The most aggressive type of brain tumor, grade IV glioma known as glioblastoma (GBM), is one of the few tumor
types with both a poor outcome and minimal improvement in survival in the past decades. For GBM, like other
solid cancers, intratumoral heterogeneity is likely an important factor in mediating therapeutic response. In
particular, quiescent, G0-like subpopulations may engender tumors with more robust responses to treatment
regimens and allow for tumor regrowth after standard of care (SOC). However, G0-like tumor populations are
currently ill-defined, even after application of single cell genomics to GBM. Our failure to fully comprehend and
experimentally model quiescent/G0-like states represents a critical knowledge gap, but also a key opportunity,
for glioma and other cancers, as neutralizing G0 cells could effectively prevent chemoradiotherapy resistance
and tumor recurrence.
The purpose of this grant is to provide a functional and molecular definition of G0-like states in GBM tumors and
their responses to SOC. In Aim 1, we will define molecular networks governing long- and short-term quiescent
states in GBM patient tumors using a novel G0 reporter system in combination with single cell genomic analysis.
In Aim 2, we will test the hypothesis that dormant G0 GBM cells have unique RNA and chromatin signatures
required for SOC survival and tumor regrowth. In Aim 3, we will study and nominate the NuA4/KAT5 lysine
acetyltransferase complex as a key regulator of G0-like states in GBM and candidate therapeutic target.
The Aims are built on strong preliminary data, including: the creation of a machine learning-based method for
identifying G0-like cells in gliomas, integrated analysis of single cell RNA and chromatin analysis of primary and
PDX GBM tumors with standard of care, a functional genomic screen to identify regulators of GBM G0, and key
experimental models to functionally dissect G0 states in GBM tumor models.
If successful, this grant will produce a new working model for GBM G0-like states, provide key genes and gene
networks associated with G0, and analysis tools for identifying G0-like states in clinical samples. It will also
define how these populations respond to SOC and shift tumor dynamics during recurrence. Finally, it will provide
data for a new therapeutic strategy, "downgrading", where grade IV tumors are made less aggressive by
triggering extended or permanent G0-like states in tumor cells.