagnosis and Treatment
3.25
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.