# Glioblastoma, Glioblastoma Stem Cells and Radiotherapy

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2020 · $370,789

## Abstract

Abstract
Despite a tremendous effort in basic science, clinical trials, drug development, and technical advances in
radiation oncology, glioblastoma remains incurable and improvements in overall survival have been marginal.
While radiotherapy is one of the most effective treatment options for glioblastoma it cannot control the disease
over time. This led us to conclude that novel combination therapies are desperately needed to improve
radiation treatment outcome for patients suffering from this disease. The studies outlined in this proposal make
are base on a hypothesis that is backed by our extensive preliminary data and published data in the literature.
Specifically, that radiation causes a phenotype conversion of differentiated glioma cells into therapy-resistant
glioma-initiating cells (GICs) and that interfering with this process will increase the efficiency of radiotherapy.
The three aims of this study will address this aspect of glioma biology using an innovative tool to track GICs
and their progeny and make use of unique resources and expertise available at UCLA. If successful, results
from these studies and in particular Aim 2 and 3 will have wider impact on radiation oncology as these
principles apply not only to glioblastoma but many other solid cancer.

## Key facts

- **NIH application ID:** 9991802
- **Project number:** 5R01CA200234-05
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Frank Pajonk
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $370,789
- **Award type:** 5
- **Project period:** 2016-08-03 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9991802

## Citation

> US National Institutes of Health, RePORTER application 9991802, Glioblastoma, Glioblastoma Stem Cells and Radiotherapy (5R01CA200234-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9991802. Licensed CC0.

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