The goal of my proposed work is to use genomics and three-dimensional (3D) spatial data from imaging to study spatial patterns of tumor heterogeneity in vivo. Most tumors are not comprised of identical copies of one cell, but instead a mixture of cells with different genetic alterations. The co-existence of these genetically distinct populations is thought to underlie treatment failure. Previous intratumoral heterogeneity studies have been limited by insufficient sampling, typically just one biopsy per tumor, and a lack of knowledge of where within the heterogeneous tumor the sample was obtained. This is a particularly important and challenging issue to address in glioma, the most common form of brain tumor. While a handful of studies have used multi-region sampling to characterize glioma heterogeneity within subtypes, these studies have not determined how spatial patterns of heterogeneity differ among subtypes, nor how these divergent patterns impact therapeutically relevant characteristics, such as the tumor microenvironment. Previous work and my preliminary data suggest that relatively indolent low-grade glioma (LGG) has distinct spatial patterns of heterogeneity from more aggressive high-grade glioma (HGG). To evaluate this, I propose to use 8-10 spatially-distinct biopsies from each tumor (LGG or HGG), each removed using a surgical navigation system that records the precise, 3D location of each of biopsy. With this unique data set, I will determine how 3D patterns of genetic heterogeneity differ between LGG and HGG, as well as evaluate the consequences of divergent 3D heterogeneity patterns on the tumor microenvironment. The knowledge gained from this work will allow for the development of more informed, tumor subtype-specific biopsy strategies. It will also reveal therapeutically-relevant relationships between patterns of genetic heterogeneity and the tumor microenvironment, knowledge of which has the potential to serve as a foundation for the development of more effective treatments for brain tumors.
Ascertaining how diverse populations of cells evolve in vivo within brain tumors, and how this diversity shapes the tumor microenvironment, are essential next steps to developing more informed treatments that overcome the barrier currently posed by intratumoral heterogeneity. The goal of the proposed work is to integrate genomics and three-dimensional positional data from imaging to study spatial patterns of brain tumor heterogeneity, as well as the impact of this heterogeneity on the tumor microenvironment, in vivo. The knowledge gained from this work will allow for the development of more informed, tumor subtype-specific biopsy strategies and will reveal therapeutically-relevant relationships between patterns of genetic heterogeneity and the tumor microenvironment, knowledge of which has the potential to serve as a foundation for the development of more effective treatments for brain tumors.
